feat: bump versions to 1.3.0 (#3820)

* feat: bump versions to 1.3.0

* chore: update crew and flow templates to use crewai[tools] version 1.3.0

docs/en/learn/a2a-agent-delegation.mdx

@@ -0,0 +1,291 @@
+---
+title: Agent-to-Agent (A2A) Protocol
+description: Enable CrewAI agents to delegate tasks to remote A2A-compliant agents for specialized handling
+icon: network-wired
+mode: "wide"
+---
+
+## A2A Agent Delegation
+
+CrewAI supports the Agent-to-Agent (A2A) protocol, allowing agents to delegate tasks to remote specialized agents. The agent's LLM automatically decides whether to handle a task directly or delegate to an A2A agent based on the task requirements.
+
+<Note>
+  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
+</Note>
+
+## How It Works
+
+When an agent is configured with A2A capabilities:
+
+1. The LLM analyzes each task
+2. It decides to either:
+   - Handle the task directly using its own capabilities
+   - Delegate to a remote A2A agent for specialized handling
+3. If delegating, the agent communicates with the remote A2A agent through the protocol
+4. Results are returned to the CrewAI workflow
+
+## Basic Configuration
+
+Configure an agent for A2A delegation by setting the `a2a` parameter:
+
+```python Code
+from crewai import Agent, Crew, Task
+from crewai.a2a import A2AConfig
+
+agent = Agent(
+    role="Research Coordinator",
+    goal="Coordinate research tasks efficiently",
+    backstory="Expert at delegating to specialized research agents",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://example.com/.well-known/agent-card.json",
+        timeout=120,
+        max_turns=10
+    )
+)
+
+task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="A comprehensive research report",
+    agent=agent
+)
+
+crew = Crew(agents=[agent], tasks=[task], verbose=True)
+result = crew.kickoff()
+```
+
+## Configuration Options
+
+The `A2AConfig` class accepts the following parameters:
+
+<ParamField path="endpoint" type="str" required>
+  The A2A agent endpoint URL (typically points to `.well-known/agent-card.json`)
+</ParamField>
+
+<ParamField path="auth" type="AuthScheme" default="None">
+  Authentication scheme for the A2A agent. Supports Bearer tokens, OAuth2, API keys, and HTTP authentication.
+</ParamField>
+
+<ParamField path="timeout" type="int" default="120">
+  Request timeout in seconds
+</ParamField>
+
+<ParamField path="max_turns" type="int" default="10">
+  Maximum number of conversation turns with the A2A agent
+</ParamField>
+
+<ParamField path="response_model" type="type[BaseModel]" default="None">
+  Optional Pydantic model for requesting structured output from an A2A agent. A2A protocol does not
+  enforce this, so an A2A agent does not need to honor this request.
+</ParamField>
+
+<ParamField path="fail_fast" type="bool" default="True">
+  Whether to raise an error immediately if agent connection fails. When `False`, the agent continues with available agents and informs the LLM about unavailable ones.
+</ParamField>
+
+## Authentication
+
+For A2A agents that require authentication, use one of the provided auth schemes:
+
+<Tabs>
+  <Tab title="Bearer Token">
+    ```python Code
+from crewai.a2a import A2AConfig
+from crewai.a2a.auth import BearerTokenAuth
+
+agent = Agent(
+    role="Secure Coordinator",
+    goal="Coordinate tasks with secured agents",
+    backstory="Manages secure agent communications",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://secure-agent.example.com/.well-known/agent-card.json",
+        auth=BearerTokenAuth(token="your-bearer-token"),
+        timeout=120
+    )
+)
+    ```
+  </Tab>
+
+  <Tab title="API Key">
+    ```python Code
+from crewai.a2a import A2AConfig
+from crewai.a2a.auth import APIKeyAuth
+
+agent = Agent(
+    role="API Coordinator",
+    goal="Coordinate with API-based agents",
+    backstory="Manages API-authenticated communications",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://api-agent.example.com/.well-known/agent-card.json",
+        auth=APIKeyAuth(
+            api_key="your-api-key",
+            location="header",  # or "query" or "cookie"
+            name="X-API-Key"
+        ),
+        timeout=120
+    )
+)
+    ```
+  </Tab>
+
+  <Tab title="OAuth2">
+    ```python Code
+from crewai.a2a import A2AConfig
+from crewai.a2a.auth import OAuth2ClientCredentials
+
+agent = Agent(
+    role="OAuth Coordinator",
+    goal="Coordinate with OAuth-secured agents",
+    backstory="Manages OAuth-authenticated communications",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://oauth-agent.example.com/.well-known/agent-card.json",
+        auth=OAuth2ClientCredentials(
+            token_url="https://auth.example.com/oauth/token",
+            client_id="your-client-id",
+            client_secret="your-client-secret",
+            scopes=["read", "write"]
+        ),
+        timeout=120
+    )
+)
+    ```
+  </Tab>
+
+  <Tab title="HTTP Basic">
+    ```python Code
+from crewai.a2a import A2AConfig
+from crewai.a2a.auth import HTTPBasicAuth
+
+agent = Agent(
+    role="Basic Auth Coordinator",
+    goal="Coordinate with basic auth agents",
+    backstory="Manages basic authentication communications",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://basic-agent.example.com/.well-known/agent-card.json",
+        auth=HTTPBasicAuth(
+            username="your-username",
+            password="your-password"
+        ),
+        timeout=120
+    )
+)
+    ```
+  </Tab>
+</Tabs>
+
+## Multiple A2A Agents
+
+Configure multiple A2A agents for delegation by passing a list:
+
+```python Code
+from crewai.a2a import A2AConfig
+from crewai.a2a.auth import BearerTokenAuth
+
+agent = Agent(
+    role="Multi-Agent Coordinator",
+    goal="Coordinate with multiple specialized agents",
+    backstory="Expert at delegating to the right specialist",
+    llm="gpt-4o",
+    a2a=[
+        A2AConfig(
+            endpoint="https://research.example.com/.well-known/agent-card.json",
+            timeout=120
+        ),
+        A2AConfig(
+            endpoint="https://data.example.com/.well-known/agent-card.json",
+            auth=BearerTokenAuth(token="data-token"),
+            timeout=90
+        )
+    ]
+)
+```
+
+The LLM will automatically choose which A2A agent to delegate to based on the task requirements.
+
+## Error Handling
+
+Control how agent connection failures are handled using the `fail_fast` parameter:
+
+```python Code
+from crewai.a2a import A2AConfig
+
+# Fail immediately on connection errors (default)
+agent = Agent(
+    role="Research Coordinator",
+    goal="Coordinate research tasks",
+    backstory="Expert at delegation",
+    llm="gpt-4o",
+    a2a=A2AConfig(
+        endpoint="https://research.example.com/.well-known/agent-card.json",
+        fail_fast=True
+    )
+)
+
+# Continue with available agents
+agent = Agent(
+    role="Multi-Agent Coordinator",
+    goal="Coordinate with multiple agents",
+    backstory="Expert at working with available resources",
+    llm="gpt-4o",
+    a2a=[
+        A2AConfig(
+            endpoint="https://primary.example.com/.well-known/agent-card.json",
+            fail_fast=False
+        ),
+        A2AConfig(
+            endpoint="https://backup.example.com/.well-known/agent-card.json",
+            fail_fast=False
+        )
+    ]
+)
+```
+
+When `fail_fast=False`:
+- If some agents fail, the LLM is informed which agents are unavailable and can delegate to working agents
+- If all agents fail, the LLM receives a notice about unavailable agents and handles the task directly
+- Connection errors are captured and included in the context for better decision-making
+
+## Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Set Appropriate Timeouts" icon="clock">
+    Configure timeouts based on expected A2A agent response times. Longer-running tasks may need higher timeout values.
+  </Card>
+
+  <Card title="Limit Conversation Turns" icon="comments">
+    Use `max_turns` to prevent excessive back-and-forth. The agent will automatically conclude conversations before hitting the limit.
+  </Card>
+
+  <Card title="Use Resilient Error Handling" icon="shield-check">
+    Set `fail_fast=False` for production environments with multiple agents to gracefully handle connection failures and maintain workflow continuity.
+  </Card>
+
+  <Card title="Secure Your Credentials" icon="lock">
+    Store authentication tokens and credentials as environment variables, not in code.
+  </Card>
+
+  <Card title="Monitor Delegation Decisions" icon="eye">
+    Use verbose mode to observe when the LLM chooses to delegate versus handle tasks directly.
+  </Card>
+</CardGroup>
+
+## Supported Authentication Methods
+
+- **Bearer Token** - Simple token-based authentication
+- **OAuth2 Client Credentials** - OAuth2 flow for machine-to-machine communication
+- **OAuth2 Authorization Code** - OAuth2 flow requiring user authorization
+- **API Key** - Key-based authentication (header, query param, or cookie)
+- **HTTP Basic** - Username/password authentication
+- **HTTP Digest** - Digest authentication (requires `httpx-auth` package)
+
+## Learn More
+
+For more information about the A2A protocol and reference implementations:
+
+- [A2A Protocol Documentation](https://a2a-protocol.org)
+- [A2A Sample Implementations](https://github.com/a2aproject/a2a-samples)
+- [A2A Python SDK](https://github.com/a2aproject/a2a-python)

docs/en/observability/datadog.mdx

@@ -93,11 +93,15 @@ After running the application, you can view the traces in [Datadog LLM Observabi
 
 Clicking on a trace will show you the details of the trace, including total tokens used, number of LLM calls, models used, and estimated cost. Clicking into a specific span will narrow down these details, and show related input, output, and metadata.
 
-![Datadog LLM Observability Trace View](/images/datadog-llm-observability-1.png)
+<Frame>
+<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM Observability Trace View" />
+</Frame>
 
 Additionally, you can view the execution graph view of the trace, which shows the control and data flow of the trace, which will scale with larger agents to show handoffs and relationships between LLM calls, tool calls, and agent interactions.
 
-![Datadog LLM Observability Agent Execution Flow View](/images/datadog-llm-observability-2.png)
+<Frame>
+<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability Agent Execution Flow View" />
+</Frame>
 
 ## References
 

docs/en/tools/database-data/qdrantvectorsearchtool.mdx

@@ -23,13 +23,15 @@ Here's a minimal example of how to use the tool:
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
 
-# Initialize the tool
+# Initialize the tool with QdrantConfig
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url="your_qdrant_url",
-    qdrant_api_key="your_qdrant_api_key",
-    collection_name="your_collection"
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_qdrant_url",
+        qdrant_api_key="your_qdrant_api_key",
+        collection_name="your_collection"
+    )
 )
 
 # Create an agent that uses the tool
@@ -82,7 +84,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-small"
+        model="text-embedding-3-large"
     )
     return response.data[0].embedding
 
@@ -90,13 +92,13 @@ def get_openai_embedding(text):
 def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
     # Extract text from PDF
     text_chunks = extract_text_from_pdf(pdf_path)
-    
+
     # Create Qdrant collection
     if qdrant.collection_exists(collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
     # Store embeddings
@@ -120,19 +122,23 @@ pdf_path = "path/to/your/document.pdf"
 load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
 
 # Initialize Qdrant search tool
+from crewai_tools import QdrantConfig
+
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url=os.getenv("QDRANT_URL"),
-    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-    collection_name=collection_name,
-    limit=3,
-    score_threshold=0.35
+    qdrant_config=QdrantConfig(
+        qdrant_url=os.getenv("QDRANT_URL"),
+        qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+        collection_name=collection_name,
+        limit=3,
+        score_threshold=0.35
+    )
 )
 
 # Create CrewAI agents
 search_agent = Agent(
     role="Senior Semantic Search Agent",
     goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant 
+    backstory="""You are an expert research assistant who can find relevant
     information using semantic search in a Qdrant database.""",
     tools=[qdrant_tool],
     verbose=True
@@ -141,7 +147,7 @@ search_agent = Agent(
 answer_agent = Agent(
     role="Senior Answer Assistant",
     goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate 
+    backstory="""You are an expert answer assistant who can generate
     answers to questions based on the context provided.""",
     tools=[qdrant_tool],
     verbose=True
@@ -180,21 +186,82 @@ print(result)
 ## Tool Parameters
 
 ### Required Parameters
-- `qdrant_url` (str): The URL of your Qdrant server
-- `qdrant_api_key` (str): API key for authentication with Qdrant
-- `collection_name` (str): Name of the Qdrant collection to search
+- `qdrant_config` (QdrantConfig): Configuration object containing all Qdrant settings
 
-### Optional Parameters
+### QdrantConfig Parameters
+- `qdrant_url` (str): The URL of your Qdrant server
+- `qdrant_api_key` (str, optional): API key for authentication with Qdrant
+- `collection_name` (str): Name of the Qdrant collection to search
 - `limit` (int): Maximum number of results to return (default: 3)
 - `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
+- `filter` (Any, optional): Qdrant Filter instance for advanced filtering (default: None)
+
+### Optional Tool Parameters
 - `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
+- `qdrant_package` (str): Base package path for Qdrant (default: "qdrant_client")
+- `client` (Any): Pre-initialized Qdrant client (optional)
+
+## Advanced Filtering
+
+The QdrantVectorSearchTool supports powerful filtering capabilities to refine your search results:
+
+### Dynamic Filtering
+Use `filter_by` and `filter_value` parameters in your search to filter results on-the-fly:
+
+```python
+# Agent will use these parameters when calling the tool
+# The tool schema accepts filter_by and filter_value
+# Example: search with category filter
+# Results will be filtered where category == "technology"
+```
+
+### Preset Filters with QdrantConfig
+For complex filtering, use Qdrant Filter instances in your configuration:
+
+```python
+from qdrant_client.http import models as qmodels
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
+
+# Create a filter for specific conditions
+preset_filter = qmodels.Filter(
+    must=[
+        qmodels.FieldCondition(
+            key="category",
+            match=qmodels.MatchValue(value="research")
+        ),
+        qmodels.FieldCondition(
+            key="year",
+            match=qmodels.MatchValue(value=2024)
+        )
+    ]
+)
+
+# Initialize tool with preset filter
+qdrant_tool = QdrantVectorSearchTool(
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection",
+        filter=preset_filter  # Preset filter applied to all searches
+    )
+)
+```
+
+### Combining Filters
+The tool automatically combines preset filters from `QdrantConfig` with dynamic filters from `filter_by` and `filter_value`:
+
+```python
+# If QdrantConfig has a preset filter for category="research"
+# And the search uses filter_by="year", filter_value=2024
+# Both filters will be combined (AND logic)
+```
 
 ## Search Parameters
 
 The tool accepts these parameters in its schema:
 - `query` (str): The search query to find similar documents
 - `filter_by` (str, optional): Metadata field to filter on
-- `filter_value` (str, optional): Value to filter by
+- `filter_value` (Any, optional): Value to filter by
 
 ## Return Format
 
@@ -214,7 +281,7 @@ The tool returns results in JSON format:
 
 ## Default Embedding
 
-By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
+By default, the tool uses OpenAI's `text-embedding-3-large` model for vectorization. This requires:
 - OpenAI API key set in environment: `OPENAI_API_KEY`
 
 ## Custom Embeddings
@@ -240,18 +307,22 @@ def custom_embeddings(text: str) -> list[float]:
     # Tokenize and get model outputs
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
-    
+
     # Use mean pooling to get text embedding
     embeddings = outputs.last_hidden_state.mean(dim=1)
-    
+
     # Convert to list of floats and return
     return embeddings[0].tolist()
 
 # Use custom embeddings with the tool
+from crewai_tools import QdrantConfig
+
 tool = QdrantVectorSearchTool(
-    qdrant_url="your_url",
-    qdrant_api_key="your_key",
-    collection_name="your_collection",
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection"
+    ),
     custom_embedding_fn=custom_embeddings  # Pass your custom function
 )
 ```
@@ -269,4 +340,4 @@ Required environment variables:
 ```bash
 export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
 export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
-export OPENAI_API_KEY="your_openai_key"  # If using default embeddings
+export OPENAI_API_KEY="your_openai_key"  # If using default embeddings

docs/en/tools/file-document/csvsearchtool.mdx

@@ -54,25 +54,25 @@ The following parameters can be used to customize the `CSVSearchTool`'s behavior
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = CSVSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/en/tools/file-document/directorysearchtool.mdx

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
 The DirectorySearchTool uses OpenAI for embeddings and summarization by default. Customization options for these settings include changing the model provider and configuration, enhancing flexibility for advanced users.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DirectorySearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # Options include ollama, google, anthropic, llama2, and more
-            config=dict(
-                model="llama2",
-                # Additional configurations here
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/en/tools/file-document/docxsearchtool.mdx

@@ -56,25 +56,25 @@ The following parameters can be used to customize the `DOCXSearchTool`'s behavio
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DOCXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/en/tools/file-document/mdxsearchtool.mdx

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
 The tool defaults to using OpenAI for embeddings and summarization. For customization, utilize a configuration dictionary as shown below:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = MDXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # Options include google, openai, anthropic, llama2, etc.
-            config=dict(
-                model="llama2",
-                # Optional parameters can be included here.
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # Optional title for the embeddings can be added here.
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/en/tools/file-document/pdfsearchtool.mdx

@@ -45,28 +45,64 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 
 ## Custom model and embeddings
 
-By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
+By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows. Note: a vector database is required because generated embeddings must be stored and queried from a vectordb.
 
 ```python Code
+from crewai_tools import PDFSearchTool
+
+# - embedding_model (required): choose provider + provider-specific config
+# - vectordb (required): choose vector DB and pass its config
+
 tool = PDFSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            # Supported providers: "openai", "azure", "google-generativeai", "google-vertex",
+            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
+            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
+            "provider": "openai",  # or: "google-generativeai", "cohere", "ollama", ...
+            "config": {
+                # Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
+                "model": "text-embedding-3-small",
+                # Optional: API key. If omitted, the tool will use provider-specific env vars when available
+                # (e.g., OPENAI_API_KEY for provider="openai").
+                # "api_key": "sk-...",
+
+                # Provider-specific examples:
+                # --- Google Generative AI ---
+                # (Set provider="google-generativeai" above)
+                # "model": "models/embedding-001",
+                # "task_type": "retrieval_document",
+                # "title": "Embeddings",
+
+                # --- Cohere ---
+                # (Set provider="cohere" above)
+                # "model": "embed-english-v3.0",
+
+                # --- Ollama (local) ---
+                # (Set provider="ollama" above)
+                # "model": "nomic-embed-text",
+            },
+        },
+        "vectordb": {
+                    "provider": "chromadb",  # or "qdrant"
+                    "config": {
+                        # For ChromaDB: pass "settings" (chromadb.config.Settings) or rely on defaults.
+                        # Example (uncomment and import):
+                        # from chromadb.config import Settings
+                        # "settings": Settings(
+                        #     persist_directory="/content/chroma",
+                        #     allow_reset=True,
+                        #     is_persistent=True,
+                        # ),
+
+                        # For Qdrant: pass "vectors_config" (qdrant_client.models.VectorParams).
+                        # Example (uncomment and import):
+                        # from qdrant_client.models import VectorParams, Distance
+                        # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                        # Note: collection name is controlled by the tool (default: "rag_tool_collection"), not set here.
+                    }
+        },
+    }
 )
 ```

docs/en/tools/file-document/txtsearchtool.mdx

@@ -57,25 +57,41 @@ By default, the tool uses OpenAI for both embeddings and summarization.
 To customize the model, you can use a config dictionary as follows:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = TXTSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        # Required: embeddings provider + config
+        "embedding_model": {
+            "provider": "openai",  # or google-generativeai, cohere, ollama, ...
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",  # optional if env var is set
+                # Provider examples:
+                # Google → model: "models/embedding-001", task_type: "retrieval_document"
+                # Cohere → model: "embed-english-v3.0"
+                # Ollama → model: "nomic-embed-text"
+            },
+        },
+
+        # Required: vector database config
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # Chroma settings (optional persistence)
+                # "settings": Settings(
+                #     persist_directory="/content/chroma",
+                #     allow_reset=True,
+                #     is_persistent=True,
+                # ),
+
+                # Qdrant vector params example:
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                # Note: collection name is controlled by the tool (default: "rag_tool_collection").
+            }
+        },
+    }
 )
 ```

docs/en/tools/file-document/xmlsearchtool.mdx

@@ -54,25 +54,25 @@ It is an optional parameter during the tool's initialization but must be provide
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code  
+from chromadb.config import Settings
+
 tool = XMLSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # or "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/ko/observability/datadog.mdx

@@ -93,11 +93,15 @@ ddtrace-run python crewai_agent.py
 
 트레이스를 클릭하면 사용된 총 토큰, LLM 호출 수, 사용된 모델, 예상 비용 등 트레이스에 대한 세부 정보가 표시됩니다. 특정 스팬(span)을 클릭하면 이러한 세부 정보의 범위가 좁혀지고 관련 입력, 출력 및 메타데이터가 표시됩니다.
 
-![Datadog LLM 옵저버빌리티 추적 보기](/images/datadog-llm-observability-1.png)
+<Frame>
+<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM 옵저버빌리티 추적 보기" />
+</Frame>
 
 또한, 트레이스의 제어 및 데이터 흐름을 보여주는 트레이스의 실행 그래프 보기를 볼 수 있으며, 이는 더 큰 에이전트로 확장하여 LLM 호출, 도구 호출 및 에이전트 상호 작용 간의 핸드오프와 관계를 보여줍니다.
 
-![Datadog LLM Observability 에이전트 실행 흐름 보기](/images/datadog-llm-observability-2.png)
+<Frame>
+<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability 에이전트 실행 흐름 보기" />
+</Frame>
 
 ## 참조
 

docs/ko/tools/database-data/qdrantvectorsearchtool.mdx

@@ -23,13 +23,15 @@ uv add qdrant-client
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
 
-# Initialize the tool
+# QdrantConfig로 도구 초기화
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url="your_qdrant_url",
-    qdrant_api_key="your_qdrant_api_key",
-    collection_name="your_collection"
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_qdrant_url",
+        qdrant_api_key="your_qdrant_api_key",
+        collection_name="your_collection"
+    )
 )
 
 # Create an agent that uses the tool
@@ -82,7 +84,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-small"
+        model="text-embedding-3-large"
     )
     return response.data[0].embedding
 
@@ -90,13 +92,13 @@ def get_openai_embedding(text):
 def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
     # Extract text from PDF
     text_chunks = extract_text_from_pdf(pdf_path)
-    
+
     # Create Qdrant collection
     if qdrant.collection_exists(collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
     # Store embeddings
@@ -120,19 +122,23 @@ pdf_path = "path/to/your/document.pdf"
 load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
 
 # Initialize Qdrant search tool
+from crewai_tools import QdrantConfig
+
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url=os.getenv("QDRANT_URL"),
-    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-    collection_name=collection_name,
-    limit=3,
-    score_threshold=0.35
+    qdrant_config=QdrantConfig(
+        qdrant_url=os.getenv("QDRANT_URL"),
+        qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+        collection_name=collection_name,
+        limit=3,
+        score_threshold=0.35
+    )
 )
 
 # Create CrewAI agents
 search_agent = Agent(
     role="Senior Semantic Search Agent",
     goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant 
+    backstory="""You are an expert research assistant who can find relevant
     information using semantic search in a Qdrant database.""",
     tools=[qdrant_tool],
     verbose=True
@@ -141,7 +147,7 @@ search_agent = Agent(
 answer_agent = Agent(
     role="Senior Answer Assistant",
     goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate 
+    backstory="""You are an expert answer assistant who can generate
     answers to questions based on the context provided.""",
     tools=[qdrant_tool],
     verbose=True
@@ -180,21 +186,82 @@ print(result)
 ## 도구 매개변수
 
 ### 필수 파라미터
-- `qdrant_url` (str): Qdrant 서버의 URL
-- `qdrant_api_key` (str): Qdrant 인증을 위한 API 키
-- `collection_name` (str): 검색할 Qdrant 컬렉션의 이름
+- `qdrant_config` (QdrantConfig): 모든 Qdrant 설정을 포함하는 구성 객체
 
-### 선택적 매개변수
+### QdrantConfig 매개변수
+- `qdrant_url` (str): Qdrant 서버의 URL
+- `qdrant_api_key` (str, 선택 사항): Qdrant 인증을 위한 API 키
+- `collection_name` (str): 검색할 Qdrant 컬렉션의 이름
 - `limit` (int): 반환할 최대 결과 수 (기본값: 3)
 - `score_threshold` (float): 최소 유사도 점수 임계값 (기본값: 0.35)
+- `filter` (Any, 선택 사항): 고급 필터링을 위한 Qdrant Filter 인스턴스 (기본값: None)
+
+### 선택적 도구 매개변수
 - `custom_embedding_fn` (Callable[[str], list[float]]): 텍스트 벡터화를 위한 사용자 지정 함수
+- `qdrant_package` (str): Qdrant의 기본 패키지 경로 (기본값: "qdrant_client")
+- `client` (Any): 사전 초기화된 Qdrant 클라이언트 (선택 사항)
+
+## 고급 필터링
+
+QdrantVectorSearchTool은 검색 결과를 세밀하게 조정할 수 있는 강력한 필터링 기능을 지원합니다:
+
+### 동적 필터링
+검색 시 `filter_by` 및 `filter_value` 매개변수를 사용하여 즉석에서 결과를 필터링할 수 있습니다:
+
+```python
+# 에이전트는 도구를 호출할 때 이러한 매개변수를 사용합니다
+# 도구 스키마는 filter_by 및 filter_value를 허용합니다
+# 예시: 카테고리 필터를 사용한 검색
+# 결과는 category == "기술"인 항목으로 필터링됩니다
+```
+
+### QdrantConfig를 사용한 사전 설정 필터
+복잡한 필터링의 경우 구성에서 Qdrant Filter 인스턴스를 사용하세요:
+
+```python
+from qdrant_client.http import models as qmodels
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
+
+# 특정 조건에 대한 필터 생성
+preset_filter = qmodels.Filter(
+    must=[
+        qmodels.FieldCondition(
+            key="category",
+            match=qmodels.MatchValue(value="research")
+        ),
+        qmodels.FieldCondition(
+            key="year",
+            match=qmodels.MatchValue(value=2024)
+        )
+    ]
+)
+
+# 사전 설정 필터로 도구 초기화
+qdrant_tool = QdrantVectorSearchTool(
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection",
+        filter=preset_filter  # 모든 검색에 적용되는 사전 설정 필터
+    )
+)
+```
+
+### 필터 결합
+도구는 `QdrantConfig`의 사전 설정 필터와 `filter_by` 및 `filter_value`의 동적 필터를 자동으로 결합합니다:
+
+```python
+# QdrantConfig에 category="research"에 대한 사전 설정 필터가 있고
+# 검색에서 filter_by="year", filter_value=2024를 사용하는 경우
+# 두 필터가 모두 결합됩니다 (AND 논리)
+```
 
 ## 검색 매개변수
 
 이 도구는 스키마에서 다음과 같은 매개변수를 허용합니다:
 - `query` (str): 유사한 문서를 찾기 위한 검색 쿼리
 - `filter_by` (str, 선택 사항): 필터링할 메타데이터 필드
-- `filter_value` (str, 선택 사항): 필터 기준 값
+- `filter_value` (Any, 선택 사항): 필터 기준 값
 
 ## 반환 형식
 
@@ -214,7 +281,7 @@ print(result)
 
 ## 기본 임베딩
 
-기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-small` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
+기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-large` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
 - 환경변수에 설정된 OpenAI API 키: `OPENAI_API_KEY`
 
 ## 커스텀 임베딩
@@ -240,18 +307,22 @@ def custom_embeddings(text: str) -> list[float]:
     # Tokenize and get model outputs
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
-    
+
     # Use mean pooling to get text embedding
     embeddings = outputs.last_hidden_state.mean(dim=1)
-    
+
     # Convert to list of floats and return
     return embeddings[0].tolist()
 
 # Use custom embeddings with the tool
+from crewai_tools import QdrantConfig
+
 tool = QdrantVectorSearchTool(
-    qdrant_url="your_url",
-    qdrant_api_key="your_key",
-    collection_name="your_collection",
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection"
+    ),
     custom_embedding_fn=custom_embeddings  # Pass your custom function
 )
 ```
@@ -270,4 +341,4 @@ tool = QdrantVectorSearchTool(
 export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
 export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
 export OPENAI_API_KEY="your_openai_key"  # If using default embeddings
-```
+```

docs/ko/tools/file-document/csvsearchtool.mdx

@@ -54,25 +54,25 @@ tool = CSVSearchTool()
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 사용자 지정하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = CSVSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/directorysearchtool.mdx

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
 DirectorySearchTool은 기본적으로 OpenAI를 사용하여 임베딩 및 요약을 수행합니다. 이 설정의 커스터마이즈 옵션에는 모델 공급자 및 구성을 변경하는 것이 포함되어 있어, 고급 사용자를 위한 유연성을 향상시킵니다.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DirectorySearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # Options include ollama, google, anthropic, llama2, and more
-            config=dict(
-                model="llama2",
-                # Additional configurations here
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/docxsearchtool.mdx

@@ -56,25 +56,25 @@ tool = DOCXSearchTool(docx='path/to/your/document.docx')
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DOCXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/mdxsearchtool.mdx

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
 이 도구는 기본적으로 임베딩과 요약을 위해 OpenAI를 사용합니다. 커스터마이징을 위해 아래와 같이 설정 딕셔너리를 사용할 수 있습니다.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = MDXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # 옵션에는 google, openai, anthropic, llama2 등이 있습니다.
-            config=dict(
-                model="llama2",
-                # 선택적 파라미터를 여기에 포함할 수 있습니다.
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # 또는 openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # 임베딩에 대한 선택적 제목을 여기에 추가할 수 있습니다.
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/pdfsearchtool.mdx

@@ -45,28 +45,60 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 
 ## 커스텀 모델 및 임베딩
 
-기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
+기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다. 참고: 임베딩은 벡터DB에 저장되어야 하므로 vectordb 설정이 필요합니다.
 
 ```python Code
+from crewai_tools import PDFSearchTool
+from chromadb.config import Settings  # Chroma 영속성 설정
+
 tool = PDFSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        # 필수: 임베딩 제공자와 설정
+        "embedding_model": {
+            # 사용 가능 공급자: "openai", "azure", "google-generativeai", "google-vertex",
+            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
+            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
+            "provider": "openai",
+            "config": {
+                # "model" 키는 내부적으로 "model_name"으로 매핑됩니다.
+                "model": "text-embedding-3-small",
+                # 선택: API 키 (미설정 시 환경변수 사용)
+                # "api_key": "sk-...",
+
+                # 공급자별 예시
+                # --- Google ---
+                # (provider를 "google-generativeai"로 설정)
+                # "model": "models/embedding-001",
+                # "task_type": "retrieval_document",
+
+                # --- Cohere ---
+                # (provider를 "cohere"로 설정)
+                # "model": "embed-english-v3.0",
+
+                # --- Ollama(로컬) ---
+                # (provider를 "ollama"로 설정)
+                # "model": "nomic-embed-text",
+            },
+        },
+
+        # 필수: 벡터DB 설정
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # Chroma 설정 예시
+                # "settings": Settings(
+                #     persist_directory="/content/chroma",
+                #     allow_reset=True,
+                #     is_persistent=True,
+                # ),
+
+                # Qdrant 설정 예시
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                # 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "rag_tool_collection").
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/txtsearchtool.mdx

@@ -57,25 +57,34 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
 모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = TXTSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        # 필수: 임베딩 제공자 + 설정
+        "embedding_model": {
+            "provider": "openai",  # 또는 google-generativeai, cohere, ollama 등
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",  # 환경변수 사용 시 생략 가능
+                # 공급자별 예시: Google → model: "models/embedding-001", task_type: "retrieval_document"
+            },
+        },
+
+        # 필수: 벡터DB 설정
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # Chroma 설정(영속성 예시)
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+
+                # Qdrant 벡터 파라미터 예시:
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                # 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "rag_tool_collection").
+            }
+        },
+    }
 )
 ```

docs/ko/tools/file-document/xmlsearchtool.mdx

@@ -54,25 +54,25 @@ tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = XMLSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # or openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # 또는 "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/pt-BR/observability/datadog.mdx

@@ -93,11 +93,14 @@ Depois de executar o aplicativo, você pode visualizar os traços na [Datadog LL
 
 Ao clicar em um rastreamento, você verá os detalhes do rastreamento, incluindo o total de tokens usados, o número de chamadas LLM, os modelos usados e o custo estimado. Clicar em um intervalo específico reduzirá esses detalhes e mostrará a entrada, a saída e os metadados relacionados.
 
-![Visualização do rastreamento de observabilidade do Datadog LLM](/images/datadog-llm-observability-1.png)
-
+<Frame>
+<img src="/images/datadog-llm-observability-1.png" alt="Visualização do rastreamento de observabilidade do Datadog LLM" />
+</Frame>    
 Além disso, você pode visualizar a visualização do gráfico de execução do rastreamento, que mostra o controle e o fluxo de dados do rastreamento, que será dimensionado com agentes maiores para mostrar transferências e relacionamentos entre chamadas LLM, chamadas de ferramentas e interações de agentes.
 
-![Visualização do fluxo de execução do agente de observabilidade do Datadog LLM](/images/datadog-llm-observability-2.png)
+<Frame>
+<img src="/images/datadog-llm-observability-2.png" alt="Visualização do fluxo de execução do agente de observabilidade do Datadog LLM" />
+</Frame>
 
 ## Referências
 

docs/pt-BR/tools/database-data/qdrantvectorsearchtool.mdx

@@ -23,13 +23,15 @@ Veja um exemplo mínimo de como utilizar a ferramenta:
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
 
-# Inicialize a ferramenta
+# Inicialize a ferramenta com QdrantConfig
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url="your_qdrant_url",
-    qdrant_api_key="your_qdrant_api_key",
-    collection_name="your_collection"
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_qdrant_url",
+        qdrant_api_key="your_qdrant_api_key",
+        collection_name="your_collection"
+    )
 )
 
 # Crie um agente que utiliza a ferramenta
@@ -82,7 +84,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-small"
+        model="text-embedding-3-large"
     )
     return response.data[0].embedding
 
@@ -90,13 +92,13 @@ def get_openai_embedding(text):
 def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
     # Extrair texto do PDF
     text_chunks = extract_text_from_pdf(pdf_path)
-    
+
     # Criar coleção no Qdrant
     if qdrant.collection_exists(collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
     # Armazenar embeddings
@@ -120,19 +122,23 @@ pdf_path = "path/to/your/document.pdf"
 load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
 
 # Inicializar ferramenta de busca Qdrant
+from crewai_tools import QdrantConfig
+
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url=os.getenv("QDRANT_URL"),
-    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-    collection_name=collection_name,
-    limit=3,
-    score_threshold=0.35
+    qdrant_config=QdrantConfig(
+        qdrant_url=os.getenv("QDRANT_URL"),
+        qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+        collection_name=collection_name,
+        limit=3,
+        score_threshold=0.35
+    )
 )
 
 # Criar agentes CrewAI
 search_agent = Agent(
     role="Senior Semantic Search Agent",
     goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant 
+    backstory="""You are an expert research assistant who can find relevant
     information using semantic search in a Qdrant database.""",
     tools=[qdrant_tool],
     verbose=True
@@ -141,7 +147,7 @@ search_agent = Agent(
 answer_agent = Agent(
     role="Senior Answer Assistant",
     goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate 
+    backstory="""You are an expert answer assistant who can generate
     answers to questions based on the context provided.""",
     tools=[qdrant_tool],
     verbose=True
@@ -180,21 +186,82 @@ print(result)
 ## Parâmetros da Ferramenta
 
 ### Parâmetros Obrigatórios
-- `qdrant_url` (str): URL do seu servidor Qdrant
-- `qdrant_api_key` (str): Chave de API para autenticação com o Qdrant
-- `collection_name` (str): Nome da coleção Qdrant a ser pesquisada
+- `qdrant_config` (QdrantConfig): Objeto de configuração contendo todas as configurações do Qdrant
 
-### Parâmetros Opcionais
+### Parâmetros do QdrantConfig
+- `qdrant_url` (str): URL do seu servidor Qdrant
+- `qdrant_api_key` (str, opcional): Chave de API para autenticação com o Qdrant
+- `collection_name` (str): Nome da coleção Qdrant a ser pesquisada
 - `limit` (int): Número máximo de resultados a serem retornados (padrão: 3)
 - `score_threshold` (float): Limite mínimo de similaridade (padrão: 0.35)
+- `filter` (Any, opcional): Instância de Filter do Qdrant para filtragem avançada (padrão: None)
+
+### Parâmetros Opcionais da Ferramenta
 - `custom_embedding_fn` (Callable[[str], list[float]]): Função personalizada para vetorização de textos
+- `qdrant_package` (str): Caminho base do pacote Qdrant (padrão: "qdrant_client")
+- `client` (Any): Cliente Qdrant pré-inicializado (opcional)
+
+## Filtragem Avançada
+
+A ferramenta QdrantVectorSearchTool oferece recursos poderosos de filtragem para refinar os resultados da busca:
+
+### Filtragem Dinâmica
+Use os parâmetros `filter_by` e `filter_value` na sua busca para filtrar resultados dinamicamente:
+
+```python
+# O agente usará esses parâmetros ao chamar a ferramenta
+# O schema da ferramenta aceita filter_by e filter_value
+# Exemplo: busca com filtro de categoria
+# Os resultados serão filtrados onde categoria == "tecnologia"
+```
+
+### Filtros Pré-definidos com QdrantConfig
+Para filtragens complexas, use instâncias de Filter do Qdrant na sua configuração:
+
+```python
+from qdrant_client.http import models as qmodels
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
+
+# Criar um filtro para condições específicas
+preset_filter = qmodels.Filter(
+    must=[
+        qmodels.FieldCondition(
+            key="categoria",
+            match=qmodels.MatchValue(value="pesquisa")
+        ),
+        qmodels.FieldCondition(
+            key="ano",
+            match=qmodels.MatchValue(value=2024)
+        )
+    ]
+)
+
+# Inicializar ferramenta com filtro pré-definido
+qdrant_tool = QdrantVectorSearchTool(
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection",
+        filter=preset_filter  # Filtro pré-definido aplicado a todas as buscas
+    )
+)
+```
+
+### Combinando Filtros
+A ferramenta combina automaticamente os filtros pré-definidos do `QdrantConfig` com os filtros dinâmicos de `filter_by` e `filter_value`:
+
+```python
+# Se QdrantConfig tem um filtro pré-definido para categoria="pesquisa"
+# E a busca usa filter_by="ano", filter_value=2024
+# Ambos os filtros serão combinados (lógica AND)
+```
 
 ## Parâmetros de Busca
 
 A ferramenta aceita estes parâmetros em seu schema:
 - `query` (str): Consulta de busca para encontrar documentos similares
 - `filter_by` (str, opcional): Campo de metadado para filtrar
-- `filter_value` (str, opcional): Valor para filtrar
+- `filter_value` (Any, opcional): Valor para filtrar
 
 ## Formato de Retorno
 
@@ -214,7 +281,7 @@ A ferramenta retorna resultados no formato JSON:
 
 ## Embedding Padrão
 
-Por padrão, a ferramenta utiliza o modelo `text-embedding-3-small` da OpenAI para vetorização. Isso requer:
+Por padrão, a ferramenta utiliza o modelo `text-embedding-3-large` da OpenAI para vetorização. Isso requer:
 - Chave de API da OpenAI definida na variável de ambiente: `OPENAI_API_KEY`
 
 ## Embeddings Personalizados
@@ -240,18 +307,22 @@ def custom_embeddings(text: str) -> list[float]:
     # Tokenizar e obter saídas do modelo
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
-    
+
     # Usar mean pooling para obter o embedding do texto
     embeddings = outputs.last_hidden_state.mean(dim=1)
-    
+
     # Converter para lista de floats e retornar
     return embeddings[0].tolist()
 
 # Usar embeddings personalizados com a ferramenta
+from crewai_tools import QdrantConfig
+
 tool = QdrantVectorSearchTool(
-    qdrant_url="your_url",
-    qdrant_api_key="your_key",
-    collection_name="your_collection",
+    qdrant_config=QdrantConfig(
+        qdrant_url="your_url",
+        qdrant_api_key="your_key",
+        collection_name="your_collection"
+    ),
     custom_embedding_fn=custom_embeddings  # Passe sua função personalizada
 )
 ```
@@ -270,4 +341,4 @@ Variáveis de ambiente obrigatórias:
 export QDRANT_URL="your_qdrant_url"  # Se não for informado no construtor
 export QDRANT_API_KEY="your_api_key"  # Se não for informado no construtor
 export OPENAI_API_KEY="your_openai_key"  # Se estiver usando embeddings padrão
-```
+```

docs/pt-BR/tools/file-document/directorysearchtool.mdx

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
 O DirectorySearchTool utiliza OpenAI para embeddings e sumarização por padrão. As opções de personalização dessas configurações incluem a alteração do provedor de modelo e configurações, ampliando a flexibilidade para usuários avançados.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DirectorySearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # As opções incluem ollama, google, anthropic, llama2 e mais
-            config=dict(
-                model="llama2",
-                # Configurações adicionais aqui
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/pt-BR/tools/file-document/docxsearchtool.mdx

@@ -56,25 +56,25 @@ Os seguintes parâmetros podem ser usados para customizar o comportamento da `DO
 Por padrão, a ferramenta utiliza o OpenAI tanto para embeddings quanto para sumarização. Para customizar o modelo, você pode usar um dicionário de configuração como no exemplo:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = DOCXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # ou google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/pt-BR/tools/file-document/mdxsearchtool.mdx

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
 A ferramenta utiliza, por padrão, o OpenAI para embeddings e sumarização. Para personalizar, utilize um dicionário de configuração conforme exemplo abaixo:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = MDXSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # As opções incluem google, openai, anthropic, llama2, etc.
-            config=dict(
-                model="llama2",
-                # Parâmetros opcionais podem ser incluídos aqui.
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # Um título opcional para os embeddings pode ser adicionado aqui.
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

docs/pt-BR/tools/file-document/pdfsearchtool.mdx

@@ -45,28 +45,60 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 
 ## Modelo e embeddings personalizados
 
-Por padrão, a ferramenta utiliza OpenAI tanto para embeddings quanto para sumarização. Para personalizar o modelo, você pode usar um dicionário de configuração como no exemplo abaixo:
+Por padrão, a ferramenta utiliza OpenAI para embeddings e sumarização. Para personalizar, use um dicionário de configuração conforme abaixo. Observação: um banco vetorial (vectordb) é necessário, pois os embeddings gerados precisam ser armazenados e consultados.
 
 ```python Code
+from crewai_tools import PDFSearchTool
+from chromadb.config import Settings  # Persistência no Chroma
+
 tool = PDFSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # ou google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        # Obrigatório: provedor de embeddings + configuração
+        "embedding_model": {
+            # Provedores suportados: "openai", "azure", "google-generativeai", "google-vertex",
+            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
+            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
+            "provider": "openai",
+            "config": {
+                # "model" é mapeado internamente para "model_name".
+                "model": "text-embedding-3-small",
+                # Opcional: chave da API (se ausente, usa variáveis de ambiente do provedor)
+                # "api_key": "sk-...",
+
+                # Exemplos específicos por provedor
+                # --- Google ---
+                # (defina provider="google-generativeai")
+                # "model": "models/embedding-001",
+                # "task_type": "retrieval_document",
+
+                # --- Cohere ---
+                # (defina provider="cohere")
+                # "model": "embed-english-v3.0",
+
+                # --- Ollama (local) ---
+                # (defina provider="ollama")
+                # "model": "nomic-embed-text",
+            },
+        },
+
+        # Obrigatório: configuração do banco vetorial
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # Exemplo Chroma:
+                # "settings": Settings(
+                #     persist_directory="/content/chroma",
+                #     allow_reset=True,
+                #     is_persistent=True,
+                # ),
+
+                # Exemplo Qdrant:
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                # Observação: o nome da coleção é controlado pela ferramenta (padrão: "rag_tool_collection").
+            }
+        },
+    }
 )
 ```

docs/pt-BR/tools/file-document/txtsearchtool.mdx

@@ -57,25 +57,39 @@ Por padrão, a ferramenta utiliza o OpenAI tanto para embeddings quanto para sum
 Para personalizar o modelo, você pode usar um dicionário de configuração como o exemplo a seguir:
 
 ```python Code
+from chromadb.config import Settings
+
 tool = TXTSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # ou google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        # Obrigatório: provedor de embeddings + configuração
+        "embedding_model": {
+            "provider": "openai",  # ou google-generativeai, cohere, ollama, ...
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",  # opcional se variável de ambiente estiver definida
+                # Exemplos por provedor:
+                # Google → model: "models/embedding-001", task_type: "retrieval_document"
+            },
+        },
+
+        # Obrigatório: configuração do banco vetorial
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # Configurações do Chroma (persistência opcional)
+                # "settings": Settings(
+                #     persist_directory="/content/chroma",
+                #     allow_reset=True,
+                #     is_persistent=True,
+                # ),
+
+                # Exemplo de parâmetros de vetor do Qdrant:
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+
+                # Observação: o nome da coleção é controlado pela ferramenta (padrão: "rag_tool_collection").
+            }
+        },
+    }
 )
 ```

docs/pt-BR/tools/file-document/xmlsearchtool.mdx

@@ -54,25 +54,25 @@ Este parâmetro é opcional durante a inicialização da ferramenta, mas deve se
 Por padrão, a ferramenta utiliza a OpenAI tanto para embeddings quanto para sumarização. Para personalizar o modelo, você pode usar um dicionário de configuração conforme o exemplo a seguir:
 
 ```python Code  
+from chromadb.config import Settings
+
 tool = XMLSearchTool(
-    config=dict(
-        llm=dict(
-            provider="ollama", # ou google, openai, anthropic, llama2, ...
-            config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google", # ou openai, ollama, ...
-            config=dict(
-                model="models/embedding-001",
-                task_type="retrieval_document",
-                # title="Embeddings",
-            ),
-        ),
-    )
+    config={
+        "embedding_model": {
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small",
+                # "api_key": "sk-...",
+            },
+        },
+        "vectordb": {
+            "provider": "chromadb",  # ou "qdrant"
+            "config": {
+                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
+                # from qdrant_client.models import VectorParams, Distance
+                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
+            }
+        },
+    }
 )
 ```

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.2.1",
+    "crewai==1.3.0",
     "lancedb>=0.5.4",
     "tiktoken>=0.8.0",
     "beautifulsoup4>=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -287,4 +287,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.2.1"
+__version__ = "1.3.0"

lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/firecrawl_crawl_website_tool.py

@@ -22,22 +22,23 @@ class FirecrawlCrawlWebsiteToolSchema(BaseModel):
 
 
 class FirecrawlCrawlWebsiteTool(BaseTool):
-    """Tool for crawling websites using Firecrawl. To run this tool, you need to have a Firecrawl API key.
+    """Tool for crawling websites using Firecrawl v2 API. To run this tool, you need to have a Firecrawl API key.
 
     Args:
         api_key (str): Your Firecrawl API key.
-        config (dict): Optional. It contains Firecrawl API parameters.
+        config (dict): Optional. It contains Firecrawl v2 API parameters.
 
-    Default configuration options:
-        max_depth (int): Maximum depth to crawl. Default: 2
+    Default configuration options (Firecrawl v2 API):
+        max_discovery_depth (int): Maximum depth for discovering pages. Default: 2
         ignore_sitemap (bool): Whether to ignore sitemap. Default: True
-        limit (int): Maximum number of pages to crawl. Default: 100
-        allow_backward_links (bool): Allow crawling backward links. Default: False
+        limit (int): Maximum number of pages to crawl. Default: 10
         allow_external_links (bool): Allow crawling external links. Default: False
-        scrape_options (ScrapeOptions): Options for scraping content
-            - formats (list[str]): Content formats to return. Default: ["markdown", "screenshot", "links"]
+        allow_subdomains (bool): Allow crawling subdomains. Default: False
+        delay (int): Delay between requests in milliseconds. Default: None
+        scrape_options (dict): Options for scraping content
+            - formats (list[str]): Content formats to return. Default: ["markdown"]
             - only_main_content (bool): Only return main content. Default: True
-            - timeout (int): Timeout in milliseconds. Default: 30000
+            - timeout (int): Timeout in milliseconds. Default: 10000
     """
 
     model_config = ConfigDict(
@@ -49,14 +50,15 @@ class FirecrawlCrawlWebsiteTool(BaseTool):
     api_key: str | None = None
     config: dict[str, Any] | None = Field(
         default_factory=lambda: {
-            "maxDepth": 2,
-            "ignoreSitemap": True,
+            "max_discovery_depth": 2,
+            "ignore_sitemap": True,
             "limit": 10,
-            "allowBackwardLinks": False,
-            "allowExternalLinks": False,
-            "scrapeOptions": {
-                "formats": ["markdown", "screenshot", "links"],
-                "onlyMainContent": True,
+            "allow_external_links": False,
+            "allow_subdomains": False,
+            "delay": None,
+            "scrape_options": {
+                "formats": ["markdown"],
+                "only_main_content": True,
                 "timeout": 10000,
             },
         }
@@ -107,7 +109,7 @@ class FirecrawlCrawlWebsiteTool(BaseTool):
         if not self._firecrawl:
             raise RuntimeError("FirecrawlApp not properly initialized")
 
-        return self._firecrawl.crawl_url(url, poll_interval=2, params=self.config)
+        return self._firecrawl.crawl(url=url, poll_interval=2, **self.config)
 
 
 try:

lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py

@@ -22,20 +22,27 @@ class FirecrawlScrapeWebsiteToolSchema(BaseModel):
 
 
 class FirecrawlScrapeWebsiteTool(BaseTool):
-    """Tool for scraping webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key.
+    """Tool for scraping webpages using Firecrawl v2 API. To run this tool, you need to have a Firecrawl API key.
 
     Args:
         api_key (str): Your Firecrawl API key.
-        config (dict): Optional. It contains Firecrawl API parameters.
+        config (dict): Optional. It contains Firecrawl v2 API parameters.
 
-    Default configuration options:
+    Default configuration options (Firecrawl v2 API):
         formats (list[str]): Content formats to return. Default: ["markdown"]
-        onlyMainContent (bool): Only return main content. Default: True
-        includeTags (list[str]): Tags to include. Default: []
-        excludeTags (list[str]): Tags to exclude. Default: []
-        headers (dict): Headers to include. Default: {}
-        waitFor (int): Time to wait for page to load in ms. Default: 0
-        json_options (dict): Options for JSON extraction. Default: None
+        only_main_content (bool): Only return main content excluding headers, navs, footers, etc. Default: True
+        include_tags (list[str]): Tags to include in the output. Default: []
+        exclude_tags (list[str]): Tags to exclude from the output. Default: []
+        max_age (int): Returns cached version if younger than this age in milliseconds. Default: 172800000 (2 days)
+        headers (dict): Headers to send with the request (e.g., cookies, user-agent). Default: {}
+        wait_for (int): Delay in milliseconds before fetching content. Default: 0
+        mobile (bool): Emulate scraping from a mobile device. Default: False
+        skip_tls_verification (bool): Skip TLS certificate verification. Default: True
+        timeout (int): Request timeout in milliseconds. Default: None
+        remove_base64_images (bool): Remove base64 images from output. Default: True
+        block_ads (bool): Enable ad-blocking and cookie popup blocking. Default: True
+        proxy (str): Proxy type ("basic", "stealth", "auto"). Default: "auto"
+        store_in_cache (bool): Store page in Firecrawl index and cache. Default: True
     """
 
     model_config = ConfigDict(
@@ -48,11 +55,18 @@ class FirecrawlScrapeWebsiteTool(BaseTool):
     config: dict[str, Any] = Field(
         default_factory=lambda: {
             "formats": ["markdown"],
-            "onlyMainContent": True,
-            "includeTags": [],
-            "excludeTags": [],
+            "only_main_content": True,
+            "include_tags": [],
+            "exclude_tags": [],
+            "max_age": 172800000,  # 2 days cache
             "headers": {},
-            "waitFor": 0,
+            "wait_for": 0,
+            "mobile": False,
+            "skip_tls_verification": True,
+            "remove_base64_images": True,
+            "block_ads": True,
+            "proxy": "auto",
+            "store_in_cache": True,
         }
     )
 
@@ -95,7 +109,7 @@ class FirecrawlScrapeWebsiteTool(BaseTool):
         if not self._firecrawl:
             raise RuntimeError("FirecrawlApp not properly initialized")
 
-        return self._firecrawl.scrape_url(url, params=self.config)
+        return self._firecrawl.scrape(url=url, **self.config)
 
 
 try:

lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/firecrawl_search_tool.py

@@ -23,19 +23,24 @@ class FirecrawlSearchToolSchema(BaseModel):
 
 
 class FirecrawlSearchTool(BaseTool):
-    """Tool for searching webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key.
+    """Tool for searching webpages using Firecrawl v2 API. To run this tool, you need to have a Firecrawl API key.
 
     Args:
         api_key (str): Your Firecrawl API key.
-        config (dict): Optional. It contains Firecrawl API parameters.
+        config (dict): Optional. It contains Firecrawl v2 API parameters.
 
-    Default configuration options:
-        limit (int): Maximum number of pages to crawl. Default: 5
-        tbs (str): Time before search. Default: None
-        lang (str): Language. Default: "en"
-        country (str): Country. Default: "us"
-        location (str): Location. Default: None
-        timeout (int): Timeout in milliseconds. Default: 60000
+    Default configuration options (Firecrawl v2 API):
+        limit (int): Maximum number of search results to return. Default: 5
+        tbs (str): Time-based search filter (e.g., "qdr:d" for past day). Default: None
+        location (str): Location for search results. Default: None
+        timeout (int): Request timeout in milliseconds. Default: None
+        scrape_options (dict): Options for scraping the search results. Default: {"formats": ["markdown"]}
+            - formats (list[str]): Content formats to return. Default: ["markdown"]
+            - only_main_content (bool): Only return main content. Default: True
+            - include_tags (list[str]): Tags to include. Default: []
+            - exclude_tags (list[str]): Tags to exclude. Default: []
+            - wait_for (int): Delay before fetching content in ms. Default: 0
+            - timeout (int): Request timeout in milliseconds. Default: None
     """
 
     model_config = ConfigDict(
@@ -49,10 +54,15 @@ class FirecrawlSearchTool(BaseTool):
         default_factory=lambda: {
             "limit": 5,
             "tbs": None,
-            "lang": "en",
-            "country": "us",
             "location": None,
-            "timeout": 60000,
+            "timeout": None,
+            "scrape_options": {
+                "formats": ["markdown"],
+                "only_main_content": True,
+                "include_tags": [],
+                "exclude_tags": [],
+                "wait_for": 0,
+            },
         }
     )
     _firecrawl: FirecrawlApp | None = PrivateAttr(None)
@@ -106,7 +116,7 @@ class FirecrawlSearchTool(BaseTool):
 
         return self._firecrawl.search(
             query=query,
-            params=self.config,
+            **self.config,
         )
 
 

lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
+from collections.abc import Callable
 import importlib
 import json
 import os
-from collections.abc import Callable
 from typing import Any
 
 from crewai.tools import BaseTool, EnvVar
@@ -12,9 +12,13 @@ from pydantic.types import ImportString
 
 
 class QdrantToolSchema(BaseModel):
-    query: str = Field(..., description="Query to search in Qdrant DB.")
-    filter_by: str | None = None
-    filter_value: str | None = None
+    query: str = Field(..., description="Query to search in Qdrant DB")
+    filter_by: str | None = Field(
+        default=None, description="Parameter to filter the search by."
+    )
+    filter_value: Any | None = Field(
+        default=None, description="Value to filter the search by."
+    )
 
 
 class QdrantConfig(BaseModel):
@@ -25,7 +29,9 @@ class QdrantConfig(BaseModel):
     collection_name: str
     limit: int = 3
     score_threshold: float = 0.35
-    filter_conditions: list[tuple[str, Any]] = Field(default_factory=list)
+    filter: Any | None = Field(
+        default=None, description="Qdrant Filter instance for advanced filtering."
+    )
 
 
 class QdrantVectorSearchTool(BaseTool):
@@ -76,23 +82,26 @@ class QdrantVectorSearchTool(BaseTool):
         filter_value: Any | None = None,
     ) -> str:
         """Perform vector similarity search."""
-        filter_ = self.qdrant_package.http.models.Filter
-        field_condition = self.qdrant_package.http.models.FieldCondition
-        match_value = self.qdrant_package.http.models.MatchValue
-        conditions = self.qdrant_config.filter_conditions.copy()
-        if filter_by and filter_value is not None:
-            conditions.append((filter_by, filter_value))
 
         search_filter = (
-            filter_(
-                must=[
-                    field_condition(key=k, match=match_value(value=v))
-                    for k, v in conditions
-                ]
-            )
-            if conditions
-            else None
+            self.qdrant_config.filter.model_copy()
+            if self.qdrant_config.filter is not None
+            else self.qdrant_package.http.models.Filter(must=[])
         )
+        if filter_by and filter_value is not None:
+            if not hasattr(search_filter, "must") or not isinstance(
+                search_filter.must, list
+            ):
+                search_filter.must = []
+            search_filter.must.append(
+                self.qdrant_package.http.models.FieldCondition(
+                    key=filter_by,
+                    match=self.qdrant_package.http.models.MatchValue(
+                        value=filter_value
+                    ),
+                )
+            )
+
         query_vector = (
             self.custom_embedding_fn(query)
             if self.custom_embedding_fn

lib/crewai-tools/tests/tools/cassettes/firecrawl_scrape_website_tool_test/test_firecrawl_scrape_tool_integration.yaml

@@ -0,0 +1,289 @@
+interactions:
+- request:
+    body: '{"url": "https://firecrawl.dev", "includeTags": [], "excludeTags": [],
+      "onlyMainContent": true, "waitFor": 0, "skipTlsVerification": true, "removeBase64Images":
+      true, "fastMode": false, "blockAds": true, "storeInCache": true, "maxAge": 172800000,
+      "formats": ["markdown"], "headers": {}, "mobile": false, "proxy": "auto", "origin":
+      "python-sdk@4.5.0"}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '350'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - python-requests/2.32.5
+    method: POST
+    uri: https://api.firecrawl.dev/v2/scrape
+  response:
+    body:
+      string: "{\"success\":true,\"data\":{\"markdown\":\"We just raised our Series
+        A and shipped Firecrawl /v2 \U0001F389. [Read the blog.](https://www.firecrawl.dev/blog/firecrawl-v2-series-a-announcement)\\n\\n[2
+        Months Free \u2014 Annually](https://www.firecrawl.dev/pricing)\\n\\n# Turn
+        websites into   LLM-ready data\\n\\nPower your AI apps with clean web data\\n\\nfrom
+        any website. [It's also open source.](https://github.com/firecrawl/firecrawl)\\n\\nScrape\\n\\nSearch\\nNew\\n\\nMap\\n\\nCrawl\\n\\nScrape\\n\\nLogo\\n\\nNavigation\\n\\nButton\\n\\nH1
+        Title\\n\\nDescription\\n\\nCTA Button\\n\\n\\\\[ .JSON \\\\]\\n\\n```json\\n1[\\\\\\n2
+        \ {\\\\\\n3    \\\"url\\\": \\\"https://example.com\\\",\\\\\\n4    \\\"markdown\\\":
+        \\\"# Getting Started...\\\",\\\\\\n5    \\\"json\\\": { \\\"title\\\": \\\"Guide\\\",
+        \\\"docs\\\": \\\"...\\\" },\\\\\\n6    \\\"screenshot\\\": \\\"https://example.com/hero.png\\\"\\\\\\n7
+        \ }\\\\\\n8]\\n```\\n\\nScrape Completed\\n\\nTrusted by5000+\\n\\ncompaniesof
+        all sizes\\n\\n![Logo 17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets-original/logocloud/1.png)\\n\\n![Logo
+        2](https://www.firecrawl.dev/assets-original/logocloud/2.png)\\n\\n![Logo
+        3](https://www.firecrawl.dev/assets-original/logocloud/3.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets-original/logocloud/4.png)\\n\\n![Logo
+        5](https://www.firecrawl.dev/assets-original/logocloud/5.png)\\n\\n![Logo
+        6](https://www.firecrawl.dev/assets-original/logocloud/6.png)\\n\\n![Logo
+        7](https://www.firecrawl.dev/assets-original/logocloud/7.png)\\n\\n![Logo
+        8](https://www.firecrawl.dev/assets-original/logocloud/8.png)\\n\\n![Logo
+        9](https://www.firecrawl.dev/assets-original/logocloud/9.png)\\n\\n![Logo
+        10](https://www.firecrawl.dev/assets-original/logocloud/10.png)\\n\\n![Logo
+        11](https://www.firecrawl.dev/assets-original/logocloud/11.png)\\n\\n![Logo
+        12](https://www.firecrawl.dev/assets-original/logocloud/12.png)\\n\\n![Logo
+        13](https://www.firecrawl.dev/assets-original/logocloud/13.png)\\n\\n![Logo
+        14](https://www.firecrawl.dev/assets-original/logocloud/14.png)\\n\\n![Logo
+        15](https://www.firecrawl.dev/assets-original/logocloud/15.png)\\n\\n![Logo
+        16](https://www.firecrawl.dev/assets-original/logocloud/16.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        19](https://www.firecrawl.dev/assets-original/logocloud/19.png)\\n\\n![Logo
+        20](https://www.firecrawl.dev/assets-original/logocloud/20.png)\\n\\n![Logo
+        21](https://www.firecrawl.dev/assets-original/logocloud/21.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets-original/logocloud/1.png)\\n\\n![Logo
+        2](https://www.firecrawl.dev/assets-original/logocloud/2.png)\\n\\n![Logo
+        3](https://www.firecrawl.dev/assets-original/logocloud/3.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets-original/logocloud/4.png)\\n\\n![Logo
+        5](https://www.firecrawl.dev/assets-original/logocloud/5.png)\\n\\n![Logo
+        6](https://www.firecrawl.dev/assets-original/logocloud/6.png)\\n\\n![Logo
+        7](https://www.firecrawl.dev/assets-original/logocloud/7.png)\\n\\n![Logo
+        8](https://www.firecrawl.dev/assets-original/logocloud/8.png)\\n\\n![Logo
+        9](https://www.firecrawl.dev/assets-original/logocloud/9.png)\\n\\n![Logo
+        10](https://www.firecrawl.dev/assets-original/logocloud/10.png)\\n\\n![Logo
+        11](https://www.firecrawl.dev/assets-original/logocloud/11.png)\\n\\n![Logo
+        12](https://www.firecrawl.dev/assets-original/logocloud/12.png)\\n\\n![Logo
+        13](https://www.firecrawl.dev/assets-original/logocloud/13.png)\\n\\n![Logo
+        14](https://www.firecrawl.dev/assets-original/logocloud/14.png)\\n\\n![Logo
+        15](https://www.firecrawl.dev/assets-original/logocloud/15.png)\\n\\n![Logo
+        16](https://www.firecrawl.dev/assets-original/logocloud/16.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        19](https://www.firecrawl.dev/assets-original/logocloud/19.png)\\n\\n![Logo
+        20](https://www.firecrawl.dev/assets-original/logocloud/20.png)\\n\\n![Logo
+        21](https://www.firecrawl.dev/assets-original/logocloud/21.png)\\n\\n\\\\[01/
+        07 \\\\]\\n\\n\xB7\\n\\nMain Features\\n\\n//\\n\\nDeveloper First\\n\\n//\\n\\n##
+        Startscraping   today\\n\\nEnhance your apps with industry leading web scraping
+        and crawling capabilities.\\n\\nScrape\\n\\nGet llm-ready data from websites.
+        Markdown, JSON, screenshot, etc.\\n\\nSearch\\n\\nNew\\n\\nSearch the web
+        and get full content from results.\\n\\nCrawl\\n\\nCrawl all the pages on
+        a website and get data for each page.\\n\\nPython\\n\\nNode.js\\n\\nCurl\\n\\nCopy
+        code\\n\\n```python\\n1# pip install firecrawl-py\\n2from firecrawl import
+        Firecrawl\\n3\\n4app = Firecrawl(api_key=\\\"fc-YOUR_API_KEY\\\")\\n5\\n6#
+        Scrape a website:\\n7app.scrape('firecrawl.dev')\\n8\\n9\\n10\\n```\\n\\n\\\\[
+        .MD \\\\]\\n\\n```markdown\\n1# Firecrawl\\n2\\n3Firecrawl is a powerful web
+        scraping\\n4library that makes it easy to extract\\n5data from websites.\\n6\\n7##
+        Installation\\n8\\n9To install Firecrawl, run:\\n10\\n11\\n```\\n\\n![developer-1](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-2](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-3](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-4](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-5](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-6](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-7](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-8](https://www.firecrawl.dev/assets/developer/8.png)\\n\\n![developer-9](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-10](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-11](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-12](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-13](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-14](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-15](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-16](https://www.firecrawl.dev/assets/developer/8.png)\\n\\n![developer-17](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-18](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-19](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-20](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-21](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-22](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-23](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-24](https://www.firecrawl.dev/assets/developer/8.png)\\n\\nIntegrations\\n\\n###
+        Use well-known tools\\n\\nAlready fully integrated with the greatest existing
+        tools and workflows.\\n\\n[See all integrations](https://www.firecrawl.dev/app)\\n\\n![Firecrawl
+        icon (blueprint)](https://www.firecrawl.dev/assets-original/developer-os-icon.png)\\n\\nmendableai/firecrawl\\n\\nPublic\\n\\nStar\\n\\n65.3K\\n\\n\\\\[python-SDK\\\\]
+        improvs/async\\n\\n#1337\\n\\n\xB7\\n\\nApr 18, 2025\\n\\n\xB7\\n\\n![rafaelsideguide](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nrafaelsideguide\\n\\nfeat(extract):
+        cost limit\\n\\n#1473\\n\\n\xB7\\n\\nApr 17, 2025\\n\\n\xB7\\n\\n![mogery](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nmogery\\n\\nfeat(scrape):
+        get job result from GCS, avoid Redis\\n\\n#1461\\n\\n\xB7\\n\\nApr 15, 2025\\n\\n\xB7\\n\\n![mogery](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nmogery\\n\\nExtract
+        v2/rerank improvs\\n\\n#1437\\n\\n\xB7\\n\\nApr 11, 2025\\n\\n\xB7\\n\\n![rafaelsideguide](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nrafaelsideguide\\n\\n![https://avatars.githubusercontent.com/u/150964962?v=4](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=96&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\n![https://avatars.githubusercontent.com/u/66118807?v=4](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=96&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\n+90\\n\\nOpen
+        Source\\n\\n### Code you can trust\\n\\nDeveloped transparently and collaboratively.
+        Join our community of contributors.\\n\\n[Check out our repo](https://github.com/firecrawl/firecrawl)\\n\\n\\\\[02/
+        07 \\\\]\\n\\n\xB7\\n\\nCore\\n\\n//\\n\\nBuilt to outperform\\n\\n//\\n\\n##
+        Core principles,    provenperformance\\n\\nBuilt from the ground up to outperform
+        traditional scrapers.\\n\\nNo proxy headaches\\n\\nReliable.Covers 96% of
+        the web,\\n\\nincluding JS-heavy and protected pages. No proxies, no puppets,
+        just clean data.\\n\\nFirecrawl\\n\\n96%\\n\\n![Puppeteer icon](https://www.firecrawl.dev/assets/puppeteer.png)\\n\\nPuppeteer\\n\\n79%\\n\\ncURL\\n\\n75%\\n\\nSpeed
+        that feels invisible\\n\\nBlazingly fast.Delivers results in less than 1 second,
+        fast for real-time agents\\n\\nand dynamic apps.\\n\\nURL\\n\\nCrawl\\n\\nScrape\\n\\nfirecrawl.dev/docs\\n\\n50ms\\n\\n51ms\\n\\nfirecrawl.dev/templates\\n\\n52ms\\n\\n50ms\\n\\nfirecrawl.dev/changelog\\n\\n49ms\\n\\n52ms\\n\\nfirecrawl.dev/about\\n\\n52ms\\n\\n50ms\\n\\nfirecrawl.dev/changelog\\n\\n50ms\\n\\n52ms\\n\\nfirecrawl.dev/playground\\n\\n51ms\\n\\n49ms\\n\\n\\\\[
+        CTA \\\\]\\n\\n\\\\[ CRAWL \\\\]\\n\\n\\\\[ SCRAPE \\\\]\\n\\n\\\\[ CTA \\\\]\\n\\n//\\n\\nGet
+        started\\n\\n//\\n\\nReady to build?\\n\\nStart getting Web Data for free
+        and scale seamlessly as your project expands. No credit card needed.\\n\\n[Start
+        for free](https://www.firecrawl.dev/signin) [See our plans](https://www.firecrawl.dev/pricing)\\n\\n\\\\[03/
+        07 \\\\]\\n\\n\xB7\\n\\nFeatures\\n\\n//\\n\\nZero configuration\\n\\n//\\n\\n##
+        We handle the hard stuff\\n\\nRotating proxies, orchestration, rate limits,
+        js-blocked content and more.\\n\\nDocs to data\\n\\nMedia parsing.Firecrawl
+        can parse and output content from web hosted pdfs, docx, and more.\\n\\nhttps://example.com/docs/report.pdf\\n\\nhttps://example.com/files/brief.docx\\n\\nhttps://example.com/docs/guide.html\\n\\ndocx\\n\\nParsing...\\n\\nKnows
+        the moment\\n\\nSmart wait.Firecrawl intelligently waits for content to load,
+        making scraping faster and more reliable.\\n\\nhttps://example-spa.com\\n\\nRequest
+        Sent\\n\\nScrapes the real thing\\n\\nCached, when you need it.Selective caching,
+        you choose your caching patterns, growing web index.\\n\\n![User](https://www.firecrawl.dev/_next/image?url=%2Fassets-original%2Ffeatures%2Fcached-user.png&w=256&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nUser\\n\\nFirecrawl\\n\\nCache\\n\\nInvisible
+        access\\n\\nStealth mode.Crawls the web without\\n\\nbeing blocked, mimics
+        real users to access protected or dynamic content.\\n\\nInteractive scraping\\n\\nActions.Click,
+        scroll, write, wait, press and more before extracting content.\\n\\nhttps://example.com\\n\\nNavigate\\n\\nClick\\n\\nType\\n\\nWait\\n\\nScroll\\n\\nPress\\n\\nScreenshot\\n\\nScrape\\n\\n\\\\[04/
+        07 \\\\]\\n\\n\xB7\\n\\nPricing\\n\\n//\\n\\nTransparent\\n\\n//\\n\\n## Flexible
+        pricing\\n\\nExplore transparent pricing built for real-world scraping.  Start
+        for free, then scale as you grow.\\n\\n\U0001F1FA\U0001F1F8USD\\n\\nFree Plan\\n\\nA
+        lightweight way to try scraping.\\n\\nNo cost, no card, no hassle.\\n\\n500
+        credits\\n\\n$0123456789\\n\\none-time\\n\\nGet started\\n\\nScrape 500 pages\\n\\n2
+        concurrent requests\\n\\nLow rate limits\\n\\nHobby\\n\\nGreat for side projects
+        and small tools.\\n\\nFast, simple, no overkill.\\n\\n3,000 credits\\n\\n$01234567890123456789\\n\\n/monthly\\n\\nBilled
+        yearly\\n\\n2 months free\\n\\nSubscribe\\n\\nScrape 3,000 pages\\n\\n5 concurrent
+        requests\\n\\nBasic support\\n\\n$9 per extra 1k credits\\n\\nStandard\\n\\nMost
+        popular\\n\\nPerfect for scaling with less effort.\\n\\nSimple, solid, dependable.\\n\\n100,000
+        credits\\n\\n$01234567890123456789\\n\\n/monthly\\n\\nBilled yearly\\n\\n2
+        months free\\n\\nSubscribe\\n\\nScrape 100,000 pages\\n\\n50 concurrent requests\\n\\nStandard
+        support\\n\\n$47 per extra 35k credits\\n\\nGrowth\\n\\nBuilt for high volume
+        and speed.\\n\\nFirecrawl at full force.\\n\\n500,000 credits\\n\\n$012345678901234567890123456789\\n\\n/monthly\\n\\nBilled
+        yearly\\n\\n2 months free\\n\\nSubscribe\\n\\nScrape 500,000 pages\\n\\n100
+        concurrent requests\\n\\nPriority support\\n\\n$177 per extra 175k credits\\n\\nExtra
+        credits are available via auto-recharge packs. [Enable](https://www.firecrawl.dev/signin/signup)\\n\\nEnterprise\\n\\nPower
+        at your pace\\n\\nUnlimited credits. Custom RPMs.\\n\\n[Contact sales](https://fk4bvu0n5qp.typeform.com/to/Ej6oydlg)
+        [More details](https://www.firecrawl.dev/enterprise)\\n\\nBulk discounts\\n\\nTop
+        priority support\\n\\nCustom concurrency limits\\n\\nImproved stealth proxies\\n\\nSLAs\\n\\nAdvanced
+        security & controls\\n\\n\\\\[05/ 07 \\\\]\\n\\n\xB7\\n\\nTestimonials\\n\\n//\\n\\nCommunity\\n\\n//\\n\\n##
+        People love    building withFirecrawl\\n\\nDiscover why developers choose
+        Firecrawl every day.\\n\\n[![Morgan Linton](https://www.firecrawl.dev/assets/testimonials/morgan-linton.png)Morgan
+        Linton@morganlinton\\\"If you're coding with AI, and haven't discovered @firecrawl\\\\_dev
+        yet, prepare to have your mind blown \U0001F92F\\\"](https://x.com/morganlinton/status/1839454165703204955)
+        [![Chris DeWeese](https://www.firecrawl.dev/assets/testimonials/chris-deweese.png)Chris
+        DeWeese@chrisdeweese\\\\_\\\"Started using @firecrawl\\\\_dev for a project,
+        I wish I used this sooner.\\\"](https://x.com/chrisdeweese_/status/1853587120406876601)
+        [![Alex Reibman](https://www.firecrawl.dev/assets/testimonials/alex-reibman.png)Alex
+        Reibman@AlexReibman\\\"Moved our internal agent's web scraping tool from Apify
+        to Firecrawl because it benchmarked 50x faster with AgentOps.\\\"](https://x.com/AlexReibman/status/1780299595484131836)
+        [![Tom - Morpho](https://www.firecrawl.dev/assets/testimonials/tom-morpho.png)Tom
+        - Morpho@TomReppelin\\\"I found gold today. Thank you @firecrawl\\\\_dev\\\"](https://x.com/TomReppelin/status/1844382491014201613)\\n\\n[![Morgan
+        Linton](https://www.firecrawl.dev/assets/testimonials/morgan-linton.png)Morgan
+        Linton@morganlinton\\\"If you're coding with AI, and haven't discovered @firecrawl\\\\_dev
+        yet, prepare to have your mind blown \U0001F92F\\\"](https://x.com/morganlinton/status/1839454165703204955)
+        [![Chris DeWeese](https://www.firecrawl.dev/assets/testimonials/chris-deweese.png)Chris
+        DeWeese@chrisdeweese\\\\_\\\"Started using @firecrawl\\\\_dev for a project,
+        I wish I used this sooner.\\\"](https://x.com/chrisdeweese_/status/1853587120406876601)
+        [![Alex Reibman](https://www.firecrawl.dev/assets/testimonials/alex-reibman.png)Alex
+        Reibman@AlexReibman\\\"Moved our internal agent's web scraping tool from Apify
+        to Firecrawl because it benchmarked 50x faster with AgentOps.\\\"](https://x.com/AlexReibman/status/1780299595484131836)
+        [![Tom - Morpho](https://www.firecrawl.dev/assets/testimonials/tom-morpho.png)Tom
+        - Morpho@TomReppelin\\\"I found gold today. Thank you @firecrawl\\\\_dev\\\"](https://x.com/TomReppelin/status/1844382491014201613)\\n\\n[![Bardia](https://www.firecrawl.dev/assets/testimonials/bardia.png)Bardia@thepericulum\\\"The
+        Firecrawl team ships. I wanted types for their node SDK, and less than an
+        hour later, I got them.\\\"](https://x.com/thepericulum/status/1781397799487078874)
+        [![Matt Busigin](https://www.firecrawl.dev/assets/testimonials/matt-busigin.png)Matt
+        Busigin@mbusigin\\\"Firecrawl is dope. Congrats guys \U0001F44F\\\"](https://x.com/mbusigin/status/1836065372010656069)
+        [![Sumanth](https://www.firecrawl.dev/assets/testimonials/sumanth.png)Sumanth@Sumanth\\\\_077\\\"Web
+        scraping will never be the same!\\\\\\\\\\n\\\\\\\\\\nFirecrawl is an open-source
+        framework that takes a URL, crawls it, and conver...\\\"](https://x.com/Sumanth_077/status/1940049003074478511)
+        [![Steven Tey](https://www.firecrawl.dev/assets/testimonials/steven-tey.png)Steven
+        Tey@steventey\\\"Open-source Clay alternative just dropped\\\\\\\\\\n\\\\\\\\\\nUpload
+        a CSV of emails and...\\\"](https://x.com/steventey/status/1932945651761098889)\\n\\n[![Bardia](https://www.firecrawl.dev/assets/testimonials/bardia.png)Bardia@thepericulum\\\"The
+        Firecrawl team ships. I wanted types for their node SDK, and less than an
+        hour later, I got them.\\\"](https://x.com/thepericulum/status/1781397799487078874)
+        [![Matt Busigin](https://www.firecrawl.dev/assets/testimonials/matt-busigin.png)Matt
+        Busigin@mbusigin\\\"Firecrawl is dope. Congrats guys \U0001F44F\\\"](https://x.com/mbusigin/status/1836065372010656069)
+        [![Sumanth](https://www.firecrawl.dev/assets/testimonials/sumanth.png)Sumanth@Sumanth\\\\_077\\\"Web
+        scraping will never be the same!\\\\\\\\\\n\\\\\\\\\\nFirecrawl is an open-source
+        framework that takes a URL, crawls it, and conver...\\\"](https://x.com/Sumanth_077/status/1940049003074478511)
+        [![Steven Tey](https://www.firecrawl.dev/assets/testimonials/steven-tey.png)Steven
+        Tey@steventey\\\"Open-source Clay alternative just dropped\\\\\\\\\\n\\\\\\\\\\nUpload
+        a CSV of emails and...\\\"](https://x.com/steventey/status/1932945651761098889)\\n\\n\\\\[06/
+        07 \\\\]\\n\\n\xB7\\n\\nUse Cases\\n\\n//\\n\\nUse cases\\n\\n//\\n\\n## Transform
+        \   web data into  AI-powered solutions\\n\\nDiscover how Firecrawl customers
+        are getting the most out of our API.\\n\\n[View all use cases](https://docs.firecrawl.dev/use-cases/overview)\\n\\nChat
+        with context\\n\\nSmarter AI chats\\n\\nPower your AI assistants with real-time,
+        accurate web content.\\n\\n[View docs](https://docs.firecrawl.dev/introduction)\\n\\n![AI
+        Assistant](https://www.firecrawl.dev/assets/ai/bot.png)\\n\\nAI Assistant\\n\\nwithFirecrawl\\n\\nReal-time\xB7Updated
+        2 min ago\\n\\nAsk anything...\\n\\nKnow your leads\\n\\nLead enrichment\\n\\nEnhance
+        your sales data with\\n\\nweb information.\\n\\n[Check out Extract](https://www.firecrawl.dev/extract)\\n\\nExtracting
+        leads from directory...\\n\\nTech startups\\n\\nWith contact info\\n\\nDecision
+        makers\\n\\nFunding stage\\n\\nReady to engage\\n\\n![Emily Tran](https://www.firecrawl.dev/assets/ai/leads-1.png)\\n\\n![James
+        Carter](https://www.firecrawl.dev/assets/ai/leads-2.png)\\n\\n![Sophia Kim](https://www.firecrawl.dev/assets/ai/leads-3.png)\\n\\n![Michael
+        Rivera](https://www.firecrawl.dev/assets/ai/leads-4.png)\\n\\nKnow your leads\\n\\nMCPs\\n\\nAdd
+        powerful scraping to your\\n\\ncode editors.\\n\\n[Get started](https://docs.firecrawl.dev/mcp-server)\\n\\n![Claude
+        Code](https://www.firecrawl.dev/assets/ai/mcps-claude.png)\\n\\nClaude Code\\n\\n![Cursor](https://www.firecrawl.dev/assets/ai/mcps-cursor.png)\\n\\nCursor\\n\\n![Windsurf](https://www.firecrawl.dev/assets/ai/mcps-windsurf.png)\\n\\nWindsurf\\n\\n\u273B\\n\\nWelcome
+        to Claude Code!\\n\\n/help for help, /status for your current setup\\n\\n>Try
+        \\\"how do I log an error?\\\"\\n\\nBuild with context\\n\\nAI platforms\\n\\nLet
+        your customers build AI apps\\n\\nwith web data.\\n\\n[Check out Map](https://docs.firecrawl.dev/features/map)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets/ai/platforms-1.png)\\n\\n![Logo 2](https://www.firecrawl.dev/assets/ai/platforms-2.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets/ai/platforms-4.png)\\n\\n![Logo 3](https://www.firecrawl.dev/assets/ai/platforms-3.png)\\n\\nExtracting
+        text...\\n\\nNo insight missed\\n\\nDeep research\\n\\nExtract comprehensive
+        information for\\n\\nin-depth research.\\n\\n[Build your own with Search](https://docs.firecrawl.dev/features/search)\\n\\nDeep
+        research in progress...\\n\\nAcademic papers\\n\\n0 found\\n\\nNews articles\\n\\n0
+        found\\n\\nExpert opinions\\n\\n0 found\\n\\nResearch reports\\n\\n0 found\\n\\nIndustry
+        data\\n\\n0 found\\n\\nAsk anything...\\n\\n\\\\[ CTA \\\\]\\n\\n\\\\[ CRAWL
+        \\\\]\\n\\n\\\\[ SCRAPE \\\\]\\n\\n\\\\[ CTA \\\\]\\n\\n//\\n\\nGet started\\n\\n//\\n\\nReady
+        to build?\\n\\nStart getting Web Data for free and scale seamlessly as your
+        project expands. No credit card needed.\\n\\n[Start for free](https://www.firecrawl.dev/signin)
+        [See our plans](https://www.firecrawl.dev/pricing)\\n\\n\\\\[07/ 07 \\\\]\\n\\n\xB7\\n\\nFAQ\\n\\n//\\n\\nFAQ\\n\\n//\\n\\n##
+        Frequently    askedquestions\\n\\nEverything you need to know about Firecrawl.\\n\\nGeneral\\n\\nWhat
+        is Firecrawl?\\n\\nWhat sites work?\\n\\nWho can benefit from using Firecrawl?\\n\\nIs
+        Firecrawl open-source?\\n\\nWhat is the difference between Firecrawl and other
+        web scrapers?\\n\\nWhat is the difference between the open-source version
+        and the hosted version?\\n\\nScraping & Crawling\\n\\nHow does Firecrawl handle
+        dynamic content on websites?\\n\\nWhy is it not crawling all the pages?\\n\\nCan
+        Firecrawl crawl websites without a sitemap?\\n\\nWhat formats can Firecrawl
+        convert web data into?\\n\\nHow does Firecrawl ensure the cleanliness of the
+        data?\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nDoes
+        it respect robots.txt?\\n\\nWhat measures does Firecrawl take to handle web
+        scraping challenges like rate limits and caching?\\n\\nDoes Firecrawl handle
+        captcha or authentication?\\n\\nAPI Related\\n\\nWhere can I find my API key?\\n\\nBilling\\n\\nIs
+        Firecrawl free?\\n\\nIs there a pay-per-use plan instead of monthly?\\n\\nDo
+        credits roll over to the next month?\\n\\nHow many credits do scraping and
+        crawling cost?\\n\\nDo you charge for failed requests?\\n\\nWhat payment methods
+        do you accept?\\n\\nFOOTER\\n\\nThe easiest way to extract\\n\\ndata from
+        the web\\n\\nBacked by\\n\\nY Combinator\\n\\n[Linkedin](https://www.linkedin.com/company/firecrawl)
+        [Github](https://github.com/firecrawl/firecrawl)\\n\\nSOC II \xB7 Type 2\\n\\nAICPA\\n\\nSOC
+        2\\n\\n[X (Twitter)](https://x.com/firecrawl_dev) [Discord](https://discord.gg/gSmWdAkdwd)\\n\\nProducts\\n\\n[Playground](https://www.firecrawl.dev/playground)
+        [Extract](https://www.firecrawl.dev/extract) [Pricing](https://www.firecrawl.dev/pricing)
+        [Templates](https://www.firecrawl.dev/templates) [Changelog](https://www.firecrawl.dev/changelog)\\n\\nUse
+        Cases\\n\\n[AI Platforms](https://docs.firecrawl.dev/use-cases/ai-platforms)
+        [Lead Enrichment](https://docs.firecrawl.dev/use-cases/lead-enrichment) [SEO
+        Platforms](https://docs.firecrawl.dev/use-cases/seo-platforms) [Deep Research](https://docs.firecrawl.dev/use-cases/deep-research)\\n\\nDocumentation\\n\\n[Getting
+        started](https://docs.firecrawl.dev/introduction) [API Reference](https://docs.firecrawl.dev/api-reference/introduction)
+        [Integrations](https://www.firecrawl.dev/app) [Examples](https://docs.firecrawl.dev/use-cases/overview)
+        [SDKs](https://docs.firecrawl.dev/sdks/overview)\\n\\nCompany\\n\\n[Blog](https://www.firecrawl.dev/blog)
+        [Careers](https://www.firecrawl.dev/careers) [Creator & OSS program](https://www.firecrawl.dev/creator-oss-program)
+        [Student program](https://www.firecrawl.dev/student-program)\\n\\n\xA9 2025
+        Firecrawl\\n\\n[Terms of Service](https://www.firecrawl.dev/terms-of-service)
+        [Privacy Policy](https://www.firecrawl.dev/privacy-policy) [Report Abuse](mailto:help@firecrawl.com?subject=Issue:)\\n\\n[All
+        systems normal](https://status.firecrawl.dev/)\\n\\nStripeM-Inner\",\"metadata\":{\"twitter:title\":\"Firecrawl
+        - The Web Data API for AI\",\"publisher\":\"Firecrawl\",\"ogUrl\":\"https://www.firecrawl.dev\",\"robots\":\"follow,
+        index\",\"title\":\"Firecrawl - The Web Data API for AI\",\"ogDescription\":\"The
+        web crawling, scraping, and search API for AI. Built for scale. Firecrawl
+        delivers the entire internet to AI agents and builders. Clean, structured,
+        and ready to reason with.\",\"ogImage\":\"https://www.firecrawl.dev/og.png\",\"viewport\":\"width=device-width,
+        initial-scale=1, maximum-scale=1, user-scalable=no\",\"og:url\":\"https://www.firecrawl.dev\",\"og:site_name\":\"Firecrawl
+        - The Web Data API for AI\",\"og:type\":\"website\",\"twitter:image\":\"https://www.firecrawl.dev/og.png\",\"author\":\"Firecrawl\",\"og:title\":\"Firecrawl
+        - The Web Data API for AI\",\"favicon\":\"https://www.firecrawl.dev/favicon.png\",\"description\":\"The
+        web crawling, scraping, and search API for AI. Built for scale. Firecrawl
+        delivers the entire internet to AI agents and builders. Clean, structured,
+        and ready to reason with.\",\"referrer\":\"origin-when-cross-origin\",\"twitter:site\":\"@Vercel\",\"ogSiteName\":\"Firecrawl
+        - The Web Data API for AI\",\"og:image\":\"https://www.firecrawl.dev/og.png\",\"twitter:card\":\"summary_large_image\",\"twitter:creator\":\"@Vercel\",\"twitter:description\":\"The
+        web crawling, scraping, and search API for AI. Built for scale. Firecrawl
+        delivers the entire internet to AI agents and builders. Clean, structured,
+        and ready to reason with.\",\"language\":\"en\",\"keywords\":\"Firecrawl,Markdown,Data,Mendable,Langchain\",\"creator\":\"Firecrawl\",\"ogTitle\":\"Firecrawl
+        - The Web Data API for AI\",\"og:description\":\"The web crawling, scraping,
+        and search API for AI. Built for scale. Firecrawl delivers the entire internet
+        to AI agents and builders. Clean, structured, and ready to reason with.\",\"scrapeId\":\"e78d8060-d581-4e5e-b25a-90cfdad48530\",\"sourceURL\":\"https://firecrawl.dev\",\"url\":\"https://www.firecrawl.dev/\",\"statusCode\":200,\"contentType\":\"text/html;
+        charset=utf-8\",\"proxyUsed\":\"basic\",\"cacheState\":\"hit\",\"cachedAt\":\"2025-10-29T13:09:07.713Z\",\"creditsUsed\":1}}}"
+    headers:
+      Access-Control-Allow-Origin:
+      - '*'
+      Alt-Svc:
+      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
+      Content-Length:
+      - '24693'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Wed, 29 Oct 2025 14:34:03 GMT
+      ETag:
+      - W/"6075-Q1W6uMv95JKEZARbtaiPYYMojlU"
+      Via:
+      - 1.1 google
+      X-Powered-By:
+      - Express
+      X-Response-Time:
+      - 4719.998ms
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai-tools/tests/tools/cassettes/firecrawl_search_tool_test/test_firecrawl_search_tool_integration.yaml

@@ -0,0 +1,937 @@
+interactions:
+- request:
+    body: '{"query": "firecrawl", "limit": 5, "scrapeOptions": {"includeTags": [],
+      "excludeTags": [], "onlyMainContent": true, "waitFor": 0, "skipTlsVerification":
+      true, "removeBase64Images": true, "fastMode": false, "blockAds": true, "storeInCache":
+      true, "maxAge": 14400000, "formats": ["markdown"], "mobile": false}, "origin":
+      "python-sdk@4.5.0"}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '338'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - python-requests/2.32.5
+    method: POST
+    uri: https://api.firecrawl.dev/v2/search
+  response:
+    body:
+      string: "{\"success\":true,\"data\":{\"web\":[{\"url\":\"https://www.firecrawl.dev/\",\"title\":\"Firecrawl
+        - The Web Data API for AI\",\"description\":\"The web crawling, scraping,
+        and search API for AI. Built for scale. Firecrawl delivers the entire internet
+        to AI agents and builders.\",\"position\":1,\"markdown\":\"We just raised
+        our Series A and shipped Firecrawl /v2 \U0001F389. [Read the blog.](https://www.firecrawl.dev/blog/firecrawl-v2-series-a-announcement)\\n\\n[2
+        Months Free \u2014 Annually](https://www.firecrawl.dev/pricing)\\n\\n# Turn
+        websites into   LLM-ready data\\n\\nPower your AI apps with clean web data\\n\\nfrom
+        any website. [It's also open source.](https://github.com/firecrawl/firecrawl)\\n\\nScrape\\n\\nSearch\\nNew\\n\\nMap\\n\\nCrawl\\n\\nScrape\\n\\nLogo\\n\\nNavigation\\n\\nButton\\n\\nH1
+        Title\\n\\nDescription\\n\\nCTA Button\\n\\n\\\\[ .JSON \\\\]\\n\\n```json\\n1[\\\\\\n2
+        \ {\\\\\\n3    \\\"url\\\": \\\"https://example.com\\\",\\\\\\n4    \\\"markdown\\\":
+        \\\"# Getting Started...\\\",\\\\\\n5    \\\"json\\\": { \\\"title\\\": \\\"Guide\\\",
+        \\\"docs\\\": \\\"...\\\" },\\\\\\n6    \\\"screenshot\\\": \\\"https://example.com/hero.png\\\"\\\\\\n7
+        \ }\\\\\\n8]\\n```\\n\\nScrape Completed\\n\\nTrusted by5000+\\n\\ncompaniesof
+        all sizes\\n\\n![Logo 17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets-original/logocloud/1.png)\\n\\n![Logo
+        2](https://www.firecrawl.dev/assets-original/logocloud/2.png)\\n\\n![Logo
+        3](https://www.firecrawl.dev/assets-original/logocloud/3.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets-original/logocloud/4.png)\\n\\n![Logo
+        5](https://www.firecrawl.dev/assets-original/logocloud/5.png)\\n\\n![Logo
+        6](https://www.firecrawl.dev/assets-original/logocloud/6.png)\\n\\n![Logo
+        7](https://www.firecrawl.dev/assets-original/logocloud/7.png)\\n\\n![Logo
+        8](https://www.firecrawl.dev/assets-original/logocloud/8.png)\\n\\n![Logo
+        9](https://www.firecrawl.dev/assets-original/logocloud/9.png)\\n\\n![Logo
+        10](https://www.firecrawl.dev/assets-original/logocloud/10.png)\\n\\n![Logo
+        11](https://www.firecrawl.dev/assets-original/logocloud/11.png)\\n\\n![Logo
+        12](https://www.firecrawl.dev/assets-original/logocloud/12.png)\\n\\n![Logo
+        13](https://www.firecrawl.dev/assets-original/logocloud/13.png)\\n\\n![Logo
+        14](https://www.firecrawl.dev/assets-original/logocloud/14.png)\\n\\n![Logo
+        15](https://www.firecrawl.dev/assets-original/logocloud/15.png)\\n\\n![Logo
+        16](https://www.firecrawl.dev/assets-original/logocloud/16.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        19](https://www.firecrawl.dev/assets-original/logocloud/19.png)\\n\\n![Logo
+        20](https://www.firecrawl.dev/assets-original/logocloud/20.png)\\n\\n![Logo
+        21](https://www.firecrawl.dev/assets-original/logocloud/21.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets-original/logocloud/1.png)\\n\\n![Logo
+        2](https://www.firecrawl.dev/assets-original/logocloud/2.png)\\n\\n![Logo
+        3](https://www.firecrawl.dev/assets-original/logocloud/3.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets-original/logocloud/4.png)\\n\\n![Logo
+        5](https://www.firecrawl.dev/assets-original/logocloud/5.png)\\n\\n![Logo
+        6](https://www.firecrawl.dev/assets-original/logocloud/6.png)\\n\\n![Logo
+        7](https://www.firecrawl.dev/assets-original/logocloud/7.png)\\n\\n![Logo
+        8](https://www.firecrawl.dev/assets-original/logocloud/8.png)\\n\\n![Logo
+        9](https://www.firecrawl.dev/assets-original/logocloud/9.png)\\n\\n![Logo
+        10](https://www.firecrawl.dev/assets-original/logocloud/10.png)\\n\\n![Logo
+        11](https://www.firecrawl.dev/assets-original/logocloud/11.png)\\n\\n![Logo
+        12](https://www.firecrawl.dev/assets-original/logocloud/12.png)\\n\\n![Logo
+        13](https://www.firecrawl.dev/assets-original/logocloud/13.png)\\n\\n![Logo
+        14](https://www.firecrawl.dev/assets-original/logocloud/14.png)\\n\\n![Logo
+        15](https://www.firecrawl.dev/assets-original/logocloud/15.png)\\n\\n![Logo
+        16](https://www.firecrawl.dev/assets-original/logocloud/16.png)\\n\\n![Logo
+        17](https://www.firecrawl.dev/assets-original/logocloud/17.png)\\n\\n![Logo
+        18](https://www.firecrawl.dev/assets-original/logocloud/18.png)\\n\\n![Logo
+        19](https://www.firecrawl.dev/assets-original/logocloud/19.png)\\n\\n![Logo
+        20](https://www.firecrawl.dev/assets-original/logocloud/20.png)\\n\\n![Logo
+        21](https://www.firecrawl.dev/assets-original/logocloud/21.png)\\n\\n\\\\[01/
+        07 \\\\]\\n\\n\xB7\\n\\nMain Features\\n\\n//\\n\\nDeveloper First\\n\\n//\\n\\n##
+        Startscraping   today\\n\\nEnhance your apps with industry leading web scraping
+        and crawling capabilities.\\n\\nScrape\\n\\nGet llm-ready data from websites.
+        Markdown, JSON, screenshot, etc.\\n\\nSearch\\n\\nNew\\n\\nSearch the web
+        and get full content from results.\\n\\nCrawl\\n\\nCrawl all the pages on
+        a website and get data for each page.\\n\\nPython\\n\\nNode.js\\n\\nCurl\\n\\nCopy
+        code\\n\\n```python\\n1# pip install firecrawl-py\\n2from firecrawl import
+        Firecrawl\\n3\\n4app = Firecrawl(api_key=\\\"fc-YOUR_API_KEY\\\")\\n5\\n6#
+        Scrape a website:\\n7app.scrape('firecrawl.dev')\\n8\\n9\\n10\\n```\\n\\n\\\\[
+        .MD \\\\]\\n\\n```markdown\\n1# Firecrawl\\n2\\n3Firecrawl is a powerful web
+        scraping\\n4library that makes it easy to extract\\n5data from websites.\\n6\\n7##
+        Installation\\n8\\n9To install Firecrawl, run:\\n10\\n11\\n```\\n\\n![developer-1](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-2](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-3](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-4](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-5](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-6](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-7](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-8](https://www.firecrawl.dev/assets/developer/8.png)\\n\\n![developer-9](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-10](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-11](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-12](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-13](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-14](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-15](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-16](https://www.firecrawl.dev/assets/developer/8.png)\\n\\n![developer-17](https://www.firecrawl.dev/assets/developer/1.png)\\n\\n![developer-18](https://www.firecrawl.dev/assets/developer/2.png)\\n\\n![developer-19](https://www.firecrawl.dev/assets/developer/3.png)\\n\\n![developer-20](https://www.firecrawl.dev/assets/developer/4.png)\\n\\n![developer-21](https://www.firecrawl.dev/assets/developer/5.png)\\n\\n![developer-22](https://www.firecrawl.dev/assets/developer/6.png)\\n\\n![developer-23](https://www.firecrawl.dev/assets/developer/7.png)\\n\\n![developer-24](https://www.firecrawl.dev/assets/developer/8.png)\\n\\nIntegrations\\n\\n###
+        Use well-known tools\\n\\nAlready fully integrated with the greatest existing
+        tools and workflows.\\n\\n[See all integrations](https://www.firecrawl.dev/app)\\n\\n![Firecrawl
+        icon (blueprint)](https://www.firecrawl.dev/assets-original/developer-os-icon.png)\\n\\nmendableai/firecrawl\\n\\nPublic\\n\\nStar\\n\\n65.3K\\n\\n\\\\[python-SDK\\\\]
+        improvs/async\\n\\n#1337\\n\\n\xB7\\n\\nApr 18, 2025\\n\\n\xB7\\n\\n![rafaelsideguide](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nrafaelsideguide\\n\\nfeat(extract):
+        cost limit\\n\\n#1473\\n\\n\xB7\\n\\nApr 17, 2025\\n\\n\xB7\\n\\n![mogery](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nmogery\\n\\nfeat(scrape):
+        get job result from GCS, avoid Redis\\n\\n#1461\\n\\n\xB7\\n\\nApr 15, 2025\\n\\n\xB7\\n\\n![mogery](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nmogery\\n\\nExtract
+        v2/rerank improvs\\n\\n#1437\\n\\n\xB7\\n\\nApr 11, 2025\\n\\n\xB7\\n\\n![rafaelsideguide](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=48&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nrafaelsideguide\\n\\n![https://avatars.githubusercontent.com/u/150964962?v=4](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F150964962%3Fv%3D4&w=96&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\n![https://avatars.githubusercontent.com/u/66118807?v=4](https://www.firecrawl.dev/_next/image?url=https%3A%2F%2Favatars.githubusercontent.com%2Fu%2F66118807%3Fv%3D4&w=96&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\n+90\\n\\nOpen
+        Source\\n\\n### Code you can trust\\n\\nDeveloped transparently and collaboratively.
+        Join our community of contributors.\\n\\n[Check out our repo](https://github.com/firecrawl/firecrawl)\\n\\n\\\\[02/
+        07 \\\\]\\n\\n\xB7\\n\\nCore\\n\\n//\\n\\nBuilt to outperform\\n\\n//\\n\\n##
+        Core principles,    provenperformance\\n\\nBuilt from the ground up to outperform
+        traditional scrapers.\\n\\nNo proxy headaches\\n\\nReliable.Covers 96% of
+        the web,\\n\\nincluding JS-heavy and protected pages. No proxies, no puppets,
+        just clean data.\\n\\nFirecrawl\\n\\n96%\\n\\n![Puppeteer icon](https://www.firecrawl.dev/assets/puppeteer.png)\\n\\nPuppeteer\\n\\n79%\\n\\ncURL\\n\\n75%\\n\\nSpeed
+        that feels invisible\\n\\nBlazingly fast.Delivers results in less than 1 second,
+        fast for real-time agents\\n\\nand dynamic apps.\\n\\nURL\\n\\nCrawl\\n\\nScrape\\n\\nfirecrawl.dev/docs\\n\\n50ms\\n\\n51ms\\n\\nfirecrawl.dev/templates\\n\\n52ms\\n\\n50ms\\n\\nfirecrawl.dev/changelog\\n\\n49ms\\n\\n52ms\\n\\nfirecrawl.dev/about\\n\\n52ms\\n\\n50ms\\n\\nfirecrawl.dev/changelog\\n\\n50ms\\n\\n52ms\\n\\nfirecrawl.dev/playground\\n\\n51ms\\n\\n49ms\\n\\n\\\\[
+        CTA \\\\]\\n\\n\\\\[ CRAWL \\\\]\\n\\n\\\\[ SCRAPE \\\\]\\n\\n\\\\[ CTA \\\\]\\n\\n//\\n\\nGet
+        started\\n\\n//\\n\\nReady to build?\\n\\nStart getting Web Data for free
+        and scale seamlessly as your project expands. No credit card needed.\\n\\n[Start
+        for free](https://www.firecrawl.dev/signin) [See our plans](https://www.firecrawl.dev/pricing)\\n\\n\\\\[03/
+        07 \\\\]\\n\\n\xB7\\n\\nFeatures\\n\\n//\\n\\nZero configuration\\n\\n//\\n\\n##
+        We handle the hard stuff\\n\\nRotating proxies, orchestration, rate limits,
+        js-blocked content and more.\\n\\nDocs to data\\n\\nMedia parsing.Firecrawl
+        can parse and output content from web hosted pdfs, docx, and more.\\n\\nhttps://example.com/docs/report.pdf\\n\\nhttps://example.com/files/brief.docx\\n\\nhttps://example.com/docs/guide.html\\n\\ndocx\\n\\nParsing...\\n\\nKnows
+        the moment\\n\\nSmart wait.Firecrawl intelligently waits for content to load,
+        making scraping faster and more reliable.\\n\\nhttps://example-spa.com\\n\\nRequest
+        Sent\\n\\nScrapes the real thing\\n\\nCached, when you need it.Selective caching,
+        you choose your caching patterns, growing web index.\\n\\n![User](https://www.firecrawl.dev/_next/image?url=%2Fassets-original%2Ffeatures%2Fcached-user.png&w=256&q=75&dpl=dpl_7RqvseQXNVYetFdhTKj6RntohhL1)\\n\\nUser\\n\\nFirecrawl\\n\\nCache\\n\\nInvisible
+        access\\n\\nStealth mode.Crawls the web without\\n\\nbeing blocked, mimics
+        real users to access protected or dynamic content.\\n\\nInteractive scraping\\n\\nActions.Click,
+        scroll, write, wait, press and more before extracting content.\\n\\nhttps://example.com\\n\\nNavigate\\n\\nClick\\n\\nType\\n\\nWait\\n\\nScroll\\n\\nPress\\n\\nScreenshot\\n\\nScrape\\n\\n\\\\[04/
+        07 \\\\]\\n\\n\xB7\\n\\nPricing\\n\\n//\\n\\nTransparent\\n\\n//\\n\\n## Flexible
+        pricing\\n\\nExplore transparent pricing built for real-world scraping.  Start
+        for free, then scale as you grow.\\n\\n\U0001F1FA\U0001F1F8USD\\n\\nFree Plan\\n\\nA
+        lightweight way to try scraping.\\n\\nNo cost, no card, no hassle.\\n\\n500
+        credits\\n\\n$0123456789\\n\\none-time\\n\\nGet started\\n\\nScrape 500 pages\\n\\n2
+        concurrent requests\\n\\nLow rate limits\\n\\nHobby\\n\\nGreat for side projects
+        and small tools.\\n\\nFast, simple, no overkill.\\n\\n3,000 credits\\n\\n$01234567890123456789\\n\\n/monthly\\n\\nBilled
+        yearly\\n\\n2 months free\\n\\nSubscribe\\n\\nScrape 3,000 pages\\n\\n5 concurrent
+        requests\\n\\nBasic support\\n\\n$9 per extra 1k credits\\n\\nStandard\\n\\nMost
+        popular\\n\\nPerfect for scaling with less effort.\\n\\nSimple, solid, dependable.\\n\\n100,000
+        credits\\n\\n$01234567890123456789\\n\\n/monthly\\n\\nBilled yearly\\n\\n2
+        months free\\n\\nSubscribe\\n\\nScrape 100,000 pages\\n\\n50 concurrent requests\\n\\nStandard
+        support\\n\\n$47 per extra 35k credits\\n\\nGrowth\\n\\nBuilt for high volume
+        and speed.\\n\\nFirecrawl at full force.\\n\\n500,000 credits\\n\\n$012345678901234567890123456789\\n\\n/monthly\\n\\nBilled
+        yearly\\n\\n2 months free\\n\\nSubscribe\\n\\nScrape 500,000 pages\\n\\n100
+        concurrent requests\\n\\nPriority support\\n\\n$177 per extra 175k credits\\n\\nExtra
+        credits are available via auto-recharge packs. [Enable](https://www.firecrawl.dev/signin/signup)\\n\\nEnterprise\\n\\nPower
+        at your pace\\n\\nUnlimited credits. Custom RPMs.\\n\\n[Contact sales](https://fk4bvu0n5qp.typeform.com/to/Ej6oydlg)
+        [More details](https://www.firecrawl.dev/enterprise)\\n\\nBulk discounts\\n\\nTop
+        priority support\\n\\nCustom concurrency limits\\n\\nImproved stealth proxies\\n\\nSLAs\\n\\nAdvanced
+        security & controls\\n\\n\\\\[05/ 07 \\\\]\\n\\n\xB7\\n\\nTestimonials\\n\\n//\\n\\nCommunity\\n\\n//\\n\\n##
+        People love    building withFirecrawl\\n\\nDiscover why developers choose
+        Firecrawl every day.\\n\\n[![Morgan Linton](https://www.firecrawl.dev/assets/testimonials/morgan-linton.png)Morgan
+        Linton@morganlinton\\\"If you're coding with AI, and haven't discovered @firecrawl\\\\_dev
+        yet, prepare to have your mind blown \U0001F92F\\\"](https://x.com/morganlinton/status/1839454165703204955)
+        [![Chris DeWeese](https://www.firecrawl.dev/assets/testimonials/chris-deweese.png)Chris
+        DeWeese@chrisdeweese\\\\_\\\"Started using @firecrawl\\\\_dev for a project,
+        I wish I used this sooner.\\\"](https://x.com/chrisdeweese_/status/1853587120406876601)
+        [![Alex Reibman](https://www.firecrawl.dev/assets/testimonials/alex-reibman.png)Alex
+        Reibman@AlexReibman\\\"Moved our internal agent's web scraping tool from Apify
+        to Firecrawl because it benchmarked 50x faster with AgentOps.\\\"](https://x.com/AlexReibman/status/1780299595484131836)
+        [![Tom - Morpho](https://www.firecrawl.dev/assets/testimonials/tom-morpho.png)Tom
+        - Morpho@TomReppelin\\\"I found gold today. Thank you @firecrawl\\\\_dev\\\"](https://x.com/TomReppelin/status/1844382491014201613)\\n\\n[![Morgan
+        Linton](https://www.firecrawl.dev/assets/testimonials/morgan-linton.png)Morgan
+        Linton@morganlinton\\\"If you're coding with AI, and haven't discovered @firecrawl\\\\_dev
+        yet, prepare to have your mind blown \U0001F92F\\\"](https://x.com/morganlinton/status/1839454165703204955)
+        [![Chris DeWeese](https://www.firecrawl.dev/assets/testimonials/chris-deweese.png)Chris
+        DeWeese@chrisdeweese\\\\_\\\"Started using @firecrawl\\\\_dev for a project,
+        I wish I used this sooner.\\\"](https://x.com/chrisdeweese_/status/1853587120406876601)
+        [![Alex Reibman](https://www.firecrawl.dev/assets/testimonials/alex-reibman.png)Alex
+        Reibman@AlexReibman\\\"Moved our internal agent's web scraping tool from Apify
+        to Firecrawl because it benchmarked 50x faster with AgentOps.\\\"](https://x.com/AlexReibman/status/1780299595484131836)
+        [![Tom - Morpho](https://www.firecrawl.dev/assets/testimonials/tom-morpho.png)Tom
+        - Morpho@TomReppelin\\\"I found gold today. Thank you @firecrawl\\\\_dev\\\"](https://x.com/TomReppelin/status/1844382491014201613)\\n\\n[![Bardia](https://www.firecrawl.dev/assets/testimonials/bardia.png)Bardia@thepericulum\\\"The
+        Firecrawl team ships. I wanted types for their node SDK, and less than an
+        hour later, I got them.\\\"](https://x.com/thepericulum/status/1781397799487078874)
+        [![Matt Busigin](https://www.firecrawl.dev/assets/testimonials/matt-busigin.png)Matt
+        Busigin@mbusigin\\\"Firecrawl is dope. Congrats guys \U0001F44F\\\"](https://x.com/mbusigin/status/1836065372010656069)
+        [![Sumanth](https://www.firecrawl.dev/assets/testimonials/sumanth.png)Sumanth@Sumanth\\\\_077\\\"Web
+        scraping will never be the same!\\\\\\\\\\n\\\\\\\\\\nFirecrawl is an open-source
+        framework that takes a URL, crawls it, and conver...\\\"](https://x.com/Sumanth_077/status/1940049003074478511)
+        [![Steven Tey](https://www.firecrawl.dev/assets/testimonials/steven-tey.png)Steven
+        Tey@steventey\\\"Open-source Clay alternative just dropped\\\\\\\\\\n\\\\\\\\\\nUpload
+        a CSV of emails and...\\\"](https://x.com/steventey/status/1932945651761098889)\\n\\n[![Bardia](https://www.firecrawl.dev/assets/testimonials/bardia.png)Bardia@thepericulum\\\"The
+        Firecrawl team ships. I wanted types for their node SDK, and less than an
+        hour later, I got them.\\\"](https://x.com/thepericulum/status/1781397799487078874)
+        [![Matt Busigin](https://www.firecrawl.dev/assets/testimonials/matt-busigin.png)Matt
+        Busigin@mbusigin\\\"Firecrawl is dope. Congrats guys \U0001F44F\\\"](https://x.com/mbusigin/status/1836065372010656069)
+        [![Sumanth](https://www.firecrawl.dev/assets/testimonials/sumanth.png)Sumanth@Sumanth\\\\_077\\\"Web
+        scraping will never be the same!\\\\\\\\\\n\\\\\\\\\\nFirecrawl is an open-source
+        framework that takes a URL, crawls it, and conver...\\\"](https://x.com/Sumanth_077/status/1940049003074478511)
+        [![Steven Tey](https://www.firecrawl.dev/assets/testimonials/steven-tey.png)Steven
+        Tey@steventey\\\"Open-source Clay alternative just dropped\\\\\\\\\\n\\\\\\\\\\nUpload
+        a CSV of emails and...\\\"](https://x.com/steventey/status/1932945651761098889)\\n\\n\\\\[06/
+        07 \\\\]\\n\\n\xB7\\n\\nUse Cases\\n\\n//\\n\\nUse cases\\n\\n//\\n\\n## Transform
+        \   web data into  AI-powered solutions\\n\\nDiscover how Firecrawl customers
+        are getting the most out of our API.\\n\\n[View all use cases](https://docs.firecrawl.dev/use-cases/overview)\\n\\nChat
+        with context\\n\\nSmarter AI chats\\n\\nPower your AI assistants with real-time,
+        accurate web content.\\n\\n[View docs](https://docs.firecrawl.dev/introduction)\\n\\n![AI
+        Assistant](https://www.firecrawl.dev/assets/ai/bot.png)\\n\\nAI Assistant\\n\\nwithFirecrawl\\n\\nReal-time\xB7Updated
+        2 min ago\\n\\nAsk anything...\\n\\nKnow your leads\\n\\nLead enrichment\\n\\nEnhance
+        your sales data with\\n\\nweb information.\\n\\n[Check out Extract](https://www.firecrawl.dev/extract)\\n\\nExtracting
+        leads from directory...\\n\\nTech startups\\n\\nWith contact info\\n\\nDecision
+        makers\\n\\nFunding stage\\n\\nReady to engage\\n\\n![Emily Tran](https://www.firecrawl.dev/assets/ai/leads-1.png)\\n\\n![James
+        Carter](https://www.firecrawl.dev/assets/ai/leads-2.png)\\n\\n![Sophia Kim](https://www.firecrawl.dev/assets/ai/leads-3.png)\\n\\n![Michael
+        Rivera](https://www.firecrawl.dev/assets/ai/leads-4.png)\\n\\nKnow your leads\\n\\nMCPs\\n\\nAdd
+        powerful scraping to your\\n\\ncode editors.\\n\\n[Get started](https://docs.firecrawl.dev/mcp-server)\\n\\n![Claude
+        Code](https://www.firecrawl.dev/assets/ai/mcps-claude.png)\\n\\nClaude Code\\n\\n![Cursor](https://www.firecrawl.dev/assets/ai/mcps-cursor.png)\\n\\nCursor\\n\\n![Windsurf](https://www.firecrawl.dev/assets/ai/mcps-windsurf.png)\\n\\nWindsurf\\n\\n\u273B\\n\\nWelcome
+        to Claude Code!\\n\\n/help for help, /status for your current setup\\n\\n>Try
+        \\\"how do I log an error?\\\"\\n\\nBuild with context\\n\\nAI platforms\\n\\nLet
+        your customers build AI apps\\n\\nwith web data.\\n\\n[Check out Map](https://docs.firecrawl.dev/features/map)\\n\\n![Logo
+        1](https://www.firecrawl.dev/assets/ai/platforms-1.png)\\n\\n![Logo 2](https://www.firecrawl.dev/assets/ai/platforms-2.png)\\n\\n![Logo
+        4](https://www.firecrawl.dev/assets/ai/platforms-4.png)\\n\\n![Logo 3](https://www.firecrawl.dev/assets/ai/platforms-3.png)\\n\\nExtracting
+        text...\\n\\nNo insight missed\\n\\nDeep research\\n\\nExtract comprehensive
+        information for\\n\\nin-depth research.\\n\\n[Build your own with Search](https://docs.firecrawl.dev/features/search)\\n\\nDeep
+        research in progress...\\n\\nAcademic papers\\n\\n0 found\\n\\nNews articles\\n\\n0
+        found\\n\\nExpert opinions\\n\\n0 found\\n\\nResearch reports\\n\\n0 found\\n\\nIndustry
+        data\\n\\n0 found\\n\\nAsk anything...\\n\\n\\\\[ CTA \\\\]\\n\\n\\\\[ CRAWL
+        \\\\]\\n\\n\\\\[ SCRAPE \\\\]\\n\\n\\\\[ CTA \\\\]\\n\\n//\\n\\nGet started\\n\\n//\\n\\nReady
+        to build?\\n\\nStart getting Web Data for free and scale seamlessly as your
+        project expands. No credit card needed.\\n\\n[Start for free](https://www.firecrawl.dev/signin)
+        [See our plans](https://www.firecrawl.dev/pricing)\\n\\n\\\\[07/ 07 \\\\]\\n\\n\xB7\\n\\nFAQ\\n\\n//\\n\\nFAQ\\n\\n//\\n\\n##
+        Frequently    askedquestions\\n\\nEverything you need to know about Firecrawl.\\n\\nGeneral\\n\\nWhat
+        is Firecrawl?\\n\\nWhat sites work?\\n\\nWho can benefit from using Firecrawl?\\n\\nIs
+        Firecrawl open-source?\\n\\nWhat is the difference between Firecrawl and other
+        web scrapers?\\n\\nWhat is the difference between the open-source version
+        and the hosted version?\\n\\nScraping & Crawling\\n\\nHow does Firecrawl handle
+        dynamic content on websites?\\n\\nWhy is it not crawling all the pages?\\n\\nCan
+        Firecrawl crawl websites without a sitemap?\\n\\nWhat formats can Firecrawl
+        convert web data into?\\n\\nHow does Firecrawl ensure the cleanliness of the
+        data?\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nDoes
+        it respect robots.txt?\\n\\nWhat measures does Firecrawl take to handle web
+        scraping challenges like rate limits and caching?\\n\\nDoes Firecrawl handle
+        captcha or authentication?\\n\\nAPI Related\\n\\nWhere can I find my API key?\\n\\nBilling\\n\\nIs
+        Firecrawl free?\\n\\nIs there a pay-per-use plan instead of monthly?\\n\\nDo
+        credits roll over to the next month?\\n\\nHow many credits do scraping and
+        crawling cost?\\n\\nDo you charge for failed requests?\\n\\nWhat payment methods
+        do you accept?\\n\\nFOOTER\\n\\nThe easiest way to extract\\n\\ndata from
+        the web\\n\\nBacked by\\n\\nY Combinator\\n\\n[Linkedin](https://www.linkedin.com/company/firecrawl)
+        [Github](https://github.com/firecrawl/firecrawl)\\n\\nSOC II \xB7 Type 2\\n\\nAICPA\\n\\nSOC
+        2\\n\\n[X (Twitter)](https://x.com/firecrawl_dev) [Discord](https://discord.gg/gSmWdAkdwd)\\n\\nProducts\\n\\n[Playground](https://www.firecrawl.dev/playground)
+        [Extract](https://www.firecrawl.dev/extract) [Pricing](https://www.firecrawl.dev/pricing)
+        [Templates](https://www.firecrawl.dev/templates) [Changelog](https://www.firecrawl.dev/changelog)\\n\\nUse
+        Cases\\n\\n[AI Platforms](https://docs.firecrawl.dev/use-cases/ai-platforms)
+        [Lead Enrichment](https://docs.firecrawl.dev/use-cases/lead-enrichment) [SEO
+        Platforms](https://docs.firecrawl.dev/use-cases/seo-platforms) [Deep Research](https://docs.firecrawl.dev/use-cases/deep-research)\\n\\nDocumentation\\n\\n[Getting
+        started](https://docs.firecrawl.dev/introduction) [API Reference](https://docs.firecrawl.dev/api-reference/introduction)
+        [Integrations](https://www.firecrawl.dev/app) [Examples](https://docs.firecrawl.dev/use-cases/overview)
+        [SDKs](https://docs.firecrawl.dev/sdks/overview)\\n\\nCompany\\n\\n[Blog](https://www.firecrawl.dev/blog)
+        [Careers](https://www.firecrawl.dev/careers) [Creator & OSS program](https://www.firecrawl.dev/creator-oss-program)
+        [Student program](https://www.firecrawl.dev/student-program)\\n\\n\xA9 2025
+        Firecrawl\\n\\n[Terms of Service](https://www.firecrawl.dev/terms-of-service)
+        [Privacy Policy](https://www.firecrawl.dev/privacy-policy) [Report Abuse](mailto:help@firecrawl.com?subject=Issue:)\\n\\n[All
+        systems normal](https://status.firecrawl.dev/)\\n\\nStripeM-Inner\",\"metadata\":{\"favicon\":\"https://www.firecrawl.dev/favicon.png\",\"ogUrl\":\"https://www.firecrawl.dev\",\"ogImage\":\"https://www.firecrawl.dev/og.png\",\"referrer\":\"origin-when-cross-origin\",\"ogDescription\":\"The
+        web crawling, scraping, and search API for AI. Built for scale. Firecrawl
+        delivers the entire internet to AI agents and builders. Clean, structured,
+        and ready to reason with.\",\"robots\":\"follow, index\",\"twitter:card\":\"summary_large_image\",\"og:site_name\":\"Firecrawl
+        - The Web Data API for AI\",\"twitter:title\":\"Firecrawl - The Web Data API
+        for AI\",\"og:image\":\"https://www.firecrawl.dev/og.png\",\"title\":\"Firecrawl
+        - The Web Data API for AI\",\"og:description\":\"The web crawling, scraping,
+        and search API for AI. Built for scale. Firecrawl delivers the entire internet
+        to AI agents and builders. Clean, structured, and ready to reason with.\",\"twitter:image\":\"https://www.firecrawl.dev/og.png\",\"viewport\":\"width=device-width,
+        initial-scale=1, maximum-scale=1, user-scalable=no\",\"ogSiteName\":\"Firecrawl
+        - The Web Data API for AI\",\"keywords\":\"Firecrawl,Markdown,Data,Mendable,Langchain\",\"author\":\"Firecrawl\",\"og:title\":\"Firecrawl
+        - The Web Data API for AI\",\"twitter:description\":\"The web crawling, scraping,
+        and search API for AI. Built for scale. Firecrawl delivers the entire internet
+        to AI agents and builders. Clean, structured, and ready to reason with.\",\"description\":\"The
+        web crawling, scraping, and search API for AI. Built for scale. Firecrawl
+        delivers the entire internet to AI agents and builders. Clean, structured,
+        and ready to reason with.\",\"twitter:site\":\"@Vercel\",\"og:url\":\"https://www.firecrawl.dev\",\"og:type\":\"website\",\"ogTitle\":\"Firecrawl
+        - The Web Data API for AI\",\"language\":\"en\",\"creator\":\"Firecrawl\",\"publisher\":\"Firecrawl\",\"twitter:creator\":\"@Vercel\",\"scrapeId\":\"57b0586f-36e8-4923-aaa2-88ff58c03999\",\"sourceURL\":\"https://www.firecrawl.dev/\",\"url\":\"https://www.firecrawl.dev/\",\"statusCode\":200,\"contentType\":\"text/html;
+        charset=utf-8\",\"proxyUsed\":\"basic\",\"cacheState\":\"hit\",\"cachedAt\":\"2025-10-29T13:09:07.713Z\"}},{\"url\":\"https://github.com/firecrawl/firecrawl\",\"title\":\"firecrawl/firecrawl:
+        The Web Data API for AI - Turn entire ... - GitHub\",\"description\":\"Firecrawl
+        is an API service that takes a URL, crawls it, and converts it into clean
+        markdown or structured data. We crawl all accessible subpages and give you
+        ...\",\"position\":2,\"category\":\"github\",\"markdown\":\"[Skip to content](https://github.com/firecrawl/firecrawl#start-of-content)\\n\\nYou
+        signed in with another tab or window. [Reload](https://github.com/firecrawl/firecrawl)
+        to refresh your session.You signed out in another tab or window. [Reload](https://github.com/firecrawl/firecrawl)
+        to refresh your session.You switched accounts on another tab or window. [Reload](https://github.com/firecrawl/firecrawl)
+        to refresh your session.Dismiss alert\\n\\n{{ message }}\\n\\n[firecrawl](https://github.com/firecrawl)/
+        **[firecrawl](https://github.com/firecrawl/firecrawl)** Public\\n\\n- Couldn't
+        load subscription status.\\nRetry\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n### Uh
+        oh!\\n\\n\\n\\n\\n\\n\\n\\nThere was an error while loading. [Please reload
+        this page](https://github.com/firecrawl/firecrawl).\\n\\n- [Fork\\\\\\\\\\n5.1k](https://github.com/login?return_to=%2Ffirecrawl%2Ffirecrawl)\\n-
+        [Star\\\\\\\\\\n65.2k](https://github.com/login?return_to=%2Ffirecrawl%2Ffirecrawl)\\n\\n\\n\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data\\n\\n\\n[firecrawl.dev](https://firecrawl.dev/ \\\"https://firecrawl.dev\\\")\\n\\n###
+        License\\n\\n[AGPL-3.0 license](https://github.com/firecrawl/firecrawl/blob/main/LICENSE)\\n\\n[65.2k\\\\\\\\\\nstars](https://github.com/firecrawl/firecrawl/stargazers)
+        [5.1k\\\\\\\\\\nforks](https://github.com/firecrawl/firecrawl/forks) [Branches](https://github.com/firecrawl/firecrawl/branches)
+        [Tags](https://github.com/firecrawl/firecrawl/tags) [Activity](https://github.com/firecrawl/firecrawl/activity)\\n\\n[Star](https://github.com/login?return_to=%2Ffirecrawl%2Ffirecrawl)\\n\\nCouldn't
+        load subscription status.\\nRetry\\n\\n### Uh oh!\\n\\nThere was an error
+        while loading. [Please reload this page](https://github.com/firecrawl/firecrawl).\\n\\n#
+        firecrawl/firecrawl\\n\\nmain\\n\\n[**887** Branches](https://github.com/firecrawl/firecrawl/branches)
+        [**28** Tags](https://github.com/firecrawl/firecrawl/tags)\\n\\n[Go to Branches
+        page](https://github.com/firecrawl/firecrawl/branches)[Go to Tags page](https://github.com/firecrawl/firecrawl/tags)\\n\\nGo
+        to file\\n\\nCode\\n\\nOpen more actions menu\\n\\n## Folders and files\\n\\n|
+        Name | Name | Last commit message | Last commit date |\\n| --- | --- | ---
+        | --- |\\n| ## Latest commit<br>[![amplitudesxd](https://avatars.githubusercontent.com/u/62763456?v=4&size=40)](https://github.com/amplitudesxd)[amplitudesxd](https://github.com/firecrawl/firecrawl/commits?author=amplitudesxd)<br>[chore:
+        update last scrape rpc (](https://github.com/firecrawl/firecrawl/commit/37de2877fab4bae2de297e37bad3c9bcd49a64bc)
+        [#2339](https://github.com/firecrawl/firecrawl/pull/2339) [)](https://github.com/firecrawl/firecrawl/commit/37de2877fab4bae2de297e37bad3c9bcd49a64bc)<br>success<br>20
+        hours agoOct 27, 2025<br>[37de287](https://github.com/firecrawl/firecrawl/commit/37de2877fab4bae2de297e37bad3c9bcd49a64bc)\_\xB7\_20
+        hours agoOct 27, 2025<br>## History<br>[4,487 Commits](https://github.com/firecrawl/firecrawl/commits/main/)
+        <br>Open commit details<br>[View commit history for this file.](https://github.com/firecrawl/firecrawl/commits/main/)
+        |\\n| [.github](https://github.com/firecrawl/firecrawl/tree/main/.github \\\".github\\\")
+        | [.github](https://github.com/firecrawl/firecrawl/tree/main/.github \\\".github\\\")
+        | [fix(ci): temp disabled prod env tests](https://github.com/firecrawl/firecrawl/commit/42fc149c1ab738da0e15e772817774aa35273f8e
+        \\\"fix(ci): temp disabled prod env tests\\\") | 5 days agoOct 23, 2025 |\\n|
+        [apps](https://github.com/firecrawl/firecrawl/tree/main/apps \\\"apps\\\")
+        | [apps](https://github.com/firecrawl/firecrawl/tree/main/apps \\\"apps\\\")
+        | [chore: update last scrape rpc (](https://github.com/firecrawl/firecrawl/commit/37de2877fab4bae2de297e37bad3c9bcd49a64bc
+        \\\"chore: update last scrape rpc (#2339)\\\") [#2339](https://github.com/firecrawl/firecrawl/pull/2339)
+        [)](https://github.com/firecrawl/firecrawl/commit/37de2877fab4bae2de297e37bad3c9bcd49a64bc
+        \\\"chore: update last scrape rpc (#2339)\\\") | 20 hours agoOct 27, 2025
+        |\\n| [examples](https://github.com/firecrawl/firecrawl/tree/main/examples
+        \\\"examples\\\") | [examples](https://github.com/firecrawl/firecrawl/tree/main/examples
+        \\\"examples\\\") | [Merge pull request](https://github.com/firecrawl/firecrawl/commit/7ad57003b4ad8b230ba8252129e52bafa62dfae9
+        \\\"Merge pull request #2172 from MAVRICK-1/firecrawl-gemini-screenshot-editor
+        \ feat: Add Firecrawl + Gemini 2.5 Flash Image CLI Editor\\\") [#2172](https://github.com/firecrawl/firecrawl/pull/2172)
+        [from MAVRICK-1/firecrawl-gemini-screenshot-e\u2026](https://github.com/firecrawl/firecrawl/commit/7ad57003b4ad8b230ba8252129e52bafa62dfae9
+        \\\"Merge pull request #2172 from MAVRICK-1/firecrawl-gemini-screenshot-editor
+        \ feat: Add Firecrawl + Gemini 2.5 Flash Image CLI Editor\\\") | last monthSep
+        23, 2025 |\\n| [img](https://github.com/firecrawl/firecrawl/tree/main/img
+        \\\"img\\\") | [img](https://github.com/firecrawl/firecrawl/tree/main/img
+        \\\"img\\\") | [updated readme](https://github.com/firecrawl/firecrawl/commit/4f904e774831dc598681d3e998d0e5e15abcec27
+        \\\"updated readme\\\") | 2 months agoAug 18, 2025 |\\n| [.gitattributes](https://github.com/firecrawl/firecrawl/blob/main/.gitattributes
+        \\\".gitattributes\\\") | [.gitattributes](https://github.com/firecrawl/firecrawl/blob/main/.gitattributes
+        \\\".gitattributes\\\") | [Initial commit](https://github.com/firecrawl/firecrawl/commit/a6c2a878119321a196f720cce4195e086f1c6b46
+        \\\"Initial commit\\\") | last yearApr 15, 2024 |\\n| [.gitignore](https://github.com/firecrawl/firecrawl/blob/main/.gitignore
+        \\\".gitignore\\\") | [.gitignore](https://github.com/firecrawl/firecrawl/blob/main/.gitignore
+        \\\".gitignore\\\") | [Nick: init](https://github.com/firecrawl/firecrawl/commit/ab3fa4838458c8303a67dd30fdd75a16b89cc20b
+        \\\"Nick: init\\\") | 3 weeks agoOct 10, 2025 |\\n| [.gitmodules](https://github.com/firecrawl/firecrawl/blob/main/.gitmodules
+        \\\".gitmodules\\\") | [.gitmodules](https://github.com/firecrawl/firecrawl/blob/main/.gitmodules
+        \\\".gitmodules\\\") | [mendableai -> firecrawl](https://github.com/firecrawl/firecrawl/commit/2f3bc4e7a7b1a67a29c06df629f79402ee1aad1b
+        \\\"mendableai -> firecrawl\\\") | 2 months agoAug 18, 2025 |\\n| [CLAUDE.md](https://github.com/firecrawl/firecrawl/blob/main/CLAUDE.md
+        \\\"CLAUDE.md\\\") | [CLAUDE.md](https://github.com/firecrawl/firecrawl/blob/main/CLAUDE.md
+        \\\"CLAUDE.md\\\") | [add claude file](https://github.com/firecrawl/firecrawl/commit/3f0873c788823258a7d9f55d1c8772aed4e1a8de
+        \\\"add claude file\\\") | 2 months agoAug 6, 2025 |\\n| [CONTRIBUTING.md](https://github.com/firecrawl/firecrawl/blob/main/CONTRIBUTING.md
+        \\\"CONTRIBUTING.md\\\") | [CONTRIBUTING.md](https://github.com/firecrawl/firecrawl/blob/main/CONTRIBUTING.md
+        \\\"CONTRIBUTING.md\\\") | [Add Rust to CONTRIBUTING (](https://github.com/firecrawl/firecrawl/commit/f396cb20b54c3c2d7e64882642c5df6310a01002
+        \\\"Add Rust to CONTRIBUTING (#2180)\\\") [#2180](https://github.com/firecrawl/firecrawl/pull/2180)
+        [)](https://github.com/firecrawl/firecrawl/commit/f396cb20b54c3c2d7e64882642c5df6310a01002
+        \\\"Add Rust to CONTRIBUTING (#2180)\\\") | last monthSep 18, 2025 |\\n| [LICENSE](https://github.com/firecrawl/firecrawl/blob/main/LICENSE
+        \\\"LICENSE\\\") | [LICENSE](https://github.com/firecrawl/firecrawl/blob/main/LICENSE
+        \\\"LICENSE\\\") | [Update SDKs to MIT license](https://github.com/firecrawl/firecrawl/commit/afb49e21e7cff595ebad9ce0b7aba13b88f39cf8
+        \\\"Update SDKs to MIT license\\\") | last yearJul 8, 2024 |\\n| [README.md](https://github.com/firecrawl/firecrawl/blob/main/README.md
+        \\\"README.md\\\") | [README.md](https://github.com/firecrawl/firecrawl/blob/main/README.md
+        \\\"README.md\\\") | [Update README.md](https://github.com/firecrawl/firecrawl/commit/a21430e97818d95099bb365be711d9227bd75590
+        \\\"Update README.md\\\") | 3 weeks agoOct 6, 2025 |\\n| [SELF\\\\_HOST.md](https://github.com/firecrawl/firecrawl/blob/main/SELF_HOST.md
+        \\\"SELF_HOST.md\\\") | [SELF\\\\_HOST.md](https://github.com/firecrawl/firecrawl/blob/main/SELF_HOST.md
+        \\\"SELF_HOST.md\\\") | [Allow self-hosted webhook delivery to private IP
+        addresses (](https://github.com/firecrawl/firecrawl/commit/5756b834884d481382ce1f5674836a56b7fee33d
+        \\\"Allow self-hosted webhook delivery to private IP addresses (#2232)\\\")
+        [#2232](https://github.com/firecrawl/firecrawl/pull/2232) [)](https://github.com/firecrawl/firecrawl/commit/5756b834884d481382ce1f5674836a56b7fee33d
+        \\\"Allow self-hosted webhook delivery to private IP addresses (#2232)\\\")
+        | 27 days agoOct 1, 2025 |\\n| [docker-compose.yaml](https://github.com/firecrawl/firecrawl/blob/main/docker-compose.yaml
+        \\\"docker-compose.yaml\\\") | [docker-compose.yaml](https://github.com/firecrawl/firecrawl/blob/main/docker-compose.yaml
+        \\\"docker-compose.yaml\\\") | [Fix a self-hosted docker-compose.yaml bug
+        caused by a recent firecraw\u2026](https://github.com/firecrawl/firecrawl/commit/7d4100b274889977fa1ba26344532d9d8747494c
+        \\\"Fix a self-hosted docker-compose.yaml bug caused by a recent firecrawl
+        change (#2252)  Add EXTRACT_WORKER_PORT to docker-compose environment\\\")
+        | 3 weeks agoOct 4, 2025 |\\n| View all files |\\n\\n## Repository files navigation\\n\\n###
+        [![](https://raw.githubusercontent.com/firecrawl/firecrawl/main/img/firecrawl_logo.png)](https://raw.githubusercontent.com/firecrawl/firecrawl/main/img/firecrawl_logo.png)\\n\\n[Permalink:
+        ](https://github.com/firecrawl/firecrawl#----)\\n\\n[![License](https://camo.githubusercontent.com/d8ec6c81115d21c81bc26f2c80f8987a4d2a72e538b88afaa738fad5cd6289ff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f66697265637261776c2f66697265637261776c)](https://github.com/firecrawl/firecrawl/blob/main/LICENSE)[![Downloads](https://camo.githubusercontent.com/9d76afe428b4085c8b7103f2f4e31da110ee154ad7320bace4348d92ac0c2450/68747470733a2f2f7374617469632e706570792e746563682f62616467652f66697265637261776c2d7079)](https://pepy.tech/project/firecrawl-py)[![GitHub
+        Contributors](https://camo.githubusercontent.com/a9eabcb95ba00300afa51ce546660540c1f65764492cb2ba8fb67fe541c7e97f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f66697265637261776c2f66697265637261776c2e737667)](https://github.com/firecrawl/firecrawl/graphs/contributors)[![Visit
+        firecrawl.dev](https://camo.githubusercontent.com/3576b8cb0e77344c001cc8456d28c830691cb96480d4b65be90f8a4c99dead56/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f56697369742d66697265637261776c2e6465762d6f72616e6765)](https://firecrawl.dev/)\\n\\n[![Follow
+        on X](https://camo.githubusercontent.com/610127222e603752676f0275682f12398f8e434706861d577c1f6688d999191c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f466f6c6c6f772532306f6e253230582d3030303030303f7374796c653d666f722d7468652d6261646765266c6f676f3d78266c6f676f436f6c6f723d7768697465)](https://twitter.com/firecrawl_dev)[![Follow
+        on LinkedIn](https://camo.githubusercontent.com/8741d51bb8e1c8ae576ac05e875f826bcf80e8711dcf9225935bb78d5bb03802/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f466f6c6c6f772532306f6e2532304c696e6b6564496e2d3030373742353f7374796c653d666f722d7468652d6261646765266c6f676f3d6c696e6b6564696e266c6f676f436f6c6f723d7768697465)](https://www.linkedin.com/company/104100957)[![Join
+        our Discord](https://camo.githubusercontent.com/886138c89a84dc2ad74d06900f364d736ccf753b2732d59fbd4106f6310f3616/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4a6f696e2532306f7572253230446973636f72642d3538363546323f7374796c653d666f722d7468652d6261646765266c6f676f3d646973636f7264266c6f676f436f6c6f723d7768697465)](https://discord.com/invite/gSmWdAkdwd)\\n\\n#
+        \U0001F525 Firecrawl\\n\\n[Permalink: \U0001F525 Firecrawl](https://github.com/firecrawl/firecrawl#-firecrawl)\\n\\nEmpower
+        your AI apps with clean data from any website. Featuring advanced scraping,
+        crawling, and data extraction capabilities.\\n\\n_This repository is in development,
+        and we\u2019re still integrating custom modules into the mono repo. It's not
+        fully ready for self-hosted deployment yet, but you can run it locally._\\n\\n##
+        What is Firecrawl?\\n\\n[Permalink: What is Firecrawl?](https://github.com/firecrawl/firecrawl#what-is-firecrawl)\\n\\n[Firecrawl](https://firecrawl.dev/?ref=github)
+        is an API service that takes a URL, crawls it, and converts it into clean
+        markdown or structured data. We crawl all accessible subpages and give you
+        clean data for each. No sitemap required. Check out our [documentation](https://docs.firecrawl.dev/).\\n\\nLooking
+        for our MCP? Check out the [repo here](https://github.com/firecrawl/firecrawl-mcp-server).\\n\\n_Pst.
+        hey, you, join our stargazers :)_\\n\\n[![GitHub stars](https://camo.githubusercontent.com/11f7ce76e9f1608b3470b3a23a1db3a7d9ec083ee18f2d826862f420bac800dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f66697265637261776c2f66697265637261776c2e7376673f7374796c653d736f6369616c266c6162656c3d53746172266d61784167653d32353932303030)](https://github.com/firecrawl/firecrawl)\\n\\n##
+        How to use it?\\n\\n[Permalink: How to use it?](https://github.com/firecrawl/firecrawl#how-to-use-it)\\n\\nWe
+        provide an easy to use API with our hosted version. You can find the playground
+        and documentation [here](https://firecrawl.dev/playground). You can also self
+        host the backend if you'd like.\\n\\nCheck out the following resources to
+        get started:\\n\\n- [x] **API**: [Documentation](https://docs.firecrawl.dev/api-reference/introduction)\\n-
+        [x] **SDKs**: [Python](https://docs.firecrawl.dev/sdks/python), [Node](https://docs.firecrawl.dev/sdks/node)\\n-
+        [x] **LLM Frameworks**: [Langchain (python)](https://python.langchain.com/docs/integrations/document_loaders/firecrawl/),
+        [Langchain (js)](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/firecrawl),
+        [Llama Index](https://docs.llamaindex.ai/en/latest/examples/data_connectors/WebPageDemo/#using-firecrawl-reader),
+        [Crew.ai](https://docs.crewai.com/), [Composio](https://composio.dev/tools/firecrawl/all),
+        [PraisonAI](https://docs.praison.ai/firecrawl/), [Superinterface](https://superinterface.ai/docs/assistants/functions/firecrawl),
+        [Vectorize](https://docs.vectorize.io/integrations/source-connectors/firecrawl)\\n-
+        [x] **Low-code Frameworks**: [Dify](https://dify.ai/blog/dify-ai-blog-integrated-with-firecrawl),
+        [Langflow](https://docs.langflow.org/), [Flowise AI](https://docs.flowiseai.com/integrations/langchain/document-loaders/firecrawl),
+        [Cargo](https://docs.getcargo.io/integration/firecrawl), [Pipedream](https://pipedream.com/apps/firecrawl/)\\n-
+        [x] **Community SDKs**: [Go](https://docs.firecrawl.dev/sdks/go), [Rust](https://docs.firecrawl.dev/sdks/rust)\\n-
+        [x] **Others**: [Zapier](https://zapier.com/apps/firecrawl/integrations),
+        [Pabbly Connect](https://www.pabbly.com/connect/integrations/firecrawl/)\\n-
+        [ ]  Want an SDK or Integration? Let us know by opening an issue.\\n\\nTo
+        run locally, refer to guide [here](https://github.com/firecrawl/firecrawl/blob/main/CONTRIBUTING.md).\\n\\n###
+        API Key\\n\\n[Permalink: API Key](https://github.com/firecrawl/firecrawl#api-key)\\n\\nTo
+        use the API, you need to sign up on [Firecrawl](https://firecrawl.dev/) and
+        get an API key.\\n\\n### Features\\n\\n[Permalink: Features](https://github.com/firecrawl/firecrawl#features)\\n\\n-
+        [**Scrape**](https://github.com/firecrawl/firecrawl#scraping): scrapes a URL
+        and get its content in LLM-ready format (markdown, structured data via [LLM
+        Extract](https://github.com/firecrawl/firecrawl#llm-extraction-beta), screenshot,
+        html)\\n- [**Crawl**](https://github.com/firecrawl/firecrawl#crawling): scrapes
+        all the URLs of a web page and return content in LLM-ready format\\n- [**Map**](https://github.com/firecrawl/firecrawl#map):
+        input a website and get all the website urls - extremely fast\\n- [**Search**](https://github.com/firecrawl/firecrawl#search):
+        search the web and get full content from results\\n- [**Extract**](https://github.com/firecrawl/firecrawl#extract):
+        get structured data from single page, multiple pages or entire websites with
+        AI.\\n\\n### Powerful Capabilities\\n\\n[Permalink: Powerful Capabilities](https://github.com/firecrawl/firecrawl#powerful-capabilities)\\n\\n-
+        **LLM-ready formats**: markdown, structured data, screenshot, HTML, links,
+        metadata\\n- **The hard stuff**: proxies, anti-bot mechanisms, dynamic content
+        (js-rendered), output parsing, orchestration\\n- **Customizability**: exclude
+        tags, crawl behind auth walls with custom headers, max crawl depth, etc...\\n-
+        **Media parsing**: pdfs, docx, images\\n- **Reliability first**: designed
+        to get the data you need - no matter how hard it is\\n- **Actions**: click,
+        scroll, input, wait and more before extracting data\\n- **Batching**: scrape
+        thousands of URLs at the same time with a new async endpoint\\n- **Change
+        Tracking**: monitor and detect changes in website content over time\\n\\nYou
+        can find all of Firecrawl's capabilities and how to use them in our [documentation](https://docs.firecrawl.dev/)\\n\\n###
+        Crawling\\n\\n[Permalink: Crawling](https://github.com/firecrawl/firecrawl#crawling)\\n\\nUsed
+        to crawl a URL and all accessible subpages. This submits a crawl job and returns
+        a job ID to check the status of the crawl.\\n\\n```\\ncurl -X POST https://api.firecrawl.dev/v2/crawl
+        \\\\\\n    -H 'Content-Type: application/json' \\\\\\n    -H 'Authorization:
+        Bearer fc-YOUR_API_KEY' \\\\\\n    -d '{\\n      \\\"url\\\": \\\"https://docs.firecrawl.dev\\\",\\n
+        \     \\\"limit\\\": 10,\\n      \\\"scrapeOptions\\\": {\\n        \\\"formats\\\":
+        [\\\"markdown\\\", \\\"html\\\"]\\n      }\\n    }'\\n```\\n\\nReturns a crawl
+        job id and the url to check the status of the crawl.\\n\\n```\\n{\\n  \\\"success\\\":
+        true,\\n  \\\"id\\\": \\\"123-456-789\\\",\\n  \\\"url\\\": \\\"https://api.firecrawl.dev/v2/crawl/123-456-789\\\"\\n}\\n```\\n\\n###
+        Check Crawl Job\\n\\n[Permalink: Check Crawl Job](https://github.com/firecrawl/firecrawl#check-crawl-job)\\n\\nUsed
+        to check the status of a crawl job and get its result.\\n\\n```\\ncurl -X
+        GET https://api.firecrawl.dev/v2/crawl/123-456-789 \\\\\\n  -H 'Content-Type:
+        application/json' \\\\\\n  -H 'Authorization: Bearer YOUR_API_KEY'\\n```\\n\\n```\\n{\\n
+        \ \\\"status\\\": \\\"completed\\\",\\n  \\\"total\\\": 36,\\n  \\\"creditsUsed\\\":
+        36,\\n  \\\"expiresAt\\\": \\\"2024-00-00T00:00:00.000Z\\\",\\n  \\\"data\\\":
+        [\\\\\\n    {\\\\\\n      \\\"markdown\\\": \\\"[Firecrawl Docs home page![light
+        logo](https://mintlify.s3-us-west-1.amazonaws.com/firecrawl/logo/light.svg)!...\\\",\\\\\\n
+        \     \\\"html\\\": \\\"<!DOCTYPE html><html lang=\\\\\\\"en\\\\\\\" class=\\\\\\\"js-focus-visible
+        lg:[--scroll-mt:9.5rem]\\\\\\\" data-js-focus-visible=\\\\\\\"\\\\\\\">...\\\",\\\\\\n
+        \     \\\"metadata\\\": {\\\\\\n        \\\"title\\\": \\\"Build a 'Chat with
+        website' using Groq Llama 3 | Firecrawl\\\",\\\\\\n        \\\"language\\\":
+        \\\"en\\\",\\\\\\n        \\\"sourceURL\\\": \\\"https://docs.firecrawl.dev/learn/rag-llama3\\\",\\\\\\n
+        \       \\\"description\\\": \\\"Learn how to use Firecrawl, Groq Llama 3,
+        and Langchain to build a 'Chat with your website' bot.\\\",\\\\\\n        \\\"ogLocaleAlternate\\\":
+        [],\\\\\\n        \\\"statusCode\\\": 200\\\\\\n      }\\\\\\n    }\\\\\\n
+        \ ]\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n### Scraping\\\\\\n\\\\\\n[Permalink: Scraping](https://github.com/firecrawl/firecrawl#scraping)\\\\\\n\\\\\\nUsed
+        to scrape a URL and get its content in the specified formats.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/scrape \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n      \\\"url\\\": \\\"https://docs.firecrawl.dev\\\",\\\\\\n
+        \     \\\"formats\\\" : [\\\"markdown\\\", \\\"html\\\"]\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\nResponse:\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"data\\\": {\\\\\\n    \\\"markdown\\\":
+        \\\"Launch Week I is here! [See our Day 2 Release \U0001F680](https://www.firecrawl.dev/blog/launch-week-i-day-2-doubled-rate-limits)[\U0001F4A5
+        Get 2 months free...\\\",\\\\\\n    \\\"html\\\": \\\"<!DOCTYPE html><html
+        lang=\\\\\\\"en\\\\\\\" class=\\\\\\\"light\\\\\\\" style=\\\\\\\"color-scheme:
+        light;\\\\\\\"><body class=\\\\\\\"__variable_36bd41 __variable_d7dc5d font-inter
+        ...\\\",\\\\\\n    \\\"metadata\\\": {\\\\\\n      \\\"title\\\": \\\"Home
+        - Firecrawl\\\",\\\\\\n      \\\"description\\\": \\\"Firecrawl crawls and
+        converts any website into clean markdown.\\\",\\\\\\n      \\\"language\\\":
+        \\\"en\\\",\\\\\\n      \\\"keywords\\\": \\\"Firecrawl,Markdown,Data,Mendable,Langchain\\\",\\\\\\n
+        \     \\\"robots\\\": \\\"follow, index\\\",\\\\\\n      \\\"ogTitle\\\":
+        \\\"Firecrawl\\\",\\\\\\n      \\\"ogDescription\\\": \\\"Turn any website
+        into LLM-ready data.\\\",\\\\\\n      \\\"ogUrl\\\": \\\"https://www.firecrawl.dev/\\\",\\\\\\n
+        \     \\\"ogImage\\\": \\\"https://www.firecrawl.dev/og.png?123\\\",\\\\\\n
+        \     \\\"ogLocaleAlternate\\\": [],\\\\\\n      \\\"ogSiteName\\\": \\\"Firecrawl\\\",\\\\\\n
+        \     \\\"sourceURL\\\": \\\"https://firecrawl.dev\\\",\\\\\\n      \\\"statusCode\\\":
+        200\\\\\\n    }\\\\\\n  }\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n### Map\\\\\\n\\\\\\n[Permalink:
+        Map](https://github.com/firecrawl/firecrawl#map)\\\\\\n\\\\\\nUsed to map
+        a URL and get urls of the website. This returns most links present on the
+        website.\\\\\\n\\\\\\n```\\\\\\ncurl -X POST https://api.firecrawl.dev/v2/map
+        \\\\\\\\\\n    -H 'Content-Type: application/json' \\\\\\\\\\n    -H 'Authorization:
+        Bearer YOUR_API_KEY' \\\\\\\\\\n    -d '{\\\\\\n      \\\"url\\\": \\\"https://firecrawl.dev\\\"\\\\\\n
+        \   }'\\\\\\n```\\\\\\n\\\\\\nResponse:\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n  \\\"success\\\":
+        true,\\\\\\n  \\\"links\\\": [\\\\\\n    { \\\"url\\\": \\\"https://firecrawl.dev\\\",
+        \\\"title\\\": \\\"Firecrawl\\\", \\\"description\\\": \\\"Firecrawl is a
+        tool that allows you to crawl a website and get the data you need.\\\" },\\\\\\n
+        \   { \\\"url\\\": \\\"https://www.firecrawl.dev/pricing\\\", \\\"title\\\":
+        \\\"Firecrawl Pricing\\\", \\\"description\\\": \\\"Firecrawl Pricing\\\"
+        },\\\\\\n    { \\\"url\\\": \\\"https://www.firecrawl.dev/blog\\\", \\\"title\\\":
+        \\\"Firecrawl Blog\\\", \\\"description\\\": \\\"Firecrawl Blog\\\" },\\\\\\n
+        \   { \\\"url\\\": \\\"https://www.firecrawl.dev/playground\\\", \\\"title\\\":
+        \\\"Firecrawl Playground\\\", \\\"description\\\": \\\"Firecrawl Playground\\\"
+        },\\\\\\n    { \\\"url\\\": \\\"https://www.firecrawl.dev/smart-crawl\\\",
+        \\\"title\\\": \\\"Firecrawl Smart Crawl\\\", \\\"description\\\": \\\"Firecrawl
+        Smart Crawl\\\" }\\\\\\n  ]\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n#### Map with search\\\\\\n\\\\\\n[Permalink:
+        Map with search](https://github.com/firecrawl/firecrawl#map-with-search)\\\\\\n\\\\\\nMap
+        with `search` param allows you to search for specific urls inside a website.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/map \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n      \\\"url\\\": \\\"https://firecrawl.dev\\\",\\\\\\n      \\\"search\\\":
+        \\\"docs\\\"\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\nResponse will be an ordered
+        list from the most relevant to the least relevant.\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"links\\\": [\\\\\\n    { \\\"url\\\":
+        \\\"https://docs.firecrawl.dev\\\", \\\"title\\\": \\\"Firecrawl Docs\\\",
+        \\\"description\\\": \\\"Firecrawl Docs\\\" },\\\\\\n    { \\\"url\\\": \\\"https://docs.firecrawl.dev/sdks/python\\\",
+        \\\"title\\\": \\\"Firecrawl Python SDK\\\", \\\"description\\\": \\\"Firecrawl
+        Python SDK\\\" },\\\\\\n    { \\\"url\\\": \\\"https://docs.firecrawl.dev/learn/rag-llama3\\\",
+        \\\"title\\\": \\\"Firecrawl RAG Llama 3\\\", \\\"description\\\": \\\"Firecrawl
+        RAG Llama 3\\\" }\\\\\\n  ]\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n### Search\\\\\\n\\\\\\n[Permalink:
+        Search](https://github.com/firecrawl/firecrawl#search)\\\\\\n\\\\\\nSearch
+        the web and get full content from results\\\\\\n\\\\\\nFirecrawl\u2019s search
+        API allows you to perform web searches and optionally scrape the search results
+        in one operation.\\\\\\n\\\\\\n- Choose specific output formats (markdown,
+        HTML, links, screenshots)\\\\\\n- Search the web with customizable parameters
+        (language, country, etc.)\\\\\\n- Optionally retrieve content from search
+        results in various formats\\\\\\n- Control the number of results and set timeouts\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/search \\\\\\\\\\n  -H \\\"Content-Type:
+        application/json\\\" \\\\\\\\\\n  -H \\\"Authorization: Bearer fc-YOUR_API_KEY\\\"
+        \\\\\\\\\\n  -d '{\\\\\\n    \\\"query\\\": \\\"what is firecrawl?\\\",\\\\\\n
+        \   \\\"limit\\\": 5\\\\\\n  }'\\\\\\n```\\\\\\n\\\\\\n#### Response\\\\\\n\\\\\\n[Permalink:
+        Response](https://github.com/firecrawl/firecrawl#response)\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"data\\\": [\\\\\\n    {\\\\\\n      \\\"url\\\":
+        \\\"https://firecrawl.dev\\\",\\\\\\n      \\\"title\\\": \\\"Firecrawl |
+        Home Page\\\",\\\\\\n      \\\"description\\\": \\\"Turn websites into LLM-ready
+        data with Firecrawl\\\"\\\\\\n    },\\\\\\n    {\\\\\\n      \\\"url\\\":
+        \\\"https://docs.firecrawl.dev\\\",\\\\\\n      \\\"title\\\": \\\"Documentation
+        | Firecrawl\\\",\\\\\\n      \\\"description\\\": \\\"Learn how to use Firecrawl
+        in your own applications\\\"\\\\\\n    }\\\\\\n  ]\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n####
+        With content scraping\\\\\\n\\\\\\n[Permalink: With content scraping](https://github.com/firecrawl/firecrawl#with-content-scraping)\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/search \\\\\\\\\\n  -H \\\"Content-Type:
+        application/json\\\" \\\\\\\\\\n  -H \\\"Authorization: Bearer fc-YOUR_API_KEY\\\"
+        \\\\\\\\\\n  -d '{\\\\\\n    \\\"query\\\": \\\"what is firecrawl?\\\",\\\\\\n
+        \   \\\"limit\\\": 5,\\\\\\n    \\\"scrapeOptions\\\": {\\\\\\n      \\\"formats\\\":
+        [\\\"markdown\\\", \\\"links\\\"]\\\\\\n    }\\\\\\n  }'\\\\\\n```\\\\\\n\\\\\\n###
+        Extract (Beta)\\\\\\n\\\\\\n[Permalink: Extract (Beta)](https://github.com/firecrawl/firecrawl#extract-beta)\\\\\\n\\\\\\nGet
+        structured data from entire websites with a prompt and/or a schema.\\\\\\n\\\\\\nYou
+        can extract structured data from one or multiple URLs, including wildcards:\\\\\\n\\\\\\nSingle
+        Page:\\\\\\nExample: [https://firecrawl.dev/some-page](https://firecrawl.dev/some-page)\\\\\\n\\\\\\nMultiple
+        Pages / Full Domain\\\\\\nExample: [https://firecrawl.dev/](https://firecrawl.dev/)\\\\*\\\\\\n\\\\\\nWhen
+        you use /\\\\*, Firecrawl will automatically crawl and parse all URLs it can
+        discover in that domain, then extract the requested data.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/extract \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n      \\\"urls\\\": [\\\\\\n        \\\"https://firecrawl.dev/*\\\",\\\\\\n
+        \       \\\"https://docs.firecrawl.dev/\\\",\\\\\\n        \\\"https://www.ycombinator.com/companies\\\"\\\\\\n
+        \     ],\\\\\\n      \\\"prompt\\\": \\\"Extract the company mission, whether
+        it is open source, and whether it is in Y Combinator from the page.\\\",\\\\\\n
+        \     \\\"schema\\\": {\\\\\\n        \\\"type\\\": \\\"object\\\",\\\\\\n
+        \       \\\"properties\\\": {\\\\\\n          \\\"company_mission\\\": {\\\\\\n
+        \           \\\"type\\\": \\\"string\\\"\\\\\\n          },\\\\\\n          \\\"is_open_source\\\":
+        {\\\\\\n            \\\"type\\\": \\\"boolean\\\"\\\\\\n          },\\\\\\n
+        \         \\\"is_in_yc\\\": {\\\\\\n            \\\"type\\\": \\\"boolean\\\"\\\\\\n
+        \         }\\\\\\n        },\\\\\\n        \\\"required\\\": [\\\\\\n          \\\"company_mission\\\",\\\\\\n
+        \         \\\"is_open_source\\\",\\\\\\n          \\\"is_in_yc\\\"\\\\\\n
+        \       ]\\\\\\n      }\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"id\\\": \\\"44aa536d-f1cb-4706-ab87-ed0386685740\\\",\\\\\\n
+        \ \\\"urlTrace\\\": []\\\\\\n}\\\\\\n```\\\\\\n\\\\\\nIf you are using the
+        sdks, it will auto pull the response for you:\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"data\\\": {\\\\\\n    \\\"company_mission\\\":
+        \\\"Firecrawl is the easiest way to extract data from the web. Developers
+        use us to reliably convert URLs into LLM-ready markdown or structured data
+        with a single API call.\\\",\\\\\\n    \\\"supports_sso\\\": false,\\\\\\n
+        \   \\\"is_open_source\\\": true,\\\\\\n    \\\"is_in_yc\\\": true\\\\\\n
+        \ }\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n### LLM Extraction (Beta)\\\\\\n\\\\\\n[Permalink:
+        LLM Extraction (Beta)](https://github.com/firecrawl/firecrawl#llm-extraction-beta)\\\\\\n\\\\\\nUsed
+        to extract structured data from scraped pages.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/scrape \\\\\\\\\\n  -H 'Content-Type:
+        application/json' \\\\\\\\\\n  -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \ -d '{\\\\\\n    \\\"url\\\": \\\"https://www.mendable.ai/\\\",\\\\\\n    \\\"formats\\\":
+        [\\\\\\n      {\\\\\\n        \\\"type\\\": \\\"json\\\",\\\\\\n        \\\"schema\\\":
+        {\\\\\\n          \\\"type\\\": \\\"object\\\",\\\\\\n          \\\"properties\\\":
+        {\\\\\\n            \\\"company_mission\\\": { \\\"type\\\": \\\"string\\\"
+        },\\\\\\n            \\\"supports_sso\\\": { \\\"type\\\": \\\"boolean\\\"
+        },\\\\\\n            \\\"is_open_source\\\": { \\\"type\\\": \\\"boolean\\\"
+        },\\\\\\n            \\\"is_in_yc\\\": { \\\"type\\\": \\\"boolean\\\" }\\\\\\n
+        \         }\\\\\\n        }\\\\\\n      }\\\\\\n    ]\\\\\\n  }'\\\\\\n```\\\\\\n\\\\\\n```\\\\\\n{\\\\\\n
+        \ \\\"success\\\": true,\\\\\\n  \\\"data\\\": {\\\\\\n    \\\"content\\\":
+        \\\"Raw Content\\\",\\\\\\n    \\\"metadata\\\": {\\\\\\n      \\\"title\\\":
+        \\\"Mendable\\\",\\\\\\n      \\\"description\\\": \\\"Mendable allows you
+        to easily build AI chat applications. Ingest, customize, then deploy with
+        one line of code anywhere you want. Brought to you by SideGuide\\\",\\\\\\n
+        \     \\\"robots\\\": \\\"follow, index\\\",\\\\\\n      \\\"ogTitle\\\":
+        \\\"Mendable\\\",\\\\\\n      \\\"ogDescription\\\": \\\"Mendable allows you
+        to easily build AI chat applications. Ingest, customize, then deploy with
+        one line of code anywhere you want. Brought to you by SideGuide\\\",\\\\\\n
+        \     \\\"ogUrl\\\": \\\"https://mendable.ai/\\\",\\\\\\n      \\\"ogImage\\\":
+        \\\"https://mendable.ai/mendable_new_og1.png\\\",\\\\\\n      \\\"ogLocaleAlternate\\\":
+        [],\\\\\\n      \\\"ogSiteName\\\": \\\"Mendable\\\",\\\\\\n      \\\"sourceURL\\\":
+        \\\"https://mendable.ai/\\\"\\\\\\n    },\\\\\\n    \\\"json\\\": {\\\\\\n
+        \     \\\"company_mission\\\": \\\"Train a secure AI on your technical resources
+        that answers customer and employee questions so your team doesn't have to\\\",\\\\\\n
+        \     \\\"supports_sso\\\": true,\\\\\\n      \\\"is_open_source\\\": false,\\\\\\n
+        \     \\\"is_in_yc\\\": true\\\\\\n    }\\\\\\n  }\\\\\\n}\\\\\\n```\\\\\\n\\\\\\n###
+        Extracting without a schema (New)\\\\\\n\\\\\\n[Permalink: Extracting without
+        a schema (New)](https://github.com/firecrawl/firecrawl#extracting-without-a-schema-new)\\\\\\n\\\\\\nYou
+        can now extract without a schema by just passing a `prompt` to the endpoint.
+        The llm chooses the structure of the data.\\\\\\n\\\\\\n```\\\\\\ncurl -X
+        POST https://api.firecrawl.dev/v2/scrape \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n      \\\"url\\\": \\\"https://docs.firecrawl.dev/\\\",\\\\\\n
+        \     \\\"formats\\\": [\\\\\\n        {\\\\\\n          \\\"type\\\": \\\"json\\\",\\\\\\n
+        \         \\\"prompt\\\": \\\"Extract the company mission from the page.\\\"\\\\\\n
+        \       }\\\\\\n      ]\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\n### Interacting
+        with the page with Actions (Cloud-only)\\\\\\n\\\\\\n[Permalink: Interacting
+        with the page with Actions (Cloud-only)](https://github.com/firecrawl/firecrawl#interacting-with-the-page-with-actions-cloud-only)\\\\\\n\\\\\\nFirecrawl
+        allows you to perform various actions on a web page before scraping its content.
+        This is particularly useful for interacting with dynamic content, navigating
+        through pages, or accessing content that requires user interaction.\\\\\\n\\\\\\nHere
+        is an example of how to use actions to navigate to google.com, search for
+        Firecrawl, click on the first result, and take a screenshot.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/scrape \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n        \\\"url\\\": \\\"google.com\\\",\\\\\\n        \\\"formats\\\":
+        [\\\"markdown\\\"],\\\\\\n        \\\"actions\\\": [\\\\\\n            {\\\"type\\\":
+        \\\"wait\\\", \\\"milliseconds\\\": 2000},\\\\\\n            {\\\"type\\\":
+        \\\"click\\\", \\\"selector\\\": \\\"textarea[title=\\\\\\\"Search\\\\\\\"]\\\"},\\\\\\n
+        \           {\\\"type\\\": \\\"wait\\\", \\\"milliseconds\\\": 2000},\\\\\\n
+        \           {\\\"type\\\": \\\"write\\\", \\\"text\\\": \\\"firecrawl\\\"},\\\\\\n
+        \           {\\\"type\\\": \\\"wait\\\", \\\"milliseconds\\\": 2000},\\\\\\n
+        \           {\\\"type\\\": \\\"press\\\", \\\"key\\\": \\\"ENTER\\\"},\\\\\\n
+        \           {\\\"type\\\": \\\"wait\\\", \\\"milliseconds\\\": 3000},\\\\\\n
+        \           {\\\"type\\\": \\\"click\\\", \\\"selector\\\": \\\"h3\\\"},\\\\\\n
+        \           {\\\"type\\\": \\\"wait\\\", \\\"milliseconds\\\": 3000},\\\\\\n
+        \           {\\\"type\\\": \\\"screenshot\\\"}\\\\\\n        ]\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\n###
+        Batch Scraping Multiple URLs (New)\\\\\\n\\\\\\n[Permalink: Batch Scraping
+        Multiple URLs (New)](https://github.com/firecrawl/firecrawl#batch-scraping-multiple-urls-new)\\\\\\n\\\\\\nYou
+        can now batch scrape multiple URLs at the same time. It is very similar to
+        how the /crawl endpoint works. It submits a batch scrape job and returns a
+        job ID to check the status of the batch scrape.\\\\\\n\\\\\\n```\\\\\\ncurl
+        -X POST https://api.firecrawl.dev/v2/batch/scrape \\\\\\\\\\n    -H 'Content-Type:
+        application/json' \\\\\\\\\\n    -H 'Authorization: Bearer YOUR_API_KEY' \\\\\\\\\\n
+        \   -d '{\\\\\\n      \\\"urls\\\": [\\\"https://docs.firecrawl.dev\\\", \\\"https://docs.firecrawl.dev/sdks/overview\\\"],\\\\\\n
+        \     \\\"formats\\\" : [\\\"markdown\\\", \\\"html\\\"]\\\\\\n    }'\\\\\\n```\\\\\\n\\\\\\n##
+        Using Python SDK\\\\\\n\\\\\\n[Permalink: Using Python SDK](https://github.com/firecrawl/firecrawl#using-python-sdk)\\\\\\n\\\\\\n###
+        Installing Python SDK\\\\\\n\\\\\\n[Permalink: Installing Python SDK](https://github.com/firecrawl/firecrawl#installing-python-sdk)\\\\\\n\\\\\\n```\\\\\\npip
+        install firecrawl-py\\\\\\n```\\\\\\n\\\\\\n### Crawl a website\\\\\\n\\\\\\n[Permalink:
+        Crawl a website](https://github.com/firecrawl/firecrawl#crawl-a-website)\\\\\\n\\\\\\n```\\\\\\nfrom
+        firecrawl import Firecrawl\\\\\\n\\\\\\nfirecrawl = Firecrawl(api_key=\\\"fc-YOUR_API_KEY\\\")\\\\\\n\\\\\\n#
+        Scrape a website (returns a Document)\\\\\\ndoc = firecrawl.scrape(\\\\\\n
+        \   \\\"https://firecrawl.dev\\\",\\\\\\n    formats=[\\\"markdown\\\", \\\"html\\\"],\\\\\\n)\\\\\\nprint(doc.markdown)\\\\\\n\\\\\\n#
+        Crawl a website\\\\\\nresponse = firecrawl.crawl(\\\\\\n    \\\"https://firecrawl.dev\\\",\\\\\\n
+        \   limit=100,\\\\\\n    scrape_options={\\\"formats\\\": [\\\"markdown\\\",
+        \\\"html\\\"]},\\\\\\n    poll_interval=30,\\\\\\n)\\\\\\nprint(response)\\\\\\n```\\\\\\n\\\\\\n###
+        Extracting structured data from a URL\\\\\\n\\\\\\n[Permalink: Extracting
+        structured data from a URL](https://github.com/firecrawl/firecrawl#extracting-structured-data-from-a-url)\\\\\\n\\\\\\nWith
+        LLM extraction, you can easily extract structured data from any URL. We support
+        pydantic schemas to make it easier for you too. Here is how you to use it:\\\\\\n\\\\\\n```\\\\\\nfrom
+        pydantic import BaseModel, Field\\\\\\nfrom typing import List\\\\\\n\\\\\\nclass
+        Article(BaseModel):\\\\\\n    title: str\\\\\\n    points: int\\\\\\n    by:
+        str\\\\\\n    commentsURL: str\\\\\\n\\\\\\nclass TopArticles(BaseModel):\\\\\\n
+        \   top: List[Article] = Field(..., description=\\\"Top 5 stories\\\")\\\\\\n\\\\\\n#
+        Use JSON format with a Pydantic schema\\\\\\ndoc = firecrawl.scrape(\\\\\\n
+        \   \\\"https://news.ycombinator.com\\\",\\\\\\n    formats=[{\\\"type\\\":
+        \\\"json\\\", \\\"schema\\\": TopArticles}],\\\\\\n)\\\\\\nprint(doc.json)\\\\\\n```\\\\\\n\\\\\\n##
+        Using the Node SDK\\\\\\n\\\\\\n[Permalink: Using the Node SDK](https://github.com/firecrawl/firecrawl#using-the-node-sdk)\\\\\\n\\\\\\n###
+        Installation\\\\\\n\\\\\\n[Permalink: Installation](https://github.com/firecrawl/firecrawl#installation)\\\\\\n\\\\\\nTo
+        install the Firecrawl Node SDK, you can use npm:\\\\\\n\\\\\\n```\\\\\\nnpm
+        install @mendable/firecrawl-js\\\\\\n```\\\\\\n\\\\\\n### Usage\\\\\\n\\\\\\n[Permalink:
+        Usage](https://github.com/firecrawl/firecrawl#usage)\\\\\\n\\\\\\n1. Get an
+        API key from [firecrawl.dev](https://firecrawl.dev/)\\\\\\n2. Set the API
+        key as an environment variable named `FIRECRAWL_API_KEY` or pass it as a parameter
+        to the `Firecrawl` class.\\\\\\n\\\\\\n```\\\\\\nimport Firecrawl from '@mendable/firecrawl-js';\\\\\\n\\\\\\nconst
+        firecrawl = new Firecrawl({ apiKey: 'fc-YOUR_API_KEY' });\\\\\\n\\\\\\n//
+        Scrape a website\\\\\\nconst doc = await firecrawl.scrape('https://firecrawl.dev',
+        {\\\\\\n  formats: ['markdown', 'html'],\\\\\\n});\\\\\\nconsole.log(doc);\\\\\\n\\\\\\n//
+        Crawl a website\\\\\\nconst response = await firecrawl.crawl('https://firecrawl.dev',
+        {\\\\\\n  limit: 100,\\\\\\n  scrapeOptions: { formats: ['markdown', 'html']
+        },\\\\\\n});\\\\\\nconsole.log(response);\\\\\\n```\\\\\\n\\\\\\n### Extracting
+        structured data from a URL\\\\\\n\\\\\\n[Permalink: Extracting structured
+        data from a URL](https://github.com/firecrawl/firecrawl#extracting-structured-data-from-a-url-1)\\\\\\n\\\\\\nWith
+        LLM extraction, you can easily extract structured data from any URL. We support
+        zod schema to make it easier for you too. Here is how to use it:\\\\\\n\\\\\\n```\\\\\\nimport
+        Firecrawl from '@mendable/firecrawl-js';\\\\\\nimport { z } from 'zod';\\\\\\n\\\\\\nconst
+        firecrawl = new Firecrawl({ apiKey: 'fc-YOUR_API_KEY' });\\\\\\n\\\\\\n//
+        Define schema to extract contents into\\\\\\nconst schema = z.object({\\\\\\n
+        \ top: z\\\\\\n    .array(\\\\\\n      z.object({\\\\\\n        title: z.string(),\\\\\\n
+        \       points: z.number(),\\\\\\n        by: z.string(),\\\\\\n        commentsURL:
+        z.string(),\\\\\\n      })\\\\\\n    )\\\\\\n    .length(5)\\\\\\n    .describe('Top
+        5 stories on Hacker News'),\\\\\\n});\\\\\\n\\\\\\n// Use the v2 extract API
+        with direct Zod schema support\\\\\\nconst extractRes = await firecrawl.extract({\\\\\\n
+        \ urls: ['https://news.ycombinator.com'],\\\\\\n  schema,\\\\\\n  prompt:
+        'Extract the top 5 stories',\\\\\\n});\\\\\\n\\\\\\nconsole.log(extractRes);\\\\\\n```\\\\\\n\\\\\\n##
+        Open Source vs Cloud Offering\\\\\\n\\\\\\n[Permalink: Open Source vs Cloud
+        Offering](https://github.com/firecrawl/firecrawl#open-source-vs-cloud-offering)\\\\\\n\\\\\\nFirecrawl
+        is open source available under the AGPL-3.0 license.\\\\\\n\\\\\\nTo deliver
+        the best possible product, we offer a hosted version of Firecrawl alongside
+        our open-source offering. The cloud solution allows us to continuously innovate
+        and maintain a high-quality, sustainable service for all users.\\\\\\n\\\\\\nFirecrawl
+        Cloud is available at [firecrawl.dev](https://firecrawl.dev/) and offers a
+        range of features that are not available in the open source version:\\\\\\n\\\\\\n[![Open
+        Source vs Cloud Offering](https://raw.githubusercontent.com/firecrawl/firecrawl/main/img/open-source-cloud.png)](https://raw.githubusercontent.com/firecrawl/firecrawl/main/img/open-source-cloud.png)\\\\\\n\\\\\\n##
+        Contributing\\\\\\n\\\\\\n[Permalink: Contributing](https://github.com/firecrawl/firecrawl#contributing)\\\\\\n\\\\\\nWe
+        love contributions! Please read our [contributing guide](https://github.com/firecrawl/firecrawl/blob/main/CONTRIBUTING.md)
+        before submitting a pull request. If you'd like to self-host, refer to the
+        [self-hosting guide](https://github.com/firecrawl/firecrawl/blob/main/SELF_HOST.md).\\\\\\n\\\\\\n_It
+        is the sole responsibility of the end users to respect websites' policies
+        when scraping, searching and crawling with Firecrawl. Users are advised to
+        adhere to the applicable privacy policies and terms of use of the websites
+        prior to initiating any scraping activities. By default, Firecrawl respects
+        the directives specified in the websites' robots.txt files when crawling.
+        By utilizing Firecrawl, you expressly agree to comply with these conditions._\\\\\\n\\\\\\n##
+        Contributors\\\\\\n\\\\\\n[Permalink: Contributors](https://github.com/firecrawl/firecrawl#contributors)\\\\\\n\\\\\\n[![contributors](https://camo.githubusercontent.com/e3b2e7ad4c1f76e68fc11ede158c87a0f039052f649002a1ff855d13fb9294fb/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d66697265637261776c2f66697265637261776c)](https://github.com/firecrawl/firecrawl/graphs/contributors)\\\\\\n\\\\\\n##
+        License Disclaimer\\\\\\n\\\\\\n[Permalink: License Disclaimer](https://github.com/firecrawl/firecrawl#license-disclaimer)\\\\\\n\\\\\\nThis
+        project is primarily licensed under the GNU Affero General Public License
+        v3.0 (AGPL-3.0), as specified in the LICENSE file in the root directory of
+        this repository. However, certain components of this project are licensed
+        under the MIT License. Refer to the LICENSE files in these specific directories
+        for details.\\\\\\n\\\\\\nPlease note:\\\\\\n\\\\\\n- The AGPL-3.0 license
+        applies to all parts of the project unless otherwise specified.\\\\\\n- The
+        SDKs and some UI components are licensed under the MIT License. Refer to the
+        LICENSE files in these specific directories for details.\\\\\\n- When using
+        or contributing to this project, ensure you comply with the appropriate license
+        terms for the specific component you are working with.\\\\\\n\\\\\\nFor more
+        details on the licensing of specific components, please refer to the LICENSE
+        files in the respective directories or contact the project maintainers.\\\\\\n\\\\\\n[\u2191
+        Back to Top \u2191](https://github.com/firecrawl/firecrawl#readme-top)\\\\\\n\\\\\\n##
+        About\\\\\\n\\\\\\n\U0001F525 The Web Data API for AI - Turn entire websites
+        into LLM-ready markdown or structured data\\\\\\n\\\\\\n\\\\\\n[firecrawl.dev](https://firecrawl.dev/
+        \\\"https://firecrawl.dev\\\")\\\\\\n\\\\\\n### Topics\\\\\\n\\\\\\n[markdown](https://github.com/topics/markdown
+        \\\"Topic: markdown\\\") [crawler](https://github.com/topics/crawler \\\"Topic:
+        crawler\\\") [scraper](https://github.com/topics/scraper \\\"Topic: scraper\\\")
+        [ai](https://github.com/topics/ai \\\"Topic: ai\\\") [html-to-markdown](https://github.com/topics/html-to-markdown
+        \\\"Topic: html-to-markdown\\\") [web-crawler](https://github.com/topics/web-crawler
+        \\\"Topic: web-crawler\\\") [scraping](https://github.com/topics/scraping
+        \\\"Topic: scraping\\\") [web-scraper](https://github.com/topics/web-scraper
+        \\\"Topic: web-scraper\\\") [web-scraping](https://github.com/topics/web-scraping
+        \\\"Topic: web-scraping\\\") [data-extraction](https://github.com/topics/data-extraction
+        \\\"Topic: data-extraction\\\") [webscraping](https://github.com/topics/webscraping
+        \\\"Topic: webscraping\\\") [web-data-extraction](https://github.com/topics/web-data-extraction
+        \\\"Topic: web-data-extraction\\\") [ai-agents](https://github.com/topics/ai-agents
+        \\\"Topic: ai-agents\\\") [web-search](https://github.com/topics/web-search
+        \\\"Topic: web-search\\\") [ai-search](https://github.com/topics/ai-search
+        \\\"Topic: ai-search\\\") [web-data](https://github.com/topics/web-data \\\"Topic:
+        web-data\\\") [llm](https://github.com/topics/llm \\\"Topic: llm\\\") [ai-crawler](https://github.com/topics/ai-crawler
+        \\\"Topic: ai-crawler\\\") [ai-scraping](https://github.com/topics/ai-scraping
+        \\\"Topic: ai-scraping\\\")\\\\\\n\\\\\\n### Resources\\\\\\n\\\\\\n[Readme](https://github.com/firecrawl/firecrawl#readme-ov-file)\\\\\\n\\\\\\n###
+        License\\\\\\n\\\\\\n[AGPL-3.0 license](https://github.com/firecrawl/firecrawl#AGPL-3.0-1-ov-file)\\\\\\n\\\\\\n###
+        Contributing\\\\\\n\\\\\\n[Contributing](https://github.com/firecrawl/firecrawl#contributing-ov-file)\\\\\\n\\\\\\n###
+        Uh oh!\\\\\\n\\\\\\nThere was an error while loading. [Please reload this
+        page](https://github.com/firecrawl/firecrawl).\\\\\\n\\\\\\n[Activity](https://github.com/firecrawl/firecrawl/activity)\\\\\\n\\\\\\n[Custom
+        properties](https://github.com/firecrawl/firecrawl/custom-properties)\\\\\\n\\\\\\n###
+        Stars\\\\\\n\\\\\\n[**65.2k**\\\\\\\\\\nstars](https://github.com/firecrawl/firecrawl/stargazers)\\\\\\n\\\\\\n###
+        Watchers\\\\\\n\\\\\\n[**256**\\\\\\\\\\nwatching](https://github.com/firecrawl/firecrawl/watchers)\\\\\\n\\\\\\n###
+        Forks\\\\\\n\\\\\\n[**5.1k**\\\\\\\\\\nforks](https://github.com/firecrawl/firecrawl/forks)\\\\\\n\\\\\\n[Report
+        repository](https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Ffirecrawl%2Ffirecrawl&report=firecrawl+%28user%29)\\\\\\n\\\\\\n##
+        [Releases\\\\  28](https://github.com/firecrawl/firecrawl/releases)\\\\\\n\\\\\\n[v2.4.0\\\\\\\\\\nLatest\\\\\\\\\\n\\\\\\\\\\n2
+        weeks agoOct 13, 2025](https://github.com/firecrawl/firecrawl/releases/tag/v2.4.0)\\\\\\n\\\\\\n[\\\\+
+        27 releases](https://github.com/firecrawl/firecrawl/releases)\\\\\\n\\\\\\n##
+        [Packages\\\\  3](https://github.com/orgs/firecrawl/packages?repo_name=firecrawl)\\\\\\n\\\\\\n-
+        [firecrawl](https://github.com/orgs/firecrawl/packages/container/package/firecrawl)\\\\\\n-
+        [playwright-service](https://github.com/orgs/firecrawl/packages/container/package/playwright-service)\\\\\\n-
+        [nuq-postgres](https://github.com/orgs/firecrawl/packages/container/package/nuq-postgres)\\\\\\n\\\\\\n##
+        [Contributors\\\\  121](https://github.com/firecrawl/firecrawl/graphs/contributors)\\\\\\n\\\\\\n[\\\\+
+        107 contributors](https://github.com/firecrawl/firecrawl/graphs/contributors)\\\\\\n\\\\\\n##
+        Languages\\\\\\n\\\\\\n- [TypeScript73.5%](https://github.com/firecrawl/firecrawl/search?l=typescript)\\\\\\n-
+        [Python18.9%](https://github.com/firecrawl/firecrawl/search?l=python)\\\\\\n-
+        [Rust6.0%](https://github.com/firecrawl/firecrawl/search?l=rust)\\\\\\n- [Astro0.6%](https://github.com/firecrawl/firecrawl/search?l=astro)\\\\\\n-
+        [JavaScript0.3%](https://github.com/firecrawl/firecrawl/search?l=javascript)\\\\\\n-
+        [Jupyter Notebook0.2%](https://github.com/firecrawl/firecrawl/search?l=jupyter-notebook)\\\\\\n-
+        Other0.5%\",\"metadata\":{\"octolytics-dimension-repository_network_root_id\":\"787076358\",\"visitor-hmac\":\"163b2538b2335f7d4000a770785477e15881e1101708ca5ff1e8c021095f6ca1\",\"og:type\":\"object\",\"language\":\"en\",\"route-action\":\"disambiguate\",\"og:title\":\"GitHub
+        - firecrawl/firecrawl: \U0001F525 The Web Data API for AI - Turn entire websites
+        into LLM-ready markdown or structured data\",\"octolytics-dimension-repository_public\":\"true\",\"octolytics-dimension-repository_network_root_nwo\":\"firecrawl/firecrawl\",\"browser-errors-url\":\"https://api.github.com/_private/browser/errors\",\"browser-stats-url\":\"https://api.github.com/_private/browser/stats\",\"twitter:title\":\"GitHub
+        - firecrawl/firecrawl: \U0001F525 The Web Data API for AI - Turn entire websites
+        into LLM-ready markdown or structured data\",\"ui-target\":\"full\",\"og:image\":\"https://repository-images.githubusercontent.com/787076358/f9616c09-3701-41ef-b5a6-fdf912ffb15b\",\"google-site-verification\":\"Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I\",\"ogSiteName\":\"GitHub\",\"route-pattern\":\"/:user_id/:repository\",\"visitor-payload\":\"eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiJBMTVGOjE3OUI0RDo2MzdFQUREOjg3MzEwOTM6NjkwMTE4MjUiLCJ2aXNpdG9yX2lkIjoiNDkyNzk2MzExNjg5OTIxMTMwMSIsInJlZ2lvbl9lZGdlIjoiaWFkIiwicmVnaW9uX3JlbmRlciI6ImlhZCJ9\",\"og:description\":\"\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data - firecrawl/firecrawl\",\"expected-hostname\":\"github.com\",\"release\":\"66136a30a16cc69206f1249b6ba072daa2174535\",\"title\":\"GitHub
+        - firecrawl/firecrawl: \U0001F525 The Web Data API for AI - Turn entire websites
+        into LLM-ready markdown or structured data\",\"ogDescription\":\"\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data - firecrawl/firecrawl\",\"twitter:card\":\"summary_large_image\",\"fb:app_id\":\"1401488693436528\",\"color-scheme\":\"light
+        dark\",\"twitter:description\":\"\U0001F525 The Web Data API for AI - Turn
+        entire websites into LLM-ready markdown or structured data - firecrawl/firecrawl\",\"favicon\":\"https://github.githubassets.com/favicons/favicon.svg\",\"viewport\":\"width=device-width\",\"twitter:image\":\"https://repository-images.githubusercontent.com/787076358/f9616c09-3701-41ef-b5a6-fdf912ffb15b\",\"user-login\":\"\",\"description\":\"\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data - firecrawl/firecrawl\",\"octolytics-dimension-repository_nwo\":\"firecrawl/firecrawl\",\"octolytics-dimension-user_id\":\"135057108\",\"twitter:site\":\"@github\",\"og:url\":\"https://github.com/firecrawl/firecrawl\",\"octolytics-dimension-user_login\":\"firecrawl\",\"hostname\":\"github.com\",\"current-catalog-service-hash\":\"f3abb0cc802f3d7b95fc8762b94bdcb13bf39634c40c357301c4aa1d67a256fb\",\"html-safe-nonce\":\"e5653da3800db7de2c8b4a64ff6367242043a9e452608e9a6941a1c9e8346cfc\",\"apple-itunes-app\":\"app-id=1477376905,
+        app-argument=https://github.com/firecrawl/firecrawl\",\"turbo-cache-control\":\"no-preview\",\"og:site_name\":\"GitHub\",\"request-id\":\"A15F:179B4D:637EADD:8731093:69011825\",\"octolytics-url\":\"https://collector.github.com/github/collect\",\"octolytics-dimension-repository_is_fork\":\"false\",\"fetch-nonce\":\"v2:bc80e85b-edaf-e746-9f2c-ffdc75854b07\",\"og:image:alt\":\"\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data - firecrawl/firecrawl\",\"github-keyboard-shortcuts\":\"repository,copilot\",\"ogTitle\":\"GitHub
+        - firecrawl/firecrawl: \U0001F525 The Web Data API for AI - Turn entire websites
+        into LLM-ready markdown or structured data\",\"ogImage\":\"https://repository-images.githubusercontent.com/787076358/f9616c09-3701-41ef-b5a6-fdf912ffb15b\",\"analytics-location\":\"/<user-name>/<repo-name>\",\"route-controller\":\"files\",\"octolytics-dimension-repository_id\":\"787076358\",\"ogUrl\":\"https://github.com/firecrawl/firecrawl\",\"go-import\":\"github.com/firecrawl/firecrawl
+        git https://github.com/firecrawl/firecrawl.git\",\"hovercard-subject-tag\":\"repository:787076358\",\"theme-color\":\"#1e2327\",\"turbo-body-classes\":\"logged-out
+        env-production page-responsive\",\"scrapeId\":\"ec4d99a0-4c4f-4d1a-9fd2-08b8f891f883\",\"sourceURL\":\"https://github.com/firecrawl/firecrawl\",\"url\":\"https://github.com/firecrawl/firecrawl\",\"statusCode\":200,\"contentType\":\"text/html;
+        charset=utf-8\",\"proxyUsed\":\"basic\",\"cacheState\":\"hit\",\"cachedAt\":\"2025-10-28T19:23:20.106Z\"}},{\"url\":\"https://x.com/firecrawl_dev?lang=en\",\"title\":\"Firecrawl
+        (@firecrawl_dev) / Posts / X\",\"description\":\"Firecrawl (@firecrawl_dev)
+        - Posts - Turn websites into LLM-ready data. Built by @mendableai team Open
+        source: | X (formerly Twitter)\",\"position\":3},{\"url\":\"https://github.com/firecrawl\",\"title\":\"Firecrawl
+        - GitHub\",\"description\":\"Building AI applications? You need clean, structured
+        data from the web. Firecrawl handles the complexity of modern web scraping
+        so you can focus on building ...\",\"position\":4,\"category\":\"github\",\"markdown\":\"[Skip
+        to content](https://github.com/firecrawl#start-of-content)\\n\\nYou signed
+        in with another tab or window. [Reload](https://github.com/firecrawl) to refresh
+        your session.You signed out in another tab or window. [Reload](https://github.com/firecrawl)
+        to refresh your session.You switched accounts on another tab or window. [Reload](https://github.com/firecrawl)
+        to refresh your session.Dismiss alert\\n\\n{{ message }}\\n\\n[README.md](https://github.com/firecrawl/.github/tree/main/profile/README.md)\\n\\n#
+        \U0001F525 Firecrawl\\n\\n[Permalink: \U0001F525 Firecrawl](https://github.com/firecrawl#-firecrawl)\\n\\n[![Firecrawl
+        Logo](https://raw.githubusercontent.com/mendableai/firecrawl/main/img/firecrawl_logo.png)](https://raw.githubusercontent.com/mendableai/firecrawl/main/img/firecrawl_logo.png)\\n\\n###
+        Transform any website into LLM-ready data\\n\\n[Permalink: Transform any website
+        into LLM-ready data](https://github.com/firecrawl#transform-any-website-into-llm-ready-data)\\n\\nAdvanced
+        web scraping, crawling, and data extraction infrastructure for AI applications\\n\\n[![Get
+        Started](https://camo.githubusercontent.com/85b729c7fb201b60a98279ddc4e70268281fc6df999e110276f798cf5c050126/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f9a805f4765745f537461727465642d4646364233353f7374796c653d666f722d7468652d6261646765)](https://firecrawl.dev/)
+        [![Documentation](https://camo.githubusercontent.com/f7531cb91d3d3dcac76ac7c2ba9d36c1a74728016c64d942cfaf954f5ae4a238/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f939a5f446f63756d656e746174696f6e2d3441393045323f7374796c653d666f722d7468652d6261646765)](https://docs.firecrawl.dev/)
+        [![Discord](https://camo.githubusercontent.com/8c2d9f948c1d79b69e26d25add89a17a734b48cc6fd6fc0040f34f31c6a11774/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f92ac5f4a6f696e5f446973636f72642d3538363546323f7374796c653d666f722d7468652d6261646765)](https://discord.com/invite/gSmWdAkdwd)\\n\\n[![License](https://camo.githubusercontent.com/a6f4431b80529dbeaa43c3c5fbcf4649f6b4ebbeb82d5a58abeb39ca3eeca8be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d656e6461626c6561692f66697265637261776c)](https://github.com/mendableai/firecrawl/blob/main/LICENSE)[![GitHub
+        Stars](https://camo.githubusercontent.com/f42ce9a4d46d07baa67b74b49277e72ed33877743358deebfa774e17532eb4ff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6d656e6461626c6561692f66697265637261776c3f7374796c653d736f6369616c)](https://github.com/mendableai/firecrawl/stargazers)[![Python
+        Downloads](https://camo.githubusercontent.com/9d76afe428b4085c8b7103f2f4e31da110ee154ad7320bace4348d92ac0c2450/68747470733a2f2f7374617469632e706570792e746563682f62616467652f66697265637261776c2d7079)](https://pepy.tech/project/firecrawl-py)[![Follow
+        on X](https://camo.githubusercontent.com/e32f3aece18eaab32ee100cadb592843d985aa1171a8da08ae047626363d65ae/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f66697265637261776c5f6465763f7374796c653d736f6369616c)](https://x.com/firecrawl_dev)\\n\\n*
+        * *\\n\\n## Why Firecrawl?\\n\\n[Permalink: Why Firecrawl?](https://github.com/firecrawl#why-firecrawl)\\n\\n**Building
+        AI applications?** You need clean, structured data from the web. Firecrawl
+        handles the complexity of modern web scraping so you can focus on building
+        great products.\\n\\n## Our Core Ecosystem\\n\\n[Permalink: Our Core Ecosystem](https://github.com/firecrawl#our-core-ecosystem)\\n\\n###
+        Main Repository\\n\\n[Permalink: Main Repository](https://github.com/firecrawl#main-repository)\\n\\n[![](https://camo.githubusercontent.com/97aa9741f2773cb2d192c516c7689f4e2bbab89403aa868d842487551743626a/68747470733a2f2f6769746875622d726561646d652d73746174732e76657263656c2e6170702f6170692f70696e2f3f757365726e616d653d6d656e6461626c656169267265706f3d66697265637261776c267468656d653d6c69676874)](https://github.com/mendableai/firecrawl)\\n\\n**[firecrawl](https://github.com/mendableai/firecrawl)**
+        \\\\- Core API & SDK\\n\\nTurn entire websites into LLM-ready markdown or
+        structured data. Our flagship product with 40k+ stars.\\n\\n### Cloud API\\n\\n[Permalink:
+        Cloud API](https://github.com/firecrawl#cloud-api)\\n\\n[![Cloud API](https://camo.githubusercontent.com/6ad9773ed98c84d54b1546ffbf8b0fbb085be0c60580ae5a1ead2d23ddf1b121/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436c6f75645f4150492d4646364233353f7374796c653d666f722d7468652d6261646765266c6f676f3d636c6f7564266c6f676f436f6c6f723d7768697465)](https://firecrawl.dev/)\\n\\n**[Firecrawl](https://firecrawl.dev/)**
+        \\\\- Hosted API Service\\n\\nProduction-ready web scraping without infrastructure
+        management. Get your API key and start scraping in minutes with our reliable,
+        scalable cloud service.\\n\\n### MCP Integration\\n\\n[Permalink: MCP Integration](https://github.com/firecrawl#mcp-integration)\\n\\n[![MCP
+        Server](https://camo.githubusercontent.com/ea9cbd6a754e0932d17ae393ff53566bfb681492abddd08704dbe04b122dfaa1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d43505f5365727665722d3441393045323f7374796c653d666f722d7468652d6261646765266c6f676f3d736572766572266c6f676f436f6c6f723d7768697465)](https://github.com/mendableai/firecrawl-mcp-server)\\n\\n**[firecrawl-mcp-server](https://github.com/mendableai/firecrawl-mcp-server)**
+        \\\\- Model Context Protocol Server\\n\\nAdd powerful web scraping capabilities
+        to Claude, Cursor, and any MCP-compatible LLM client.\\n\\n## Community &
+        Support\\n\\n[Permalink: Community & Support](https://github.com/firecrawl#community--support)\\n\\n[![Discord](https://camo.githubusercontent.com/62d3d35241760cf174631c4e6b5f4503c0a6b34640fd306e36a829ab5ec47b14/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446973636f72642d3538363546323f7374796c653d666f722d7468652d6261646765266c6f676f3d646973636f7264266c6f676f436f6c6f723d7768697465)](https://discord.com/invite/gSmWdAkdwd)[![X](https://camo.githubusercontent.com/8c709aaebc7feee6050eba44984b294d9da3ace3353bd5eed8b499dd04af3c06/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f582d3030303030303f7374796c653d666f722d7468652d6261646765266c6f676f3d78266c6f676f436f6c6f723d7768697465)](https://x.com/firecrawl_dev)[![LinkedIn](https://camo.githubusercontent.com/8c0692475a5bfc1d9e7361074bdb648e567cae7b5b40ffd32adae31180b0d7b6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c696e6b6564496e2d3030373742353f7374796c653d666f722d7468652d6261646765266c6f676f3d6c696e6b6564696e266c6f676f436f6c6f723d7768697465)](https://www.linkedin.com/company/104100957/)[![Discussions](https://camo.githubusercontent.com/9403fd9d6d54f5a23a79f9a8a6a256ae82159fb626710fd56c2495fff1257d62/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4769744875625f44697363757373696f6e732d3138313731373f7374796c653d666f722d7468652d6261646765266c6f676f3d676974687562266c6f676f436f6c6f723d7768697465)](https://github.com/mendableai/firecrawl/discussions)[![Documentation](https://camo.githubusercontent.com/9d518c9da8018ae3524a2580522bd1ef591f343cc4df7983b4476e117fa70bba/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f63756d656e746174696f6e2d3441393045323f7374796c653d666f722d7468652d6261646765266c6f676f3d626f6f6b266c6f676f436f6c6f723d7768697465)](https://docs.firecrawl.dev/)\\n\\n##
+        Built By Mendable\\n\\n[Permalink: Built By Mendable](https://github.com/firecrawl#built-by-mendable)\\n\\nWe're
+        the team behind [Mendable.ai](https://mendable.ai/), passionate about making
+        web data accessible for AI applications. Firecrawl powers thousands of AI
+        products worldwide.\\n\\n* * *\\n\\n**Ready to build something amazing?**\\n\\n[Get
+        your API key](https://firecrawl.dev/) and start scraping in minutes\\n\\n\\n[Star
+        our main repo](https://github.com/mendableai/firecrawl) \u2022\\n[Try the
+        playground](https://firecrawl.dev/playground) \u2022\\n[Read the docs](https://docs.firecrawl.dev/)\\n\\n##
+        Pinned   Loading\\n\\n1. [firecrawl](https://github.com/firecrawl/firecrawl)
+        firecrawlPublic\\n\\n\\n\\n\\n\\n\\n\U0001F525 The Web Data API for AI - Turn
+        entire websites into LLM-ready markdown or structured data\\n\\n\\n\\n\\nTypeScript[64.9k](https://github.com/firecrawl/firecrawl/stargazers)
+        [5.1k](https://github.com/firecrawl/firecrawl/forks)\\n\\n2. [mendable-nextjs-chatbot](https://github.com/firecrawl/mendable-nextjs-chatbot)
+        mendable-nextjs-chatbotPublic template\\n\\n\\n\\n\\n\\n\\nNext.js Starter
+        Template for building chatbots with Mendable\\n\\n\\n\\n\\nTypeScript[256](https://github.com/firecrawl/mendable-nextjs-chatbot/stargazers)
+        [52](https://github.com/firecrawl/mendable-nextjs-chatbot/forks)\\n\\n3. [rag-arena](https://github.com/firecrawl/rag-arena)
+        rag-arenaPublic\\n\\n\\n\\n\\n\\n\\nOpen-source RAG evaluation through users'
+        feedback\\n\\n\\n\\n\\nTypeScript[206](https://github.com/firecrawl/rag-arena/stargazers)
+        [32](https://github.com/firecrawl/rag-arena/forks)\\n\\n4. [QA\\\\_clustering](https://github.com/firecrawl/QA_clustering)
+        QA\\\\_clusteringPublic\\n\\n\\n\\n\\n\\n\\nAnalyzing chat interactions w/
+        LLMs to improve \U0001F99C\U0001F517 Langchain docs\\n\\n\\n\\n\\nJupyter
+        Notebook[80](https://github.com/firecrawl/QA_clustering/stargazers) [12](https://github.com/firecrawl/QA_clustering/forks)\\n\\n5.
+        [data-connectors](https://github.com/firecrawl/data-connectors) data-connectorsPublic\\n\\n\\n\\n\\n\\n\\nLLM-ready
+        data connectors\\n\\n\\n\\n\\nTypeScript[95](https://github.com/firecrawl/data-connectors/stargazers)
+        [23](https://github.com/firecrawl/data-connectors/forks)\\n\\n6. [mendable-py](https://github.com/firecrawl/mendable-py)
+        mendable-pyPublic\\n\\n\\n\\n\\n\\n\\nBuild Production Ready LLM Chat Apps
+        in Minutes\\n\\n\\n\\n\\nPython[33](https://github.com/firecrawl/mendable-py/stargazers)
+        [7](https://github.com/firecrawl/mendable-py/forks)\\n\\n\\n### Repositories\\n\\nLoading\\n\\nType\\n\\nAllPublicSourcesForksArchivedMirrorsTemplates\\n\\nLanguage\\n\\nAllCSSGoJavaJavaScriptJupyter
+        NotebookMDXPythonRustTypeScript\\n\\nSort\\n\\nLast updatedNameStars\\n\\nShowing
+        10 of 61 repositories\\n\\n- [firecrawl](https://github.com/firecrawl/firecrawl)\\nPublic\\n\\n\\n\\n\U0001F525
+        The Web Data API for AI - Turn entire websites into LLM-ready markdown or
+        structured data\\n\\n\\n\\n\\n\\n\\nfirecrawl/firecrawl\u2019s past year of
+        commit activity\\n\\n\\n\\nTypeScript[64,949](https://github.com/firecrawl/firecrawl/stargazers)AGPL-3.0\\n[5,132](https://github.com/firecrawl/firecrawl/forks)
+        [27](https://github.com/firecrawl/firecrawl/issues) [(2 issues need help)](https://github.com/firecrawl/firecrawl/issues?q=label%3A%22good+first+issue%22+is%3Aissue+is%3Aopen)
+        [85](https://github.com/firecrawl/firecrawl/pulls)\\nUpdated 2 hours agoOct
+        27, 2025\\n\\n- [firecrawl-docs](https://github.com/firecrawl/firecrawl-docs)\\nPublic\\n\\n\\n\\nDocumentation
+        for Firecrawl.\\n\\n\\n\\n\\n\\n\\nfirecrawl/firecrawl-docs\u2019s past year
+        of commit activity\\n\\n\\n\\nMDX[17](https://github.com/firecrawl/firecrawl-docs/stargazers)
+        [35](https://github.com/firecrawl/firecrawl-docs/forks) [10](https://github.com/firecrawl/firecrawl-docs/issues)
+        [5](https://github.com/firecrawl/firecrawl-docs/pulls)\\nUpdated 20 hours
+        agoOct 26, 2025\\n\\n- [open-agent-builder](https://github.com/firecrawl/open-agent-builder)\\nPublic\\n\\n\\n\\n\U0001F525
+        Visual workflow builder for AI agents powered by Firecrawl - drag-and-drop
+        web scraping pipelines with real-time execution\\n\\n\\n\\n\\n\\n\\nfirecrawl/open-agent-builder\u2019s
+        past year of commit activity\\n\\n\\n\\nTypeScript[1,673](https://github.com/firecrawl/open-agent-builder/stargazers)
+        [274](https://github.com/firecrawl/open-agent-builder/forks) [4](https://github.com/firecrawl/open-agent-builder/issues)
+        [2](https://github.com/firecrawl/open-agent-builder/pulls)\\nUpdated last
+        weekOct 20, 2025\\n\\n- [firecrawl-mcp-server](https://github.com/firecrawl/firecrawl-mcp-server)\\nPublic\\n\\n\\n\\n\U0001F525
+        Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor,
+        Claude and any other LLM clients.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n[**Uh
+        oh!**](https://github.com/firecrawl/firecrawl-mcp-server/graphs/commit-activity)\\n\\n[There
+        was an error while loading.](https://github.com/firecrawl/firecrawl-mcp-server/graphs/commit-activity)
+        [Please reload this page](https://github.com/firecrawl).\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nfirecrawl/firecrawl-mcp-server\u2019s
+        past year of commit activity\\n\\n\\n\\nJavaScript[4,794](https://github.com/firecrawl/firecrawl-mcp-server/stargazers)MIT\\n[519](https://github.com/firecrawl/firecrawl-mcp-server/forks)
+        [44](https://github.com/firecrawl/firecrawl-mcp-server/issues) [17](https://github.com/firecrawl/firecrawl-mcp-server/pulls)\\nUpdated
+        last weekOct 19, 2025\\n\\n- [n8n-nodes-firecrawl](https://github.com/firecrawl/n8n-nodes-firecrawl)\\nPublic\\n\\n\\n\\nn8n
+        node to interact with Firecrawl\\n\\n\\n\\n\\n\\n\\nfirecrawl/n8n-nodes-firecrawl\u2019s
+        past year of commit activity\\n\\n\\n\\nTypeScript[21](https://github.com/firecrawl/n8n-nodes-firecrawl/stargazers)MIT\\n[13](https://github.com/firecrawl/n8n-nodes-firecrawl/forks)
+        [3](https://github.com/firecrawl/n8n-nodes-firecrawl/issues) [0](https://github.com/firecrawl/n8n-nodes-firecrawl/pulls)\\nUpdated
+        2 weeks agoOct 17, 2025\\n\\n- [.github](https://github.com/firecrawl/.github)\\nPublic\\n\\n\\n\\n\\nfirecrawl/.github\u2019s
+        past year of commit activity\\n\\n\\n\\n0\\n[1](https://github.com/firecrawl/.github/forks)
+        [0](https://github.com/firecrawl/.github/issues) [0](https://github.com/firecrawl/.github/pulls)\\nUpdated
+        2 weeks agoOct 12, 2025\\n\\n- [fire-enrich](https://github.com/firecrawl/fire-enrich)\\nPublic\\n\\n\\n\\n\U0001F525
+        AI-powered data enrichment tool that transforms emails into rich datasets
+        with company profiles, funding data, tech stacks, and more using Firecrawl
+        and multi-agent AI\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n[**Uh oh!**](https://github.com/firecrawl/fire-enrich/graphs/commit-activity)\\n\\n[There
+        was an error while loading.](https://github.com/firecrawl/fire-enrich/graphs/commit-activity)
+        [Please reload this page](https://github.com/firecrawl).\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nfirecrawl/fire-enrich\u2019s
+        past year of commit activity\\n\\n\\n\\nTypeScript[953](https://github.com/firecrawl/fire-enrich/stargazers)MIT\\n[239](https://github.com/firecrawl/fire-enrich/forks)
+        [12](https://github.com/firecrawl/fire-enrich/issues) [3](https://github.com/firecrawl/fire-enrich/pulls)\\nUpdated
+        3 weeks agoOct 8, 2025\\n\\n- [firecrawl-java-sdk](https://github.com/firecrawl/firecrawl-java-sdk)\\nPublic\\n\\n\\n\\n\\nfirecrawl/firecrawl-java-sdk\u2019s
+        past year of commit activity\\n\\n\\n\\nJava[11](https://github.com/firecrawl/firecrawl-java-sdk/stargazers)MIT\\n[4](https://github.com/firecrawl/firecrawl-java-sdk/forks)
+        [0](https://github.com/firecrawl/firecrawl-java-sdk/issues) [0](https://github.com/firecrawl/firecrawl-java-sdk/pulls)\\nUpdated
+        last monthSep 28, 2025\\n\\n- [open-lovable](https://github.com/firecrawl/open-lovable)\\nPublic\\n\\n\\n\\n\U0001F525
+        Clone and recreate any website as a modern React app in seconds\\n\\n\\n\\n\\n\\n\\nfirecrawl/open-lovable\u2019s
+        past year of commit activity\\n\\n\\n\\nTypeScript[21,320](https://github.com/firecrawl/open-lovable/stargazers)MIT\\n[3,986](https://github.com/firecrawl/open-lovable/forks)
+        [70](https://github.com/firecrawl/open-lovable/issues) [33](https://github.com/firecrawl/open-lovable/pulls)\\nUpdated
+        last monthSep 27, 2025\\n\\n- [mineru-api](https://github.com/firecrawl/mineru-api)\\nPublic\\n\\n\\n\\n\\nfirecrawl/mineru-api\u2019s
+        past year of commit activity\\n\\n\\n\\nPython[12](https://github.com/firecrawl/mineru-api/stargazers)AGPL-3.0\\n[2](https://github.com/firecrawl/mineru-api/forks)
+        [1](https://github.com/firecrawl/mineru-api/issues) [1](https://github.com/firecrawl/mineru-api/pulls)\\nUpdated
+        on Sep 26Sep 26, 2025\\n\\n\\n[View all repositories](https://github.com/orgs/firecrawl/repositories?type=all)\\n\\n[**People**](https://github.com/orgs/firecrawl/people)\\n\\n[![@alexnucci](https://avatars.githubusercontent.com/u/1919849?s=70&v=4)](https://github.com/alexnucci)[![@micahstairs](https://avatars.githubusercontent.com/u/7231485?s=70&v=4)](https://github.com/micahstairs)[![@nickscamara](https://avatars.githubusercontent.com/u/20311743?s=70&v=4)](https://github.com/nickscamara)[![@mogery](https://avatars.githubusercontent.com/u/66118807?s=70&v=4)](https://github.com/mogery)[![@developersdigest](https://avatars.githubusercontent.com/u/124798203?s=70&v=4)](https://github.com/developersdigest)\\n\\n####
+        Top languages\\n\\n[TypeScript](https://github.com/orgs/firecrawl/repositories?language=typescript&type=all)
+        [Python](https://github.com/orgs/firecrawl/repositories?language=python&type=all)
+        [JavaScript](https://github.com/orgs/firecrawl/repositories?language=javascript&type=all)
+        [Go](https://github.com/orgs/firecrawl/repositories?language=go&type=all)
+        [MDX](https://github.com/orgs/firecrawl/repositories?language=mdx&type=all)\\n\\n####
+        Most used topics\\n\\n[ai](https://github.com/search?q=topic%3Aai+org%3Afirecrawl+fork%3Atrue&type=repositories
+        \\\"Topic: ai\\\") [firecrawl](https://github.com/search?q=topic%3Afirecrawl+org%3Afirecrawl+fork%3Atrue&type=repositories
+        \\\"Topic: firecrawl\\\") [llm](https://github.com/search?q=topic%3Allm+org%3Afirecrawl+fork%3Atrue&type=repositories
+        \\\"Topic: llm\\\") [web-crawler](https://github.com/search?q=topic%3Aweb-crawler+org%3Afirecrawl+fork%3Atrue&type=repositories
+        \\\"Topic: web-crawler\\\") [web-scraping](https://github.com/search?q=topic%3Aweb-scraping+org%3Afirecrawl+fork%3Atrue&type=repositories
+        \\\"Topic: web-scraping\\\")\\n\\nYou can\u2019t perform that action at this
+        time.\",\"metadata\":{\"analytics-location\":\"/<org-login>\",\"apple-itunes-app\":\"app-id=1477376905,
+        app-argument=https://github.com/firecrawl\",\"twitter:card\":\"summary_large_image\",\"google-site-verification\":\"Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I\",\"description\":\"Web
+        data API for AI. Firecrawl has 61 repositories available. Follow their code
+        on GitHub.\",\"og:image\":\"https://avatars.githubusercontent.com/u/135057108?s=280&v=4\",\"og:type\":\"profile\",\"visitor-payload\":\"eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiI5REFEOjIzM0ExMzo4RDgzNEI6QzQ5OUNCOjY4RkYzODlGIiwidmlzaXRvcl9pZCI6IjQwMDQ0MTI5MTkxMDA5NDY1OTEiLCJyZWdpb25fZWRnZSI6ImlhZCIsInJlZ2lvbl9yZW5kZXIiOiJpYWQifQ==\",\"github-keyboard-shortcuts\":\"copilot\",\"user-login\":\"\",\"viewport\":\"width=device-width\",\"og:description\":\"Web
+        data API for AI. Firecrawl has 61 repositories available. Follow their code
+        on GitHub.\",\"turbo-cache-control\":\"no-preview\",\"fetch-nonce\":\"v2:35a2e032-0081-3f6b-595e-967e32025c6f\",\"og:url\":\"https://github.com/firecrawl\",\"title\":\"Firecrawl
+        \xB7 GitHub\",\"route-pattern\":\"/:user_id(.:format)\",\"route-action\":\"show\",\"octolytics-url\":\"https://collector.github.com/github/collect\",\"og:site_name\":\"GitHub\",\"twitter:title\":\"Firecrawl\",\"request-id\":\"9DAD:233A13:8D834B:C499CB:68FF389F\",\"ogSiteName\":\"GitHub\",\"fb:app_id\":\"1401488693436528\",\"language\":\"en\",\"twitter:image\":\"https://avatars.githubusercontent.com/u/135057108?s=280&v=4\",\"ogImage\":\"https://avatars.githubusercontent.com/u/135057108?s=280&v=4\",\"release\":\"c44b7f7aa5c70f3296484971978c9f4b1b473352\",\"theme-color\":\"#1e2327\",\"color-scheme\":\"light
+        dark\",\"html-safe-nonce\":\"2d932295da6aa360d861f16279839ab109dc4e977a51bc99204477969d7d12c6\",\"ogTitle\":\"Firecrawl\",\"hovercard-subject-tag\":\"organization:135057108\",\"twitter:description\":\"Web
+        data API for AI. Firecrawl has 61 repositories available. Follow their code
+        on GitHub.\",\"hostname\":\"github.com\",\"ogUrl\":\"https://github.com/firecrawl\",\"ogDescription\":\"Web
+        data API for AI. Firecrawl has 61 repositories available. Follow their code
+        on GitHub.\",\"route-controller\":\"profiles\",\"favicon\":\"https://github.githubassets.com/favicons/favicon.svg\",\"visitor-hmac\":\"43cf3bbb9c57a3bcf0b1857fbcabcc78c274e7082e0d28c76cd6d4651bc2a920\",\"twitter:site\":\"@github\",\"ui-target\":\"full\",\"og:title\":\"Firecrawl\",\"expected-hostname\":\"github.com\",\"og:image:alt\":\"Web
+        data API for AI. Firecrawl has 61 repositories available. Follow their code
+        on GitHub.\",\"profile:username\":\"firecrawl\",\"current-catalog-service-hash\":\"4a1c50a83cf6cc4b55b6b9c53e553e3f847c876b87fb333f71f5d05db8f1a7db\",\"turbo-body-classes\":\"logged-out
+        env-production page-responsive\",\"browser-stats-url\":\"https://api.github.com/_private/browser/stats\",\"browser-errors-url\":\"https://api.github.com/_private/browser/errors\",\"scrapeId\":\"65cbc300-be11-4a1a-9d20-c114fb8473a7\",\"sourceURL\":\"https://github.com/firecrawl\",\"url\":\"https://github.com/firecrawl\",\"statusCode\":200,\"contentType\":\"text/html;
+        charset=utf-8\",\"proxyUsed\":\"basic\",\"cacheState\":\"hit\",\"cachedAt\":\"2025-10-27T09:17:20.756Z\"}},{\"url\":\"https://www.linkedin.com/company/firecrawl\",\"title\":\"Firecrawl
+        | LinkedIn\",\"description\":\"Our Dify integration now uses Firecrawl /v2
+        endpoints Scraping is 10x faster thanks to intelligent caching, plus we've
+        added semantic ...\",\"position\":5}]},\"creditsUsed\":3}"
+    headers:
+      Access-Control-Allow-Origin:
+      - '*'
+      Alt-Svc:
+      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
+      Content-Length:
+      - '93428'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Wed, 29 Oct 2025 14:37:39 GMT
+      ETag:
+      - W/"16cf4-kHwVbMu4CCVG2UIt6p1g/gz5M4M"
+      Via:
+      - 1.1 google
+      X-Powered-By:
+      - Express
+      X-Response-Time:
+      - 13172.495ms
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai-tools/tests/tools/firecrawl_crawl_website_tool_test.py

@@ -0,0 +1,18 @@
+import pytest
+
+from crewai_tools.tools.firecrawl_crawl_website_tool.firecrawl_crawl_website_tool import (
+    FirecrawlCrawlWebsiteTool,
+)
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_firecrawl_crawl_tool_integration():
+    tool = FirecrawlCrawlWebsiteTool(config={
+        "limit": 2,
+        "max_discovery_depth": 1,
+        "scrape_options": {"formats": ["markdown"]}
+    })
+    result = tool.run(url="https://firecrawl.dev")
+
+    assert result is not None
+    assert hasattr(result, 'status')
+    assert result.status in ["completed", "scraping"]

lib/crewai-tools/tests/tools/firecrawl_scrape_website_tool_test.py

@@ -0,0 +1,15 @@
+import pytest
+
+from crewai_tools.tools.firecrawl_scrape_website_tool.firecrawl_scrape_website_tool import (
+    FirecrawlScrapeWebsiteTool,
+)
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_firecrawl_scrape_tool_integration():
+    tool = FirecrawlScrapeWebsiteTool()
+    result = tool.run(url="https://firecrawl.dev")
+
+    assert result is not None
+    assert hasattr(result, 'markdown')
+    assert len(result.markdown) > 0
+    assert "Firecrawl" in result.markdown or "firecrawl" in result.markdown.lower()

lib/crewai-tools/tests/tools/firecrawl_search_tool_test.py

@@ -0,0 +1,12 @@
+import pytest
+
+from crewai_tools.tools.firecrawl_search_tool.firecrawl_search_tool import FirecrawlSearchTool
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_firecrawl_search_tool_integration():
+    tool = FirecrawlSearchTool()
+    result = tool.run(query="firecrawl")
+
+    assert result is not None
+    assert hasattr(result, 'web') or hasattr(result, 'news') or hasattr(result, 'images')

lib/crewai/pyproject.toml

@@ -23,7 +23,6 @@ dependencies = [
     "chromadb~=1.1.0",
     "tokenizers>=0.20.3",
     "openpyxl>=3.1.5",
-    "pyvis>=0.3.2",
     # Authentication and Security
     "python-dotenv>=1.1.1",
     "pyjwt>=2.9.0",
@@ -49,7 +48,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.2.1",
+    "crewai-tools==1.3.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -94,10 +93,11 @@ azure-ai-inference = [
 anthropic = [
     "anthropic>=0.69.0",
 ]
-# a2a = [
-#     "a2a-sdk~=0.3.9",
-#     "httpx-sse>=0.4.0",
-# ]
+ a2a = [
+     "a2a-sdk~=0.3.10",
+     "httpx-auth>=0.23.1",
+     "httpx-sse>=0.4.0",
+]
 
 
 [project.scripts]

lib/crewai/src/crewai/__init__.py

@@ -3,7 +3,7 @@ from typing import Any
 import urllib.request
 import warnings
 
-from crewai.agent import Agent
+from crewai.agent.core import Agent
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.2.1"
+__version__ = "1.3.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/a2a/__init__.py

@@ -0,0 +1,6 @@
+"""Agent-to-Agent (A2A) protocol communication module for CrewAI."""
+
+from crewai.a2a.config import A2AConfig
+
+
+__all__ = ["A2AConfig"]

lib/crewai/src/crewai/a2a/auth/__init__.py

@@ -0,0 +1,20 @@
+"""A2A authentication schemas."""
+
+from crewai.a2a.auth.schemas import (
+    APIKeyAuth,
+    BearerTokenAuth,
+    HTTPBasicAuth,
+    HTTPDigestAuth,
+    OAuth2AuthorizationCode,
+    OAuth2ClientCredentials,
+)
+
+
+__all__ = [
+    "APIKeyAuth",
+    "BearerTokenAuth",
+    "HTTPBasicAuth",
+    "HTTPDigestAuth",
+    "OAuth2AuthorizationCode",
+    "OAuth2ClientCredentials",
+]

lib/crewai/src/crewai/a2a/auth/schemas.py

@@ -0,0 +1,392 @@
+"""Authentication schemes for A2A protocol agents.
+
+Supported authentication methods:
+- Bearer tokens
+- OAuth2 (Client Credentials, Authorization Code)
+- API Keys (header, query, cookie)
+- HTTP Basic authentication
+- HTTP Digest authentication
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+import base64
+from collections.abc import Awaitable, Callable, MutableMapping
+import time
+from typing import Literal
+import urllib.parse
+
+import httpx
+from httpx import DigestAuth
+from pydantic import BaseModel, Field, PrivateAttr
+
+
+class AuthScheme(ABC, BaseModel):
+    """Base class for authentication schemes."""
+
+    @abstractmethod
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply authentication to request headers.
+
+        Args:
+            client: HTTP client for making auth requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with authentication applied.
+        """
+        ...
+
+
+class BearerTokenAuth(AuthScheme):
+    """Bearer token authentication (Authorization: Bearer <token>).
+
+    Attributes:
+        token: Bearer token for authentication.
+    """
+
+    token: str = Field(description="Bearer token")
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply Bearer token to Authorization header.
+
+        Args:
+            client: HTTP client for making auth requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with Bearer token in Authorization header.
+        """
+        headers["Authorization"] = f"Bearer {self.token}"
+        return headers
+
+
+class HTTPBasicAuth(AuthScheme):
+    """HTTP Basic authentication.
+
+    Attributes:
+        username: Username for Basic authentication.
+        password: Password for Basic authentication.
+    """
+
+    username: str = Field(description="Username")
+    password: str = Field(description="Password")
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply HTTP Basic authentication.
+
+        Args:
+            client: HTTP client for making auth requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with Basic auth in Authorization header.
+        """
+        credentials = f"{self.username}:{self.password}"
+        encoded = base64.b64encode(credentials.encode()).decode()
+        headers["Authorization"] = f"Basic {encoded}"
+        return headers
+
+
+class HTTPDigestAuth(AuthScheme):
+    """HTTP Digest authentication.
+
+    Note: Uses httpx-auth library for digest implementation.
+
+    Attributes:
+        username: Username for Digest authentication.
+        password: Password for Digest authentication.
+    """
+
+    username: str = Field(description="Username")
+    password: str = Field(description="Password")
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Digest auth is handled by httpx auth flow, not headers.
+
+        Args:
+            client: HTTP client for making auth requests.
+            headers: Current request headers.
+
+        Returns:
+            Unchanged headers (Digest auth handled by httpx auth flow).
+        """
+        return headers
+
+    def configure_client(self, client: httpx.AsyncClient) -> None:
+        """Configure client with Digest auth.
+
+        Args:
+            client: HTTP client to configure with Digest authentication.
+        """
+        client.auth = DigestAuth(self.username, self.password)
+
+
+class APIKeyAuth(AuthScheme):
+    """API Key authentication (header, query, or cookie).
+
+    Attributes:
+        api_key: API key value for authentication.
+        location: Where to send the API key (header, query, or cookie).
+        name: Parameter name for the API key (default: X-API-Key).
+    """
+
+    api_key: str = Field(description="API key value")
+    location: Literal["header", "query", "cookie"] = Field(
+        default="header", description="Where to send the API key"
+    )
+    name: str = Field(default="X-API-Key", description="Parameter name for the API key")
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply API key authentication.
+
+        Args:
+            client: HTTP client for making auth requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with API key (for header/cookie locations).
+        """
+        if self.location == "header":
+            headers[self.name] = self.api_key
+        elif self.location == "cookie":
+            headers["Cookie"] = f"{self.name}={self.api_key}"
+        return headers
+
+    def configure_client(self, client: httpx.AsyncClient) -> None:
+        """Configure client for query param API keys.
+
+        Args:
+            client: HTTP client to configure with query param API key hook.
+        """
+        if self.location == "query":
+
+            async def _add_api_key_param(request: httpx.Request) -> None:
+                url = httpx.URL(request.url)
+                request.url = url.copy_add_param(self.name, self.api_key)
+
+            client.event_hooks["request"].append(_add_api_key_param)
+
+
+class OAuth2ClientCredentials(AuthScheme):
+    """OAuth2 Client Credentials flow authentication.
+
+    Attributes:
+        token_url: OAuth2 token endpoint URL.
+        client_id: OAuth2 client identifier.
+        client_secret: OAuth2 client secret.
+        scopes: List of required OAuth2 scopes.
+    """
+
+    token_url: str = Field(description="OAuth2 token endpoint")
+    client_id: str = Field(description="OAuth2 client ID")
+    client_secret: str = Field(description="OAuth2 client secret")
+    scopes: list[str] = Field(
+        default_factory=list, description="Required OAuth2 scopes"
+    )
+
+    _access_token: str | None = PrivateAttr(default=None)
+    _token_expires_at: float | None = PrivateAttr(default=None)
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply OAuth2 access token to Authorization header.
+
+        Args:
+            client: HTTP client for making token requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with OAuth2 access token in Authorization header.
+        """
+        if (
+            self._access_token is None
+            or self._token_expires_at is None
+            or time.time() >= self._token_expires_at
+        ):
+            await self._fetch_token(client)
+
+        if self._access_token:
+            headers["Authorization"] = f"Bearer {self._access_token}"
+
+        return headers
+
+    async def _fetch_token(self, client: httpx.AsyncClient) -> None:
+        """Fetch OAuth2 access token using client credentials flow.
+
+        Args:
+            client: HTTP client for making token request.
+
+        Raises:
+            httpx.HTTPStatusError: If token request fails.
+        """
+        data = {
+            "grant_type": "client_credentials",
+            "client_id": self.client_id,
+            "client_secret": self.client_secret,
+        }
+
+        if self.scopes:
+            data["scope"] = " ".join(self.scopes)
+
+        response = await client.post(self.token_url, data=data)
+        response.raise_for_status()
+
+        token_data = response.json()
+        self._access_token = token_data["access_token"]
+        expires_in = token_data.get("expires_in", 3600)
+        self._token_expires_at = time.time() + expires_in - 60
+
+
+class OAuth2AuthorizationCode(AuthScheme):
+    """OAuth2 Authorization Code flow authentication.
+
+    Note: Requires interactive authorization.
+
+    Attributes:
+        authorization_url: OAuth2 authorization endpoint URL.
+        token_url: OAuth2 token endpoint URL.
+        client_id: OAuth2 client identifier.
+        client_secret: OAuth2 client secret.
+        redirect_uri: OAuth2 redirect URI for callback.
+        scopes: List of required OAuth2 scopes.
+    """
+
+    authorization_url: str = Field(description="OAuth2 authorization endpoint")
+    token_url: str = Field(description="OAuth2 token endpoint")
+    client_id: str = Field(description="OAuth2 client ID")
+    client_secret: str = Field(description="OAuth2 client secret")
+    redirect_uri: str = Field(description="OAuth2 redirect URI")
+    scopes: list[str] = Field(
+        default_factory=list, description="Required OAuth2 scopes"
+    )
+
+    _access_token: str | None = PrivateAttr(default=None)
+    _refresh_token: str | None = PrivateAttr(default=None)
+    _token_expires_at: float | None = PrivateAttr(default=None)
+    _authorization_callback: Callable[[str], Awaitable[str]] | None = PrivateAttr(
+        default=None
+    )
+
+    def set_authorization_callback(
+        self, callback: Callable[[str], Awaitable[str]] | None
+    ) -> None:
+        """Set callback to handle authorization URL.
+
+        Args:
+            callback: Async function that receives authorization URL and returns auth code.
+        """
+        self._authorization_callback = callback
+
+    async def apply_auth(
+        self, client: httpx.AsyncClient, headers: MutableMapping[str, str]
+    ) -> MutableMapping[str, str]:
+        """Apply OAuth2 access token to Authorization header.
+
+        Args:
+            client: HTTP client for making token requests.
+            headers: Current request headers.
+
+        Returns:
+            Updated headers with OAuth2 access token in Authorization header.
+
+        Raises:
+            ValueError: If authorization callback is not set.
+        """
+
+        if self._access_token is None:
+            if self._authorization_callback is None:
+                msg = "Authorization callback not set. Use set_authorization_callback()"
+                raise ValueError(msg)
+            await self._fetch_initial_token(client)
+        elif self._token_expires_at and time.time() >= self._token_expires_at:
+            await self._refresh_access_token(client)
+
+        if self._access_token:
+            headers["Authorization"] = f"Bearer {self._access_token}"
+
+        return headers
+
+    async def _fetch_initial_token(self, client: httpx.AsyncClient) -> None:
+        """Fetch initial access token using authorization code flow.
+
+        Args:
+            client: HTTP client for making token request.
+
+        Raises:
+            ValueError: If authorization callback is not set.
+            httpx.HTTPStatusError: If token request fails.
+        """
+        params = {
+            "response_type": "code",
+            "client_id": self.client_id,
+            "redirect_uri": self.redirect_uri,
+            "scope": " ".join(self.scopes),
+        }
+        auth_url = f"{self.authorization_url}?{urllib.parse.urlencode(params)}"
+
+        if self._authorization_callback is None:
+            msg = "Authorization callback not set"
+            raise ValueError(msg)
+        auth_code = await self._authorization_callback(auth_url)
+
+        data = {
+            "grant_type": "authorization_code",
+            "code": auth_code,
+            "client_id": self.client_id,
+            "client_secret": self.client_secret,
+            "redirect_uri": self.redirect_uri,
+        }
+
+        response = await client.post(self.token_url, data=data)
+        response.raise_for_status()
+
+        token_data = response.json()
+        self._access_token = token_data["access_token"]
+        self._refresh_token = token_data.get("refresh_token")
+
+        expires_in = token_data.get("expires_in", 3600)
+        self._token_expires_at = time.time() + expires_in - 60
+
+    async def _refresh_access_token(self, client: httpx.AsyncClient) -> None:
+        """Refresh the access token using refresh token.
+
+        Args:
+            client: HTTP client for making token request.
+
+        Raises:
+            httpx.HTTPStatusError: If token refresh request fails.
+        """
+        if not self._refresh_token:
+            await self._fetch_initial_token(client)
+            return
+
+        data = {
+            "grant_type": "refresh_token",
+            "refresh_token": self._refresh_token,
+            "client_id": self.client_id,
+            "client_secret": self.client_secret,
+        }
+
+        response = await client.post(self.token_url, data=data)
+        response.raise_for_status()
+
+        token_data = response.json()
+        self._access_token = token_data["access_token"]
+        if "refresh_token" in token_data:
+            self._refresh_token = token_data["refresh_token"]
+
+        expires_in = token_data.get("expires_in", 3600)
+        self._token_expires_at = time.time() + expires_in - 60

lib/crewai/src/crewai/a2a/auth/utils.py

@@ -0,0 +1,236 @@
+"""Authentication utilities for A2A protocol agent communication.
+
+Provides validation and retry logic for various authentication schemes including
+OAuth2, API keys, and HTTP authentication methods.
+"""
+
+import asyncio
+from collections.abc import Awaitable, Callable, MutableMapping
+import re
+from typing import Final
+
+from a2a.client.errors import A2AClientHTTPError
+from a2a.types import (
+    APIKeySecurityScheme,
+    AgentCard,
+    HTTPAuthSecurityScheme,
+    OAuth2SecurityScheme,
+)
+from httpx import AsyncClient, Response
+
+from crewai.a2a.auth.schemas import (
+    APIKeyAuth,
+    AuthScheme,
+    BearerTokenAuth,
+    HTTPBasicAuth,
+    HTTPDigestAuth,
+    OAuth2AuthorizationCode,
+    OAuth2ClientCredentials,
+)
+
+
+_auth_store: dict[int, AuthScheme | None] = {}
+
+_SCHEME_PATTERN: Final[re.Pattern[str]] = re.compile(r"(\w+)\s+(.+?)(?=,\s*\w+\s+|$)")
+_PARAM_PATTERN: Final[re.Pattern[str]] = re.compile(r'(\w+)=(?:"([^"]*)"|([^\s,]+))')
+
+_SCHEME_AUTH_MAPPING: Final[dict[type, tuple[type[AuthScheme], ...]]] = {
+    OAuth2SecurityScheme: (
+        OAuth2ClientCredentials,
+        OAuth2AuthorizationCode,
+        BearerTokenAuth,
+    ),
+    APIKeySecurityScheme: (APIKeyAuth,),
+}
+
+_HTTP_SCHEME_MAPPING: Final[dict[str, type[AuthScheme]]] = {
+    "basic": HTTPBasicAuth,
+    "digest": HTTPDigestAuth,
+    "bearer": BearerTokenAuth,
+}
+
+
+def _raise_auth_mismatch(
+    expected_classes: type[AuthScheme] | tuple[type[AuthScheme], ...],
+    provided_auth: AuthScheme,
+) -> None:
+    """Raise authentication mismatch error.
+
+    Args:
+        expected_classes: Expected authentication class or tuple of classes.
+        provided_auth: Actually provided authentication instance.
+
+    Raises:
+        A2AClientHTTPError: Always raises with 401 status code.
+    """
+    if isinstance(expected_classes, tuple):
+        if len(expected_classes) == 1:
+            required = expected_classes[0].__name__
+        else:
+            names = [cls.__name__ for cls in expected_classes]
+            required = f"one of ({', '.join(names)})"
+    else:
+        required = expected_classes.__name__
+
+    msg = (
+        f"AgentCard requires {required} authentication, "
+        f"but {type(provided_auth).__name__} was provided"
+    )
+    raise A2AClientHTTPError(401, msg)
+
+
+def parse_www_authenticate(header_value: str) -> dict[str, dict[str, str]]:
+    """Parse WWW-Authenticate header into auth challenges.
+
+    Args:
+        header_value: The WWW-Authenticate header value.
+
+    Returns:
+        Dictionary mapping auth scheme to its parameters.
+        Example: {"Bearer": {"realm": "api", "scope": "read write"}}
+    """
+    if not header_value:
+        return {}
+
+    challenges: dict[str, dict[str, str]] = {}
+
+    for match in _SCHEME_PATTERN.finditer(header_value):
+        scheme = match.group(1)
+        params_str = match.group(2)
+
+        params: dict[str, str] = {}
+
+        for param_match in _PARAM_PATTERN.finditer(params_str):
+            key = param_match.group(1)
+            value = param_match.group(2) or param_match.group(3)
+            params[key] = value
+
+        challenges[scheme] = params
+
+    return challenges
+
+
+def validate_auth_against_agent_card(
+    agent_card: AgentCard, auth: AuthScheme | None
+) -> None:
+    """Validate that provided auth matches AgentCard security requirements.
+
+    Args:
+        agent_card: The A2A AgentCard containing security requirements.
+        auth: User-provided authentication scheme (or None).
+
+    Raises:
+        A2AClientHTTPError: If auth doesn't match AgentCard requirements (status_code=401).
+    """
+
+    if not agent_card.security or not agent_card.security_schemes:
+        return
+
+    if not auth:
+        msg = "AgentCard requires authentication but no auth scheme provided"
+        raise A2AClientHTTPError(401, msg)
+
+    first_security_req = agent_card.security[0] if agent_card.security else {}
+
+    for scheme_name in first_security_req.keys():
+        security_scheme_wrapper = agent_card.security_schemes.get(scheme_name)
+        if not security_scheme_wrapper:
+            continue
+
+        scheme = security_scheme_wrapper.root
+
+        if allowed_classes := _SCHEME_AUTH_MAPPING.get(type(scheme)):
+            if not isinstance(auth, allowed_classes):
+                _raise_auth_mismatch(allowed_classes, auth)
+            return
+
+        if isinstance(scheme, HTTPAuthSecurityScheme):
+            if required_class := _HTTP_SCHEME_MAPPING.get(scheme.scheme.lower()):
+                if not isinstance(auth, required_class):
+                    _raise_auth_mismatch(required_class, auth)
+            return
+
+    msg = "Could not validate auth against AgentCard security requirements"
+    raise A2AClientHTTPError(401, msg)
+
+
+async def retry_on_401(
+    request_func: Callable[[], Awaitable[Response]],
+    auth_scheme: AuthScheme | None,
+    client: AsyncClient,
+    headers: MutableMapping[str, str],
+    max_retries: int = 3,
+) -> Response:
+    """Retry a request on 401 authentication error.
+
+    Handles 401 errors by:
+    1. Parsing WWW-Authenticate header
+    2. Re-acquiring credentials
+    3. Retrying the request
+
+    Args:
+        request_func: Async function that makes the HTTP request.
+        auth_scheme: Authentication scheme to refresh credentials with.
+        client: HTTP client for making requests.
+        headers: Request headers to update with new auth.
+        max_retries: Maximum number of retry attempts (default: 3).
+
+    Returns:
+        HTTP response from the request.
+
+    Raises:
+        httpx.HTTPStatusError: If retries are exhausted or auth scheme is None.
+    """
+    last_response: Response | None = None
+    last_challenges: dict[str, dict[str, str]] = {}
+
+    for attempt in range(max_retries):
+        response = await request_func()
+
+        if response.status_code != 401:
+            return response
+
+        last_response = response
+
+        if auth_scheme is None:
+            response.raise_for_status()
+            return response
+
+        www_authenticate = response.headers.get("WWW-Authenticate", "")
+        challenges = parse_www_authenticate(www_authenticate)
+        last_challenges = challenges
+
+        if attempt >= max_retries - 1:
+            break
+
+        backoff_time = 2**attempt
+        await asyncio.sleep(backoff_time)
+
+        await auth_scheme.apply_auth(client, headers)
+
+    if last_response:
+        last_response.raise_for_status()
+        return last_response
+
+    msg = "retry_on_401 failed without making any requests"
+    if last_challenges:
+        challenge_info = ", ".join(
+            f"{scheme} (realm={params.get('realm', 'N/A')})"
+            for scheme, params in last_challenges.items()
+        )
+        msg = f"{msg}. Server challenges: {challenge_info}"
+    raise RuntimeError(msg)
+
+
+def configure_auth_client(
+    auth: HTTPDigestAuth | APIKeyAuth, client: AsyncClient
+) -> None:
+    """Configure HTTP client with auth-specific settings.
+
+    Only HTTPDigestAuth and APIKeyAuth need client configuration.
+
+    Args:
+        auth: Authentication scheme that requires client configuration.
+        client: HTTP client to configure.
+    """
+    auth.configure_client(client)

lib/crewai/src/crewai/a2a/config.py

@@ -0,0 +1,59 @@
+"""A2A configuration types.
+
+This module is separate from experimental.a2a to avoid circular imports.
+"""
+
+from __future__ import annotations
+
+from typing import Annotated
+
+from pydantic import (
+    BaseModel,
+    BeforeValidator,
+    Field,
+    HttpUrl,
+    TypeAdapter,
+)
+
+from crewai.a2a.auth.schemas import AuthScheme
+
+
+http_url_adapter = TypeAdapter(HttpUrl)
+
+Url = Annotated[
+    str,
+    BeforeValidator(
+        lambda value: str(http_url_adapter.validate_python(value, strict=True))
+    ),
+]
+
+
+class A2AConfig(BaseModel):
+    """Configuration for A2A protocol integration.
+
+    Attributes:
+        endpoint: A2A agent endpoint URL.
+        auth: Authentication scheme (Bearer, OAuth2, API Key, HTTP Basic/Digest).
+        timeout: Request timeout in seconds (default: 120).
+        max_turns: Maximum conversation turns with A2A agent (default: 10).
+        response_model: Optional Pydantic model for structured A2A agent responses.
+        fail_fast: If True, raise error when agent unreachable; if False, skip and continue (default: True).
+    """
+
+    endpoint: Url = Field(description="A2A agent endpoint URL")
+    auth: AuthScheme | None = Field(
+        default=None,
+        description="Authentication scheme (Bearer, OAuth2, API Key, HTTP Basic/Digest)",
+    )
+    timeout: int = Field(default=120, description="Request timeout in seconds")
+    max_turns: int = Field(
+        default=10, description="Maximum conversation turns with A2A agent"
+    )
+    response_model: type[BaseModel] | None = Field(
+        default=None,
+        description="Optional Pydantic model for structured A2A agent responses. When specified, the A2A agent is expected to return JSON matching this schema.",
+    )
+    fail_fast: bool = Field(
+        default=True,
+        description="If True, raise an error immediately when the A2A agent is unreachable. If False, skip the A2A agent and continue execution.",
+    )

lib/crewai/src/crewai/a2a/templates.py

@@ -0,0 +1,29 @@
+"""String templates for A2A (Agent-to-Agent) protocol messaging and status."""
+
+from string import Template
+from typing import Final
+
+
+AVAILABLE_AGENTS_TEMPLATE: Final[Template] = Template(
+    "\n<AVAILABLE_A2A_AGENTS>\n    $available_a2a_agents\n</AVAILABLE_A2A_AGENTS>\n"
+)
+PREVIOUS_A2A_CONVERSATION_TEMPLATE: Final[Template] = Template(
+    "\n<PREVIOUS_A2A_CONVERSATION>\n"
+    "    $previous_a2a_conversation"
+    "\n</PREVIOUS_A2A_CONVERSATION>\n"
+)
+CONVERSATION_TURN_INFO_TEMPLATE: Final[Template] = Template(
+    "\n<CONVERSATION_PROGRESS>\n"
+    '    turn="$turn_count"\n'
+    '    max_turns="$max_turns"\n'
+    "    $warning"
+    "\n</CONVERSATION_PROGRESS>\n"
+)
+UNAVAILABLE_AGENTS_NOTICE_TEMPLATE: Final[Template] = Template(
+    "\n<A2A_AGENTS_STATUS>\n"
+    "   NOTE: A2A agents were configured but are currently unavailable.\n"
+    "   You cannot delegate to remote agents for this task.\n\n"
+    "   Unavailable Agents:\n"
+    "     $unavailable_agents"
+    "\n</A2A_AGENTS_STATUS>\n"
+)

lib/crewai/src/crewai/a2a/types.py

@@ -0,0 +1,38 @@
+"""Type definitions for A2A protocol message parts."""
+
+from typing import Any, Literal, Protocol, TypedDict, runtime_checkable
+
+from typing_extensions import NotRequired
+
+
+@runtime_checkable
+class AgentResponseProtocol(Protocol):
+    """Protocol for the dynamically created AgentResponse model."""
+
+    a2a_ids: tuple[str, ...]
+    message: str
+    is_a2a: bool
+
+
+class PartsMetadataDict(TypedDict, total=False):
+    """Metadata for A2A message parts.
+
+    Attributes:
+        mimeType: MIME type for the part content.
+        schema: JSON schema for the part content.
+    """
+
+    mimeType: Literal["application/json"]
+    schema: dict[str, Any]
+
+
+class PartsDict(TypedDict):
+    """A2A message part containing text and optional metadata.
+
+    Attributes:
+        text: The text content of the message part.
+        metadata: Optional metadata describing the part content.
+    """
+
+    text: str
+    metadata: NotRequired[PartsMetadataDict]

lib/crewai/src/crewai/a2a/utils.py

@@ -0,0 +1,755 @@
+"""Utility functions for A2A (Agent-to-Agent) protocol delegation."""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, MutableMapping
+from contextlib import asynccontextmanager
+from functools import lru_cache
+import time
+from typing import TYPE_CHECKING, Any
+import uuid
+
+from a2a.client import Client, ClientConfig, ClientFactory
+from a2a.client.errors import A2AClientHTTPError
+from a2a.types import (
+    AgentCard,
+    Message,
+    Part,
+    Role,
+    TaskArtifactUpdateEvent,
+    TaskState,
+    TaskStatusUpdateEvent,
+    TextPart,
+    TransportProtocol,
+)
+import httpx
+from pydantic import BaseModel, Field, create_model
+
+from crewai.a2a.auth.schemas import APIKeyAuth, HTTPDigestAuth
+from crewai.a2a.auth.utils import (
+    _auth_store,
+    configure_auth_client,
+    retry_on_401,
+    validate_auth_against_agent_card,
+)
+from crewai.a2a.config import A2AConfig
+from crewai.a2a.types import PartsDict, PartsMetadataDict
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.a2a_events import (
+    A2AConversationStartedEvent,
+    A2ADelegationCompletedEvent,
+    A2ADelegationStartedEvent,
+    A2AMessageSentEvent,
+    A2AResponseReceivedEvent,
+)
+from crewai.types.utils import create_literals_from_strings
+
+
+if TYPE_CHECKING:
+    from a2a.types import Message, Task as A2ATask
+
+    from crewai.a2a.auth.schemas import AuthScheme
+
+
+@lru_cache()
+def _fetch_agent_card_cached(
+    endpoint: str,
+    auth_hash: int,
+    timeout: int,
+    _ttl_hash: int,
+) -> AgentCard:
+    """Cached version of fetch_agent_card with auth support.
+
+    Args:
+        endpoint: A2A agent endpoint URL
+        auth_hash: Hash of the auth object
+        timeout: Request timeout
+        _ttl_hash: Time-based hash for cache invalidation (unused in body)
+
+    Returns:
+        Cached AgentCard
+    """
+    auth = _auth_store.get(auth_hash)
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(
+            _fetch_agent_card_async(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
+
+
+def fetch_agent_card(
+    endpoint: str,
+    auth: AuthScheme | None = None,
+    timeout: int = 30,
+    use_cache: bool = True,
+    cache_ttl: int = 300,
+) -> AgentCard:
+    """Fetch AgentCard from an A2A endpoint with optional caching.
+
+    Args:
+        endpoint: A2A agent endpoint URL (AgentCard URL)
+        auth: Optional AuthScheme for authentication
+        timeout: Request timeout in seconds
+        use_cache: Whether to use caching (default True)
+        cache_ttl: Cache TTL in seconds (default 300 = 5 minutes)
+
+    Returns:
+        AgentCard object with agent capabilities and skills
+
+    Raises:
+        httpx.HTTPStatusError: If the request fails
+        A2AClientHTTPError: If authentication fails
+    """
+    if use_cache:
+        auth_hash = hash((type(auth).__name__, id(auth))) if auth else 0
+        _auth_store[auth_hash] = auth
+        ttl_hash = int(time.time() // cache_ttl)
+        return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(
+            _fetch_agent_card_async(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
+
+
+async def _fetch_agent_card_async(
+    endpoint: str,
+    auth: AuthScheme | None,
+    timeout: int,
+) -> AgentCard:
+    """Async implementation of AgentCard fetching.
+
+    Args:
+        endpoint: A2A agent endpoint URL
+        auth: Optional AuthScheme for authentication
+        timeout: Request timeout in seconds
+
+    Returns:
+        AgentCard object
+    """
+    if "/.well-known/agent-card.json" in endpoint:
+        base_url = endpoint.replace("/.well-known/agent-card.json", "")
+        agent_card_path = "/.well-known/agent-card.json"
+    else:
+        url_parts = endpoint.split("/", 3)
+        base_url = f"{url_parts[0]}//{url_parts[2]}"
+        agent_card_path = f"/{url_parts[3]}" if len(url_parts) > 3 else "/"
+
+    headers: MutableMapping[str, str] = {}
+    if auth:
+        async with httpx.AsyncClient(timeout=timeout) as temp_auth_client:
+            if isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+                configure_auth_client(auth, temp_auth_client)
+            headers = await auth.apply_auth(temp_auth_client, {})
+
+    async with httpx.AsyncClient(timeout=timeout, headers=headers) as temp_client:
+        if auth and isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+            configure_auth_client(auth, temp_client)
+
+        agent_card_url = f"{base_url}{agent_card_path}"
+
+        async def _fetch_agent_card_request() -> httpx.Response:
+            return await temp_client.get(agent_card_url)
+
+        try:
+            response = await retry_on_401(
+                request_func=_fetch_agent_card_request,
+                auth_scheme=auth,
+                client=temp_client,
+                headers=temp_client.headers,
+                max_retries=2,
+            )
+            response.raise_for_status()
+
+            return AgentCard.model_validate(response.json())
+
+        except httpx.HTTPStatusError as e:
+            if e.response.status_code == 401:
+                error_details = ["Authentication failed"]
+                www_auth = e.response.headers.get("WWW-Authenticate")
+                if www_auth:
+                    error_details.append(f"WWW-Authenticate: {www_auth}")
+                if not auth:
+                    error_details.append("No auth scheme provided")
+                msg = " | ".join(error_details)
+                raise A2AClientHTTPError(401, msg) from e
+            raise
+
+
+def execute_a2a_delegation(
+    endpoint: str,
+    auth: AuthScheme | None,
+    timeout: int,
+    task_description: str,
+    context: str | None = None,
+    context_id: str | None = None,
+    task_id: str | None = None,
+    reference_task_ids: list[str] | None = None,
+    metadata: dict[str, Any] | None = None,
+    extensions: dict[str, Any] | None = None,
+    conversation_history: list[Message] | None = None,
+    agent_id: str | None = None,
+    agent_role: Role | None = None,
+    agent_branch: Any | None = None,
+    response_model: type[BaseModel] | None = None,
+    turn_number: int | None = None,
+) -> dict[str, Any]:
+    """Execute a task delegation to a remote A2A agent with multi-turn support.
+
+    Handles:
+    - AgentCard discovery
+    - Authentication setup
+    - Message creation and sending
+    - Response parsing
+    - Multi-turn conversations
+
+    Args:
+        endpoint: A2A agent endpoint URL (AgentCard URL)
+        auth: Optional AuthScheme for authentication (Bearer, OAuth2, API Key, HTTP Basic/Digest)
+        timeout: Request timeout in seconds
+        task_description: The task to delegate
+        context: Optional context information
+        context_id: Context ID for correlating messages/tasks
+        task_id: Specific task identifier
+        reference_task_ids: List of related task IDs
+        metadata: Additional metadata (external_id, request_id, etc.)
+        extensions: Protocol extensions for custom fields
+        conversation_history: Previous Message objects from conversation
+        agent_id: Agent identifier for logging
+        agent_role: Role of the CrewAI agent delegating the task
+        agent_branch: Optional agent tree branch for logging
+        response_model: Optional Pydantic model for structured outputs
+        turn_number: Optional turn number for multi-turn conversations
+
+    Returns:
+        Dictionary with:
+        - status: "completed", "input_required", "failed", etc.
+        - result: Result string (if completed)
+        - error: Error message (if failed)
+        - history: List of new Message objects from this exchange
+
+    Raises:
+        ImportError: If a2a-sdk is not installed
+    """
+    is_multiturn = bool(conversation_history and len(conversation_history) > 0)
+    if turn_number is None:
+        turn_number = (
+            len([m for m in (conversation_history or []) if m.role == Role.user]) + 1
+        )
+    crewai_event_bus.emit(
+        agent_branch,
+        A2ADelegationStartedEvent(
+            endpoint=endpoint,
+            task_description=task_description,
+            agent_id=agent_id,
+            is_multiturn=is_multiturn,
+            turn_number=turn_number,
+        ),
+    )
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        result = loop.run_until_complete(
+            _execute_a2a_delegation_async(
+                endpoint=endpoint,
+                auth=auth,
+                timeout=timeout,
+                task_description=task_description,
+                context=context,
+                context_id=context_id,
+                task_id=task_id,
+                reference_task_ids=reference_task_ids,
+                metadata=metadata,
+                extensions=extensions,
+                conversation_history=conversation_history or [],
+                is_multiturn=is_multiturn,
+                turn_number=turn_number,
+                agent_branch=agent_branch,
+                agent_id=agent_id,
+                agent_role=agent_role,
+                response_model=response_model,
+            )
+        )
+
+        crewai_event_bus.emit(
+            agent_branch,
+            A2ADelegationCompletedEvent(
+                status=result["status"],
+                result=result.get("result"),
+                error=result.get("error"),
+                is_multiturn=is_multiturn,
+            ),
+        )
+
+        return result
+    finally:
+        loop.close()
+
+
+async def _execute_a2a_delegation_async(
+    endpoint: str,
+    auth: AuthScheme | None,
+    timeout: int,
+    task_description: str,
+    context: str | None,
+    context_id: str | None,
+    task_id: str | None,
+    reference_task_ids: list[str] | None,
+    metadata: dict[str, Any] | None,
+    extensions: dict[str, Any] | None,
+    conversation_history: list[Message],
+    is_multiturn: bool = False,
+    turn_number: int = 1,
+    agent_branch: Any | None = None,
+    agent_id: str | None = None,
+    agent_role: str | None = None,
+    response_model: type[BaseModel] | None = None,
+) -> dict[str, Any]:
+    """Async implementation of A2A delegation with multi-turn support.
+
+    Args:
+        endpoint: A2A agent endpoint URL
+        auth: Optional AuthScheme for authentication
+        timeout: Request timeout in seconds
+        task_description: Task to delegate
+        context: Optional context
+        context_id: Context ID for correlation
+        task_id: Specific task identifier
+        reference_task_ids: Related task IDs
+        metadata: Additional metadata
+        extensions: Protocol extensions
+        conversation_history: Previous Message objects
+        is_multiturn: Whether this is a multi-turn conversation
+        turn_number: Current turn number
+        agent_branch: Agent tree branch for logging
+        agent_id: Agent identifier for logging
+        agent_role: Agent role for logging
+        response_model: Optional Pydantic model for structured outputs
+
+    Returns:
+        Dictionary with status, result/error, and new history
+    """
+    agent_card = await _fetch_agent_card_async(endpoint, auth, timeout)
+
+    validate_auth_against_agent_card(agent_card, auth)
+
+    headers: MutableMapping[str, str] = {}
+    if auth:
+        async with httpx.AsyncClient(timeout=timeout) as temp_auth_client:
+            if isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+                configure_auth_client(auth, temp_auth_client)
+            headers = await auth.apply_auth(temp_auth_client, {})
+
+    a2a_agent_name = None
+    if agent_card.name:
+        a2a_agent_name = agent_card.name
+
+    if turn_number == 1:
+        agent_id_for_event = agent_id or endpoint
+        crewai_event_bus.emit(
+            agent_branch,
+            A2AConversationStartedEvent(
+                agent_id=agent_id_for_event,
+                endpoint=endpoint,
+                a2a_agent_name=a2a_agent_name,
+            ),
+        )
+
+    message_parts = []
+
+    if context:
+        message_parts.append(f"Context:\n{context}\n\n")
+    message_parts.append(f"{task_description}")
+    message_text = "".join(message_parts)
+
+    if is_multiturn and conversation_history and not task_id:
+        if first_task_id := conversation_history[0].task_id:
+            task_id = first_task_id
+
+    parts: PartsDict = {"text": message_text}
+    if response_model:
+        parts.update(
+            {
+                "metadata": PartsMetadataDict(
+                    mimeType="application/json",
+                    schema=response_model.model_json_schema(),
+                )
+            }
+        )
+
+    message = Message(
+        role=Role.user,
+        message_id=str(uuid.uuid4()),
+        parts=[Part(root=TextPart(**parts))],
+        context_id=context_id,
+        task_id=task_id,
+        reference_task_ids=reference_task_ids,
+        metadata=metadata,
+        extensions=extensions,
+    )
+
+    transport_protocol = TransportProtocol("JSONRPC")
+    new_messages: list[Message] = [*conversation_history, message]
+    crewai_event_bus.emit(
+        None,
+        A2AMessageSentEvent(
+            message=message_text,
+            turn_number=turn_number,
+            is_multiturn=is_multiturn,
+            agent_role=agent_role,
+        ),
+    )
+
+    async with _create_a2a_client(
+        agent_card=agent_card,
+        transport_protocol=transport_protocol,
+        timeout=timeout,
+        headers=headers,
+        streaming=True,
+        auth=auth,
+    ) as client:
+        result_parts: list[str] = []
+        final_result: dict[str, Any] | None = None
+        event_stream = client.send_message(message)
+
+        try:
+            async for event in event_stream:
+                if isinstance(event, Message):
+                    new_messages.append(event)
+                    for part in event.parts:
+                        if part.root.kind == "text":
+                            text = part.root.text
+                            result_parts.append(text)
+
+                elif isinstance(event, tuple):
+                    a2a_task, update = event
+
+                    if isinstance(update, TaskArtifactUpdateEvent):
+                        artifact = update.artifact
+                        result_parts.extend(
+                            part.root.text
+                            for part in artifact.parts
+                            if part.root.kind == "text"
+                        )
+
+                    is_final_update = False
+                    if isinstance(update, TaskStatusUpdateEvent):
+                        is_final_update = update.final
+
+                    if not is_final_update and a2a_task.status.state not in [
+                        TaskState.completed,
+                        TaskState.input_required,
+                        TaskState.failed,
+                        TaskState.rejected,
+                        TaskState.auth_required,
+                        TaskState.canceled,
+                    ]:
+                        continue
+
+                    if a2a_task.status.state == TaskState.completed:
+                        extracted_parts = _extract_task_result_parts(a2a_task)
+                        result_parts.extend(extracted_parts)
+                        if a2a_task.history:
+                            new_messages.extend(a2a_task.history)
+
+                        response_text = " ".join(result_parts) if result_parts else ""
+                        crewai_event_bus.emit(
+                            None,
+                            A2AResponseReceivedEvent(
+                                response=response_text,
+                                turn_number=turn_number,
+                                is_multiturn=is_multiturn,
+                                status="completed",
+                                agent_role=agent_role,
+                            ),
+                        )
+
+                        final_result = {
+                            "status": "completed",
+                            "result": response_text,
+                            "history": new_messages,
+                            "agent_card": agent_card,
+                        }
+                        break
+
+                    if a2a_task.status.state == TaskState.input_required:
+                        if a2a_task.history:
+                            new_messages.extend(a2a_task.history)
+
+                        response_text = _extract_error_message(
+                            a2a_task, "Additional input required"
+                        )
+                        if response_text and not a2a_task.history:
+                            agent_message = Message(
+                                role=Role.agent,
+                                message_id=str(uuid.uuid4()),
+                                parts=[Part(root=TextPart(text=response_text))],
+                                context_id=a2a_task.context_id
+                                if hasattr(a2a_task, "context_id")
+                                else None,
+                                task_id=a2a_task.task_id
+                                if hasattr(a2a_task, "task_id")
+                                else None,
+                            )
+                            new_messages.append(agent_message)
+                        crewai_event_bus.emit(
+                            None,
+                            A2AResponseReceivedEvent(
+                                response=response_text,
+                                turn_number=turn_number,
+                                is_multiturn=is_multiturn,
+                                status="input_required",
+                                agent_role=agent_role,
+                            ),
+                        )
+
+                        final_result = {
+                            "status": "input_required",
+                            "error": response_text,
+                            "history": new_messages,
+                            "agent_card": agent_card,
+                        }
+                        break
+
+                    if a2a_task.status.state in [TaskState.failed, TaskState.rejected]:
+                        error_msg = _extract_error_message(
+                            a2a_task, "Task failed without error message"
+                        )
+                        if a2a_task.history:
+                            new_messages.extend(a2a_task.history)
+                        final_result = {
+                            "status": "failed",
+                            "error": error_msg,
+                            "history": new_messages,
+                        }
+                        break
+
+                    if a2a_task.status.state == TaskState.auth_required:
+                        error_msg = _extract_error_message(
+                            a2a_task, "Authentication required"
+                        )
+                        final_result = {
+                            "status": "auth_required",
+                            "error": error_msg,
+                            "history": new_messages,
+                        }
+                        break
+
+                    if a2a_task.status.state == TaskState.canceled:
+                        error_msg = _extract_error_message(
+                            a2a_task, "Task was canceled"
+                        )
+                        final_result = {
+                            "status": "canceled",
+                            "error": error_msg,
+                            "history": new_messages,
+                        }
+                        break
+        except Exception as e:
+            current_exception: Exception | BaseException | None = e
+            while current_exception:
+                if hasattr(current_exception, "response"):
+                    response = current_exception.response
+                    if hasattr(response, "text"):
+                        break
+                if current_exception and hasattr(current_exception, "__cause__"):
+                    current_exception = current_exception.__cause__
+            raise
+        finally:
+            if hasattr(event_stream, "aclose"):
+                await event_stream.aclose()
+
+    if final_result:
+        return final_result
+
+    return {
+        "status": "completed",
+        "result": " ".join(result_parts) if result_parts else "",
+        "history": new_messages,
+    }
+
+
+@asynccontextmanager
+async def _create_a2a_client(
+    agent_card: AgentCard,
+    transport_protocol: TransportProtocol,
+    timeout: int,
+    headers: MutableMapping[str, str],
+    streaming: bool,
+    auth: AuthScheme | None = None,
+) -> AsyncIterator[Client]:
+    """Create and configure an A2A client.
+
+    Args:
+        agent_card: The A2A agent card
+        transport_protocol: Transport protocol to use
+        timeout: Request timeout in seconds
+        headers: HTTP headers (already with auth applied)
+        streaming: Enable streaming responses
+        auth: Optional AuthScheme for client configuration
+
+    Yields:
+        Configured A2A client instance
+    """
+
+    async with httpx.AsyncClient(
+        timeout=timeout,
+        headers=headers,
+    ) as httpx_client:
+        if auth and isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+            configure_auth_client(auth, httpx_client)
+
+        config = ClientConfig(
+            httpx_client=httpx_client,
+            supported_transports=[str(transport_protocol.value)],
+            streaming=streaming,
+            accepted_output_modes=["application/json"],
+        )
+
+        factory = ClientFactory(config)
+        client = factory.create(agent_card)
+        yield client
+
+
+def _extract_task_result_parts(a2a_task: A2ATask) -> list[str]:
+    """Extract result parts from A2A task history and artifacts.
+
+    Args:
+        a2a_task: A2A Task object with history and artifacts
+
+    Returns:
+        List of result text parts
+    """
+
+    result_parts: list[str] = []
+
+    if a2a_task.history:
+        for history_msg in reversed(a2a_task.history):
+            if history_msg.role == Role.agent:
+                result_parts.extend(
+                    part.root.text
+                    for part in history_msg.parts
+                    if part.root.kind == "text"
+                )
+                break
+
+    if a2a_task.artifacts:
+        result_parts.extend(
+            part.root.text
+            for artifact in a2a_task.artifacts
+            for part in artifact.parts
+            if part.root.kind == "text"
+        )
+
+    return result_parts
+
+
+def _extract_error_message(a2a_task: A2ATask, default: str) -> str:
+    """Extract error message from A2A task.
+
+    Args:
+        a2a_task: A2A Task object
+        default: Default message if no error found
+
+    Returns:
+        Error message string
+    """
+    if a2a_task.status and a2a_task.status.message:
+        msg = a2a_task.status.message
+        if msg:
+            for part in msg.parts:
+                if part.root.kind == "text":
+                    return str(part.root.text)
+        return str(msg)
+
+    if a2a_task.history:
+        for history_msg in reversed(a2a_task.history):
+            for part in history_msg.parts:
+                if part.root.kind == "text":
+                    return str(part.root.text)
+
+    return default
+
+
+def create_agent_response_model(agent_ids: tuple[str, ...]) -> type[BaseModel]:
+    """Create a dynamic AgentResponse model with Literal types for agent IDs.
+
+    Args:
+        agent_ids: List of available A2A agent IDs
+
+    Returns:
+        Dynamically created Pydantic model with Literal-constrained a2a_ids field
+    """
+
+    DynamicLiteral = create_literals_from_strings(agent_ids)  # noqa: N806
+
+    return create_model(
+        "AgentResponse",
+        a2a_ids=(
+            tuple[DynamicLiteral, ...],  # type: ignore[valid-type]
+            Field(
+                default_factory=tuple,
+                max_length=len(agent_ids),
+                description="A2A agent IDs to delegate to.",
+            ),
+        ),
+        message=(
+            str,
+            Field(
+                description="The message content. If is_a2a=true, this is sent to the A2A agent. If is_a2a=false, this is your final answer ending the conversation."
+            ),
+        ),
+        is_a2a=(
+            bool,
+            Field(
+                description="Set to true to continue the conversation by sending this message to the A2A agent and awaiting their response. Set to false ONLY when you are completely done and providing your final answer (not when asking questions)."
+            ),
+        ),
+        __base__=BaseModel,
+    )
+
+
+def extract_a2a_agent_ids_from_config(
+    a2a_config: list[A2AConfig] | A2AConfig | None,
+) -> tuple[list[A2AConfig], tuple[str, ...]]:
+    """Extract A2A agent IDs from A2A configuration.
+
+    Args:
+        a2a_config: A2A configuration
+
+    Returns:
+        List of A2A agent IDs
+    """
+    if a2a_config is None:
+        return [], ()
+
+    if isinstance(a2a_config, A2AConfig):
+        a2a_agents = [a2a_config]
+    else:
+        a2a_agents = a2a_config
+    return a2a_agents, tuple(config.endpoint for config in a2a_agents)
+
+
+def get_a2a_agents_and_response_model(
+    a2a_config: list[A2AConfig] | A2AConfig | None,
+) -> tuple[list[A2AConfig], type[BaseModel]]:
+    """Get A2A agent IDs and response model.
+
+    Args:
+        a2a_config: A2A configuration
+
+    Returns:
+        Tuple of A2A agent IDs and response model
+    """
+    a2a_agents, agent_ids = extract_a2a_agent_ids_from_config(a2a_config=a2a_config)
+    return a2a_agents, create_agent_response_model(agent_ids)

lib/crewai/src/crewai/a2a/wrapper.py

@@ -0,0 +1,570 @@
+"""A2A agent wrapping logic for metaclass integration.
+
+Wraps agent classes with A2A delegation capabilities.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from functools import wraps
+from types import MethodType
+from typing import TYPE_CHECKING, Any, cast
+
+from a2a.types import Role
+from pydantic import BaseModel, ValidationError
+
+from crewai.a2a.config import A2AConfig
+from crewai.a2a.templates import (
+    AVAILABLE_AGENTS_TEMPLATE,
+    CONVERSATION_TURN_INFO_TEMPLATE,
+    PREVIOUS_A2A_CONVERSATION_TEMPLATE,
+    UNAVAILABLE_AGENTS_NOTICE_TEMPLATE,
+)
+from crewai.a2a.types import AgentResponseProtocol
+from crewai.a2a.utils import (
+    execute_a2a_delegation,
+    fetch_agent_card,
+    get_a2a_agents_and_response_model,
+)
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.a2a_events import (
+    A2AConversationCompletedEvent,
+    A2AMessageSentEvent,
+)
+
+
+if TYPE_CHECKING:
+    from a2a.types import AgentCard, Message
+
+    from crewai.agent.core import Agent
+    from crewai.task import Task
+    from crewai.tools.base_tool import BaseTool
+
+
+def wrap_agent_with_a2a_instance(agent: Agent) -> None:
+    """Wrap an agent instance's execute_task method with A2A support.
+
+    This function modifies the agent instance by wrapping its execute_task
+    method to add A2A delegation capabilities. Should only be called when
+    the agent has a2a configuration set.
+
+    Args:
+        agent: The agent instance to wrap
+    """
+    original_execute_task = agent.execute_task.__func__
+
+    @wraps(original_execute_task)
+    def execute_task_with_a2a(
+        self: Agent,
+        task: Task,
+        context: str | None = None,
+        tools: list[BaseTool] | None = None,
+    ) -> str:
+        """Execute task with A2A delegation support.
+
+        Args:
+            self: The agent instance
+            task: The task to execute
+            context: Optional context for task execution
+            tools: Optional tools available to the agent
+
+        Returns:
+            Task execution result
+        """
+        if not self.a2a:
+            return original_execute_task(self, task, context, tools)
+
+        a2a_agents, agent_response_model = get_a2a_agents_and_response_model(self.a2a)
+
+        return _execute_task_with_a2a(
+            self=self,
+            a2a_agents=a2a_agents,
+            original_fn=original_execute_task,
+            task=task,
+            agent_response_model=agent_response_model,
+            context=context,
+            tools=tools,
+        )
+
+    object.__setattr__(agent, "execute_task", MethodType(execute_task_with_a2a, agent))
+
+
+def _fetch_card_from_config(
+    config: A2AConfig,
+) -> tuple[A2AConfig, AgentCard | Exception]:
+    """Fetch agent card from A2A config.
+
+    Args:
+        config: A2A configuration
+
+    Returns:
+        Tuple of (config, card or exception)
+    """
+    try:
+        card = fetch_agent_card(
+            endpoint=config.endpoint,
+            auth=config.auth,
+            timeout=config.timeout,
+        )
+        return config, card
+    except Exception as e:
+        return config, e
+
+
+def _fetch_agent_cards_concurrently(
+    a2a_agents: list[A2AConfig],
+) -> tuple[dict[str, AgentCard], dict[str, str]]:
+    """Fetch agent cards concurrently for multiple A2A agents.
+
+    Args:
+        a2a_agents: List of A2A agent configurations
+
+    Returns:
+        Tuple of (agent_cards dict, failed_agents dict mapping endpoint to error message)
+    """
+    agent_cards: dict[str, AgentCard] = {}
+    failed_agents: dict[str, str] = {}
+
+    with ThreadPoolExecutor(max_workers=len(a2a_agents)) as executor:
+        futures = {
+            executor.submit(_fetch_card_from_config, config): config
+            for config in a2a_agents
+        }
+        for future in as_completed(futures):
+            config, result = future.result()
+            if isinstance(result, Exception):
+                if config.fail_fast:
+                    raise RuntimeError(
+                        f"Failed to fetch agent card from {config.endpoint}. "
+                        f"Ensure the A2A agent is running and accessible. Error: {result}"
+                    ) from result
+                failed_agents[config.endpoint] = str(result)
+            else:
+                agent_cards[config.endpoint] = result
+
+    return agent_cards, failed_agents
+
+
+def _execute_task_with_a2a(
+    self: Agent,
+    a2a_agents: list[A2AConfig],
+    original_fn: Callable[..., str],
+    task: Task,
+    agent_response_model: type[BaseModel],
+    context: str | None,
+    tools: list[BaseTool] | None,
+) -> str:
+    """Wrap execute_task with A2A delegation logic.
+
+    Args:
+        self: The agent instance
+        a2a_agents: Dictionary of A2A agent configurations
+        original_fn: The original execute_task method
+        task: The task to execute
+        context: Optional context for task execution
+        tools: Optional tools available to the agent
+        agent_response_model: Optional agent response model
+
+    Returns:
+        Task execution result (either from LLM or A2A agent)
+    """
+    original_description: str = task.description
+    original_output_pydantic = task.output_pydantic
+    original_response_model = task.response_model
+
+    agent_cards, failed_agents = _fetch_agent_cards_concurrently(a2a_agents)
+
+    if not agent_cards and a2a_agents and failed_agents:
+        unavailable_agents_text = ""
+        for endpoint, error in failed_agents.items():
+            unavailable_agents_text += f"  - {endpoint}: {error}\n"
+
+        notice = UNAVAILABLE_AGENTS_NOTICE_TEMPLATE.substitute(
+            unavailable_agents=unavailable_agents_text
+        )
+        task.description = f"{original_description}{notice}"
+
+        try:
+            return original_fn(self, task, context, tools)
+        finally:
+            task.description = original_description
+
+    task.description = _augment_prompt_with_a2a(
+        a2a_agents=a2a_agents,
+        task_description=original_description,
+        agent_cards=agent_cards,
+        failed_agents=failed_agents,
+    )
+    task.response_model = agent_response_model
+
+    try:
+        raw_result = original_fn(self, task, context, tools)
+        agent_response = _parse_agent_response(
+            raw_result=raw_result, agent_response_model=agent_response_model
+        )
+
+        if isinstance(agent_response, BaseModel) and isinstance(
+            agent_response, AgentResponseProtocol
+        ):
+            if agent_response.is_a2a:
+                return _delegate_to_a2a(
+                    self,
+                    agent_response=agent_response,
+                    task=task,
+                    original_fn=original_fn,
+                    context=context,
+                    tools=tools,
+                    agent_cards=agent_cards,
+                    original_task_description=original_description,
+                )
+            return str(agent_response.message)
+
+        return raw_result
+    finally:
+        task.description = original_description
+        task.output_pydantic = original_output_pydantic
+        task.response_model = original_response_model
+
+
+def _augment_prompt_with_a2a(
+    a2a_agents: list[A2AConfig],
+    task_description: str,
+    agent_cards: dict[str, AgentCard],
+    conversation_history: list[Message] | None = None,
+    turn_num: int = 0,
+    max_turns: int | None = None,
+    failed_agents: dict[str, str] | None = None,
+) -> str:
+    """Add A2A delegation instructions to prompt.
+
+    Args:
+        a2a_agents: Dictionary of A2A agent configurations
+        task_description: Original task description
+        agent_cards: dictionary mapping agent IDs to AgentCards
+        conversation_history: Previous A2A Messages from conversation
+        turn_num: Current turn number (0-indexed)
+        max_turns: Maximum allowed turns (from config)
+        failed_agents: Dictionary mapping failed agent endpoints to error messages
+
+    Returns:
+        Augmented task description with A2A instructions
+    """
+
+    if not agent_cards:
+        return task_description
+
+    agents_text = ""
+
+    for config in a2a_agents:
+        if config.endpoint in agent_cards:
+            card = agent_cards[config.endpoint]
+            agents_text += f"\n{card.model_dump_json(indent=2, exclude_none=True, include={'description', 'url', 'skills'})}\n"
+
+    failed_agents = failed_agents or {}
+    if failed_agents:
+        agents_text += "\n<!-- Unavailable Agents -->\n"
+        for endpoint, error in failed_agents.items():
+            agents_text += f"\n<!-- Agent: {endpoint}\n     Status: Unavailable\n     Error: {error} -->\n"
+
+    agents_text = AVAILABLE_AGENTS_TEMPLATE.substitute(available_a2a_agents=agents_text)
+
+    history_text = ""
+    if conversation_history:
+        for msg in conversation_history:
+            history_text += f"\n{msg.model_dump_json(indent=2, exclude_none=True, exclude={'message_id'})}\n"
+
+    history_text = PREVIOUS_A2A_CONVERSATION_TEMPLATE.substitute(
+        previous_a2a_conversation=history_text
+    )
+    turn_info = ""
+
+    if max_turns is not None and conversation_history:
+        turn_count = turn_num + 1
+        warning = ""
+        if turn_count >= max_turns:
+            warning = (
+                "CRITICAL: This is the FINAL turn. You MUST conclude the conversation now.\n"
+                "Set is_a2a=false and provide your final response to complete the task."
+            )
+        elif turn_count == max_turns - 1:
+            warning = "WARNING: Next turn will be the last. Consider wrapping up the conversation."
+
+        turn_info = CONVERSATION_TURN_INFO_TEMPLATE.substitute(
+            turn_count=turn_count,
+            max_turns=max_turns,
+            warning=warning,
+        )
+
+    return f"""{task_description}
+
+IMPORTANT: You have the ability to delegate this task to remote A2A agents.
+
+{agents_text}
+{history_text}{turn_info}
+
+
+"""
+
+
+def _parse_agent_response(
+    raw_result: str | dict[str, Any], agent_response_model: type[BaseModel]
+) -> BaseModel | str:
+    """Parse LLM output as AgentResponse or return raw agent response.
+
+    Args:
+        raw_result: Raw output from LLM
+        agent_response_model: The agent response model
+
+    Returns:
+        Parsed AgentResponse or string
+    """
+    if agent_response_model:
+        try:
+            if isinstance(raw_result, str):
+                return agent_response_model.model_validate_json(raw_result)
+            if isinstance(raw_result, dict):
+                return agent_response_model.model_validate(raw_result)
+        except ValidationError:
+            return cast(str, raw_result)
+    return cast(str, raw_result)
+
+
+def _handle_agent_response_and_continue(
+    self: Agent,
+    a2a_result: dict[str, Any],
+    agent_id: str,
+    agent_cards: dict[str, AgentCard] | None,
+    a2a_agents: list[A2AConfig],
+    original_task_description: str,
+    conversation_history: list[Message],
+    turn_num: int,
+    max_turns: int,
+    task: Task,
+    original_fn: Callable[..., str],
+    context: str | None,
+    tools: list[BaseTool] | None,
+    agent_response_model: type[BaseModel],
+) -> tuple[str | None, str | None]:
+    """Handle A2A result and get CrewAI agent's response.
+
+    Args:
+        self: The agent instance
+        a2a_result: Result from A2A delegation
+        agent_id: ID of the A2A agent
+        agent_cards: Pre-fetched agent cards
+        a2a_agents: List of A2A configurations
+        original_task_description: Original task description
+        conversation_history: Conversation history
+        turn_num: Current turn number
+        max_turns: Maximum turns allowed
+        task: The task being executed
+        original_fn: Original execute_task method
+        context: Optional context
+        tools: Optional tools
+        agent_response_model: Response model for parsing
+
+    Returns:
+        Tuple of (final_result, current_request) where:
+        - final_result is not None if conversation should end
+        - current_request is the next message to send if continuing
+    """
+    agent_cards_dict = agent_cards or {}
+    if "agent_card" in a2a_result and agent_id not in agent_cards_dict:
+        agent_cards_dict[agent_id] = a2a_result["agent_card"]
+
+    task.description = _augment_prompt_with_a2a(
+        a2a_agents=a2a_agents,
+        task_description=original_task_description,
+        conversation_history=conversation_history,
+        turn_num=turn_num,
+        max_turns=max_turns,
+        agent_cards=agent_cards_dict,
+    )
+
+    raw_result = original_fn(self, task, context, tools)
+    llm_response = _parse_agent_response(
+        raw_result=raw_result, agent_response_model=agent_response_model
+    )
+
+    if isinstance(llm_response, BaseModel) and isinstance(
+        llm_response, AgentResponseProtocol
+    ):
+        if not llm_response.is_a2a:
+            final_turn_number = turn_num + 1
+            crewai_event_bus.emit(
+                None,
+                A2AMessageSentEvent(
+                    message=str(llm_response.message),
+                    turn_number=final_turn_number,
+                    is_multiturn=True,
+                    agent_role=self.role,
+                ),
+            )
+            crewai_event_bus.emit(
+                None,
+                A2AConversationCompletedEvent(
+                    status="completed",
+                    final_result=str(llm_response.message),
+                    error=None,
+                    total_turns=final_turn_number,
+                ),
+            )
+            return str(llm_response.message), None
+        return None, str(llm_response.message)
+
+    return str(raw_result), None
+
+
+def _delegate_to_a2a(
+    self: Agent,
+    agent_response: AgentResponseProtocol,
+    task: Task,
+    original_fn: Callable[..., str],
+    context: str | None,
+    tools: list[BaseTool] | None,
+    agent_cards: dict[str, AgentCard] | None = None,
+    original_task_description: str | None = None,
+) -> str:
+    """Delegate to A2A agent with multi-turn conversation support.
+
+    Args:
+        self: The agent instance
+        agent_response: The AgentResponse indicating delegation
+        task: The task being executed (for extracting A2A fields)
+        original_fn: The original execute_task method for follow-ups
+        context: Optional context for task execution
+        tools: Optional tools available to the agent
+        agent_cards: Pre-fetched agent cards from _execute_task_with_a2a
+        original_task_description: The original task description before A2A augmentation
+
+    Returns:
+        Result from A2A agent
+
+    Raises:
+        ImportError: If a2a-sdk is not installed
+    """
+    a2a_agents, agent_response_model = get_a2a_agents_and_response_model(self.a2a)
+    agent_ids = tuple(config.endpoint for config in a2a_agents)
+    current_request = str(agent_response.message)
+    agent_id = agent_response.a2a_ids[0]
+
+    if agent_id not in agent_ids:
+        raise ValueError(
+            f"Unknown A2A agent ID(s): {agent_response.a2a_ids} not in {agent_ids}"
+        )
+
+    agent_config = next(filter(lambda x: x.endpoint == agent_id, a2a_agents))
+    task_config = task.config or {}
+    context_id = task_config.get("context_id")
+    task_id_config = task_config.get("task_id")
+    reference_task_ids = task_config.get("reference_task_ids")
+    metadata = task_config.get("metadata")
+    extensions = task_config.get("extensions")
+
+    if original_task_description is None:
+        original_task_description = task.description
+
+    conversation_history: list[Message] = []
+    max_turns = agent_config.max_turns
+
+    try:
+        for turn_num in range(max_turns):
+            console_formatter = getattr(crewai_event_bus, "_console", None)
+            agent_branch = None
+            if console_formatter:
+                agent_branch = getattr(
+                    console_formatter, "current_agent_branch", None
+                ) or getattr(console_formatter, "current_task_branch", None)
+
+            a2a_result = execute_a2a_delegation(
+                endpoint=agent_config.endpoint,
+                auth=agent_config.auth,
+                timeout=agent_config.timeout,
+                task_description=current_request,
+                context_id=context_id,
+                task_id=task_id_config,
+                reference_task_ids=reference_task_ids,
+                metadata=metadata,
+                extensions=extensions,
+                conversation_history=conversation_history,
+                agent_id=agent_id,
+                agent_role=Role.user,
+                agent_branch=agent_branch,
+                response_model=agent_config.response_model,
+                turn_number=turn_num + 1,
+            )
+
+            conversation_history = a2a_result.get("history", [])
+
+            if a2a_result["status"] in ["completed", "input_required"]:
+                final_result, next_request = _handle_agent_response_and_continue(
+                    self=self,
+                    a2a_result=a2a_result,
+                    agent_id=agent_id,
+                    agent_cards=agent_cards,
+                    a2a_agents=a2a_agents,
+                    original_task_description=original_task_description,
+                    conversation_history=conversation_history,
+                    turn_num=turn_num,
+                    max_turns=max_turns,
+                    task=task,
+                    original_fn=original_fn,
+                    context=context,
+                    tools=tools,
+                    agent_response_model=agent_response_model,
+                )
+
+                if final_result is not None:
+                    return final_result
+
+                if next_request is not None:
+                    current_request = next_request
+
+                continue
+
+            error_msg = a2a_result.get("error", "Unknown error")
+            crewai_event_bus.emit(
+                None,
+                A2AConversationCompletedEvent(
+                    status="failed",
+                    final_result=None,
+                    error=error_msg,
+                    total_turns=turn_num + 1,
+                ),
+            )
+            raise Exception(f"A2A delegation failed: {error_msg}")
+
+        if conversation_history:
+            for msg in reversed(conversation_history):
+                if msg.role == Role.agent:
+                    text_parts = [
+                        part.root.text for part in msg.parts if part.root.kind == "text"
+                    ]
+                    final_message = (
+                        " ".join(text_parts) if text_parts else "Conversation completed"
+                    )
+                    crewai_event_bus.emit(
+                        None,
+                        A2AConversationCompletedEvent(
+                            status="completed",
+                            final_result=final_message,
+                            error=None,
+                            total_turns=max_turns,
+                        ),
+                    )
+                    return final_message
+
+        crewai_event_bus.emit(
+            None,
+            A2AConversationCompletedEvent(
+                status="failed",
+                final_result=None,
+                error=f"Conversation exceeded maximum turns ({max_turns})",
+                total_turns=max_turns,
+            ),
+        )
+        raise Exception(f"A2A conversation exceeded maximum turns ({max_turns})")
+
+    finally:
+        task.description = original_task_description

lib/crewai/src/crewai/agent/__init__.py

@@ -0,0 +1,5 @@
+from crewai.agent.core import Agent
+from crewai.utilities.training_handler import CrewTrainingHandler
+
+
+__all__ = ["Agent", "CrewTrainingHandler"]

lib/crewai/src/crewai/agent/core.py

@@ -2,27 +2,27 @@ from __future__ import annotations
 
 import asyncio
 from collections.abc import Sequence
+import json
 import shutil
 import subprocess
 import time
 from typing import (
     TYPE_CHECKING,
     Any,
+    Final,
     Literal,
+    cast,
 )
+from urllib.parse import urlparse
 
 from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
 from typing_extensions import Self
 
+from crewai.a2a.config import A2AConfig
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
 from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
@@ -70,14 +70,14 @@ if TYPE_CHECKING:
 
 
 # MCP Connection timeout constants (in seconds)
-MCP_CONNECTION_TIMEOUT = 10
-MCP_TOOL_EXECUTION_TIMEOUT = 30
-MCP_DISCOVERY_TIMEOUT = 15
-MCP_MAX_RETRIES = 3
+MCP_CONNECTION_TIMEOUT: Final[int] = 10
+MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30
+MCP_DISCOVERY_TIMEOUT: Final[int] = 15
+MCP_MAX_RETRIES: Final[int] = 3
 
 # Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
-_mcp_schema_cache = {}
-_cache_ttl = 300  # 5 minutes
+_mcp_schema_cache: dict[str, Any] = {}
+_cache_ttl: Final[int] = 300  # 5 minutes
 
 
 class Agent(BaseAgent):
@@ -197,6 +197,10 @@ class Agent(BaseAgent):
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
     )
+    a2a: list[A2AConfig] | A2AConfig | None = Field(
+        default=None,
+        description="A2A (Agent-to-Agent) configuration for delegating tasks to remote agents. Can be a single A2AConfig or a dict mapping agent IDs to configs.",
+    )
 
     @model_validator(mode="before")
     def validate_from_repository(cls, v: Any) -> dict[str, Any] | None | Any:  # noqa: N805
@@ -305,17 +309,19 @@ class Agent(BaseAgent):
         # If the task requires output in JSON or Pydantic format,
         # append specific instructions to the task prompt to ensure
         # that the final answer does not include any code block markers
-        if task.output_json or task.output_pydantic:
+        # Skip this if task.response_model is set, as native structured outputs handle schema automatically
+        if (task.output_json or task.output_pydantic) and not task.response_model:
             # Generate the schema based on the output format
             if task.output_json:
-                # schema = json.dumps(task.output_json, indent=2)
-                schema = generate_model_description(task.output_json)
+                schema_dict = generate_model_description(task.output_json)
+                schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
                 task_prompt += "\n" + self.i18n.slice(
                     "formatted_task_instructions"
                 ).format(output_format=schema)
 
             elif task.output_pydantic:
-                schema = generate_model_description(task.output_pydantic)
+                schema_dict = generate_model_description(task.output_pydantic)
+                schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
                 task_prompt += "\n" + self.i18n.slice(
                     "formatted_task_instructions"
                 ).format(output_format=schema)
@@ -438,6 +444,13 @@ class Agent(BaseAgent):
         else:
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
+        # Import agent events locally to avoid circular imports
+        from crewai.events.types.agent_events import (
+            AgentExecutionCompletedEvent,
+            AgentExecutionErrorEvent,
+            AgentExecutionStartedEvent,
+        )
+
         try:
             crewai_event_bus.emit(
                 self,
@@ -618,6 +631,7 @@ class Agent(BaseAgent):
                 self._rpm_controller.check_or_wait if self._rpm_controller else None
             ),
             callbacks=[TokenCalcHandler(self._token_process)],
+            response_model=task.response_model if task else None,
         )
 
     def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
@@ -709,7 +723,7 @@ class Agent(BaseAgent):
                     f"Specific tool '{specific_tool}' not found on MCP server: {server_url}",
                 )
 
-            return tools
+            return cast(list[BaseTool], tools)
 
         except Exception as e:
             self._logger.log(
@@ -739,9 +753,9 @@ class Agent(BaseAgent):
 
         return tools
 
-    def _extract_server_name(self, server_url: str) -> str:
+    @staticmethod
+    def _extract_server_name(server_url: str) -> str:
         """Extract clean server name from URL for tool prefixing."""
-        from urllib.parse import urlparse
 
         parsed = urlparse(server_url)
         domain = parsed.netloc.replace(".", "_")
@@ -778,7 +792,9 @@ class Agent(BaseAgent):
             )
             return {}
 
-    async def _get_mcp_tool_schemas_async(self, server_params: dict) -> dict[str, dict]:
+    async def _get_mcp_tool_schemas_async(
+        self, server_params: dict[str, Any]
+    ) -> dict[str, dict]:
         """Async implementation of MCP tool schema retrieval with timeouts and retries."""
         server_url = server_params["url"]
         return await self._retry_mcp_discovery(
@@ -787,7 +803,7 @@ class Agent(BaseAgent):
 
     async def _retry_mcp_discovery(
         self, operation_func, server_url: str
-    ) -> dict[str, dict]:
+    ) -> dict[str, dict[str, Any]]:
         """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop."""
         last_error = None
 
@@ -815,9 +831,10 @@ class Agent(BaseAgent):
             f"Failed to discover MCP tools after {MCP_MAX_RETRIES} attempts: {last_error}"
         )
 
+    @staticmethod
     async def _attempt_mcp_discovery(
-        self, operation_func, server_url: str
-    ) -> tuple[dict[str, dict] | None, str, bool]:
+        operation_func, server_url: str
+    ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]:
         """Attempt single MCP discovery operation and return (result, error_message, should_retry)."""
         try:
             result = await operation_func(server_url)
@@ -851,13 +868,13 @@ class Agent(BaseAgent):
 
     async def _discover_mcp_tools_with_timeout(
         self, server_url: str
-    ) -> dict[str, dict]:
+    ) -> dict[str, dict[str, Any]]:
         """Discover MCP tools with timeout wrapper."""
         return await asyncio.wait_for(
             self._discover_mcp_tools(server_url), timeout=MCP_DISCOVERY_TIMEOUT
         )
 
-    async def _discover_mcp_tools(self, server_url: str) -> dict[str, dict]:
+    async def _discover_mcp_tools(self, server_url: str) -> dict[str, dict[str, Any]]:
         """Discover tools from MCP server with proper timeout handling."""
         from mcp import ClientSession
         from mcp.client.streamable_http import streamablehttp_client
@@ -889,7 +906,9 @@ class Agent(BaseAgent):
                     }
                 return schemas
 
-    def _json_schema_to_pydantic(self, tool_name: str, json_schema: dict) -> type:
+    def _json_schema_to_pydantic(
+        self, tool_name: str, json_schema: dict[str, Any]
+    ) -> type:
         """Convert JSON Schema to Pydantic model for tool arguments.
 
         Args:
@@ -926,7 +945,7 @@ class Agent(BaseAgent):
         model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
         return create_model(model_name, **field_definitions)
 
-    def _json_type_to_python(self, field_schema: dict) -> type:
+    def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
         """Convert JSON Schema type to Python type.
 
         Args:
@@ -935,7 +954,6 @@ class Agent(BaseAgent):
         Returns:
             Python type
         """
-        from typing import Any
 
         json_type = field_schema.get("type")
 
@@ -965,13 +983,15 @@ class Agent(BaseAgent):
 
         return type_mapping.get(json_type, Any)
 
-    def _fetch_amp_mcp_servers(self, mcp_name: str) -> list[dict]:
+    @staticmethod
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict]:
         """Fetch MCP server configurations from CrewAI AMP API."""
         # TODO: Implement AMP API call to "integrations/mcps" endpoint
         # Should return list of server configs with URLs
         return []
 
-    def get_multimodal_tools(self) -> Sequence[BaseTool]:
+    @staticmethod
+    def get_multimodal_tools() -> Sequence[BaseTool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
 
         return [AddImageTool()]
@@ -991,8 +1011,9 @@ class Agent(BaseAgent):
             )
             return []
 
+    @staticmethod
     def get_output_converter(
-        self, llm: BaseLLM, text: str, model: type[BaseModel], instructions: str
+        llm: BaseLLM, text: str, model: type[BaseModel], instructions: str
     ) -> Converter:
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
@@ -1022,7 +1043,8 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: list[Any]) -> str:
+    @staticmethod
+    def _render_text_description(tools: list[Any]) -> str:
         """Render the tool name and description in plain text.
 
         Output will be in the format of:

lib/crewai/src/crewai/agent/internal/meta.py

@@ -0,0 +1,76 @@
+"""Generic metaclass for agent extensions.
+
+This metaclass enables extension capabilities for agents by detecting
+extension fields in class annotations and applying appropriate wrappers.
+"""
+
+import warnings
+from functools import wraps
+from typing import Any
+
+from pydantic import model_validator
+from pydantic._internal._model_construction import ModelMetaclass
+
+
+class AgentMeta(ModelMetaclass):
+    """Generic metaclass for agent extensions.
+
+    Detects extension fields (like 'a2a') in class annotations and applies
+    the appropriate wrapper logic to enable extension functionality.
+    """
+
+    def __new__(
+        mcs,
+        name: str,
+        bases: tuple[type, ...],
+        namespace: dict[str, Any],
+        **kwargs: Any,
+    ) -> type:
+        """Create a new class with extension support.
+
+        Args:
+            name: The name of the class being created
+            bases: Base classes
+            namespace: Class namespace dictionary
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            The newly created class with extension support if applicable
+        """
+        orig_post_init_setup = namespace.get("post_init_setup")
+
+        if orig_post_init_setup is not None:
+            original_func = (
+                orig_post_init_setup.wrapped
+                if hasattr(orig_post_init_setup, "wrapped")
+                else orig_post_init_setup
+            )
+
+            def post_init_setup_with_extensions(self: Any) -> Any:
+                """Wrap post_init_setup to apply extensions after initialization.
+
+                Args:
+                    self: The agent instance
+
+                Returns:
+                    The agent instance
+                """
+                result = original_func(self)
+
+                a2a_value = getattr(self, "a2a", None)
+                if a2a_value is not None:
+                    from crewai.a2a.wrapper import wrap_agent_with_a2a_instance
+
+                    wrap_agent_with_a2a_instance(self)
+
+                return result
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore", message=".*overrides an existing Pydantic.*"
+                )
+                namespace["post_init_setup"] = model_validator(mode="after")(
+                    post_init_setup_with_extensions
+                )
+
+        return super().__new__(mcs, name, bases, namespace, **kwargs)

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -18,6 +18,7 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
+from crewai.agent.internal.meta import AgentMeta
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
@@ -56,7 +57,7 @@ PlatformApp = Literal[
 PlatformAppOrAction = PlatformApp | str
 
 
-class BaseAgent(BaseModel, ABC):
+class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     """Abstract Base Class for all third party agents compatible with CrewAI.
 
     Attributes:

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 from collections.abc import Callable
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-from pydantic import GetCoreSchemaHandler
+from pydantic import BaseModel, GetCoreSchemaHandler
 from pydantic_core import CoreSchema, core_schema
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -65,7 +65,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def __init__(
         self,
-        llm: BaseLLM | Any,
+        llm: BaseLLM | Any | None,
         task: Task,
         crew: Crew,
         agent: Agent,
@@ -82,6 +82,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         respect_context_window: bool = False,
         request_within_rpm_limit: Callable[[], bool] | None = None,
         callbacks: list[Any] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> None:
         """Initialize executor.
 
@@ -103,6 +104,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             respect_context_window: Respect context limits.
             request_within_rpm_limit: RPM limit check function.
             callbacks: Optional callbacks list.
+            response_model: Optional Pydantic model for structured outputs.
         """
         self._i18n: I18N = I18N()
         self.llm = llm
@@ -119,23 +121,34 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tools_handler = tools_handler
         self.original_tools = original_tools or []
         self.step_callback = step_callback
-        self.use_stop_words = self.llm.supports_stop_words()
         self.tools_description = tools_description
         self.function_calling_llm = function_calling_llm
         self.respect_context_window = respect_context_window
         self.request_within_rpm_limit = request_within_rpm_limit
+        self.response_model = response_model
         self.ask_for_human_input = False
         self.messages: list[LLMMessage] = []
         self.iterations = 0
         self.log_error_after = 3
-        existing_stop = getattr(self.llm, "stop", [])
-        self.llm.stop = list(
-            set(
-                existing_stop + self.stop
-                if isinstance(existing_stop, list)
-                else self.stop
+        if self.llm:
+            # This may be mutating the shared llm object and needs further evaluation
+            existing_stop = getattr(self.llm, "stop", [])
+            self.llm.stop = list(
+                set(
+                    existing_stop + self.stop
+                    if isinstance(existing_stop, list)
+                    else self.stop
+                )
             )
-        )
+
+    @property
+    def use_stop_words(self) -> bool:
+        """Check to determine if stop words are being used.
+
+        Returns:
+            bool: True if tool should be used or not.
+        """
+        return self.llm.supports_stop_words() if self.llm else False
 
     def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent with given inputs.
@@ -211,6 +224,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     printer=self._printer,
                     from_task=self.task,
                     from_agent=self.agent,
+                    response_model=self.response_model,
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 

lib/crewai/src/crewai/cli/shared/token_manager.py

@@ -3,10 +3,17 @@ import json
 import os
 from pathlib import Path
 import sys
+from typing import BinaryIO, cast
 
 from cryptography.fernet import Fernet
 
 
+if sys.platform == "win32":
+    import msvcrt
+else:
+    import fcntl
+
+
 class TokenManager:
     def __init__(self, file_path: str = "tokens.enc") -> None:
         """
@@ -18,21 +25,74 @@ class TokenManager:
         self.key = self._get_or_create_key()
         self.fernet = Fernet(self.key)
 
+    @staticmethod
+    def _acquire_lock(file_handle: BinaryIO) -> None:
+        """
+        Acquire an exclusive lock on a file handle.
+
+        Args:
+            file_handle: Open file handle to lock.
+        """
+        if sys.platform == "win32":
+            msvcrt.locking(file_handle.fileno(), msvcrt.LK_LOCK, 1)
+        else:
+            fcntl.flock(file_handle.fileno(), fcntl.LOCK_EX)
+
+    @staticmethod
+    def _release_lock(file_handle: BinaryIO) -> None:
+        """
+        Release the lock on a file handle.
+
+        Args:
+            file_handle: Open file handle to unlock.
+        """
+        if sys.platform == "win32":
+            msvcrt.locking(file_handle.fileno(), msvcrt.LK_UNLCK, 1)
+        else:
+            fcntl.flock(file_handle.fileno(), fcntl.LOCK_UN)
+
     def _get_or_create_key(self) -> bytes:
         """
-        Get or create the encryption key.
+        Get or create the encryption key with file locking to prevent race conditions.
 
-        :return: The encryption key.
+        Returns:
+            The encryption key.
         """
         key_filename = "secret.key"
-        key = self.read_secure_file(key_filename)
+        storage_path = self.get_secure_storage_path()
 
-        if key is not None:
+        key = self.read_secure_file(key_filename)
+        if key is not None and len(key) == 44:
             return key
 
-        new_key = Fernet.generate_key()
-        self.save_secure_file(key_filename, new_key)
-        return new_key
+        lock_file_path = storage_path / f"{key_filename}.lock"
+
+        try:
+            lock_file_path.touch()
+
+            with open(lock_file_path, "r+b") as lock_file:
+                self._acquire_lock(lock_file)
+                try:
+                    key = self.read_secure_file(key_filename)
+                    if key is not None and len(key) == 44:
+                        return key
+
+                    new_key = Fernet.generate_key()
+                    self.save_secure_file(key_filename, new_key)
+                    return new_key
+                finally:
+                    try:
+                        self._release_lock(lock_file)
+                    except OSError:
+                        pass
+        except OSError:
+            key = self.read_secure_file(key_filename)
+            if key is not None and len(key) == 44:
+                return key
+
+            new_key = Fernet.generate_key()
+            self.save_secure_file(key_filename, new_key)
+            return new_key
 
     def save_tokens(self, access_token: str, expires_at: int) -> None:
         """
@@ -59,14 +119,14 @@ class TokenManager:
         if encrypted_data is None:
             return None
 
-        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
+        decrypted_data = self.fernet.decrypt(encrypted_data)
         data = json.loads(decrypted_data)
 
         expiration = datetime.fromisoformat(data["expiration"])
         if expiration <= datetime.now():
             return None
 
-        return data["access_token"]
+        return cast(str | None, data["access_token"])
 
     def clear_tokens(self) -> None:
         """
@@ -74,20 +134,18 @@ class TokenManager:
         """
         self.delete_secure_file(self.file_path)
 
-    def get_secure_storage_path(self) -> Path:
+    @staticmethod
+    def get_secure_storage_path() -> Path:
         """
         Get the secure storage path based on the operating system.
 
         :return: The secure storage path.
         """
         if sys.platform == "win32":
-            # Windows: Use %LOCALAPPDATA%
             base_path = os.environ.get("LOCALAPPDATA")
         elif sys.platform == "darwin":
-            # macOS: Use ~/Library/Application Support
             base_path = os.path.expanduser("~/Library/Application Support")
         else:
-            # Linux and other Unix-like: Use ~/.local/share
             base_path = os.path.expanduser("~/.local/share")
 
         app_name = "crewai/credentials"
@@ -110,7 +168,6 @@ class TokenManager:
         with open(file_path, "wb") as f:
             f.write(content)
 
-        # Set appropriate permissions (read/write for owner only)
         os.chmod(file_path, 0o600)
 
     def read_secure_file(self, filename: str) -> bytes | None:

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.2.1"
+    "crewai[tools]==1.3.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.2.1"
+    "crewai[tools]==1.3.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/events/__init__.py

@@ -8,21 +8,15 @@ This module provides the event infrastructure that allows users to:
 - Declare handler dependencies for ordered execution
 """
 
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.depends import Depends
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.handler_graph import CircularDependencyError
-from crewai.events.types.agent_events import (
-    AgentEvaluationCompletedEvent,
-    AgentEvaluationFailedEvent,
-    AgentEvaluationStartedEvent,
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent,
-    LiteAgentExecutionErrorEvent,
-    LiteAgentExecutionStartedEvent,
-)
+
 from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -100,6 +94,20 @@ from crewai.events.types.tool_usage_events import (
 )
 
 
+if TYPE_CHECKING:
+    from crewai.events.types.agent_events import (
+        AgentEvaluationCompletedEvent,
+        AgentEvaluationFailedEvent,
+        AgentEvaluationStartedEvent,
+        AgentExecutionCompletedEvent,
+        AgentExecutionErrorEvent,
+        AgentExecutionStartedEvent,
+        LiteAgentExecutionCompletedEvent,
+        LiteAgentExecutionErrorEvent,
+        LiteAgentExecutionStartedEvent,
+    )
+
+
 __all__ = [
     "AgentEvaluationCompletedEvent",
     "AgentEvaluationFailedEvent",
@@ -170,3 +178,27 @@ __all__ = [
     "ToolValidateInputErrorEvent",
     "crewai_event_bus",
 ]
+
+_AGENT_EVENT_MAPPING = {
+    "AgentEvaluationCompletedEvent": "crewai.events.types.agent_events",
+    "AgentEvaluationFailedEvent": "crewai.events.types.agent_events",
+    "AgentEvaluationStartedEvent": "crewai.events.types.agent_events",
+    "AgentExecutionCompletedEvent": "crewai.events.types.agent_events",
+    "AgentExecutionErrorEvent": "crewai.events.types.agent_events",
+    "AgentExecutionStartedEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionCompletedEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionErrorEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
+}
+
+
+def __getattr__(name: str):
+    """Lazy import for agent events to avoid circular imports."""
+    if name in _AGENT_EVENT_MAPPING:
+        import importlib
+
+        module_path = _AGENT_EVENT_MAPPING[name]
+        module = importlib.import_module(module_path)
+        return getattr(module, name)
+    msg = f"module {__name__!r} has no attribute {name!r}"
+    raise AttributeError(msg)

lib/crewai/src/crewai/events/base_event_listener.py

@@ -1,16 +1,26 @@
+"""Base event listener for CrewAI event system."""
+
 from abc import ABC, abstractmethod
 
 from crewai.events.event_bus import CrewAIEventsBus, crewai_event_bus
 
 
 class BaseEventListener(ABC):
+    """Abstract base class for event listeners."""
+
     verbose: bool = False
 
-    def __init__(self):
+    def __init__(self) -> None:
+        """Initialize the event listener and register handlers."""
         super().__init__()
         self.setup_listeners(crewai_event_bus)
         crewai_event_bus.validate_dependencies()
 
     @abstractmethod
-    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus):
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
+        """Setup event listeners on the event bus.
+
+        Args:
+            crewai_event_bus: The event bus to register listeners on.
+        """
         pass

lib/crewai/src/crewai/events/event_listener.py

@@ -1,12 +1,21 @@
 from __future__ import annotations
 
 from io import StringIO
-from typing import Any
+import threading
+from typing import TYPE_CHECKING, Any
 
 from pydantic import Field, PrivateAttr
 
 from crewai.events.base_event_listener import BaseEventListener
-from crewai.events.listeners.memory_listener import MemoryListener
+from crewai.events.listeners.tracing.trace_listener import TraceCollectionListener
+from crewai.events.types.a2a_events import (
+    A2AConversationCompletedEvent,
+    A2AConversationStartedEvent,
+    A2ADelegationCompletedEvent,
+    A2ADelegationStartedEvent,
+    A2AMessageSentEvent,
+    A2AResponseReceivedEvent,
+)
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionStartedEvent,
@@ -79,6 +88,10 @@ from crewai.utilities import Logger
 from crewai.utilities.constants import EMITTER_COLOR
 
 
+if TYPE_CHECKING:
+    from crewai.events.event_bus import CrewAIEventsBus
+
+
 class EventListener(BaseEventListener):
     _instance = None
     _telemetry: Telemetry = PrivateAttr(default_factory=lambda: Telemetry())
@@ -88,6 +101,7 @@ class EventListener(BaseEventListener):
     text_stream = StringIO()
     knowledge_retrieval_in_progress = False
     knowledge_query_in_progress = False
+    method_branches: dict[str, Any] = Field(default_factory=dict)
 
     def __new__(cls):
         if cls._instance is None:
@@ -101,21 +115,27 @@ class EventListener(BaseEventListener):
             self._telemetry = Telemetry()
             self._telemetry.set_tracer()
             self.execution_spans = {}
+            self.method_branches = {}
             self._initialized = True
             self.formatter = ConsoleFormatter(verbose=True)
+            self._crew_tree_lock = threading.Condition()
 
-            MemoryListener(formatter=self.formatter)
+            # Initialize trace listener with formatter for memory event handling
+            trace_listener = TraceCollectionListener()
+            trace_listener.formatter = self.formatter
 
     # ----------- CREW EVENTS -----------
 
-    def setup_listeners(self, crewai_event_bus):
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
         @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event: CrewKickoffStartedEvent):
-            self.formatter.create_crew_tree(event.crew_name or "Crew", source.id)
-            self._telemetry.crew_execution_span(source, event.inputs)
+        def on_crew_started(source, event: CrewKickoffStartedEvent) -> None:
+            with self._crew_tree_lock:
+                self.formatter.create_crew_tree(event.crew_name or "Crew", source.id)
+                self._telemetry.crew_execution_span(source, event.inputs)
+                self._crew_tree_lock.notify_all()
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event: CrewKickoffCompletedEvent):
+        def on_crew_completed(source, event: CrewKickoffCompletedEvent) -> None:
             # Handle telemetry
             final_string_output = event.output.raw
             self._telemetry.end_crew(source, final_string_output)
@@ -129,7 +149,7 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(CrewKickoffFailedEvent)
-        def on_crew_failed(source, event: CrewKickoffFailedEvent):
+        def on_crew_failed(source, event: CrewKickoffFailedEvent) -> None:
             self.formatter.update_crew_tree(
                 self.formatter.current_crew_tree,
                 event.crew_name or "Crew",
@@ -138,23 +158,23 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(CrewTrainStartedEvent)
-        def on_crew_train_started(source, event: CrewTrainStartedEvent):
+        def on_crew_train_started(source, event: CrewTrainStartedEvent) -> None:
             self.formatter.handle_crew_train_started(
                 event.crew_name or "Crew", str(event.timestamp)
             )
 
         @crewai_event_bus.on(CrewTrainCompletedEvent)
-        def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
+        def on_crew_train_completed(source, event: CrewTrainCompletedEvent) -> None:
             self.formatter.handle_crew_train_completed(
                 event.crew_name or "Crew", str(event.timestamp)
             )
 
         @crewai_event_bus.on(CrewTrainFailedEvent)
-        def on_crew_train_failed(source, event: CrewTrainFailedEvent):
+        def on_crew_train_failed(source, event: CrewTrainFailedEvent) -> None:
             self.formatter.handle_crew_train_failed(event.crew_name or "Crew")
 
         @crewai_event_bus.on(CrewTestResultEvent)
-        def on_crew_test_result(source, event: CrewTestResultEvent):
+        def on_crew_test_result(source, event: CrewTestResultEvent) -> None:
             self._telemetry.individual_test_result_span(
                 source.crew,
                 event.quality,
@@ -165,14 +185,22 @@ class EventListener(BaseEventListener):
         # ----------- TASK EVENTS -----------
 
         @crewai_event_bus.on(TaskStartedEvent)
-        def on_task_started(source, event: TaskStartedEvent):
+        def on_task_started(source, event: TaskStartedEvent) -> None:
             span = self._telemetry.task_started(crew=source.agent.crew, task=source)
             self.execution_spans[source] = span
-            # Pass both task ID and task name (if set)
-            task_name = source.name if hasattr(source, "name") and source.name else None
-            self.formatter.create_task_branch(
-                self.formatter.current_crew_tree, source.id, task_name
-            )
+
+            with self._crew_tree_lock:
+                self._crew_tree_lock.wait_for(
+                    lambda: self.formatter.current_crew_tree is not None, timeout=5.0
+                )
+
+            if self.formatter.current_crew_tree is not None:
+                task_name = (
+                    source.name if hasattr(source, "name") and source.name else None
+                )
+                self.formatter.create_task_branch(
+                    self.formatter.current_crew_tree, source.id, task_name
+                )
 
         @crewai_event_bus.on(TaskCompletedEvent)
         def on_task_completed(source, event: TaskCompletedEvent):
@@ -263,7 +291,8 @@ class EventListener(BaseEventListener):
         @crewai_event_bus.on(FlowCreatedEvent)
         def on_flow_created(source, event: FlowCreatedEvent):
             self._telemetry.flow_creation_span(event.flow_name)
-            self.formatter.create_flow_tree(event.flow_name, str(source.flow_id))
+            tree = self.formatter.create_flow_tree(event.flow_name, str(source.flow_id))
+            self.formatter.current_flow_tree = tree
 
         @crewai_event_bus.on(FlowStartedEvent)
         def on_flow_started(source, event: FlowStartedEvent):
@@ -280,30 +309,36 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(MethodExecutionStartedEvent)
         def on_method_execution_started(source, event: MethodExecutionStartedEvent):
-            self.formatter.update_method_status(
-                self.formatter.current_method_branch,
+            method_branch = self.method_branches.get(event.method_name)
+            updated_branch = self.formatter.update_method_status(
+                method_branch,
                 self.formatter.current_flow_tree,
                 event.method_name,
                 "running",
             )
+            self.method_branches[event.method_name] = updated_branch
 
         @crewai_event_bus.on(MethodExecutionFinishedEvent)
         def on_method_execution_finished(source, event: MethodExecutionFinishedEvent):
-            self.formatter.update_method_status(
-                self.formatter.current_method_branch,
+            method_branch = self.method_branches.get(event.method_name)
+            updated_branch = self.formatter.update_method_status(
+                method_branch,
                 self.formatter.current_flow_tree,
                 event.method_name,
                 "completed",
             )
+            self.method_branches[event.method_name] = updated_branch
 
         @crewai_event_bus.on(MethodExecutionFailedEvent)
         def on_method_execution_failed(source, event: MethodExecutionFailedEvent):
-            self.formatter.update_method_status(
-                self.formatter.current_method_branch,
+            method_branch = self.method_branches.get(event.method_name)
+            updated_branch = self.formatter.update_method_status(
+                method_branch,
                 self.formatter.current_flow_tree,
                 event.method_name,
                 "failed",
             )
+            self.method_branches[event.method_name] = updated_branch
 
         # ----------- TOOL USAGE EVENTS -----------
 
@@ -524,5 +559,61 @@ class EventListener(BaseEventListener):
                 event.verbose,
             )
 
+        @crewai_event_bus.on(A2ADelegationStartedEvent)
+        def on_a2a_delegation_started(source, event: A2ADelegationStartedEvent):
+            self.formatter.handle_a2a_delegation_started(
+                event.endpoint,
+                event.task_description,
+                event.agent_id,
+                event.is_multiturn,
+                event.turn_number,
+            )
+
+        @crewai_event_bus.on(A2ADelegationCompletedEvent)
+        def on_a2a_delegation_completed(source, event: A2ADelegationCompletedEvent):
+            self.formatter.handle_a2a_delegation_completed(
+                event.status,
+                event.result,
+                event.error,
+                event.is_multiturn,
+            )
+
+        @crewai_event_bus.on(A2AConversationStartedEvent)
+        def on_a2a_conversation_started(source, event: A2AConversationStartedEvent):
+            # Store A2A agent name for display in conversation tree
+            if event.a2a_agent_name:
+                self.formatter._current_a2a_agent_name = event.a2a_agent_name
+
+            self.formatter.handle_a2a_conversation_started(
+                event.agent_id,
+                event.endpoint,
+            )
+
+        @crewai_event_bus.on(A2AMessageSentEvent)
+        def on_a2a_message_sent(source, event: A2AMessageSentEvent):
+            self.formatter.handle_a2a_message_sent(
+                event.message,
+                event.turn_number,
+                event.agent_role,
+            )
+
+        @crewai_event_bus.on(A2AResponseReceivedEvent)
+        def on_a2a_response_received(source, event: A2AResponseReceivedEvent):
+            self.formatter.handle_a2a_response_received(
+                event.response,
+                event.turn_number,
+                event.status,
+                event.agent_role,
+            )
+
+        @crewai_event_bus.on(A2AConversationCompletedEvent)
+        def on_a2a_conversation_completed(source, event: A2AConversationCompletedEvent):
+            self.formatter.handle_a2a_conversation_completed(
+                event.status,
+                event.final_result,
+                event.error,
+                event.total_turns,
+            )
+
 
 event_listener = EventListener()

lib/crewai/src/crewai/events/listeners/memory_listener.py

@@ -1,106 +0,0 @@
-from crewai.events.base_event_listener import BaseEventListener
-from crewai.events.types.memory_events import (
-    MemoryQueryCompletedEvent,
-    MemoryQueryFailedEvent,
-    MemoryRetrievalCompletedEvent,
-    MemoryRetrievalStartedEvent,
-    MemorySaveCompletedEvent,
-    MemorySaveFailedEvent,
-    MemorySaveStartedEvent,
-)
-
-
-class MemoryListener(BaseEventListener):
-    def __init__(self, formatter):
-        super().__init__()
-        self.formatter = formatter
-        self.memory_retrieval_in_progress = False
-        self.memory_save_in_progress = False
-
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(MemoryRetrievalStartedEvent)
-        def on_memory_retrieval_started(source, event: MemoryRetrievalStartedEvent):
-            if self.memory_retrieval_in_progress:
-                return
-
-            self.memory_retrieval_in_progress = True
-
-            self.formatter.handle_memory_retrieval_started(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
-        def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent):
-            if not self.memory_retrieval_in_progress:
-                return
-
-            self.memory_retrieval_in_progress = False
-            self.formatter.handle_memory_retrieval_completed(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-                event.memory_content,
-                event.retrieval_time_ms,
-            )
-
-        @crewai_event_bus.on(MemoryQueryCompletedEvent)
-        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
-            if not self.memory_retrieval_in_progress:
-                return
-
-            self.formatter.handle_memory_query_completed(
-                self.formatter.current_agent_branch,
-                event.source_type,
-                event.query_time_ms,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(MemoryQueryFailedEvent)
-        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
-            if not self.memory_retrieval_in_progress:
-                return
-
-            self.formatter.handle_memory_query_failed(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-                event.error,
-                event.source_type,
-            )
-
-        @crewai_event_bus.on(MemorySaveStartedEvent)
-        def on_memory_save_started(source, event: MemorySaveStartedEvent):
-            if self.memory_save_in_progress:
-                return
-
-            self.memory_save_in_progress = True
-
-            self.formatter.handle_memory_save_started(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(MemorySaveCompletedEvent)
-        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
-            if not self.memory_save_in_progress:
-                return
-
-            self.memory_save_in_progress = False
-
-            self.formatter.handle_memory_save_completed(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-                event.save_time_ms,
-                event.source_type,
-            )
-
-        @crewai_event_bus.on(MemorySaveFailedEvent)
-        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
-            if not self.memory_save_in_progress:
-                return
-
-            self.formatter.handle_memory_save_failed(
-                self.formatter.current_agent_branch,
-                event.error,
-                event.source_type,
-                self.formatter.current_crew_tree,
-            )

lib/crewai/src/crewai/events/listeners/tracing/first_time_trace_handler.py

@@ -73,15 +73,19 @@ class FirstTimeTraceHandler:
         self.is_first_time = should_auto_collect_first_time_traces()
         return self.is_first_time
 
-    def set_batch_manager(self, batch_manager: TraceBatchManager):
-        """Set reference to batch manager for sending events."""
+    def set_batch_manager(self, batch_manager: TraceBatchManager) -> None:
+        """Set reference to batch manager for sending events.
+
+        Args:
+            batch_manager: The trace batch manager instance.
+        """
         self.batch_manager = batch_manager
 
-    def mark_events_collected(self):
+    def mark_events_collected(self) -> None:
         """Mark that events have been collected during execution."""
         self.collected_events = True
 
-    def handle_execution_completion(self):
+    def handle_execution_completion(self) -> None:
         """Handle the completion flow as shown in your diagram."""
         if not self.is_first_time or not self.collected_events:
             return

lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py

@@ -44,6 +44,7 @@ class TraceBatchManager:
 
     def __init__(self) -> None:
         self._init_lock = Lock()
+        self._batch_ready_cv = Condition(self._init_lock)
         self._pending_events_lock = Lock()
         self._pending_events_cv = Condition(self._pending_events_lock)
         self._pending_events_count = 0
@@ -94,6 +95,8 @@ class TraceBatchManager:
                 )
                 self.backend_initialized = True
 
+            self._batch_ready_cv.notify_all()
+
             return self.current_batch
 
     def _initialize_backend_batch(
@@ -161,13 +164,13 @@ class TraceBatchManager:
                 f"Error initializing trace batch: {e}. Continuing without tracing."
             )
 
-    def begin_event_processing(self):
-        """Mark that an event handler started processing (for synchronization)"""
+    def begin_event_processing(self) -> None:
+        """Mark that an event handler started processing (for synchronization)."""
         with self._pending_events_lock:
             self._pending_events_count += 1
 
-    def end_event_processing(self):
-        """Mark that an event handler finished processing (for synchronization)"""
+    def end_event_processing(self) -> None:
+        """Mark that an event handler finished processing (for synchronization)."""
         with self._pending_events_cv:
             self._pending_events_count -= 1
             if self._pending_events_count == 0:
@@ -385,6 +388,22 @@ class TraceBatchManager:
         """Check if batch is initialized"""
         return self.current_batch is not None
 
+    def wait_for_batch_initialization(self, timeout: float = 2.0) -> bool:
+        """Wait for batch to be initialized.
+
+        Args:
+            timeout: Maximum time to wait in seconds (default: 2.0)
+
+        Returns:
+            True if batch was initialized, False if timeout occurred
+        """
+        with self._batch_ready_cv:
+            if self.current_batch is not None:
+                return True
+            return self._batch_ready_cv.wait_for(
+                lambda: self.current_batch is not None, timeout=timeout
+            )
+
     def record_start_time(self, key: str):
         """Record start time for duration calculation"""
         self.execution_start_times[key] = datetime.now(timezone.utc)

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -1,10 +1,16 @@
+"""Trace collection listener for orchestrating trace collection."""
+
 import os
 from typing import Any, ClassVar
 import uuid
 
+from typing_extensions import Self
+
 from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.version import get_crewai_version
 from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import CrewAIEventsBus
+from crewai.events.utils.console_formatter import ConsoleFormatter
 from crewai.events.listeners.tracing.first_time_trace_handler import (
     FirstTimeTraceHandler,
 )
@@ -53,6 +59,8 @@ from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
     MemoryQueryStartedEvent,
+    MemoryRetrievalCompletedEvent,
+    MemoryRetrievalStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
     MemorySaveStartedEvent,
@@ -75,9 +83,7 @@ from crewai.events.types.tool_usage_events import (
 
 
 class TraceCollectionListener(BaseEventListener):
-    """
-    Trace collection listener that orchestrates trace collection
-    """
+    """Trace collection listener that orchestrates trace collection."""
 
     complex_events: ClassVar[list[str]] = [
         "task_started",
@@ -88,11 +94,12 @@ class TraceCollectionListener(BaseEventListener):
         "agent_execution_completed",
     ]
 
-    _instance = None
-    _initialized = False
-    _listeners_setup = False
+    _instance: Self | None = None
+    _initialized: bool = False
+    _listeners_setup: bool = False
 
-    def __new__(cls, batch_manager: TraceBatchManager | None = None):
+    def __new__(cls, batch_manager: TraceBatchManager | None = None) -> Self:
+        """Create or return singleton instance."""
         if cls._instance is None:
             cls._instance = super().__new__(cls)
         return cls._instance
@@ -100,7 +107,14 @@ class TraceCollectionListener(BaseEventListener):
     def __init__(
         self,
         batch_manager: TraceBatchManager | None = None,
-    ):
+        formatter: ConsoleFormatter | None = None,
+    ) -> None:
+        """Initialize trace collection listener.
+
+        Args:
+            batch_manager: Optional trace batch manager instance.
+            formatter: Optional console formatter for output.
+        """
         if self._initialized:
             return
 
@@ -108,19 +122,22 @@ class TraceCollectionListener(BaseEventListener):
         self.batch_manager = batch_manager or TraceBatchManager()
         self._initialized = True
         self.first_time_handler = FirstTimeTraceHandler()
+        self.formatter = formatter
+        self.memory_retrieval_in_progress = False
+        self.memory_save_in_progress = False
 
         if self.first_time_handler.initialize_for_first_time_user():
             self.first_time_handler.set_batch_manager(self.batch_manager)
 
     def _check_authenticated(self) -> bool:
-        """Check if tracing should be enabled"""
+        """Check if tracing should be enabled."""
         try:
             return bool(get_auth_token())
         except AuthError:
             return False
 
     def _get_user_context(self) -> dict[str, str]:
-        """Extract user context for tracing"""
+        """Extract user context for tracing."""
         return {
             "user_id": os.getenv("CREWAI_USER_ID", "anonymous"),
             "organization_id": os.getenv("CREWAI_ORG_ID", ""),
@@ -128,9 +145,12 @@ class TraceCollectionListener(BaseEventListener):
             "trace_id": str(uuid.uuid4()),
         }
 
-    def setup_listeners(self, crewai_event_bus):
-        """Setup event listeners - delegates to specific handlers"""
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
+        """Setup event listeners - delegates to specific handlers.
 
+        Args:
+            crewai_event_bus: The event bus to register listeners on.
+        """
         if self._listeners_setup:
             return
 
@@ -140,50 +160,52 @@ class TraceCollectionListener(BaseEventListener):
 
         self._listeners_setup = True
 
-    def _register_flow_event_handlers(self, event_bus):
-        """Register handlers for flow events"""
+    def _register_flow_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
+        """Register handlers for flow events."""
 
         @event_bus.on(FlowCreatedEvent)
-        def on_flow_created(source, event):
+        def on_flow_created(source: Any, event: FlowCreatedEvent) -> None:
             pass
 
         @event_bus.on(FlowStartedEvent)
-        def on_flow_started(source, event):
+        def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
             if not self.batch_manager.is_batch_initialized():
                 self._initialize_flow_batch(source, event)
             self._handle_trace_event("flow_started", source, event)
 
         @event_bus.on(MethodExecutionStartedEvent)
-        def on_method_started(source, event):
+        def on_method_started(source: Any, event: MethodExecutionStartedEvent) -> None:
             self._handle_trace_event("method_execution_started", source, event)
 
         @event_bus.on(MethodExecutionFinishedEvent)
-        def on_method_finished(source, event):
+        def on_method_finished(
+            source: Any, event: MethodExecutionFinishedEvent
+        ) -> None:
             self._handle_trace_event("method_execution_finished", source, event)
 
         @event_bus.on(MethodExecutionFailedEvent)
-        def on_method_failed(source, event):
+        def on_method_failed(source: Any, event: MethodExecutionFailedEvent) -> None:
             self._handle_trace_event("method_execution_failed", source, event)
 
         @event_bus.on(FlowFinishedEvent)
-        def on_flow_finished(source, event):
+        def on_flow_finished(source: Any, event: FlowFinishedEvent) -> None:
             self._handle_trace_event("flow_finished", source, event)
 
         @event_bus.on(FlowPlotEvent)
-        def on_flow_plot(source, event):
+        def on_flow_plot(source: Any, event: FlowPlotEvent) -> None:
             self._handle_action_event("flow_plot", source, event)
 
-    def _register_context_event_handlers(self, event_bus):
-        """Register handlers for context events (start/end)"""
+    def _register_context_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
+        """Register handlers for context events (start/end)."""
 
         @event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
+        def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
             if not self.batch_manager.is_batch_initialized():
                 self._initialize_crew_batch(source, event)
             self._handle_trace_event("crew_kickoff_started", source, event)
 
         @event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
+        def on_crew_completed(source: Any, event: CrewKickoffCompletedEvent) -> None:
             self._handle_trace_event("crew_kickoff_completed", source, event)
             if self.batch_manager.batch_owner_type == "crew":
                 if self.first_time_handler.is_first_time:
@@ -193,7 +215,7 @@ class TraceCollectionListener(BaseEventListener):
                     self.batch_manager.finalize_batch()
 
         @event_bus.on(CrewKickoffFailedEvent)
-        def on_crew_failed(source, event):
+        def on_crew_failed(source: Any, event: CrewKickoffFailedEvent) -> None:
             self._handle_trace_event("crew_kickoff_failed", source, event)
             if self.first_time_handler.is_first_time:
                 self.first_time_handler.mark_events_collected()
@@ -202,134 +224,245 @@ class TraceCollectionListener(BaseEventListener):
                 self.batch_manager.finalize_batch()
 
         @event_bus.on(TaskStartedEvent)
-        def on_task_started(source, event):
+        def on_task_started(source: Any, event: TaskStartedEvent) -> None:
             self._handle_trace_event("task_started", source, event)
 
         @event_bus.on(TaskCompletedEvent)
-        def on_task_completed(source, event):
+        def on_task_completed(source: Any, event: TaskCompletedEvent) -> None:
             self._handle_trace_event("task_completed", source, event)
 
         @event_bus.on(TaskFailedEvent)
-        def on_task_failed(source, event):
+        def on_task_failed(source: Any, event: TaskFailedEvent) -> None:
             self._handle_trace_event("task_failed", source, event)
 
         @event_bus.on(AgentExecutionStartedEvent)
-        def on_agent_started(source, event):
+        def on_agent_started(source: Any, event: AgentExecutionStartedEvent) -> None:
             self._handle_trace_event("agent_execution_started", source, event)
 
         @event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_completed(source, event):
+        def on_agent_completed(
+            source: Any, event: AgentExecutionCompletedEvent
+        ) -> None:
             self._handle_trace_event("agent_execution_completed", source, event)
 
         @event_bus.on(LiteAgentExecutionStartedEvent)
-        def on_lite_agent_started(source, event):
+        def on_lite_agent_started(
+            source: Any, event: LiteAgentExecutionStartedEvent
+        ) -> None:
             self._handle_trace_event("lite_agent_execution_started", source, event)
 
         @event_bus.on(LiteAgentExecutionCompletedEvent)
-        def on_lite_agent_completed(source, event):
+        def on_lite_agent_completed(
+            source: Any, event: LiteAgentExecutionCompletedEvent
+        ) -> None:
             self._handle_trace_event("lite_agent_execution_completed", source, event)
 
         @event_bus.on(LiteAgentExecutionErrorEvent)
-        def on_lite_agent_error(source, event):
+        def on_lite_agent_error(
+            source: Any, event: LiteAgentExecutionErrorEvent
+        ) -> None:
             self._handle_trace_event("lite_agent_execution_error", source, event)
 
         @event_bus.on(AgentExecutionErrorEvent)
-        def on_agent_error(source, event):
+        def on_agent_error(source: Any, event: AgentExecutionErrorEvent) -> None:
             self._handle_trace_event("agent_execution_error", source, event)
 
         @event_bus.on(LLMGuardrailStartedEvent)
-        def on_guardrail_started(source, event):
+        def on_guardrail_started(source: Any, event: LLMGuardrailStartedEvent) -> None:
             self._handle_trace_event("llm_guardrail_started", source, event)
 
         @event_bus.on(LLMGuardrailCompletedEvent)
-        def on_guardrail_completed(source, event):
+        def on_guardrail_completed(
+            source: Any, event: LLMGuardrailCompletedEvent
+        ) -> None:
             self._handle_trace_event("llm_guardrail_completed", source, event)
 
-    def _register_action_event_handlers(self, event_bus):
-        """Register handlers for action events (LLM calls, tool usage)"""
+    def _register_action_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
+        """Register handlers for action events (LLM calls, tool usage)."""
 
         @event_bus.on(LLMCallStartedEvent)
-        def on_llm_call_started(source, event):
+        def on_llm_call_started(source: Any, event: LLMCallStartedEvent) -> None:
             self._handle_action_event("llm_call_started", source, event)
 
         @event_bus.on(LLMCallCompletedEvent)
-        def on_llm_call_completed(source, event):
+        def on_llm_call_completed(source: Any, event: LLMCallCompletedEvent) -> None:
             self._handle_action_event("llm_call_completed", source, event)
 
         @event_bus.on(LLMCallFailedEvent)
-        def on_llm_call_failed(source, event):
+        def on_llm_call_failed(source: Any, event: LLMCallFailedEvent) -> None:
             self._handle_action_event("llm_call_failed", source, event)
 
         @event_bus.on(ToolUsageStartedEvent)
-        def on_tool_started(source, event):
+        def on_tool_started(source: Any, event: ToolUsageStartedEvent) -> None:
             self._handle_action_event("tool_usage_started", source, event)
 
         @event_bus.on(ToolUsageFinishedEvent)
-        def on_tool_finished(source, event):
+        def on_tool_finished(source: Any, event: ToolUsageFinishedEvent) -> None:
             self._handle_action_event("tool_usage_finished", source, event)
 
         @event_bus.on(ToolUsageErrorEvent)
-        def on_tool_error(source, event):
+        def on_tool_error(source: Any, event: ToolUsageErrorEvent) -> None:
             self._handle_action_event("tool_usage_error", source, event)
 
         @event_bus.on(MemoryQueryStartedEvent)
-        def on_memory_query_started(source, event):
+        def on_memory_query_started(
+            source: Any, event: MemoryQueryStartedEvent
+        ) -> None:
             self._handle_action_event("memory_query_started", source, event)
 
         @event_bus.on(MemoryQueryCompletedEvent)
-        def on_memory_query_completed(source, event):
+        def on_memory_query_completed(
+            source: Any, event: MemoryQueryCompletedEvent
+        ) -> None:
             self._handle_action_event("memory_query_completed", source, event)
+            if self.formatter and self.memory_retrieval_in_progress:
+                self.formatter.handle_memory_query_completed(
+                    self.formatter.current_agent_branch,
+                    event.source_type or "memory",
+                    event.query_time_ms,
+                    self.formatter.current_crew_tree,
+                )
 
         @event_bus.on(MemoryQueryFailedEvent)
-        def on_memory_query_failed(source, event):
+        def on_memory_query_failed(source: Any, event: MemoryQueryFailedEvent) -> None:
             self._handle_action_event("memory_query_failed", source, event)
+            if self.formatter and self.memory_retrieval_in_progress:
+                self.formatter.handle_memory_query_failed(
+                    self.formatter.current_agent_branch,
+                    self.formatter.current_crew_tree,
+                    event.error,
+                    event.source_type or "memory",
+                )
 
         @event_bus.on(MemorySaveStartedEvent)
-        def on_memory_save_started(source, event):
+        def on_memory_save_started(source: Any, event: MemorySaveStartedEvent) -> None:
             self._handle_action_event("memory_save_started", source, event)
+            if self.formatter:
+                if self.memory_save_in_progress:
+                    return
+
+                self.memory_save_in_progress = True
+
+                self.formatter.handle_memory_save_started(
+                    self.formatter.current_agent_branch,
+                    self.formatter.current_crew_tree,
+                )
 
         @event_bus.on(MemorySaveCompletedEvent)
-        def on_memory_save_completed(source, event):
+        def on_memory_save_completed(
+            source: Any, event: MemorySaveCompletedEvent
+        ) -> None:
             self._handle_action_event("memory_save_completed", source, event)
+            if self.formatter:
+                if not self.memory_save_in_progress:
+                    return
+
+                self.memory_save_in_progress = False
+
+                self.formatter.handle_memory_save_completed(
+                    self.formatter.current_agent_branch,
+                    self.formatter.current_crew_tree,
+                    event.save_time_ms,
+                    event.source_type or "memory",
+                )
 
         @event_bus.on(MemorySaveFailedEvent)
-        def on_memory_save_failed(source, event):
+        def on_memory_save_failed(source: Any, event: MemorySaveFailedEvent) -> None:
             self._handle_action_event("memory_save_failed", source, event)
+            if self.formatter and self.memory_save_in_progress:
+                self.formatter.handle_memory_save_failed(
+                    self.formatter.current_agent_branch,
+                    event.error,
+                    event.source_type or "memory",
+                    self.formatter.current_crew_tree,
+                )
+
+        @event_bus.on(MemoryRetrievalStartedEvent)
+        def on_memory_retrieval_started(
+            source: Any, event: MemoryRetrievalStartedEvent
+        ) -> None:
+            if self.formatter:
+                if self.memory_retrieval_in_progress:
+                    return
+
+                self.memory_retrieval_in_progress = True
+
+                self.formatter.handle_memory_retrieval_started(
+                    self.formatter.current_agent_branch,
+                    self.formatter.current_crew_tree,
+                )
+
+        @event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed(
+            source: Any, event: MemoryRetrievalCompletedEvent
+        ) -> None:
+            if self.formatter:
+                if not self.memory_retrieval_in_progress:
+                    return
+
+                self.memory_retrieval_in_progress = False
+                self.formatter.handle_memory_retrieval_completed(
+                    self.formatter.current_agent_branch,
+                    self.formatter.current_crew_tree,
+                    event.memory_content,
+                    event.retrieval_time_ms,
+                )
 
         @event_bus.on(AgentReasoningStartedEvent)
-        def on_agent_reasoning_started(source, event):
+        def on_agent_reasoning_started(
+            source: Any, event: AgentReasoningStartedEvent
+        ) -> None:
             self._handle_action_event("agent_reasoning_started", source, event)
 
         @event_bus.on(AgentReasoningCompletedEvent)
-        def on_agent_reasoning_completed(source, event):
+        def on_agent_reasoning_completed(
+            source: Any, event: AgentReasoningCompletedEvent
+        ) -> None:
             self._handle_action_event("agent_reasoning_completed", source, event)
 
         @event_bus.on(AgentReasoningFailedEvent)
-        def on_agent_reasoning_failed(source, event):
+        def on_agent_reasoning_failed(
+            source: Any, event: AgentReasoningFailedEvent
+        ) -> None:
             self._handle_action_event("agent_reasoning_failed", source, event)
 
         @event_bus.on(KnowledgeRetrievalStartedEvent)
-        def on_knowledge_retrieval_started(source, event):
+        def on_knowledge_retrieval_started(
+            source: Any, event: KnowledgeRetrievalStartedEvent
+        ) -> None:
             self._handle_action_event("knowledge_retrieval_started", source, event)
 
         @event_bus.on(KnowledgeRetrievalCompletedEvent)
-        def on_knowledge_retrieval_completed(source, event):
+        def on_knowledge_retrieval_completed(
+            source: Any, event: KnowledgeRetrievalCompletedEvent
+        ) -> None:
             self._handle_action_event("knowledge_retrieval_completed", source, event)
 
         @event_bus.on(KnowledgeQueryStartedEvent)
-        def on_knowledge_query_started(source, event):
+        def on_knowledge_query_started(
+            source: Any, event: KnowledgeQueryStartedEvent
+        ) -> None:
             self._handle_action_event("knowledge_query_started", source, event)
 
         @event_bus.on(KnowledgeQueryCompletedEvent)
-        def on_knowledge_query_completed(source, event):
+        def on_knowledge_query_completed(
+            source: Any, event: KnowledgeQueryCompletedEvent
+        ) -> None:
             self._handle_action_event("knowledge_query_completed", source, event)
 
         @event_bus.on(KnowledgeQueryFailedEvent)
-        def on_knowledge_query_failed(source, event):
+        def on_knowledge_query_failed(
+            source: Any, event: KnowledgeQueryFailedEvent
+        ) -> None:
             self._handle_action_event("knowledge_query_failed", source, event)
 
-    def _initialize_crew_batch(self, source: Any, event: Any):
-        """Initialize trace batch"""
+    def _initialize_crew_batch(self, source: Any, event: Any) -> None:
+        """Initialize trace batch.
+
+        Args:
+            source: Source object that triggered the event.
+            event: Event object containing crew information.
+        """
         user_context = self._get_user_context()
         execution_metadata = {
             "crew_name": getattr(event, "crew_name", "Unknown Crew"),
@@ -342,8 +475,13 @@ class TraceCollectionListener(BaseEventListener):
 
         self._initialize_batch(user_context, execution_metadata)
 
-    def _initialize_flow_batch(self, source: Any, event: Any):
-        """Initialize trace batch for Flow execution"""
+    def _initialize_flow_batch(self, source: Any, event: Any) -> None:
+        """Initialize trace batch for Flow execution.
+
+        Args:
+            source: Source object that triggered the event.
+            event: Event object containing flow information.
+        """
         user_context = self._get_user_context()
         execution_metadata = {
             "flow_name": getattr(event, "flow_name", "Unknown Flow"),
@@ -359,21 +497,32 @@ class TraceCollectionListener(BaseEventListener):
 
     def _initialize_batch(
         self, user_context: dict[str, str], execution_metadata: dict[str, Any]
-    ):
-        """Initialize trace batch - auto-enable ephemeral for first-time users."""
+    ) -> None:
+        """Initialize trace batch - auto-enable ephemeral for first-time users.
 
+        Args:
+            user_context: User context information.
+            execution_metadata: Metadata about the execution.
+        """
         if self.first_time_handler.is_first_time:
-            return self.batch_manager.initialize_batch(
+            self.batch_manager.initialize_batch(
                 user_context, execution_metadata, use_ephemeral=True
             )
+            return
 
         use_ephemeral = not self._check_authenticated()
-        return self.batch_manager.initialize_batch(
+        self.batch_manager.initialize_batch(
             user_context, execution_metadata, use_ephemeral=use_ephemeral
         )
 
-    def _handle_trace_event(self, event_type: str, source: Any, event: Any):
-        """Generic handler for context end events"""
+    def _handle_trace_event(self, event_type: str, source: Any, event: Any) -> None:
+        """Generic handler for context end events.
+
+        Args:
+            event_type: Type of the event.
+            source: Source object that triggered the event.
+            event: Event object.
+        """
         self.batch_manager.begin_event_processing()
         try:
             trace_event = self._create_trace_event(event_type, source, event)
@@ -381,9 +530,14 @@ class TraceCollectionListener(BaseEventListener):
         finally:
             self.batch_manager.end_event_processing()
 
-    def _handle_action_event(self, event_type: str, source: Any, event: Any):
-        """Generic handler for action events (LLM calls, tool usage)"""
+    def _handle_action_event(self, event_type: str, source: Any, event: Any) -> None:
+        """Generic handler for action events (LLM calls, tool usage).
 
+        Args:
+            event_type: Type of the event.
+            source: Source object that triggered the event.
+            event: Event object.
+        """
         if not self.batch_manager.is_batch_initialized():
             user_context = self._get_user_context()
             execution_metadata = {

lib/crewai/src/crewai/events/types/a2a_events.py

@@ -0,0 +1,141 @@
+"""Events for A2A (Agent-to-Agent) delegation.
+
+This module defines events emitted during A2A protocol delegation,
+including both single-turn and multiturn conversation flows.
+"""
+
+from typing import Any, Literal
+
+from crewai.events.base_events import BaseEvent
+
+
+class A2AEventBase(BaseEvent):
+    """Base class for A2A events with task/agent context."""
+
+    from_task: Any | None = None
+    from_agent: Any | None = None
+
+    def __init__(self, **data):
+        """Initialize A2A event, extracting task and agent metadata."""
+        if data.get("from_task"):
+            task = data["from_task"]
+            data["task_id"] = str(task.id)
+            data["task_name"] = task.name or task.description
+            data["from_task"] = None
+
+        if data.get("from_agent"):
+            agent = data["from_agent"]
+            data["agent_id"] = str(agent.id)
+            data["agent_role"] = agent.role
+            data["from_agent"] = None
+
+        super().__init__(**data)
+
+
+class A2ADelegationStartedEvent(A2AEventBase):
+    """Event emitted when A2A delegation starts.
+
+    Attributes:
+        endpoint: A2A agent endpoint URL (AgentCard URL)
+        task_description: Task being delegated to the A2A agent
+        agent_id: A2A agent identifier
+        is_multiturn: Whether this is part of a multiturn conversation
+        turn_number: Current turn number (1-indexed, 1 for single-turn)
+    """
+
+    type: str = "a2a_delegation_started"
+    endpoint: str
+    task_description: str
+    agent_id: str
+    is_multiturn: bool = False
+    turn_number: int = 1
+
+
+class A2ADelegationCompletedEvent(A2AEventBase):
+    """Event emitted when A2A delegation completes.
+
+    Attributes:
+        status: Completion status (completed, input_required, failed, etc.)
+        result: Result message if status is completed
+        error: Error/response message (error for failed, response for input_required)
+        is_multiturn: Whether this is part of a multiturn conversation
+    """
+
+    type: str = "a2a_delegation_completed"
+    status: str
+    result: str | None = None
+    error: str | None = None
+    is_multiturn: bool = False
+
+
+class A2AConversationStartedEvent(A2AEventBase):
+    """Event emitted when a multiturn A2A conversation starts.
+
+    This is emitted once at the beginning of a multiturn conversation,
+    before the first message exchange.
+
+    Attributes:
+        agent_id: A2A agent identifier
+        endpoint: A2A agent endpoint URL
+        a2a_agent_name: Name of the A2A agent from agent card
+    """
+
+    type: str = "a2a_conversation_started"
+    agent_id: str
+    endpoint: str
+    a2a_agent_name: str | None = None
+
+
+class A2AMessageSentEvent(A2AEventBase):
+    """Event emitted when a message is sent to the A2A agent.
+
+    Attributes:
+        message: Message content sent to the A2A agent
+        turn_number: Current turn number (1-indexed)
+        is_multiturn: Whether this is part of a multiturn conversation
+        agent_role: Role of the CrewAI agent sending the message
+    """
+
+    type: str = "a2a_message_sent"
+    message: str
+    turn_number: int
+    is_multiturn: bool = False
+    agent_role: str | None = None
+
+
+class A2AResponseReceivedEvent(A2AEventBase):
+    """Event emitted when a response is received from the A2A agent.
+
+    Attributes:
+        response: Response content from the A2A agent
+        turn_number: Current turn number (1-indexed)
+        is_multiturn: Whether this is part of a multiturn conversation
+        status: Response status (input_required, completed, etc.)
+        agent_role: Role of the CrewAI agent (for display)
+    """
+
+    type: str = "a2a_response_received"
+    response: str
+    turn_number: int
+    is_multiturn: bool = False
+    status: str
+    agent_role: str | None = None
+
+
+class A2AConversationCompletedEvent(A2AEventBase):
+    """Event emitted when a multiturn A2A conversation completes.
+
+    This is emitted once at the end of a multiturn conversation.
+
+    Attributes:
+        status: Final status (completed, failed, etc.)
+        final_result: Final result if completed successfully
+        error: Error message if failed
+        total_turns: Total number of turns in the conversation
+    """
+
+    type: str = "a2a_conversation_completed"
+    status: Literal["completed", "failed"]
+    final_result: str | None = None
+    error: str | None = None
+    total_turns: int

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -17,9 +17,16 @@ class ConsoleFormatter:
     current_method_branch: Tree | None = None
     current_lite_agent_branch: Tree | None = None
     tool_usage_counts: ClassVar[dict[str, int]] = {}
-    current_reasoning_branch: Tree | None = None  # Track reasoning status
+    current_reasoning_branch: Tree | None = None
     _live_paused: bool = False
     current_llm_tool_tree: Tree | None = None
+    current_a2a_conversation_branch: Tree | None = None
+    current_a2a_turn_count: int = 0
+    _pending_a2a_message: str | None = None
+    _pending_a2a_agent_role: str | None = None
+    _pending_a2a_turn_number: int | None = None
+    _a2a_turn_branches: ClassVar[dict[int, Tree]] = {}
+    _current_a2a_agent_name: str | None = None
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
@@ -192,7 +199,12 @@ class ConsoleFormatter:
             style,
             ID=source_id,
         )
-        content.append(f"Final Output: {final_string_output}\n", style="white")
+
+        if status == "failed" and final_string_output:
+            content.append("Error:\n", style="white bold")
+            content.append(f"{final_string_output}\n", style="red")
+        else:
+            content.append(f"Final Output: {final_string_output}\n", style="white")
 
         self.print_panel(content, title, style)
 
@@ -357,7 +369,14 @@ class ConsoleFormatter:
         return flow_tree
 
     def start_flow(self, flow_name: str, flow_id: str) -> Tree | None:
-        """Initialize a flow execution tree."""
+        """Initialize or update a flow execution tree."""
+        if self.current_flow_tree is not None:
+            for child in self.current_flow_tree.children:
+                if "Starting Flow" in str(child.label):
+                    child.label = Text("🚀 Flow Started", style="green")
+                    break
+            return self.current_flow_tree
+
         flow_tree = Tree("")
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
@@ -436,27 +455,38 @@ class ConsoleFormatter:
             prefix, style = "🔄 Running:", "yellow"
         elif status == "completed":
             prefix, style = "✅ Completed:", "green"
-            # Update initialization node when a method completes successfully
             for child in flow_tree.children:
                 if "Starting Flow" in str(child.label):
                     child.label = Text("Flow Method Step", style="white")
                     break
         else:
             prefix, style = "❌ Failed:", "red"
-            # Update initialization node on failure
             for child in flow_tree.children:
                 if "Starting Flow" in str(child.label):
                     child.label = Text("❌ Flow Step Failed", style="red")
                     break
 
-        if not method_branch:
-            # Find or create method branch
-            for branch in flow_tree.children:
-                if method_name in str(branch.label):
-                    method_branch = branch
-                    break
-            if not method_branch:
-                method_branch = flow_tree.add("")
+        if method_branch is not None:
+            if method_branch in flow_tree.children:
+                method_branch.label = Text(prefix, style=f"{style} bold") + Text(
+                    f" {method_name}", style=style
+                )
+                self.print(flow_tree)
+                self.print()
+                return method_branch
+
+        for branch in flow_tree.children:
+            label_str = str(branch.label)
+            if f" {method_name}" in label_str and (
+                "Running:" in label_str
+                or "Completed:" in label_str
+                or "Failed:" in label_str
+            ):
+                method_branch = branch
+                break
+
+        if method_branch is None:
+            method_branch = flow_tree.add("")
 
         method_branch.label = Text(prefix, style=f"{style} bold") + Text(
             f" {method_name}", style=style
@@ -464,6 +494,7 @@ class ConsoleFormatter:
 
         self.print(flow_tree)
         self.print()
+
         return method_branch
 
     def get_llm_tree(self, tool_name: str):
@@ -1455,22 +1486,37 @@ class ConsoleFormatter:
             self.print()
 
         elif isinstance(formatted_answer, AgentFinish):
-            # Create content for the finish panel
-            content = Text()
-            content.append("Agent: ", style="white")
-            content.append(f"{agent_role}\n\n", style="bright_green bold")
-            content.append("Final Answer:\n", style="white")
-            content.append(f"{formatted_answer.output}", style="bright_green")
+            is_a2a_delegation = False
+            try:
+                output_data = json.loads(formatted_answer.output)
+                if isinstance(output_data, dict):
+                    if output_data.get("is_a2a") is True:
+                        is_a2a_delegation = True
+                    elif "output" in output_data:
+                        nested_output = output_data["output"]
+                        if (
+                            isinstance(nested_output, dict)
+                            and nested_output.get("is_a2a") is True
+                        ):
+                            is_a2a_delegation = True
+            except (json.JSONDecodeError, TypeError, ValueError):
+                pass
 
-            # Create and display the finish panel
-            finish_panel = Panel(
-                content,
-                title="✅ Agent Final Answer",
-                border_style="green",
-                padding=(1, 2),
-            )
-            self.print(finish_panel)
-            self.print()
+            if not is_a2a_delegation:
+                content = Text()
+                content.append("Agent: ", style="white")
+                content.append(f"{agent_role}\n\n", style="bright_green bold")
+                content.append("Final Answer:\n", style="white")
+                content.append(f"{formatted_answer.output}", style="bright_green")
+
+                finish_panel = Panel(
+                    content,
+                    title="✅ Agent Final Answer",
+                    border_style="green",
+                    padding=(1, 2),
+                )
+                self.print(finish_panel)
+                self.print()
 
     def handle_memory_retrieval_started(
         self,
@@ -1770,3 +1816,435 @@ class ConsoleFormatter:
                 Attempts=f"{retry_count + 1}",
             )
             self.print_panel(content, "🛡️ Guardrail Failed", "red")
+
+    def handle_a2a_delegation_started(
+        self,
+        endpoint: str,
+        task_description: str,
+        agent_id: str,
+        is_multiturn: bool = False,
+        turn_number: int = 1,
+    ) -> None:
+        """Handle A2A delegation started event.
+
+        Args:
+            endpoint: A2A agent endpoint URL
+            task_description: Task being delegated
+            agent_id: A2A agent identifier
+            is_multiturn: Whether this is part of a multiturn conversation
+            turn_number: Current turn number in conversation (1-indexed)
+        """
+        branch_to_use = self.current_lite_agent_branch or self.current_task_branch
+        tree_to_use = self.current_crew_tree or branch_to_use
+        a2a_branch: Tree | None = None
+
+        if is_multiturn:
+            if self.current_a2a_turn_count == 0 and not isinstance(
+                self.current_a2a_conversation_branch, Tree
+            ):
+                if branch_to_use is not None and tree_to_use is not None:
+                    self.current_a2a_conversation_branch = branch_to_use.add("")
+                    self.update_tree_label(
+                        self.current_a2a_conversation_branch,
+                        "💬",
+                        f"Multiturn A2A Conversation ({agent_id})",
+                        "cyan",
+                    )
+                    self.print(tree_to_use)
+                    self.print()
+                else:
+                    self.current_a2a_conversation_branch = "MULTITURN_NO_TREE"
+
+                    content = Text()
+                    content.append(
+                        "Multiturn A2A Conversation Started\n\n", style="cyan bold"
+                    )
+                    content.append("Agent ID: ", style="white")
+                    content.append(f"{agent_id}\n", style="cyan")
+                    content.append("Note: ", style="white dim")
+                    content.append(
+                        "Conversation will be tracked in tree view", style="cyan dim"
+                    )
+
+                    panel = self.create_panel(
+                        content, "💬 Multiturn Conversation", "cyan"
+                    )
+                    self.print(panel)
+                    self.print()
+
+            self.current_a2a_turn_count = turn_number
+
+            return (
+                self.current_a2a_conversation_branch
+                if isinstance(self.current_a2a_conversation_branch, Tree)
+                else None
+            )
+
+        if branch_to_use is not None and tree_to_use is not None:
+            a2a_branch = branch_to_use.add("")
+            self.update_tree_label(
+                a2a_branch,
+                "🔗",
+                f"Delegating to A2A Agent ({agent_id})",
+                "cyan",
+            )
+
+            self.print(tree_to_use)
+            self.print()
+
+        content = Text()
+        content.append("A2A Delegation Started\n\n", style="cyan bold")
+        content.append("Agent ID: ", style="white")
+        content.append(f"{agent_id}\n", style="cyan")
+        content.append("Endpoint: ", style="white")
+        content.append(f"{endpoint}\n\n", style="cyan dim")
+        content.append("Task Description:\n", style="white")
+
+        task_preview = (
+            task_description
+            if len(task_description) <= 200
+            else task_description[:197] + "..."
+        )
+        content.append(task_preview, style="cyan")
+
+        panel = self.create_panel(content, "🔗 A2A Delegation", "cyan")
+        self.print(panel)
+        self.print()
+
+        return a2a_branch
+
+    def handle_a2a_delegation_completed(
+        self,
+        status: str,
+        result: str | None = None,
+        error: str | None = None,
+        is_multiturn: bool = False,
+    ) -> None:
+        """Handle A2A delegation completed event.
+
+        Args:
+            status: Completion status
+            result: Optional result message
+            error: Optional error message (or response for input_required)
+            is_multiturn: Whether this is part of a multiturn conversation
+        """
+        tree_to_use = self.current_crew_tree or self.current_task_branch
+        a2a_branch = None
+
+        if is_multiturn and self.current_a2a_conversation_branch:
+            has_tree = isinstance(self.current_a2a_conversation_branch, Tree)
+
+            if status == "input_required" and error:
+                pass
+            elif status == "completed":
+                if has_tree:
+                    final_turn = self.current_a2a_conversation_branch.add("")
+                    self.update_tree_label(
+                        final_turn,
+                        "✅",
+                        "Conversation Completed",
+                        "green",
+                    )
+
+                    if tree_to_use:
+                        self.print(tree_to_use)
+                        self.print()
+
+                self.current_a2a_conversation_branch = None
+                self.current_a2a_turn_count = 0
+            elif status == "failed":
+                if has_tree:
+                    error_turn = self.current_a2a_conversation_branch.add("")
+                    error_msg = (
+                        error[:150] + "..." if error and len(error) > 150 else error
+                    )
+                    self.update_tree_label(
+                        error_turn,
+                        "❌",
+                        f"Failed: {error_msg}" if error else "Conversation Failed",
+                        "red",
+                    )
+
+                    if tree_to_use:
+                        self.print(tree_to_use)
+                        self.print()
+
+                self.current_a2a_conversation_branch = None
+                self.current_a2a_turn_count = 0
+
+            return
+
+        if a2a_branch and tree_to_use:
+            if status == "completed":
+                self.update_tree_label(
+                    a2a_branch,
+                    "✅",
+                    "A2A Delegation Completed",
+                    "green",
+                )
+            elif status == "failed":
+                self.update_tree_label(
+                    a2a_branch,
+                    "❌",
+                    "A2A Delegation Failed",
+                    "red",
+                )
+            else:
+                self.update_tree_label(
+                    a2a_branch,
+                    "⚠️",
+                    f"A2A Delegation {status.replace('_', ' ').title()}",
+                    "yellow",
+                )
+
+            self.print(tree_to_use)
+            self.print()
+
+        if status == "completed" and result:
+            content = Text()
+            content.append("A2A Delegation Completed\n\n", style="green bold")
+            content.append("Result:\n", style="white")
+
+            result_preview = result if len(result) <= 500 else result[:497] + "..."
+            content.append(result_preview, style="green")
+
+            panel = self.create_panel(content, "✅ A2A Success", "green")
+            self.print(panel)
+            self.print()
+        elif status == "input_required" and error:
+            content = Text()
+            content.append("A2A Response\n\n", style="cyan bold")
+            content.append("Message:\n", style="white")
+
+            response_preview = error if len(error) <= 500 else error[:497] + "..."
+            content.append(response_preview, style="cyan")
+
+            panel = self.create_panel(content, "💬 A2A Response", "cyan")
+            self.print(panel)
+            self.print()
+        elif error:
+            content = Text()
+            content.append(
+                "A2A Delegation Issue\n\n",
+                style="red bold" if status == "failed" else "yellow bold",
+            )
+            content.append("Status: ", style="white")
+            content.append(
+                f"{status}\n\n", style="red" if status == "failed" else "yellow"
+            )
+            content.append("Message:\n", style="white")
+            content.append(error, style="red" if status == "failed" else "yellow")
+
+            panel_style = "red" if status == "failed" else "yellow"
+            panel_title = "❌ A2A Failed" if status == "failed" else "⚠️ A2A Status"
+            panel = self.create_panel(content, panel_title, panel_style)
+            self.print(panel)
+            self.print()
+
+    def handle_a2a_conversation_started(
+        self,
+        agent_id: str,
+        endpoint: str,
+    ) -> None:
+        """Handle A2A conversation started event.
+
+        Args:
+            agent_id: A2A agent identifier
+            endpoint: A2A agent endpoint URL
+        """
+        branch_to_use = self.current_lite_agent_branch or self.current_task_branch
+        tree_to_use = self.current_crew_tree or branch_to_use
+
+        if not isinstance(self.current_a2a_conversation_branch, Tree):
+            if branch_to_use is not None and tree_to_use is not None:
+                self.current_a2a_conversation_branch = branch_to_use.add("")
+                self.update_tree_label(
+                    self.current_a2a_conversation_branch,
+                    "💬",
+                    f"Multiturn A2A Conversation ({agent_id})",
+                    "cyan",
+                )
+                self.print(tree_to_use)
+                self.print()
+            else:
+                self.current_a2a_conversation_branch = "MULTITURN_NO_TREE"
+
+    def handle_a2a_message_sent(
+        self,
+        message: str,
+        turn_number: int,
+        agent_role: str | None = None,
+    ) -> None:
+        """Handle A2A message sent event.
+
+        Args:
+            message: Message content sent to the A2A agent
+            turn_number: Current turn number
+            agent_role: Role of the CrewAI agent sending the message
+        """
+        self._pending_a2a_message = message
+        self._pending_a2a_agent_role = agent_role
+        self._pending_a2a_turn_number = turn_number
+
+    def handle_a2a_response_received(
+        self,
+        response: str,
+        turn_number: int,
+        status: str,
+        agent_role: str | None = None,
+    ) -> None:
+        """Handle A2A response received event.
+
+        Args:
+            response: Response content from the A2A agent
+            turn_number: Current turn number
+            status: Response status (input_required, completed, etc.)
+            agent_role: Role of the CrewAI agent (for display)
+        """
+        if self.current_a2a_conversation_branch and isinstance(
+            self.current_a2a_conversation_branch, Tree
+        ):
+            if turn_number in self._a2a_turn_branches:
+                turn_branch = self._a2a_turn_branches[turn_number]
+            else:
+                turn_branch = self.current_a2a_conversation_branch.add("")
+                self.update_tree_label(
+                    turn_branch,
+                    "💬",
+                    f"Turn {turn_number}",
+                    "cyan",
+                )
+                self._a2a_turn_branches[turn_number] = turn_branch
+
+            crewai_agent_role = self._pending_a2a_agent_role or agent_role or "User"
+            message_content = self._pending_a2a_message or "sent message"
+
+            message_preview = (
+                message_content[:100] + "..."
+                if len(message_content) > 100
+                else message_content
+            )
+
+            user_node = turn_branch.add("")
+            self.update_tree_label(
+                user_node,
+                f"{crewai_agent_role} 👤 :  ",
+                f'"{message_preview}"',
+                "blue",
+            )
+
+            agent_node = turn_branch.add("")
+            response_preview = (
+                response[:100] + "..." if len(response) > 100 else response
+            )
+
+            a2a_agent_display = f"{self._current_a2a_agent_name} \U0001f916: "
+
+            if status == "completed":
+                response_color = "green"
+                status_indicator = "✓"
+            elif status == "input_required":
+                response_color = "yellow"
+                status_indicator = "❓"
+            elif status == "failed":
+                response_color = "red"
+                status_indicator = "✗"
+            elif status == "auth_required":
+                response_color = "magenta"
+                status_indicator = "🔒"
+            elif status == "canceled":
+                response_color = "dim"
+                status_indicator = "⊘"
+            else:
+                response_color = "cyan"
+                status_indicator = ""
+
+            label = f'"{response_preview}"'
+            if status_indicator:
+                label = f"{status_indicator} {label}"
+
+            self.update_tree_label(
+                agent_node,
+                a2a_agent_display,
+                label,
+                response_color,
+            )
+
+            self._pending_a2a_message = None
+            self._pending_a2a_agent_role = None
+            self._pending_a2a_turn_number = None
+
+            tree_to_use = self.current_crew_tree or self.current_task_branch
+            if tree_to_use:
+                self.print(tree_to_use)
+                self.print()
+
+    def handle_a2a_conversation_completed(
+        self,
+        status: str,
+        final_result: str | None,
+        error: str | None,
+        total_turns: int,
+    ) -> None:
+        """Handle A2A conversation completed event.
+
+        Args:
+            status: Final status (completed, failed, etc.)
+            final_result: Final result if completed successfully
+            error: Error message if failed
+            total_turns: Total number of turns in the conversation
+        """
+        if self.current_a2a_conversation_branch and isinstance(
+            self.current_a2a_conversation_branch, Tree
+        ):
+            if status == "completed":
+                if self._pending_a2a_message and self._pending_a2a_agent_role:
+                    if total_turns in self._a2a_turn_branches:
+                        turn_branch = self._a2a_turn_branches[total_turns]
+                    else:
+                        turn_branch = self.current_a2a_conversation_branch.add("")
+                        self.update_tree_label(
+                            turn_branch,
+                            "💬",
+                            f"Turn {total_turns}",
+                            "cyan",
+                        )
+                        self._a2a_turn_branches[total_turns] = turn_branch
+
+                    crewai_agent_role = self._pending_a2a_agent_role
+                    message_content = self._pending_a2a_message
+
+                    message_preview = (
+                        message_content[:100] + "..."
+                        if len(message_content) > 100
+                        else message_content
+                    )
+
+                    user_node = turn_branch.add("")
+                    self.update_tree_label(
+                        user_node,
+                        f"{crewai_agent_role} 👤 :  ",
+                        f'"{message_preview}"',
+                        "green",
+                    )
+
+                    self._pending_a2a_message = None
+                    self._pending_a2a_agent_role = None
+                    self._pending_a2a_turn_number = None
+            elif status == "failed":
+                error_turn = self.current_a2a_conversation_branch.add("")
+                error_msg = error[:150] + "..." if error and len(error) > 150 else error
+                self.update_tree_label(
+                    error_turn,
+                    "❌",
+                    f"Failed: {error_msg}" if error else "Conversation Failed",
+                    "red",
+                )
+
+            tree_to_use = self.current_crew_tree or self.current_task_branch
+            if tree_to_use:
+                self.print(tree_to_use)
+                self.print()
+
+        self.current_a2a_conversation_branch = None
+        self.current_a2a_turn_count = 0

lib/crewai/src/crewai/experimental/a2a/__init__.py

@@ -1,65 +0,0 @@
-"""A2A (Agent-to-Agent) Protocol adapter for CrewAI.
-
-This module provides integration with A2A protocol-compliant agents,
-enabling CrewAI to orchestrate external agents like ServiceNow, Bedrock Agents,
-Glean, and other A2A-compliant systems.
-
-Example:
-    ```python
-    from crewai.experimental.a2a import A2AAgentAdapter
-
-    # Create A2A agent
-    servicenow_agent = A2AAgentAdapter(
-        agent_card_url="https://servicenow.example.com/.well-known/agent-card.json",
-        auth_token="your-token",
-        role="ServiceNow Incident Manager",
-        goal="Create and manage IT incidents",
-        backstory="Expert at incident management",
-    )
-
-    # Use in crew
-    crew = Crew(agents=[servicenow_agent], tasks=[task])
-    ```
-"""
-
-from crewai.experimental.a2a.a2a_adapter import A2AAgentAdapter
-from crewai.experimental.a2a.auth import (
-    APIKeyAuth,
-    AuthScheme,
-    BearerTokenAuth,
-    HTTPBasicAuth,
-    HTTPDigestAuth,
-    OAuth2AuthorizationCode,
-    OAuth2ClientCredentials,
-    create_auth_from_agent_card,
-)
-from crewai.experimental.a2a.exceptions import (
-    A2AAuthenticationError,
-    A2AConfigurationError,
-    A2AConnectionError,
-    A2AError,
-    A2AInputRequiredError,
-    A2ATaskCanceledError,
-    A2ATaskFailedError,
-)
-
-
-__all__ = [
-    "A2AAgentAdapter",
-    "A2AAuthenticationError",
-    "A2AConfigurationError",
-    "A2AConnectionError",
-    "A2AError",
-    "A2AInputRequiredError",
-    "A2ATaskCanceledError",
-    "A2ATaskFailedError",
-    "APIKeyAuth",
-    # Authentication
-    "AuthScheme",
-    "BearerTokenAuth",
-    "HTTPBasicAuth",
-    "HTTPDigestAuth",
-    "OAuth2AuthorizationCode",
-    "OAuth2ClientCredentials",
-    "create_auth_from_agent_card",
-]

lib/crewai/src/crewai/experimental/a2a/auth.py

@@ -1,424 +0,0 @@
-"""Authentication schemes for A2A protocol agents.
-
-This module provides support for various authentication methods:
-- Bearer tokens (existing)
-- OAuth2 (Client Credentials, Authorization Code)
-- API Keys (header, query, cookie)
-- HTTP Basic authentication
-- HTTP Digest authentication
-"""
-
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-import base64
-from collections.abc import Awaitable, Callable
-from typing import TYPE_CHECKING, Any, Literal
-
-import httpx
-from pydantic import BaseModel, Field
-
-
-if TYPE_CHECKING:
-    from a2a.types import AgentCard
-
-
-class AuthScheme(ABC, BaseModel):
-    """Base class for authentication schemes."""
-
-    @abstractmethod
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply authentication to request headers.
-
-        Args:
-            client: HTTP client for making auth requests.
-            headers: Current request headers.
-
-        Returns:
-            Updated headers with authentication applied.
-        """
-        ...
-
-    @abstractmethod
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """Configure the HTTP client for this auth scheme.
-
-        Args:
-            client: HTTP client to configure.
-        """
-        ...
-
-
-class BearerTokenAuth(AuthScheme):
-    """Bearer token authentication (Authorization: Bearer <token>)."""
-
-    token: str = Field(description="Bearer token")
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply Bearer token to Authorization header."""
-        headers["Authorization"] = f"Bearer {self.token}"
-        return headers
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """No client configuration needed for Bearer tokens."""
-
-
-class HTTPBasicAuth(AuthScheme):
-    """HTTP Basic authentication."""
-
-    username: str = Field(description="Username")
-    password: str = Field(description="Password")
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply HTTP Basic authentication."""
-        credentials = f"{self.username}:{self.password}"
-        encoded = base64.b64encode(credentials.encode()).decode()
-        headers["Authorization"] = f"Basic {encoded}"
-        return headers
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """No client configuration needed for Basic auth."""
-
-
-class HTTPDigestAuth(AuthScheme):
-    """HTTP Digest authentication.
-
-    Note: Uses httpx-auth library for proper digest implementation.
-    """
-
-    username: str = Field(description="Username")
-    password: str = Field(description="Password")
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Digest auth is handled by httpx auth flow, not headers."""
-        return headers
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """Configure client with Digest auth."""
-        try:
-            from httpx_auth import DigestAuth  # type: ignore[import-not-found]
-
-            client.auth = DigestAuth(self.username, self.password)  # type: ignore[import-not-found]
-        except ImportError as e:
-            msg = "httpx-auth required for Digest authentication. Install with: pip install httpx-auth"
-            raise ImportError(msg) from e
-
-
-class APIKeyAuth(AuthScheme):
-    """API Key authentication (header, query, or cookie)."""
-
-    api_key: str = Field(description="API key value")
-    location: Literal["header", "query", "cookie"] = Field(
-        default="header", description="Where to send the API key"
-    )
-    name: str = Field(default="X-API-Key", description="Parameter name for the API key")
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply API key authentication."""
-        if self.location == "header":
-            headers[self.name] = self.api_key
-        elif self.location == "cookie":
-            headers["Cookie"] = f"{self.name}={self.api_key}"
-        # Query params are handled in configure_client via event hooks
-        return headers
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """Configure client for query param API keys."""
-        if self.location == "query":
-            # Add API key to all requests via event hook
-            async def add_api_key_param(request: httpx.Request) -> None:
-                url = httpx.URL(request.url)
-                request.url = url.copy_add_param(self.name, self.api_key)
-
-            client.event_hooks["request"].append(add_api_key_param)
-
-
-class OAuth2ClientCredentials(AuthScheme):
-    """OAuth2 Client Credentials flow authentication."""
-
-    token_url: str = Field(description="OAuth2 token endpoint")
-    client_id: str = Field(description="OAuth2 client ID")
-    client_secret: str = Field(description="OAuth2 client secret")
-    scopes: list[str] = Field(
-        default_factory=list, description="Required OAuth2 scopes"
-    )
-
-    _access_token: str | None = None
-    _token_expires_at: float | None = None
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply OAuth2 access token to Authorization header."""
-        # Get or refresh token if needed
-        import time
-
-        if (
-            self._access_token is None
-            or self._token_expires_at is None
-            or time.time() >= self._token_expires_at
-        ):
-            await self._fetch_token(client)
-
-        if self._access_token:
-            headers["Authorization"] = f"Bearer {self._access_token}"
-
-        return headers
-
-    async def _fetch_token(self, client: httpx.AsyncClient) -> None:
-        """Fetch OAuth2 access token using client credentials flow."""
-        import time
-
-        data = {
-            "grant_type": "client_credentials",
-            "client_id": self.client_id,
-            "client_secret": self.client_secret,
-        }
-
-        if self.scopes:
-            data["scope"] = " ".join(self.scopes)
-
-        response = await client.post(self.token_url, data=data)
-        response.raise_for_status()
-
-        token_data = response.json()
-        self._access_token = token_data["access_token"]
-
-        # Calculate expiration time (default to 3600 seconds if not provided)
-        expires_in = token_data.get("expires_in", 3600)
-        self._token_expires_at = time.time() + expires_in - 60  # 60s buffer
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """No client configuration needed for OAuth2."""
-
-
-class OAuth2AuthorizationCode(AuthScheme):
-    """OAuth2 Authorization Code flow authentication.
-
-    Note: This requires interactive authorization and is typically used
-    for user-facing applications. For server-to-server, use ClientCredentials.
-    """
-
-    authorization_url: str = Field(description="OAuth2 authorization endpoint")
-    token_url: str = Field(description="OAuth2 token endpoint")
-    client_id: str = Field(description="OAuth2 client ID")
-    client_secret: str = Field(description="OAuth2 client secret")
-    redirect_uri: str = Field(description="OAuth2 redirect URI")
-    scopes: list[str] = Field(
-        default_factory=list, description="Required OAuth2 scopes"
-    )
-
-    _access_token: str | None = None
-    _refresh_token: str | None = None
-    _token_expires_at: float | None = None
-    _authorization_callback: Callable[[str], Awaitable[str]] | None = None
-
-    def set_authorization_callback(
-        self, callback: Callable[[str], Awaitable[str]] | None
-    ) -> None:
-        """Set callback to handle authorization URL.
-
-        The callback receives the authorization URL and should return
-        the authorization code after user completes the flow.
-        """
-        self._authorization_callback = callback
-
-    async def apply_auth(
-        self, client: httpx.AsyncClient, headers: dict[str, str]
-    ) -> dict[str, str]:
-        """Apply OAuth2 access token to Authorization header."""
-        import time
-
-        # Get or refresh token if needed
-        if self._access_token is None:
-            if self._authorization_callback is None:
-                msg = "Authorization callback not set. Use set_authorization_callback()"
-                raise ValueError(msg)
-            await self._fetch_initial_token(client)
-        elif self._token_expires_at and time.time() >= self._token_expires_at:
-            await self._refresh_access_token(client)
-
-        if self._access_token:
-            headers["Authorization"] = f"Bearer {self._access_token}"
-
-        return headers
-
-    async def _fetch_initial_token(self, client: httpx.AsyncClient) -> None:
-        """Fetch initial access token using authorization code flow."""
-        import time
-        import urllib.parse
-
-        # Build authorization URL
-        params = {
-            "response_type": "code",
-            "client_id": self.client_id,
-            "redirect_uri": self.redirect_uri,
-            "scope": " ".join(self.scopes),
-        }
-        auth_url = f"{self.authorization_url}?{urllib.parse.urlencode(params)}"
-
-        # Get authorization code from callback
-        if self._authorization_callback is None:
-            msg = "Authorization callback not set"
-            raise ValueError(msg)
-        auth_code = await self._authorization_callback(auth_url)
-
-        # Exchange code for token
-        data = {
-            "grant_type": "authorization_code",
-            "code": auth_code,
-            "client_id": self.client_id,
-            "client_secret": self.client_secret,
-            "redirect_uri": self.redirect_uri,
-        }
-
-        response = await client.post(self.token_url, data=data)
-        response.raise_for_status()
-
-        token_data = response.json()
-        self._access_token = token_data["access_token"]
-        self._refresh_token = token_data.get("refresh_token")
-
-        expires_in = token_data.get("expires_in", 3600)
-        self._token_expires_at = time.time() + expires_in - 60
-
-    async def _refresh_access_token(self, client: httpx.AsyncClient) -> None:
-        """Refresh the access token using refresh token."""
-        import time
-
-        if not self._refresh_token:
-            # Re-authorize if no refresh token
-            await self._fetch_initial_token(client)
-            return
-
-        data = {
-            "grant_type": "refresh_token",
-            "refresh_token": self._refresh_token,
-            "client_id": self.client_id,
-            "client_secret": self.client_secret,
-        }
-
-        response = await client.post(self.token_url, data=data)
-        response.raise_for_status()
-
-        token_data = response.json()
-        self._access_token = token_data["access_token"]
-        if "refresh_token" in token_data:
-            self._refresh_token = token_data["refresh_token"]
-
-        expires_in = token_data.get("expires_in", 3600)
-        self._token_expires_at = time.time() + expires_in - 60
-
-    def configure_client(self, client: httpx.AsyncClient) -> None:
-        """No client configuration needed for OAuth2."""
-
-
-def create_auth_from_agent_card(
-    agent_card: AgentCard, credentials: dict[str, Any]
-) -> AuthScheme | None:
-    """Create an appropriate authentication scheme from AgentCard security config.
-
-    Args:
-        agent_card: The A2A AgentCard containing security requirements.
-        credentials: User-provided credentials (passwords, tokens, keys, etc.).
-
-    Returns:
-        Configured AuthScheme, or None if no authentication required.
-
-    Example:
-        ```python
-        # For OAuth2
-        credentials = {
-            "client_id": "my-app",
-            "client_secret": "secret123",
-        }
-        auth = create_auth_from_agent_card(agent_card, credentials)
-
-        # For API Key
-        credentials = {"api_key": "key-12345"}
-        auth = create_auth_from_agent_card(agent_card, credentials)
-
-        # For HTTP Basic
-        credentials = {"username": "user", "password": "pass"}
-        auth = create_auth_from_agent_card(agent_card, credentials)
-        ```
-    """
-    if not agent_card.security or not agent_card.security_schemes:
-        return None
-
-    # Get the first required security scheme
-    first_security_req = agent_card.security[0] if agent_card.security else {}
-
-    for scheme_name, _scopes in first_security_req.items():
-        security_scheme_obj = agent_card.security_schemes.get(scheme_name)
-        if not security_scheme_obj:
-            continue
-
-        # SecurityScheme is a dict-like object
-        security_scheme = dict(security_scheme_obj)  # type: ignore[arg-type]
-        scheme_type = str(security_scheme.get("type", "")).lower()
-
-        # OAuth2
-        if scheme_type == "oauth2":
-            flows = security_scheme.get("flows", {})
-
-            if "clientCredentials" in flows:
-                flow = flows["clientCredentials"]
-                return OAuth2ClientCredentials(
-                    token_url=str(flow["tokenUrl"]),
-                    client_id=str(credentials.get("client_id", "")),
-                    client_secret=str(credentials.get("client_secret", "")),
-                    scopes=list(flow.get("scopes", {}).keys()),
-                )
-
-            if "authorizationCode" in flows:
-                flow = flows["authorizationCode"]
-                return OAuth2AuthorizationCode(
-                    authorization_url=str(flow["authorizationUrl"]),
-                    token_url=str(flow["tokenUrl"]),
-                    client_id=str(credentials.get("client_id", "")),
-                    client_secret=str(credentials.get("client_secret", "")),
-                    redirect_uri=str(credentials.get("redirect_uri", "")),
-                    scopes=list(flow.get("scopes", {}).keys()),
-                )
-
-        # API Key
-        elif scheme_type == "apikey":
-            location = str(security_scheme.get("in", "header"))
-            name = str(security_scheme.get("name", "X-API-Key"))
-            return APIKeyAuth(
-                api_key=str(credentials.get("api_key", "")),
-                location=location,  # type: ignore[arg-type]
-                name=name,
-            )
-
-        # HTTP Auth
-        elif scheme_type == "http":
-            http_scheme = str(security_scheme.get("scheme", "")).lower()
-
-            if http_scheme == "basic":
-                return HTTPBasicAuth(
-                    username=str(credentials.get("username", "")),
-                    password=str(credentials.get("password", "")),
-                )
-
-            if http_scheme == "digest":
-                return HTTPDigestAuth(
-                    username=str(credentials.get("username", "")),
-                    password=str(credentials.get("password", "")),
-                )
-
-            if http_scheme == "bearer":
-                return BearerTokenAuth(token=str(credentials.get("token", "")))
-
-    return None

lib/crewai/src/crewai/experimental/a2a/exceptions.py

@@ -1,56 +0,0 @@
-"""Custom exceptions for A2A Agent Adapter."""
-
-
-class A2AError(Exception):
-    """Base exception for A2A adapter errors."""
-
-
-class A2ATaskFailedError(A2AError):
-    """Raised when A2A agent task fails or is rejected.
-
-    This exception is raised when the A2A agent reports a task
-    in the 'failed' or 'rejected' state.
-    """
-
-
-class A2AInputRequiredError(A2AError):
-    """Raised when A2A agent requires additional input.
-
-    This exception is raised when the A2A agent reports a task
-    in the 'input_required' state, indicating that it needs more
-    information to complete the task.
-    """
-
-
-class A2AConfigurationError(A2AError):
-    """Raised when A2A adapter configuration is invalid.
-
-    This exception is raised during initialization or setup when
-    the adapter configuration is invalid or incompatible.
-    """
-
-
-class A2AConnectionError(A2AError):
-    """Raised when connection to A2A agent fails.
-
-    This exception is raised when the adapter cannot establish
-    a connection to the A2A agent or when network errors occur.
-    """
-
-
-class A2AAuthenticationError(A2AError):
-    """Raised when A2A agent requires authentication.
-
-    This exception is raised when the A2A agent reports a task
-    in the 'auth_required' state, indicating that authentication
-    is needed before the task can continue.
-    """
-
-
-class A2ATaskCanceledError(A2AError):
-    """Raised when A2A task is canceled.
-
-    This exception is raised when the A2A agent reports a task
-    in the 'canceled' state, indicating the task was canceled
-    either by the user or the system.
-    """

lib/crewai/src/crewai/experimental/a2a/protocols.py

@@ -1,56 +0,0 @@
-"""Type protocols for A2A SDK components.
-
-These protocols define the expected interfaces for A2A SDK types,
-allowing for type checking without requiring the SDK to be installed.
-"""
-
-from collections.abc import AsyncIterator
-from typing import Any, Protocol, runtime_checkable
-
-
-@runtime_checkable
-class AgentCardProtocol(Protocol):
-    """Protocol for A2A AgentCard."""
-
-    name: str
-    version: str
-    description: str
-    skills: list[Any]
-    capabilities: Any
-
-
-@runtime_checkable
-class ClientProtocol(Protocol):
-    """Protocol for A2A Client."""
-
-    async def send_message(self, message: Any) -> AsyncIterator[Any]:
-        """Send message to A2A agent."""
-        ...
-
-    async def get_card(self) -> AgentCardProtocol:
-        """Get agent card."""
-        ...
-
-    async def close(self) -> None:
-        """Close client connection."""
-        ...
-
-
-@runtime_checkable
-class MessageProtocol(Protocol):
-    """Protocol for A2A Message."""
-
-    role: Any
-    message_id: str
-    parts: list[Any]
-
-
-@runtime_checkable
-class TaskProtocol(Protocol):
-    """Protocol for A2A Task."""
-
-    id: str
-    context_id: str
-    status: Any
-    history: list[Any] | None
-    artifacts: list[Any] | None

lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py

@@ -2,7 +2,7 @@ from collections.abc import Sequence
 import threading
 from typing import Any
 
-from crewai.agent import Agent
+from crewai.agent.core import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.agent_events import (

lib/crewai/src/crewai/flow/__init__.py

@@ -1,5 +1,25 @@
+from crewai.flow.visualization import (
+    FlowStructure,
+    build_flow_structure,
+    print_structure_summary,
+    structure_to_dict,
+    visualize_flow_structure,
+)
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 from crewai.flow.persistence import persist
 
 
-__all__ = ["Flow", "and_", "listen", "or_", "persist", "router", "start"]
+__all__ = [
+    "Flow",
+    "FlowStructure",
+    "and_",
+    "build_flow_structure",
+    "listen",
+    "or_",
+    "persist",
+    "print_structure_summary",
+    "router",
+    "start",
+    "structure_to_dict",
+    "visualize_flow_structure",
+]

lib/crewai/src/crewai/flow/assets/crewai_flow_visual_template.html

@@ -1,93 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <title>{{ title }}</title>
-    <script
-      src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js"
-      integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ=="
-      crossorigin="anonymous"
-      referrerpolicy="no-referrer"
-    ></script>
-    <link
-      rel="stylesheet"
-      href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css"
-      integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA=="
-      crossorigin="anonymous"
-      referrerpolicy="no-referrer"
-    />
-    <style type="text/css">
-      body {
-        font-family: verdana;
-        margin: 0;
-        padding: 0;
-      }
-      .container {
-        display: flex;
-        flex-direction: column;
-        height: 100vh;
-      }
-      #mynetwork {
-        flex-grow: 1;
-        width: 100%;
-        height: 750px;
-        background-color: #ffffff;
-      }
-      .card {
-        border: none;
-      }
-      .legend-container {
-        display: flex;
-        align-items: center;
-        justify-content: center;
-        padding: 10px;
-        background-color: #f8f9fa;
-        position: fixed; /* Make the legend fixed */
-        bottom: 0; /* Position it at the bottom */
-        width: 100%; /* Make it span the full width */
-      }
-      .legend-item {
-        display: flex;
-        align-items: center;
-        margin-right: 20px;
-      }
-      .legend-color-box {
-        width: 20px;
-        height: 20px;
-        margin-right: 5px;
-      }
-      .logo {
-        height: 50px;
-        margin-right: 20px;
-      }
-      .legend-dashed {
-        border-bottom: 2px dashed #666666;
-        width: 20px;
-        height: 0;
-        margin-right: 5px;
-      }
-      .legend-solid {
-        border-bottom: 2px solid #666666;
-        width: 20px;
-        height: 0;
-        margin-right: 5px;
-      }
-    </style>
-  </head>
-  <body>
-    <div class="container">
-      <div class="card" style="width: 100%">
-        <div id="mynetwork" class="card-body"></div>
-      </div>
-      <div class="legend-container">
-        <img
-          src="data:image/svg+xml;base64,{{ logo_svg_base64 }}"
-          alt="CrewAI logo"
-          class="logo"
-        />
-        <!-- LEGEND_ITEMS_PLACEHOLDER -->
-      </div>
-    </div>
-    {{ network_content }}
-  </body>
-</html>

lib/crewai/src/crewai/flow/constants.py

@@ -0,0 +1,4 @@
+from typing import Final, Literal
+
+AND_CONDITION: Final[Literal["AND"]] = "AND"
+OR_CONDITION: Final[Literal["OR"]] = "OR"

lib/crewai/src/crewai/flow/flow.py

@@ -1,3 +1,9 @@
+"""Core flow execution framework with decorators and state management.
+
+This module provides the Flow class and decorators (@start, @listen, @router)
+for building event-driven workflows with conditional execution and routing.
+"""
+
 from __future__ import annotations
 
 import asyncio
@@ -9,6 +15,7 @@ import logging
 from typing import (
     Any,
     ClassVar,
+    Final,
     Generic,
     Literal,
     ParamSpec,
@@ -38,7 +45,7 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.flow.flow_visualizer import plot_flow
+from crewai.flow.visualization import build_flow_structure, render_interactive
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -58,7 +65,11 @@ from crewai.flow.utils import (
     is_flow_method_callable,
     is_flow_method_name,
     is_simple_flow_condition,
+    _extract_all_methods,
+    _extract_all_methods_recursive,
+    _normalize_condition,
 )
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.utilities.printer import Printer, PrinterColor
 
 
@@ -74,95 +85,63 @@ class FlowState(BaseModel):
     )
 
 
-# type variables with explicit bounds
-T = TypeVar("T", bound=dict[str, Any] | BaseModel)  # Generic flow state type parameter
-StateT = TypeVar(
-    "StateT", bound=dict[str, Any] | BaseModel
-)  # State validation type parameter
-P = ParamSpec("P")  # ParamSpec for preserving function signatures in decorators
-R = TypeVar("R")  # Generic return type for decorated methods
-F = TypeVar("F", bound=Callable[..., Any])  # Function type for decorator preservation
-
-
-def ensure_state_type(state: Any, expected_type: type[StateT]) -> StateT:
-    """Ensure state matches expected type with proper validation.
-
-    Args:
-        state: State instance to validate
-        expected_type: Expected type for the state
-
-    Returns:
-        Validated state instance
-
-    Raises:
-        TypeError: If state doesn't match expected type
-        ValueError: If state validation fails
-    """
-    if expected_type is dict:
-        if not isinstance(state, dict):
-            raise TypeError(f"Expected dict, got {type(state).__name__}")
-        return cast(StateT, state)
-    if isinstance(expected_type, type) and issubclass(expected_type, BaseModel):
-        if not isinstance(state, expected_type):
-            raise TypeError(
-                f"Expected {expected_type.__name__}, got {type(state).__name__}"
-            )
-        return state
-    raise TypeError(f"Invalid expected_type: {expected_type}")
+T = TypeVar("T", bound=dict[str, Any] | BaseModel)
+P = ParamSpec("P")
+R = TypeVar("R")
+F = TypeVar("F", bound=Callable[..., Any])
 
 
 def start(
     condition: str | FlowCondition | Callable[..., Any] | None = None,
 ) -> Callable[[Callable[P, R]], StartMethod[P, R]]:
-    """
-    Marks a method as a flow's starting point.
+    """Marks a method as a flow's starting point.
 
     This decorator designates a method as an entry point for the flow execution.
     It can optionally specify conditions that trigger the start based on other
     method executions.
 
-    Parameters
-    ----------
-    condition : Optional[Union[str, FlowCondition, Callable[..., Any]]], optional
-        Defines when the start method should execute. Can be:
-        - str: Name of a method that triggers this start
-        - FlowCondition: Result from or_() or and_(), including nested conditions
-        - Callable[..., Any]: A method reference that triggers this start
-        Default is None, meaning unconditional start.
+    Args:
+        condition: Defines when the start method should execute. Can be:
+            - str: Name of a method that triggers this start
+            - FlowCondition: Result from or_() or and_(), including nested conditions
+            - Callable[..., Any]: A method reference that triggers this start
+            Default is None, meaning unconditional start.
 
-    Returns
-    -------
-    Callable[[Callable[P, R]], StartMethod[P, R]]
-        A decorator function that wraps the method as a flow start point
-        and preserves its signature.
+    Returns:
+        A decorator function that wraps the method as a flow start point and preserves its signature.
 
-    Raises
-    ------
-    ValueError
-        If the condition format is invalid.
+    Raises:
+        ValueError: If the condition format is invalid.
 
-    Examples
-    --------
-    >>> @start()  # Unconditional start
-    >>> def begin_flow(self):
-    ...     pass
+    Examples:
+        >>> @start()  # Unconditional start
+        >>> def begin_flow(self):
+        ...     pass
 
-    >>> @start("method_name")  # Start after specific method
-    >>> def conditional_start(self):
-    ...     pass
+        >>> @start("method_name")  # Start after specific method
+        >>> def conditional_start(self):
+        ...     pass
 
-    >>> @start(and_("method1", "method2"))  # Start after multiple methods
-    >>> def complex_start(self):
-    ...     pass
+        >>> @start(and_("method1", "method2"))  # Start after multiple methods
+        >>> def complex_start(self):
+        ...     pass
     """
 
     def decorator(func: Callable[P, R]) -> StartMethod[P, R]:
+        """Decorator that wraps a function as a start method.
+
+        Args:
+            func: The function to wrap as a start method.
+
+        Returns:
+            A StartMethod wrapper around the function.
+        """
         wrapper = StartMethod(func)
 
         if condition is not None:
             if is_flow_method_name(condition):
                 wrapper.__trigger_methods__ = [condition]
-                wrapper.__condition_type__ = "OR"
+                wrapper.__condition_type__ = OR_CONDITION
             elif is_flow_condition_dict(condition):
                 if "conditions" in condition:
                     wrapper.__trigger_condition__ = condition
@@ -177,7 +156,7 @@ def start(
                     )
             elif is_flow_method_callable(condition):
                 wrapper.__trigger_methods__ = [condition.__name__]
-                wrapper.__condition_type__ = "OR"
+                wrapper.__condition_type__ = OR_CONDITION
             else:
                 raise ValueError(
                     "Condition must be a method, string, or a result of or_() or and_()"
@@ -190,49 +169,45 @@ def start(
 def listen(
     condition: str | FlowCondition | Callable[..., Any],
 ) -> Callable[[Callable[P, R]], ListenMethod[P, R]]:
-    """
-    Creates a listener that executes when specified conditions are met.
+    """Creates a listener that executes when specified conditions are met.
 
     This decorator sets up a method to execute in response to other method
     executions in the flow. It supports both simple and complex triggering
     conditions.
 
-    Parameters
-    ----------
-    condition : Union[str, FlowCondition, Callable[..., Any]]
-        Specifies when the listener should execute. Can be:
-        - str: Name of a method that triggers this listener
-        - FlowCondition: Result from or_() or and_(), including nested conditions
-        - Callable[..., Any]: A method reference that triggers this listener
+    Args:
+        condition: Specifies when the listener should execute.
 
-    Returns
-    -------
-    Callable[[Callable[P, R]], ListenMethod[P, R]]
-        A decorator function that wraps the method as a listener
-        and preserves its signature.
+    Returns:
+        A decorator function that wraps the method as a flow listener and preserves its signature.
 
-    Raises
-    ------
-    ValueError
-        If the condition format is invalid.
+    Raises:
+        ValueError: If the condition format is invalid.
 
-    Examples
-    --------
-    >>> @listen("process_data")  # Listen to single method
-    >>> def handle_processed_data(self):
-    ...     pass
+    Examples:
+        >>> @listen("process_data")
+        >>> def handle_processed_data(self):
+        ...     pass
 
-    >>> @listen(or_("success", "failure"))  # Listen to multiple methods
-    >>> def handle_completion(self):
-    ...     pass
+        >>> @listen("method_name")
+        >>> def handle_completion(self):
+        ...     pass
     """
 
     def decorator(func: Callable[P, R]) -> ListenMethod[P, R]:
+        """Decorator that wraps a function as a listener method.
+
+        Args:
+            func: The function to wrap as a listener method.
+
+        Returns:
+            A ListenMethod wrapper around the function.
+        """
         wrapper = ListenMethod(func)
 
         if is_flow_method_name(condition):
             wrapper.__trigger_methods__ = [condition]
-            wrapper.__condition_type__ = "OR"
+            wrapper.__condition_type__ = OR_CONDITION
         elif is_flow_condition_dict(condition):
             if "conditions" in condition:
                 wrapper.__trigger_condition__ = condition
@@ -247,7 +222,7 @@ def listen(
                 )
         elif is_flow_method_callable(condition):
             wrapper.__trigger_methods__ = [condition.__name__]
-            wrapper.__condition_type__ = "OR"
+            wrapper.__condition_type__ = OR_CONDITION
         else:
             raise ValueError(
                 "Condition must be a method, string, or a result of or_() or and_()"
@@ -260,54 +235,53 @@ def listen(
 def router(
     condition: str | FlowCondition | Callable[..., Any],
 ) -> Callable[[Callable[P, R]], RouterMethod[P, R]]:
-    """
-    Creates a routing method that directs flow execution based on conditions.
+    """Creates a routing method that directs flow execution based on conditions.
 
     This decorator marks a method as a router, which can dynamically determine
     the next steps in the flow based on its return value. Routers are triggered
     by specified conditions and can return constants that determine which path
     the flow should take.
 
-    Parameters
-    ----------
-    condition : Union[str, FlowCondition, Callable[..., Any]]
-        Specifies when the router should execute. Can be:
-        - str: Name of a method that triggers this router
-        - FlowCondition: Result from or_() or and_(), including nested conditions
-        - Callable[..., Any]: A method reference that triggers this router
+    Args:
+        condition: Specifies when the router should execute. Can be:
+            - str: Name of a method that triggers this router
+            - FlowCondition: Result from or_() or and_(), including nested conditions
+            - Callable[..., Any]: A method reference that triggers this router
 
-    Returns
-    -------
-    Callable[[Callable[P, R]], RouterMethod[P, R]]
-        A decorator function that wraps the method as a router
-        and preserves its signature.
+    Returns:
+        A decorator function that wraps the method as a router and preserves its signature.
 
-    Raises
-    ------
-    ValueError
-        If the condition format is invalid.
+    Raises:
+        ValueError: If the condition format is invalid.
 
-    Examples
-    --------
-    >>> @router("check_status")
-    >>> def route_based_on_status(self):
-    ...     if self.state.status == "success":
-    ...         return SUCCESS
-    ...     return FAILURE
+    Examples:
+        >>> @router("check_status")
+        >>> def route_based_on_status(self):
+        ...     if self.state.status == "success":
+        ...         return "SUCCESS"
+        ...     return "FAILURE"
 
-    >>> @router(and_("validate", "process"))
-    >>> def complex_routing(self):
-    ...     if all([self.state.valid, self.state.processed]):
-    ...         return CONTINUE
-    ...     return STOP
+        >>> @router(and_("validate", "process"))
+        >>> def complex_routing(self):
+        ...     if all([self.state.valid, self.state.processed]):
+        ...         return "CONTINUE"
+        ...     return "STOP"
     """
 
     def decorator(func: Callable[P, R]) -> RouterMethod[P, R]:
+        """Decorator that wraps a function as a router method.
+
+        Args:
+            func: The function to wrap as a router method.
+
+        Returns:
+            A RouterMethod wrapper around the function.
+        """
         wrapper = RouterMethod(func)
 
         if is_flow_method_name(condition):
             wrapper.__trigger_methods__ = [condition]
-            wrapper.__condition_type__ = "OR"
+            wrapper.__condition_type__ = OR_CONDITION
         elif is_flow_condition_dict(condition):
             if "conditions" in condition:
                 wrapper.__trigger_condition__ = condition
@@ -322,7 +296,7 @@ def router(
                 )
         elif is_flow_method_callable(condition):
             wrapper.__trigger_methods__ = [condition.__name__]
-            wrapper.__condition_type__ = "OR"
+            wrapper.__condition_type__ = OR_CONDITION
         else:
             raise ValueError(
                 "Condition must be a method, string, or a result of or_() or and_()"
@@ -333,42 +307,29 @@ def router(
 
 
 def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
-    """
-    Combines multiple conditions with OR logic for flow control.
+    """Combines multiple conditions with OR logic for flow control.
 
     Creates a condition that is satisfied when any of the specified conditions
     are met. This is used with @start, @listen, or @router decorators to create
     complex triggering conditions.
 
-    Parameters
-    ----------
-    *conditions : Union[str, dict[str, Any], Callable[..., Any]]
-        Variable number of conditions that can be:
-        - str: Method names
-        - dict[str, Any]: Existing condition dictionaries (nested conditions)
-        - Callable[..., Any]: Method references
+    Args:
+        conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
 
-    Returns
-    -------
-    dict[str, Any]
-        A condition dictionary with format:
-        {"type": "OR", "conditions": list_of_conditions}
-        where each condition can be a string (method name) or a nested dict
+    Returns:
+        A condition dictionary with format {"type": "OR", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict
 
-    Raises
-    ------
-    ValueError
-        If any condition is invalid.
+    Raises:
+        ValueError: If condition format is invalid.
 
-    Examples
-    --------
-    >>> @listen(or_("success", "timeout"))
-    >>> def handle_completion(self):
-    ...     pass
+    Examples:
+        >>> @listen(or_("success", "timeout"))
+        >>> def handle_completion(self):
+        ...     pass
 
-    >>> @listen(or_(and_("step1", "step2"), "step3"))
-    >>> def handle_nested(self):
-    ...     pass
+        >>> @listen(or_(and_("step1", "step2"), "step3"))
+        >>> def handle_nested(self):
+        ...     pass
     """
     processed_conditions: FlowConditions = []
     for condition in conditions:
@@ -378,46 +339,34 @@ def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
             processed_conditions.append(condition.__name__)
         else:
             raise ValueError("Invalid condition in or_()")
-    return {"type": "OR", "conditions": processed_conditions}
+    return {"type": OR_CONDITION, "conditions": processed_conditions}
 
 
 def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
-    """
-    Combines multiple conditions with AND logic for flow control.
+    """Combines multiple conditions with AND logic for flow control.
 
     Creates a condition that is satisfied only when all specified conditions
     are met. This is used with @start, @listen, or @router decorators to create
     complex triggering conditions.
 
-    Parameters
-    ----------
-    *conditions : Union[str, dict[str, Any], Callable[..., Any]]
-        Variable number of conditions that can be:
-        - str: Method names
-        - dict[str, Any]: Existing condition dictionaries (nested conditions)
-        - Callable[..., Any]: Method references
+    Args:
+        *conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
 
-    Returns
-    -------
-    dict[str, Any]
-        A condition dictionary with format:
-        {"type": "AND", "conditions": list_of_conditions}
+    Returns:
+        A condition dictionary with format {"type": "AND", "conditions": list_of_conditions}
         where each condition can be a string (method name) or a nested dict
 
-    Raises
-    ------
-    ValueError
-        If any condition is invalid.
+    Raises:
+        ValueError: If any condition is invalid.
 
-    Examples
-    --------
-    >>> @listen(and_("validated", "processed"))
-    >>> def handle_complete_data(self):
-    ...     pass
+    Examples:
+        >>> @listen(and_("validated", "processed"))
+        >>> def handle_complete_data(self):
+        ...     pass
 
-    >>> @listen(and_(or_("step1", "step2"), "step3"))
-    >>> def handle_nested(self):
-    ...     pass
+        >>> @listen(and_(or_("step1", "step2"), "step3"))
+        >>> def handle_nested(self):
+        ...     pass
     """
     processed_conditions: FlowConditions = []
     for condition in conditions:
@@ -427,59 +376,7 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition
             processed_conditions.append(condition.__name__)
         else:
             raise ValueError("Invalid condition in and_()")
-    return {"type": "AND", "conditions": processed_conditions}
-
-
-def _normalize_condition(
-    condition: FlowConditions | FlowCondition | FlowMethodName,
-) -> FlowCondition:
-    """Normalize a condition to standard format with 'conditions' key.
-
-    Args:
-        condition: Can be a string (method name), dict (condition), or list
-
-    Returns:
-        Normalized dict with 'type' and 'conditions' keys
-    """
-    if is_flow_method_name(condition):
-        return {"type": "OR", "conditions": [condition]}
-    if is_flow_condition_dict(condition):
-        if "conditions" in condition:
-            return condition
-        if "methods" in condition:
-            return {"type": condition["type"], "conditions": condition["methods"]}
-        return condition
-    if is_flow_condition_list(condition):
-        return {"type": "OR", "conditions": condition}
-
-    raise ValueError(f"Cannot normalize condition: {condition}")
-
-
-def _extract_all_methods(
-    condition: str | FlowCondition | dict[str, Any] | list[Any],
-) -> list[FlowMethodName]:
-    """Extract all method names from a condition (including nested).
-
-    Args:
-        condition: Can be a string, dict, or list
-
-    Returns:
-        List of all method names in the condition tree
-    """
-    if is_flow_method_name(condition):
-        return [condition]
-    if is_flow_condition_dict(condition):
-        normalized = _normalize_condition(condition)
-        methods = []
-        for sub_cond in normalized.get("conditions", []):
-            methods.extend(_extract_all_methods(sub_cond))
-        return methods
-    if isinstance(condition, list):
-        methods = []
-        for item in condition:
-            methods.extend(_extract_all_methods(item))
-        return methods
-    return []
+    return {"type": AND_CONDITION, "conditions": processed_conditions}
 
 
 class FlowMeta(type):
@@ -515,7 +412,9 @@ class FlowMeta(type):
                     and attr_value.__trigger_methods__ is not None
                 ):
                     methods = attr_value.__trigger_methods__
-                    condition_type = getattr(attr_value, "__condition_type__", "OR")
+                    condition_type = getattr(
+                        attr_value, "__condition_type__", OR_CONDITION
+                    )
                     if (
                         hasattr(attr_value, "__trigger_condition__")
                         and attr_value.__trigger_condition__ is not None
@@ -556,7 +455,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     name: str | None = None
     tracing: bool | None = False
 
-    def __class_getitem__(cls: type[Flow[StateT]], item: type[T]) -> type[Flow[StateT]]:
+    def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:
         class _FlowGeneric(cls):  # type: ignore
             _initial_state_t = item
 
@@ -1037,24 +936,20 @@ class Flow(Generic[T], metaclass=FlowMeta):
             detach(flow_token)
 
     async def _execute_start_method(self, start_method_name: FlowMethodName) -> None:
-        """
-        Executes a flow's start method and its triggered listeners.
+        """Executes a flow's start method and its triggered listeners.
 
         This internal method handles the execution of methods marked with @start
         decorator and manages the subsequent chain of listener executions.
 
-        Parameters
-        ----------
-        start_method_name : str
-            The name of the start method to execute.
+        Args:
+            start_method_name: The name of the start method to execute.
 
-        Notes
-        -----
-        - Executes the start method and captures its result
-        - Triggers execution of any listeners waiting on this start method
-        - Part of the flow's initialization sequence
-        - Skips execution if method was already completed (e.g., after reload)
-        - Automatically injects crewai_trigger_payload if available in flow inputs
+        Note:
+            - Executes the start method and captures its result
+            - Triggers execution of any listeners waiting on this start method
+            - Part of the flow's initialization sequence
+            - Skips execution if method was already completed (e.g., after reload)
+            - Automatically injects crewai_trigger_payload if available in flow inputs
         """
         if start_method_name in self._completed_methods:
             if self._is_execution_resuming:
@@ -1174,27 +1069,21 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_listeners(
         self, trigger_method: FlowMethodName, result: Any
     ) -> None:
-        """
-        Executes all listeners and routers triggered by a method completion.
+        """Executes all listeners and routers triggered by a method completion.
 
         This internal method manages the execution flow by:
         1. First executing all triggered routers sequentially
         2. Then executing all triggered listeners in parallel
 
-        Parameters
-        ----------
-        trigger_method : str
-            The name of the method that triggered these listeners.
-        result : Any
-            The result from the triggering method, passed to listeners
-            that accept parameters.
+        Args:
+            trigger_method: The name of the method that triggered these listeners.
+            result: The result from the triggering method, passed to listeners that accept parameters.
 
-        Notes
-        -----
-        - Routers are executed sequentially to maintain flow control
-        - Each router's result becomes a new trigger_method
-        - Normal listeners are executed in parallel for efficiency
-        - Listeners can receive the trigger method's result as a parameter
+        Note:
+            - Routers are executed sequentially to maintain flow control
+            - Each router's result becomes a new trigger_method
+            - Normal listeners are executed in parallel for efficiency
+            - Listeners can receive the trigger method's result as a parameter
         """
         # First, handle routers repeatedly until no router triggers anymore
         router_results = []
@@ -1281,16 +1170,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         if is_flow_condition_dict(condition):
             normalized = _normalize_condition(condition)
-            cond_type = normalized.get("type", "OR")
+            cond_type = normalized.get("type", OR_CONDITION)
             sub_conditions = normalized.get("conditions", [])
 
-            if cond_type == "OR":
+            if cond_type == OR_CONDITION:
                 return any(
                     self._evaluate_condition(sub_cond, trigger_method, listener_name)
                     for sub_cond in sub_conditions
                 )
 
-            if cond_type == "AND":
+            if cond_type == AND_CONDITION:
                 pending_key = PendingListenerKey(f"{listener_name}:{id(condition)}")
 
                 if pending_key not in self._pending_and_listeners:
@@ -1300,7 +1189,20 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if trigger_method in self._pending_and_listeners[pending_key]:
                     self._pending_and_listeners[pending_key].discard(trigger_method)
 
-                if not self._pending_and_listeners[pending_key]:
+                direct_methods_satisfied = not self._pending_and_listeners[pending_key]
+
+                nested_conditions_satisfied = all(
+                    (
+                        self._evaluate_condition(
+                            sub_cond, trigger_method, listener_name
+                        )
+                        if is_flow_condition_dict(sub_cond)
+                        else True
+                    )
+                    for sub_cond in sub_conditions
+                )
+
+                if direct_methods_satisfied and nested_conditions_satisfied:
                     self._pending_and_listeners.pop(pending_key, None)
                     return True
 
@@ -1311,30 +1213,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def _find_triggered_methods(
         self, trigger_method: FlowMethodName, router_only: bool
     ) -> list[FlowMethodName]:
-        """
-        Finds all methods that should be triggered based on conditions.
+        """Finds all methods that should be triggered based on conditions.
 
         This internal method evaluates both OR and AND conditions to determine
         which methods should be executed next in the flow. Supports nested conditions.
 
-        Parameters
-        ----------
-        trigger_method : str
-            The name of the method that just completed execution.
-        router_only : bool
-            If True, only consider router methods.
-            If False, only consider non-router methods.
+        Args:
+            trigger_method: The name of the method that just completed execution.
+            router_only: If True, only consider router methods. If False, only consider non-router methods.
 
-        Returns
-        -------
-        list[str]
+        Returns:
             Names of methods that should be triggered.
 
-        Notes
-        -----
-        - Handles both OR and AND conditions, including nested combinations
-        - Maintains state for AND conditions using _pending_and_listeners
-        - Separates router and normal listener evaluation
+        Note:
+            - Handles both OR and AND conditions, including nested combinations
+            - Maintains state for AND conditions using _pending_and_listeners
+            - Separates router and normal listener evaluation
         """
         triggered: list[FlowMethodName] = []
 
@@ -1350,10 +1244,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if is_simple_flow_condition(condition_data):
                 condition_type, methods = condition_data
 
-                if condition_type == "OR":
+                if condition_type == OR_CONDITION:
                     if trigger_method in methods:
                         triggered.append(listener_name)
-                elif condition_type == "AND":
+                elif condition_type == AND_CONDITION:
                     pending_key = PendingListenerKey(listener_name)
                     if pending_key not in self._pending_and_listeners:
                         self._pending_and_listeners[pending_key] = set(methods)
@@ -1375,33 +1269,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_single_listener(
         self, listener_name: FlowMethodName, result: Any
     ) -> None:
-        """
-        Executes a single listener method with proper event handling.
+        """Executes a single listener method with proper event handling.
 
         This internal method manages the execution of an individual listener,
         including parameter inspection, event emission, and error handling.
 
-        Parameters
-        ----------
-        listener_name : str
-            The name of the listener method to execute.
-        result : Any
-            The result from the triggering method, which may be passed
-            to the listener if it accepts parameters.
+        Args:
+            listener_name: The name of the listener method to execute.
+            result: The result from the triggering method, which may be passed to the listener if it accepts parameters.
 
-        Notes
-        -----
-        - Inspects method signature to determine if it accepts the trigger result
-        - Emits events for method execution start and finish
-        - Handles errors gracefully with detailed logging
-        - Recursively triggers listeners of this listener
-        - Supports both parameterized and parameter-less listeners
-        - Skips execution if method was already completed (e.g., after reload)
-
-        Error Handling
-        -------------
-        Catches and logs any exceptions during execution, preventing
-        individual listener failures from breaking the entire flow.
+        Note:
+            - Inspects method signature to determine if it accepts the trigger result
+            - Emits events for method execution start and finish
+            - Handles errors gracefully with detailed logging
+            - Recursively triggers listeners of this listener
+            - Supports both parameterized and parameter-less listeners
+            - Skips execution if method was already completed (e.g., after reload)
+            - Catches and logs any exceptions during execution, preventing individual listener failures from breaking the entire flow
         """
         if listener_name in self._completed_methods:
             if self._is_execution_resuming:
@@ -1460,7 +1344,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
             logger.info(message)
         logger.warning(message)
 
-    def plot(self, filename: str = "crewai_flow") -> None:
+    def plot(self, filename: str = "crewai_flow.html", show: bool = True) -> str:
+        """Create interactive HTML visualization of Flow structure.
+
+        Args:
+            filename: Output HTML filename (default: "crewai_flow.html").
+            show: Whether to open in browser (default: True).
+
+        Returns:
+            Absolute path to generated HTML file.
+        """
         crewai_event_bus.emit(
             self,
             FlowPlotEvent(
@@ -1468,4 +1361,5 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 flow_name=self.name or self.__class__.__name__,
             ),
         )
-        plot_flow(self, filename)
+        structure = build_flow_structure(self)
+        return render_interactive(structure, filename=filename, show=show)

lib/crewai/src/crewai/flow/flow_visualizer.py

@@ -1,234 +0,0 @@
-# flow_visualizer.py
-from __future__ import annotations
-
-import os
-from typing import TYPE_CHECKING, Any
-
-from pyvis.network import Network  # type: ignore[import-untyped]
-
-from crewai.flow.config import COLORS, NODE_STYLES, NodeStyles
-from crewai.flow.html_template_handler import HTMLTemplateHandler
-from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items
-from crewai.flow.path_utils import safe_path_join
-from crewai.flow.utils import calculate_node_levels
-from crewai.flow.visualization_utils import (
-    add_edges,
-    add_nodes_to_network,
-    compute_positions,
-)
-from crewai.utilities.printer import Printer
-
-
-if TYPE_CHECKING:
-    from crewai.flow.flow import Flow
-
-
-_printer = Printer()
-
-
-class FlowPlot:
-    """Handles the creation and rendering of flow visualization diagrams."""
-
-    def __init__(self, flow: Flow[Any]) -> None:
-        """
-        Initialize FlowPlot with a flow object.
-
-        Parameters
-        ----------
-        flow : Flow
-            A Flow instance to visualize.
-
-        Raises
-        ------
-        ValueError
-            If flow object is invalid or missing required attributes.
-        """
-        self.flow = flow
-        self.colors = COLORS
-        self.node_styles: NodeStyles = NODE_STYLES
-
-    def plot(self, filename: str) -> None:
-        """
-        Generate and save an HTML visualization of the flow.
-
-        Parameters
-        ----------
-        filename : str
-            Name of the output file (without extension).
-
-        Raises
-        ------
-        ValueError
-            If filename is invalid or network generation fails.
-        IOError
-            If file operations fail or visualization cannot be generated.
-        RuntimeError
-            If network visualization generation fails.
-        """
-
-        try:
-            # Initialize network
-            net = Network(directed=True, height="750px", bgcolor=self.colors["bg"])
-
-            # Set options to disable physics
-            net.set_options(
-                """
-                var options = {
-                    "nodes": {
-                        "font": {
-                            "multi": "html"
-                        }
-                    },
-                    "physics": {
-                        "enabled": false
-                    }
-                }
-            """
-            )
-
-            # Calculate levels for nodes
-            try:
-                node_levels = calculate_node_levels(self.flow)
-            except Exception as e:
-                raise ValueError(f"Failed to calculate node levels: {e!s}") from e
-
-            # Compute positions
-            try:
-                node_positions = compute_positions(self.flow, node_levels)
-            except Exception as e:
-                raise ValueError(f"Failed to compute node positions: {e!s}") from e
-
-            # Add nodes to the network
-            try:
-                add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
-            except Exception as e:
-                raise RuntimeError(f"Failed to add nodes to network: {e!s}") from e
-
-            # Add edges to the network
-            try:
-                add_edges(net, self.flow, node_positions, self.colors)
-            except Exception as e:
-                raise RuntimeError(f"Failed to add edges to network: {e!s}") from e
-
-            # Generate HTML
-            try:
-                network_html = net.generate_html()
-                final_html_content = self._generate_final_html(network_html)
-            except Exception as e:
-                raise RuntimeError(
-                    f"Failed to generate network visualization: {e!s}"
-                ) from e
-
-            # Save the final HTML content to the file
-            try:
-                with open(f"{filename}.html", "w", encoding="utf-8") as f:
-                    f.write(final_html_content)
-                _printer.print(f"Plot saved as {filename}.html", color="green")
-            except IOError as e:
-                raise IOError(
-                    f"Failed to save flow visualization to {filename}.html: {e!s}"
-                ) from e
-
-        except (ValueError, RuntimeError, IOError) as e:
-            raise e
-        except Exception as e:
-            raise RuntimeError(
-                f"Unexpected error during flow visualization: {e!s}"
-            ) from e
-        finally:
-            self._cleanup_pyvis_lib(filename)
-
-    def _generate_final_html(self, network_html: str) -> str:
-        """
-        Generate the final HTML content with network visualization and legend.
-
-        Parameters
-        ----------
-        network_html : str
-            HTML content generated by pyvis Network.
-
-        Returns
-        -------
-        str
-            Complete HTML content with styling and legend.
-
-        Raises
-        ------
-        IOError
-            If template or logo files cannot be accessed.
-        ValueError
-            If network_html is invalid.
-        """
-        if not network_html:
-            raise ValueError("Invalid network HTML content")
-
-        try:
-            # Extract just the body content from the generated HTML
-            current_dir = os.path.dirname(__file__)
-            template_path = safe_path_join(
-                "assets", "crewai_flow_visual_template.html", root=current_dir
-            )
-            logo_path = safe_path_join("assets", "crewai_logo.svg", root=current_dir)
-
-            if not os.path.exists(template_path):
-                raise IOError(f"Template file not found: {template_path}")
-            if not os.path.exists(logo_path):
-                raise IOError(f"Logo file not found: {logo_path}")
-
-            html_handler = HTMLTemplateHandler(template_path, logo_path)
-            network_body = html_handler.extract_body_content(network_html)
-
-            # Generate the legend items HTML
-            legend_items = get_legend_items(self.colors)
-            legend_items_html = generate_legend_items_html(legend_items)
-            return html_handler.generate_final_html(network_body, legend_items_html)
-        except Exception as e:
-            raise IOError(f"Failed to generate visualization HTML: {e!s}") from e
-
-    @staticmethod
-    def _cleanup_pyvis_lib(filename: str) -> None:
-        """
-        Clean up the generated lib folder from pyvis.
-
-        This method safely removes the temporary lib directory created by pyvis
-        during network visualization generation. The lib folder is created in the
-        same directory as the output HTML file.
-
-        Parameters
-        ----------
-        filename : str
-            The output filename (without .html extension) used for the visualization.
-        """
-        try:
-            import shutil
-
-            output_dir = os.path.dirname(os.path.abspath(filename)) or os.getcwd()
-            lib_folder = os.path.join(output_dir, "lib")
-            if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
-                vis_js = os.path.join(lib_folder, "vis-network.min.js")
-                if os.path.exists(vis_js):
-                    shutil.rmtree(lib_folder)
-        except Exception as e:
-            _printer.print(f"Error cleaning up lib folder: {e}", color="red")
-
-
-def plot_flow(flow: Flow[Any], filename: str = "flow_plot") -> None:
-    """
-    Convenience function to create and save a flow visualization.
-
-    Parameters
-    ----------
-    flow : Flow
-        Flow instance to visualize.
-    filename : str, optional
-        Output filename without extension, by default "flow_plot".
-
-    Raises
-    ------
-    ValueError
-        If flow object or filename is invalid.
-    IOError
-        If file operations fail.
-    """
-    visualizer = FlowPlot(flow)
-    visualizer.plot(filename)

lib/crewai/src/crewai/flow/flow_wrappers.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 from collections.abc import Callable, Sequence
 import functools
 import inspect
-import types
 from typing import Any, Generic, Literal, ParamSpec, TypeAlias, TypeVar, TypedDict
 
 from typing_extensions import Required, Self
@@ -17,8 +16,6 @@ P = ParamSpec("P")
 R = TypeVar("R")
 
 FlowConditionType: TypeAlias = Literal["OR", "AND"]
-
-# Simple flow condition stored as tuple (condition_type, method_list)
 SimpleFlowCondition: TypeAlias = tuple[FlowConditionType, list[FlowMethodName]]
 
 
@@ -26,6 +23,11 @@ class FlowCondition(TypedDict, total=False):
     """Type definition for flow trigger conditions.
 
     This is a recursive structure where conditions can contain nested FlowConditions.
+
+    Attributes:
+        type: The type of the condition.
+        conditions: A list of conditions types.
+        methods: A list of methods.
     """
 
     type: Required[FlowConditionType]
@@ -79,8 +81,7 @@ class FlowMethod(Generic[P, R]):
             The result of calling the wrapped method.
         """
         if self._instance is not None:
-            bound = types.MethodType(self._meth, self._instance)
-            return bound(*args, **kwargs)
+            return self._meth(self._instance, *args, **kwargs)
         return self._meth(*args, **kwargs)
 
     def unwrap(self) -> Callable[P, R]:

lib/crewai/src/crewai/flow/html_template_handler.py

@@ -1,91 +0,0 @@
-"""HTML template processing and generation for flow visualization diagrams."""
-
-import base64
-import re
-from typing import Any
-
-from crewai.flow.path_utils import validate_path_exists
-
-
-class HTMLTemplateHandler:
-    """Handles HTML template processing and generation for flow visualization diagrams."""
-
-    def __init__(self, template_path: str, logo_path: str) -> None:
-        """
-        Initialize HTMLTemplateHandler with validated template and logo paths.
-
-        Parameters
-        ----------
-        template_path : str
-            Path to the HTML template file.
-        logo_path : str
-            Path to the logo image file.
-
-        Raises
-        ------
-        ValueError
-            If template or logo paths are invalid or files don't exist.
-        """
-        try:
-            self.template_path = validate_path_exists(template_path, "file")
-            self.logo_path = validate_path_exists(logo_path, "file")
-        except ValueError as e:
-            raise ValueError(f"Invalid template or logo path: {e}") from e
-
-    def read_template(self) -> str:
-        """Read and return the HTML template file contents."""
-        with open(self.template_path, "r", encoding="utf-8") as f:
-            return f.read()
-
-    def encode_logo(self) -> str:
-        """Convert the logo SVG file to base64 encoded string."""
-        with open(self.logo_path, "rb") as logo_file:
-            logo_svg_data = logo_file.read()
-            return base64.b64encode(logo_svg_data).decode("utf-8")
-
-    def extract_body_content(self, html: str) -> str:
-        """Extract and return content between body tags from HTML string."""
-        match = re.search("<body.*?>(.*?)</body>", html, re.DOTALL)
-        return match.group(1) if match else ""
-
-    def generate_legend_items_html(self, legend_items: list[dict[str, Any]]) -> str:
-        """Generate HTML markup for the legend items."""
-        legend_items_html = ""
-        for item in legend_items:
-            if "border" in item:
-                legend_items_html += f"""
-                <div class="legend-item">
-                <div class="legend-color-box" style="background-color: {item["color"]}; border: 2px dashed {item["border"]};"></div>
-                <div>{item["label"]}</div>
-                </div>
-                """
-            elif item.get("dashed") is not None:
-                style = "dashed" if item["dashed"] else "solid"
-                legend_items_html += f"""
-                <div class="legend-item">
-                <div class="legend-{style}" style="border-bottom: 2px {style} {item["color"]};"></div>
-                <div>{item["label"]}</div>
-                </div>
-                """
-            else:
-                legend_items_html += f"""
-                <div class="legend-item">
-                <div class="legend-color-box" style="background-color: {item["color"]};"></div>
-                <div>{item["label"]}</div>
-                </div>
-                """
-        return legend_items_html
-
-    def generate_final_html(
-        self, network_body: str, legend_items_html: str, title: str = "Flow Plot"
-    ) -> str:
-        """Combine all components into final HTML document with network visualization."""
-        html_template = self.read_template()
-        logo_svg_base64 = self.encode_logo()
-
-        return (
-            html_template.replace("{{ title }}", title)
-            .replace("{{ network_content }}", network_body)
-            .replace("{{ logo_svg_base64 }}", logo_svg_base64)
-            .replace("<!-- LEGEND_ITEMS_PLACEHOLDER -->", legend_items_html)
-        )

lib/crewai/src/crewai/flow/legend_generator.py

@@ -1,84 +0,0 @@
-"""Legend generation for flow visualization diagrams."""
-
-from typing import Any
-
-from crewai.flow.config import FlowColors
-
-
-def get_legend_items(colors: FlowColors) -> list[dict[str, Any]]:
-    """Generate legend items based on flow colors.
-
-    Parameters
-    ----------
-    colors : FlowColors
-        Dictionary containing color definitions for flow elements.
-
-    Returns
-    -------
-    list[dict[str, Any]]
-        List of legend item dictionaries with labels and styling.
-    """
-    return [
-        {"label": "Start Method", "color": colors["start"]},
-        {"label": "Method", "color": colors["method"]},
-        {
-            "label": "Crew Method",
-            "color": colors["bg"],
-            "border": colors["start"],
-            "dashed": False,
-        },
-        {
-            "label": "Router",
-            "color": colors["router"],
-            "border": colors["router_border"],
-            "dashed": True,
-        },
-        {"label": "Trigger", "color": colors["edge"], "dashed": False},
-        {"label": "AND Trigger", "color": colors["edge"], "dashed": True},
-        {
-            "label": "Router Trigger",
-            "color": colors["router_edge"],
-            "dashed": True,
-        },
-    ]
-
-
-def generate_legend_items_html(legend_items: list[dict[str, Any]]) -> str:
-    """Generate HTML markup for legend items.
-
-    Parameters
-    ----------
-    legend_items : list[dict[str, Any]]
-        List of legend item dictionaries containing labels and styling.
-
-    Returns
-    -------
-    str
-        HTML string containing formatted legend items.
-    """
-    legend_items_html = ""
-    for item in legend_items:
-        if "border" in item:
-            style = "dashed" if item["dashed"] else "solid"
-            legend_items_html += f"""
-            <div class="legend-item">
-            <div class="legend-color-box" style="background-color: {item["color"]}; border: 2px {style} {item["border"]}; border-radius: 5px;"></div>
-            <div>{item["label"]}</div>
-            </div>
-            """
-        elif item.get("dashed") is not None:
-            style = "dashed" if item["dashed"] else "solid"
-            legend_items_html += f"""
-            <div class="legend-item">
-            <div class="legend-{style}" style="border-bottom: 2px {style} {item["color"]}; border-radius: 5px;"></div>
-            <div>{item["label"]}</div>
-            </div>
-            """
-        else:
-            legend_items_html += f"""
-            <div class="legend-item">
-            <div class="legend-color-box" style="background-color: {item["color"]}; border-radius: 5px;"></div>
-            <div>{item["label"]}</div>
-            </div>
-            """
-    return legend_items_html

lib/crewai/src/crewai/flow/path_utils.py

@@ -1,133 +0,0 @@
-"""
-Path utilities for secure file operations in CrewAI flow module.
-
-This module provides utilities for secure path handling to prevent directory
-traversal attacks and ensure paths remain within allowed boundaries.
-"""
-
-from pathlib import Path
-
-
-def safe_path_join(*parts: str, root: str | Path | None = None) -> str:
-    """
-    Safely join path components and ensure the result is within allowed boundaries.
-
-    Parameters
-    ----------
-    *parts : str
-        Variable number of path components to join.
-    root : Union[str, Path, None], optional
-        Root directory to use as base. If None, uses current working directory.
-
-    Returns
-    -------
-    str
-        String representation of the resolved path.
-
-    Raises
-    ------
-    ValueError
-        If the resulting path would be outside the root directory
-        or if any path component is invalid.
-    """
-    if not parts:
-        raise ValueError("No path components provided")
-
-    try:
-        # Convert all parts to strings and clean them
-        clean_parts = [str(part).strip() for part in parts if part]
-        if not clean_parts:
-            raise ValueError("No valid path components provided")
-
-        # Establish root directory
-        root_path = Path(root).resolve() if root else Path.cwd()
-
-        # Join and resolve the full path
-        full_path = Path(root_path, *clean_parts).resolve()
-
-        # Check if the resolved path is within root
-        if not str(full_path).startswith(str(root_path)):
-            raise ValueError(
-                f"Invalid path: Potential directory traversal. Path must be within {root_path}"
-            )
-
-        return str(full_path)
-
-    except Exception as e:
-        if isinstance(e, ValueError):
-            raise
-        raise ValueError(f"Invalid path components: {e!s}") from e
-
-
-def validate_path_exists(path: str | Path, file_type: str = "file") -> str:
-    """
-    Validate that a path exists and is of the expected type.
-
-    Parameters
-    ----------
-    path : Union[str, Path]
-        Path to validate.
-    file_type : str, optional
-        Expected type ('file' or 'directory'), by default 'file'.
-
-    Returns
-    -------
-    str
-        Validated path as string.
-
-    Raises
-    ------
-    ValueError
-        If path doesn't exist or is not of expected type.
-    """
-    try:
-        path_obj = Path(path).resolve()
-
-        if not path_obj.exists():
-            raise ValueError(f"Path does not exist: {path}")
-
-        if file_type == "file" and not path_obj.is_file():
-            raise ValueError(f"Path is not a file: {path}")
-        if file_type == "directory" and not path_obj.is_dir():
-            raise ValueError(f"Path is not a directory: {path}")
-
-        return str(path_obj)
-
-    except Exception as e:
-        if isinstance(e, ValueError):
-            raise
-        raise ValueError(f"Invalid path: {e!s}") from e
-
-
-def list_files(directory: str | Path, pattern: str = "*") -> list[str]:
-    """
-    Safely list files in a directory matching a pattern.
-
-    Parameters
-    ----------
-    directory : Union[str, Path]
-        Directory to search in.
-    pattern : str, optional
-        Glob pattern to match files against, by default "*".
-
-    Returns
-    -------
-    List[str]
-        List of matching file paths.
-
-    Raises
-    ------
-    ValueError
-        If directory is invalid or inaccessible.
-    """
-    try:
-        dir_path = Path(directory).resolve()
-        if not dir_path.is_dir():
-            raise ValueError(f"Not a directory: {directory}")
-
-        return [str(p) for p in dir_path.glob(pattern) if p.is_file()]
-
-    except Exception as e:
-        if isinstance(e, ValueError):
-            raise
-        raise ValueError(f"Error listing files: {e!s}") from e

lib/crewai/src/crewai/flow/utils.py

@@ -13,14 +13,17 @@ Example
 >>> ancestors = build_ancestor_dict(flow)
 """
 
+from __future__ import annotations
+
 import ast
 from collections import defaultdict, deque
 import inspect
 import textwrap
-from typing import Any
+from typing import Any, TYPE_CHECKING
 
 from typing_extensions import TypeIs
 
+from crewai.flow.constants import OR_CONDITION, AND_CONDITION
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -30,6 +33,8 @@ from crewai.flow.flow_wrappers import (
 from crewai.flow.types import FlowMethodCallable, FlowMethodName
 from crewai.utilities.printer import Printer
 
+if TYPE_CHECKING:
+    from crewai.flow.flow import Flow
 
 _printer = Printer()
 
@@ -74,11 +79,22 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
         _printer.print(f"Source code:\n{source}", color="yellow")
         return None
 
-    return_values = set()
-    dict_definitions = {}
+    return_values: set[str] = set()
+    dict_definitions: dict[str, list[str]] = {}
+    variable_values: dict[str, list[str]] = {}
 
-    class DictionaryAssignmentVisitor(ast.NodeVisitor):
-        def visit_Assign(self, node):
+    def extract_string_constants(node: ast.expr) -> list[str]:
+        """Recursively extract all string constants from an AST node."""
+        strings: list[str] = []
+        if isinstance(node, ast.Constant) and isinstance(node.value, str):
+            strings.append(node.value)
+        elif isinstance(node, ast.IfExp):
+            strings.extend(extract_string_constants(node.body))
+            strings.extend(extract_string_constants(node.orelse))
+        return strings
+
+    class VariableAssignmentVisitor(ast.NodeVisitor):
+        def visit_Assign(self, node: ast.Assign) -> None:
             # Check if this assignment is assigning a dictionary literal to a variable
             if isinstance(node.value, ast.Dict) and len(node.targets) == 1:
                 target = node.targets[0]
@@ -92,29 +108,53 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
                     ]
                     if dict_values:
                         dict_definitions[var_name] = dict_values
+
+            if len(node.targets) == 1:
+                target = node.targets[0]
+                var_name_alt: str | None = None
+                if isinstance(target, ast.Name):
+                    var_name_alt = target.id
+                elif isinstance(target, ast.Attribute):
+                    var_name_alt = f"{target.value.id if isinstance(target.value, ast.Name) else '_'}.{target.attr}"
+
+                if var_name_alt:
+                    strings = extract_string_constants(node.value)
+                    if strings:
+                        variable_values[var_name_alt] = strings
+
             self.generic_visit(node)
 
     class ReturnVisitor(ast.NodeVisitor):
-        def visit_Return(self, node):
-            # Direct string return
-            if isinstance(node.value, ast.Constant) and isinstance(
-                node.value.value, str
+        def visit_Return(self, node: ast.Return) -> None:
+            if (
+                node.value
+                and isinstance(node.value, ast.Constant)
+                and isinstance(node.value.value, str)
             ):
                 return_values.add(node.value.value)
-            # Dictionary-based return, like return paths[result]
-            elif isinstance(node.value, ast.Subscript):
-                # Check if we're subscripting a known dictionary variable
+            elif node.value and isinstance(node.value, ast.Subscript):
                 if isinstance(node.value.value, ast.Name):
-                    var_name = node.value.value.id
-                    if var_name in dict_definitions:
-                        # Add all possible dictionary values
-                        for v in dict_definitions[var_name]:
+                    var_name_dict = node.value.value.id
+                    if var_name_dict in dict_definitions:
+                        for v in dict_definitions[var_name_dict]:
                             return_values.add(v)
+            elif node.value:
+                var_name_ret: str | None = None
+                if isinstance(node.value, ast.Name):
+                    var_name_ret = node.value.id
+                elif isinstance(node.value, ast.Attribute):
+                    var_name_ret = f"{node.value.value.id if isinstance(node.value.value, ast.Name) else '_'}.{node.value.attr}"
+
+                if var_name_ret and var_name_ret in variable_values:
+                    for v in variable_values[var_name_ret]:
+                        return_values.add(v)
+
             self.generic_visit(node)
 
-    # First pass: identify dictionary assignments
-    DictionaryAssignmentVisitor().visit(code_ast)
-    # Second pass: identify returns
+        def visit_If(self, node: ast.If) -> None:
+            self.generic_visit(node)
+
+    VariableAssignmentVisitor().visit(code_ast)
     ReturnVisitor().visit(code_ast)
 
     return list(return_values) if return_values else None
@@ -158,7 +198,15 @@ def calculate_node_levels(flow: Any) -> dict[str, int]:
     # Precompute listener dependencies
     or_listeners = defaultdict(list)
     and_listeners = defaultdict(set)
-    for listener_name, (condition_type, trigger_methods) in flow._listeners.items():
+    for listener_name, condition_data in flow._listeners.items():
+        if isinstance(condition_data, tuple):
+            condition_type, trigger_methods = condition_data
+        elif isinstance(condition_data, dict):
+            trigger_methods = _extract_all_methods_recursive(condition_data, flow)
+            condition_type = condition_data.get("type", "OR")
+        else:
+            continue
+
         if condition_type == "OR":
             for method in trigger_methods:
                 or_listeners[method].append(listener_name)
@@ -192,9 +240,13 @@ def calculate_node_levels(flow: Any) -> dict[str, int]:
                         if listener_name not in visited:
                             queue.append(listener_name)
 
-        # Handle router connections
         process_router_paths(flow, current, current_level, levels, queue)
 
+    max_level = max(levels.values()) if levels else 0
+    for method_name in flow._methods:
+        if method_name not in levels:
+            levels[method_name] = max_level + 1
+
     return levels
 
 
@@ -215,8 +267,14 @@ def count_outgoing_edges(flow: Any) -> dict[str, int]:
     counts = {}
     for method_name in flow._methods:
         counts[method_name] = 0
-    for method_name in flow._listeners:
-        _, trigger_methods = flow._listeners[method_name]
+    for condition_data in flow._listeners.values():
+        if isinstance(condition_data, tuple):
+            _, trigger_methods = condition_data
+        elif isinstance(condition_data, dict):
+            trigger_methods = _extract_all_methods_recursive(condition_data, flow)
+        else:
+            continue
+
         for trigger in trigger_methods:
             if trigger in flow._methods:
                 counts[trigger] += 1
@@ -271,21 +329,34 @@ def dfs_ancestors(
         return
     visited.add(node)
 
-    # Handle regular listeners
-    for listener_name, (_, trigger_methods) in flow._listeners.items():
+    for listener_name, condition_data in flow._listeners.items():
+        if isinstance(condition_data, tuple):
+            _, trigger_methods = condition_data
+        elif isinstance(condition_data, dict):
+            trigger_methods = _extract_all_methods_recursive(condition_data, flow)
+        else:
+            continue
+
         if node in trigger_methods:
             ancestors[listener_name].add(node)
             ancestors[listener_name].update(ancestors[node])
             dfs_ancestors(listener_name, ancestors, visited, flow)
 
-    # Handle router methods separately
     if node in flow._routers:
         router_method_name = node
         paths = flow._router_paths.get(router_method_name, [])
         for path in paths:
-            for listener_name, (_, trigger_methods) in flow._listeners.items():
+            for listener_name, condition_data in flow._listeners.items():
+                if isinstance(condition_data, tuple):
+                    _, trigger_methods = condition_data
+                elif isinstance(condition_data, dict):
+                    trigger_methods = _extract_all_methods_recursive(
+                        condition_data, flow
+                    )
+                else:
+                    continue
+
                 if path in trigger_methods:
-                    # Only propagate the ancestors of the router method, not the router method itself
                     ancestors[listener_name].update(ancestors[node])
                     dfs_ancestors(listener_name, ancestors, visited, flow)
 
@@ -335,19 +406,32 @@ def build_parent_children_dict(flow: Any) -> dict[str, list[str]]:
     """
     parent_children: dict[str, list[str]] = {}
 
-    # Map listeners to their trigger methods
-    for listener_name, (_, trigger_methods) in flow._listeners.items():
+    for listener_name, condition_data in flow._listeners.items():
+        if isinstance(condition_data, tuple):
+            _, trigger_methods = condition_data
+        elif isinstance(condition_data, dict):
+            trigger_methods = _extract_all_methods_recursive(condition_data, flow)
+        else:
+            continue
+
         for trigger in trigger_methods:
             if trigger not in parent_children:
                 parent_children[trigger] = []
             if listener_name not in parent_children[trigger]:
                 parent_children[trigger].append(listener_name)
 
-    # Map router methods to their paths and to listeners
     for router_method_name, paths in flow._router_paths.items():
         for path in paths:
-            # Map router method to listeners of each path
-            for listener_name, (_, trigger_methods) in flow._listeners.items():
+            for listener_name, condition_data in flow._listeners.items():
+                if isinstance(condition_data, tuple):
+                    _, trigger_methods = condition_data
+                elif isinstance(condition_data, dict):
+                    trigger_methods = _extract_all_methods_recursive(
+                        condition_data, flow
+                    )
+                else:
+                    continue
+
                 if path in trigger_methods:
                     if router_method_name not in parent_children:
                         parent_children[router_method_name] = []
@@ -382,17 +466,27 @@ def get_child_index(
     return children.index(child)
 
 
-def process_router_paths(flow, current, current_level, levels, queue):
-    """
-    Handle the router connections for the current node.
-    """
+def process_router_paths(
+    flow: Any,
+    current: str,
+    current_level: int,
+    levels: dict[str, int],
+    queue: deque[str],
+) -> None:
+    """Handle the router connections for the current node."""
     if current in flow._routers:
         paths = flow._router_paths.get(current, [])
         for path in paths:
-            for listener_name, (
-                _condition_type,
-                trigger_methods,
-            ) in flow._listeners.items():
+            for listener_name, condition_data in flow._listeners.items():
+                if isinstance(condition_data, tuple):
+                    _condition_type, trigger_methods = condition_data
+                elif isinstance(condition_data, dict):
+                    trigger_methods = _extract_all_methods_recursive(
+                        condition_data, flow
+                    )
+                else:
+                    continue
+
                 if path in trigger_methods:
                     if (
                         listener_name not in levels
@@ -413,7 +507,7 @@ def is_flow_method_name(obj: Any) -> TypeIs[FlowMethodName]:
     return isinstance(obj, str)
 
 
-def is_flow_method_callable(obj: Any) -> TypeIs[FlowMethodCallable]:
+def is_flow_method_callable(obj: Any) -> TypeIs[FlowMethodCallable[..., Any]]:
     """Check if the object is a callable flow method.
 
     Args:
@@ -517,3 +611,107 @@ def is_flow_condition_dict(obj: Any) -> TypeIs[FlowCondition]:
         return False
 
     return True
+
+
+def _extract_all_methods_recursive(
+    condition: str | FlowCondition | dict[str, Any] | list[Any],
+    flow: Flow[Any] | None = None,
+) -> list[FlowMethodName]:
+    """Extract ALL method names from a condition tree recursively.
+
+    This function recursively extracts every method name from the entire
+    condition tree, regardless of nesting. Used for visualization and debugging.
+
+    Note: Only extracts actual method names, not router output strings.
+    If flow is provided, it will filter out strings that are not in flow._methods.
+
+    Args:
+        condition: Can be a string, dict, or list
+        flow: Optional flow instance to filter out non-method strings
+
+    Returns:
+        List of all method names found in the condition tree
+    """
+    if is_flow_method_name(condition):
+        if flow is not None:
+            if condition in flow._methods:
+                return [condition]
+            return []
+        return [condition]
+    if is_flow_condition_dict(condition):
+        normalized = _normalize_condition(condition)
+        methods = []
+        for sub_cond in normalized.get("conditions", []):
+            methods.extend(_extract_all_methods_recursive(sub_cond, flow))
+        return methods
+    if isinstance(condition, list):
+        methods = []
+        for item in condition:
+            methods.extend(_extract_all_methods_recursive(item, flow))
+        return methods
+    return []
+
+
+def _normalize_condition(
+    condition: FlowConditions | FlowCondition | FlowMethodName,
+) -> FlowCondition:
+    """Normalize a condition to standard format with 'conditions' key.
+
+    Args:
+        condition: Can be a string (method name), dict (condition), or list
+
+    Returns:
+        Normalized dict with 'type' and 'conditions' keys
+    """
+    if is_flow_method_name(condition):
+        return {"type": OR_CONDITION, "conditions": [condition]}
+    if is_flow_condition_dict(condition):
+        if "conditions" in condition:
+            return condition
+        if "methods" in condition:
+            return {"type": condition["type"], "conditions": condition["methods"]}
+        return condition
+    if is_flow_condition_list(condition):
+        return {"type": OR_CONDITION, "conditions": condition}
+
+    raise ValueError(f"Cannot normalize condition: {condition}")
+
+
+def _extract_all_methods(
+    condition: str | FlowCondition | dict[str, Any] | list[Any],
+) -> list[FlowMethodName]:
+    """Extract all method names from a condition (including nested).
+
+    For AND conditions, this extracts methods that must ALL complete.
+    For OR conditions nested inside AND, we don't extract their methods
+    since only one branch of the OR needs to trigger, not all methods.
+
+    This function is used for runtime execution logic, where we need to know
+    which methods must complete for AND conditions. For visualization purposes,
+    use _extract_all_methods_recursive() instead.
+
+    Args:
+        condition: Can be a string, dict, or list
+
+    Returns:
+        List of all method names in the condition tree that must complete
+    """
+    if is_flow_method_name(condition):
+        return [condition]
+    if is_flow_condition_dict(condition):
+        normalized = _normalize_condition(condition)
+        cond_type = normalized.get("type", OR_CONDITION)
+
+        if cond_type == AND_CONDITION:
+            return [
+                sub_cond
+                for sub_cond in normalized.get("conditions", [])
+                if is_flow_method_name(sub_cond)
+            ]
+        return []
+    if isinstance(condition, list):
+        methods = []
+        for item in condition:
+            methods.extend(_extract_all_methods(item))
+        return methods
+    return []

lib/crewai/src/crewai/flow/visualization/__init__.py

@@ -0,0 +1,25 @@
+"""Flow structure visualization utilities."""
+
+from crewai.flow.visualization.builder import (
+    build_flow_structure,
+    calculate_execution_paths,
+    print_structure_summary,
+    structure_to_dict,
+)
+from crewai.flow.visualization.renderers import render_interactive
+from crewai.flow.visualization.types import FlowStructure, NodeMetadata, StructureEdge
+
+
+visualize_flow_structure = render_interactive
+
+__all__ = [
+    "FlowStructure",
+    "NodeMetadata",
+    "StructureEdge",
+    "build_flow_structure",
+    "calculate_execution_paths",
+    "print_structure_summary",
+    "render_interactive",
+    "structure_to_dict",
+    "visualize_flow_structure",
+]

lib/crewai/src/crewai/flow/visualization/assets/interactive_flow.html.j2

@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+<html lang="EN">
+<head>
+    <title>CrewAI Flow Visualization</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
+    <link rel="stylesheet" href="'{{ css_path }}'" />
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/prism.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/components/prism-python.min.js"></script>
+    <script src="'{{ js_path }}'"></script>
+</head>
+<body>
+    <!-- Drawer overlay -->
+    <div id="drawer-overlay"></div>
+
+    <!-- Highlight canvas for active nodes/edges above overlay -->
+    <canvas id="highlight-canvas"></canvas>
+
+    <!-- Side drawer -->
+    <div id="drawer" style="visibility: hidden;">
+        <div class="drawer-header">
+            <div class="drawer-title" id="drawer-node-name">Node Details</div>
+            <div style="display: flex; align-items: center;">
+                <button class="drawer-open-ide" id="drawer-open-ide" style="display: none;">
+                    <svg viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="2">
+                        <path d="M4 2 L12 2 L12 14 L4 14 Z" stroke-linecap="round" stroke-linejoin="round"/>
+                        <path d="M6 5 L10 5 M6 8 L10 8 M6 11 L10 11" stroke-linecap="round"/>
+                    </svg>
+                    Open in IDE
+                </button>
+                <button class="drawer-close" id="drawer-close">×</button>
+            </div>
+        </div>
+        <div class="drawer-content" id="drawer-content"></div>
+    </div>
+
+    <div id="info">
+        <div style="text-align: center; margin-bottom: 20px;">
+            <img src="https://cdn.prod.website-files.com/68de1ee6d7c127849807d7a6/68de1ee6d7c127849807d7ef_Logo.svg"
+                 alt="CrewAI Logo"
+                 style="width: 120px; height: auto;">
+        </div>
+        <h3>Flow Execution</h3>
+        <div class="stats">
+            <p><strong>Nodes:</strong> '{{ dag_nodes_count }}'</p>
+            <p><strong>Edges:</strong> '{{ dag_edges_count }}'</p>
+            <p><strong>Topological Paths:</strong> '{{ execution_paths }}'</p>
+        </div>
+        <div class="legend">
+            <div class="legend-title">Node Types</div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ CREWAI_ORANGE }}';"></div>
+                <span>Start Methods</span>
+            </div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ DARK_GRAY }}'; border: 3px solid '{{ CREWAI_ORANGE }}';"></div>
+                <span>Router Methods</span>
+            </div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ DARK_GRAY }}';"></div>
+                <span>Listen Methods</span>
+            </div>
+        </div>
+        <div class="legend">
+            <div class="legend-title">Edge Types</div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2"/>
+                </svg>
+                <span>Router Paths</span>
+            </div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ GRAY }}'" stroke-width="2"/>
+                </svg>
+                <span>OR Conditions</span>
+            </div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2" stroke-dasharray="5,5"/>
+                </svg>
+                <span>AND Conditions</span>
+            </div>
+        </div>
+        <div class="instructions">
+            <strong>Interactions:</strong><br>
+            • Drag to pan<br>
+            • Scroll to zoom<br><br>
+            <strong>IDE:</strong>
+            <select id="ide-selector" style="width: 100%; padding: 4px; margin-top: 4px; border-radius: 3px; border: 1px solid #e0e0e0; background: white; font-size: 12px; cursor: pointer; pointer-events: auto; position: relative; z-index: 10;">
+                <option value="auto">Auto-detect</option>
+                <option value="pycharm">PyCharm</option>
+                <option value="vscode">VS Code</option>
+                <option value="jetbrains">JetBrains (Toolbox)</option>
+            </select>
+        </div>
+    </div>
+
+    <!-- Custom navigation controls -->
+    <div class="nav-controls">
+        <div class="nav-button" id="theme-toggle" title="Toggle Dark Mode">🌙</div>
+        <div class="nav-button" id="zoom-in" title="Zoom In">+</div>
+        <div class="nav-button" id="zoom-out" title="Zoom Out">−</div>
+        <div class="nav-button" id="fit" title="Fit to Screen">⊡</div>
+        <div class="nav-button" id="export-png" title="Export to PNG">🖼</div>
+        <div class="nav-button" id="export-pdf" title="Export to PDF">📄</div>
+        <div class="nav-button" id="export-json" title="Export to JSON">{}</div>
+    </div>
+
+    <div id="network-container">
+        <div id="network"></div>
+    </div>
+</body>
+</html>

lib/crewai/src/crewai/flow/visualization/assets/style.css

@@ -0,0 +1,849 @@
+:root {
+    --bg-primary: '{{ WHITE }}';
+    --bg-secondary: rgba(255, 255, 255, 0.95);
+    --bg-drawer: rgba(255, 255, 255, 0.98);
+    --bg-overlay: rgba(0, 0, 0, 0.3);
+    --text-primary: '{{ DARK_GRAY }}';
+    --text-secondary: '{{ GRAY }}';
+    --border-color: #e0e0e0;
+    --border-subtle: rgba(102, 102, 102, 0.3);
+    --grid-color: rgba(102, 102, 102, 0.08);
+    --shadow-color: rgba(0, 0, 0, 0.1);
+    --shadow-strong: rgba(0, 0, 0, 0.15);
+    --edge-label-text: '{{ GRAY }}';
+    --edge-label-bg: rgba(255, 255, 255, 0.8);
+}
+
+[data-theme="dark"] {
+    --bg-primary: #0d1117;
+    --bg-secondary: rgba(22, 27, 34, 0.95);
+    --bg-drawer: rgba(22, 27, 34, 0.98);
+    --bg-overlay: rgba(0, 0, 0, 0.5);
+    --text-primary: #e6edf3;
+    --text-secondary: #7d8590;
+    --border-color: #30363d;
+    --border-subtle: rgba(48, 54, 61, 0.5);
+    --grid-color: rgba(255, 255, 255, 0.05);
+    --shadow-color: rgba(0, 0, 0, 0.3);
+    --shadow-strong: rgba(0, 0, 0, 0.5);
+    --edge-label-text: #c9d1d9;
+    --edge-label-bg: rgba(22, 27, 34, 0.9);
+}
+
+@keyframes dash {
+    to {
+        stroke-dashoffset: -30;
+    }
+}
+
+body {
+    margin: 0;
+    padding: 0;
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', sans-serif;
+    -webkit-font-smoothing: antialiased;
+    -moz-osx-font-smoothing: grayscale;
+    background: var(--bg-primary);
+    background-image:
+        linear-gradient(var(--grid-color) 1px, transparent 1px),
+        linear-gradient(90deg, var(--grid-color) 1px, transparent 1px);
+    background-size: 20px 20px;
+    background-position: -1px -1px;
+    transition: background-color 0.3s ease;
+}
+#network-container {
+    width: 100vw;
+    height: 100vh;
+    position: fixed;
+    top: 0;
+    left: 0;
+    z-index: 1999;
+    pointer-events: none;
+}
+#network {
+    width: 100%;
+    height: 100%;
+    border: none;
+    background: transparent;
+    pointer-events: auto;
+}
+#info {
+    position: absolute;
+    top: 20px;
+    left: 20px;
+    background: var(--bg-secondary);
+    padding: 20px;
+    border-radius: 8px;
+    box-shadow: 0 4px 12px var(--shadow-strong);
+    max-width: 320px;
+    border: 1px solid var(--border-color);
+    z-index: 10000;
+    pointer-events: auto;
+    transition: background 0.3s ease, border-color 0.3s ease, box-shadow 0.3s ease;
+}
+h3 {
+    margin: 0 0 15px 0;
+    color: var(--text-primary);
+    font-size: 18px;
+    font-weight: 600;
+}
+.stats {
+    margin-bottom: 15px;
+}
+.stats p {
+    margin: 5px 0;
+    color: var(--text-secondary);
+    font-size: 14px;
+    transition: color 0.3s ease;
+}
+.stats strong {
+    color: var(--text-primary);
+    transition: color 0.3s ease;
+}
+.legend {
+    margin-top: 15px;
+    padding-top: 15px;
+    border-top: 1px solid var(--border-color);
+    transition: border-color 0.3s ease;
+}
+.legend-title {
+    font-weight: 600;
+    color: var(--text-primary);
+    margin-bottom: 10px;
+    font-size: 14px;
+    transition: color 0.3s ease;
+}
+.legend-item {
+    margin: 8px 0;
+    display: flex;
+    align-items: center;
+}
+.legend-color {
+    width: 24px;
+    height: 24px;
+    margin-right: 12px;
+    border-radius: 3px;
+    box-sizing: border-box;
+}
+.legend-item span {
+    color: var(--text-secondary);
+    font-size: 13px;
+    transition: color 0.3s ease;
+}
+.instructions {
+    margin-top: 15px;
+    padding-top: 15px;
+    border-top: 1px solid var(--border-color);
+    font-size: 12px;
+    color: var(--text-secondary);
+    line-height: 1.5;
+    transition: color 0.3s ease, border-color 0.3s ease;
+}
+
+#ide-selector {
+    pointer-events: auto;
+    position: relative;
+    z-index: 10001;
+    cursor: pointer;
+}
+
+.nav-controls {
+    position: fixed;
+    top: auto;
+    left: 20px;
+    bottom: 20px;
+    right: auto;
+    display: grid;
+    grid-template-columns: repeat(4, 40px);
+    gap: 8px;
+    z-index: 10002;
+    pointer-events: auto;
+    max-width: 320px;
+}
+
+.nav-controls.drawer-open {
+}
+
+.nav-button {
+    width: 40px;
+    height: 40px;
+    background: var(--bg-secondary);
+    border: 1px solid var(--border-subtle);
+    border-radius: 6px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    box-shadow: 0 2px 8px var(--shadow-color);
+    font-size: 18px;
+    color: var(--text-primary);
+    user-select: none;
+    pointer-events: auto;
+    position: relative;
+    z-index: 10001;
+    transition: background 0.3s ease, border-color 0.3s ease, color 0.3s ease, box-shadow 0.3s ease;
+}
+
+.nav-button:hover {
+    background: var(--border-subtle);
+}
+
+#drawer {
+    position: fixed;
+    top: 0;
+    right: -400px;
+    width: 400px;
+    height: 100vh;
+    background: var(--bg-drawer);
+    box-shadow: -4px 0 12px var(--shadow-strong);
+    transition: right 0.3s cubic-bezier(0.4, 0, 0.2, 1), background 0.3s ease, box-shadow 0.3s ease;
+    z-index: 2000;
+    overflow-y: auto;
+    padding: 24px;
+}
+
+#drawer.open {
+    right: 0;
+}
+
+#drawer-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100vw;
+    height: 100vh;
+    background: var(--bg-overlay);
+    opacity: 0;
+    pointer-events: none;
+    transition: opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1), background 0.3s ease;
+    z-index: 1998;
+    cursor: pointer;
+}
+
+#drawer-overlay.visible {
+    opacity: 1;
+    pointer-events: auto;
+}
+
+#highlight-canvas {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100vw;
+    height: 100vh;
+    z-index: 1999;
+    pointer-events: none;
+    opacity: 0;
+    transition: opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+#highlight-canvas.visible {
+    opacity: 1;
+}
+
+.drawer-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 20px;
+    padding-bottom: 16px;
+    border-bottom: 2px solid '{{ CREWAI_ORANGE }}';
+    position: relative;
+    z-index: 2001;
+}
+
+.drawer-title {
+    font-size: 20px;
+    font-weight: 700;
+    color: var(--text-primary);
+    transition: color 0.3s ease;
+}
+
+.drawer-close {
+    background: none;
+    border: none;
+    font-size: 24px;
+    color: var(--text-secondary);
+    cursor: pointer;
+    padding: 4px 8px;
+    line-height: 1;
+    transition: color 0.3s ease;
+}
+
+.drawer-close:hover {
+    color: '{{ CREWAI_ORANGE }}';
+}
+
+.drawer-open-ide {
+    background: '{{ CREWAI_ORANGE }}';
+    border: none;
+    color: white;
+    cursor: pointer;
+    padding: 6px 12px;
+    border-radius: 4px;
+    font-size: 12px;
+    font-weight: 600;
+    transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    margin-right: 12px;
+    position: relative;
+    z-index: 9999;
+    pointer-events: auto;
+}
+
+.drawer-open-ide:hover {
+    background: #ff6b61;
+    transform: translateY(-1px);
+    box-shadow: 0 2px 8px rgba(255, 90, 80, 0.3);
+}
+
+.drawer-open-ide:active {
+    transform: translateY(0);
+    box-shadow: 0 1px 4px rgba(255, 90, 80, 0.2);
+}
+
+.drawer-open-ide svg {
+    width: 14px;
+    height: 14px;
+}
+
+.drawer-content {
+    color: '{{ DARK_GRAY }}';
+    line-height: 1.6;
+}
+
+.drawer-section {
+    margin-bottom: 20px;
+}
+
+.drawer-metadata-grid {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    grid-template-rows: 1fr 1fr;
+    gap: 0;
+    margin-bottom: 20px;
+    position: relative;
+}
+
+.drawer-metadata-grid::before {
+    content: '';
+    position: absolute;
+    left: 50%;
+    top: 0;
+    bottom: 0;
+    width: 1px;
+    background: rgba(102,102,102,0.15);
+    z-index: 1;
+}
+
+.drawer-metadata-grid::after {
+    content: '';
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 50%;
+    height: 1px;
+    background: rgba(102,102,102,0.15);
+    z-index: 1;
+}
+
+.drawer-metadata-grid .drawer-section {
+    padding: 16px;
+    position: relative;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(4):nth-last-child(1),
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(2),
+.drawer-metadata-grid .drawer-section:nth-child(2):nth-last-child(3),
+.drawer-metadata-grid .drawer-section:nth-child(1):nth-last-child(4) {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    text-align: center;
+    padding-top: 8px;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(4):nth-last-child(1) .drawer-section-title,
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(2) .drawer-section-title,
+.drawer-metadata-grid .drawer-section:nth-child(2):nth-last-child(3) .drawer-section-title,
+.drawer-metadata-grid .drawer-section:nth-child(1):nth-last-child(4) .drawer-section-title {
+    margin-bottom: auto;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(4):nth-last-child(1) > *:not(.drawer-section-title),
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(2) > *:not(.drawer-section-title),
+.drawer-metadata-grid .drawer-section:nth-child(2):nth-last-child(3) > *:not(.drawer-section-title),
+.drawer-metadata-grid .drawer-section:nth-child(1):nth-last-child(4) > *:not(.drawer-section-title) {
+    margin-top: auto;
+    margin-bottom: auto;
+    align-self: center;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(1):nth-last-child(1) {
+    grid-row: 1 / 3;
+    grid-column: 1 / 3;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    text-align: center;
+}
+
+.drawer-metadata-grid:has(.drawer-section:nth-child(1):nth-last-child(1))::before,
+.drawer-metadata-grid:has(.drawer-section:nth-child(1):nth-last-child(1))::after {
+    display: none;
+}
+
+.drawer-metadata-grid:has(.drawer-section:nth-child(2):nth-last-child(1)) {
+    grid-template-rows: 1fr;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(2):nth-last-child(1),
+.drawer-metadata-grid .drawer-section:nth-child(1):nth-last-child(2) {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    text-align: center;
+    justify-content: center;
+}
+
+.drawer-metadata-grid:has(.drawer-section:nth-child(2):nth-last-child(1))::after {
+    display: none;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(1) {
+    grid-row: 1 / 3;
+    grid-column: 2;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+}
+
+.drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1))::after {
+    right: 50%;
+}
+
+.drawer-section-title {
+    font-size: 12px;
+    text-transform: uppercase;
+    color: '{{ GRAY }}';
+    letter-spacing: 0.5px;
+    margin-bottom: 8px;
+    font-weight: 600;
+}
+
+.drawer-badge {
+    display: inline-block;
+    padding: 4px 12px;
+    border-radius: 4px;
+    font-size: 11px;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.drawer-list {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+}
+
+.drawer-list li {
+    padding: 6px 0;
+    border-bottom: 1px solid rgba(102,102,102,0.1);
+}
+
+.drawer-list li:last-child {
+    border-bottom: none;
+}
+
+.drawer-section:has(.drawer-code-link[style*="color: '{{ CREWAI_ORANGE }}'"]) .drawer-list li {
+    border-bottom: none;
+    padding: 3px 0;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(3) .drawer-list li {
+    border-bottom: none;
+    padding: 3px 0;
+}
+
+.drawer-code {
+    background: rgba(102,102,102,0.08);
+    padding: 4px 8px;
+    border-radius: 3px;
+    font-size: 12px;
+    font-family: monospace;
+    color: '{{ DARK_GRAY }}';
+    border: 1px solid rgba(102,102,102,0.12);
+}
+
+.drawer-code-link {
+    background: rgba(102,102,102,0.08);
+    padding: 4px 8px;
+    border-radius: 3px;
+    font-size: 12px;
+    font-family: monospace;
+    color: '{{ CREWAI_ORANGE }}';
+    border: 1px solid rgba(255,90,80,0.2);
+    cursor: pointer;
+    transition: all 0.2s;
+    display: inline-block;
+}
+
+.drawer-code-link:hover {
+    background: rgba(255,90,80,0.12);
+    border-color: '{{ CREWAI_ORANGE }}';
+    transform: translateX(2px);
+}
+
+.code-block-container {
+    background: transparent;
+    border: 1px solid #30363d;
+    overflow-x: auto;
+    position: relative;
+}
+
+.code-copy-button {
+    position: absolute;
+    top: 6px;
+    right: 6px;
+    background: rgba(255, 255, 255, 0.1);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    color: #c9d1d9;
+    padding: 5px;
+    border-radius: 4px;
+    cursor: pointer;
+    transition: all 0.2s;
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+    z-index: 10;
+    width: 18px;
+    height: 18px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.code-copy-button:hover {
+    background: rgba(255, 255, 255, 0.15);
+    border-color: rgba(255, 255, 255, 0.3);
+}
+
+.code-copy-button:active {
+    background: rgba(255, 255, 255, 0.2);
+}
+
+.code-copy-button.copied {
+    background: rgba(16, 185, 129, 0.2);
+    border-color: rgba(16, 185, 129, 0.4);
+    color: #10b981;
+}
+
+.code-copy-button svg {
+    width: 10px;
+    height: 10px;
+    position: absolute;
+    transition: opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1), transform 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.code-copy-button .copy-icon {
+    opacity: 1;
+    transform: scale(1) rotate(0deg);
+}
+
+.code-copy-button .check-icon {
+    opacity: 0;
+    transform: scale(0.5) rotate(-90deg);
+}
+
+.code-copy-button.copied .copy-icon {
+    opacity: 0;
+    transform: scale(0.5) rotate(90deg);
+}
+
+.code-copy-button.copied .check-icon {
+    opacity: 1;
+    transform: scale(1) rotate(0deg);
+}
+
+.code-block {
+    margin: 0;
+    padding: 0;
+    overflow-x: auto;
+}
+
+.code-block .code-line {
+    display: block;
+    white-space: pre;
+    min-height: 1.6em;
+}
+
+.code-block .line-number {
+    color: #6e7681;
+    display: inline-block;
+    padding-left: 10px;
+    padding-right: 8px;
+    margin-right: 8px;
+    text-align: right;
+    user-select: none;
+    border-right: 1px solid #30363d;
+}
+
+.code-block .json-key {
+    color: #79c0ff;
+    font-weight: 400;
+}
+
+.code-block .json-bracket {
+    color: #c9d1d9;
+    font-weight: 400;
+}
+
+.code-block .json-colon {
+    color: #c9d1d9;
+}
+
+.accordion-section {
+    border: 1px solid rgba(102,102,102,0.15);
+    border-top: 1px solid rgba(255, 255, 255, 0.1);
+    border-left: 1px solid rgba(255, 255, 255, 0.05);
+    border-radius: 6px;
+    margin-bottom: 12px;
+    overflow: hidden;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    background: #0d1117;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2), 0 1px 3px rgba(0, 0, 0, 0.3);
+}
+
+.accordion-section:hover {
+    border-color: rgba(102,102,102,0.25);
+}
+
+.accordion-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 10px 10px;
+    cursor: pointer;
+    background: rgba(102,102,102,0.05);
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    user-select: none;
+}
+
+.accordion-subheader {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 10px 10px;
+    cursor: pointer;
+    background:  #0d1117;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    user-select: none;
+    box-shadow: 0 1px 4px rgba(0, 0, 0, 0.15);
+    border-top: 1px solid rgba(255, 255, 255, 0.08);
+    border-bottom: 1px solid rgba(0, 0, 0, 0.2);
+}
+
+.accordion-header:hover {
+    background: rgba(102,102,102,0.1);
+}
+
+.accordion-header:active {
+    background: rgba(102,102,102,0.15);
+}
+
+.accordion-title {
+    font-size: 12px;
+    font-weight: 400;
+    text-transform: uppercase;
+    color: white;
+    letter-spacing: 0.5px;
+    font-family: monospace;
+}
+
+.accordion-icon {
+    width: 16px;
+    height: 16px;
+    transition: transform 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    color: '{{ GRAY }}';
+}
+
+.accordion-section.expanded .accordion-icon {
+    transform: rotate(180deg);
+}
+
+.accordion-content {
+    max-height: 0;
+    overflow: hidden;
+    transition: max-height 0.3s cubic-bezier(0.4, 0, 0.2, 1),
+                opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    opacity: 0;
+    padding: 0;
+}
+
+.accordion-section.expanded .accordion-content {
+    max-height: 2000px;
+    opacity: 1;
+    padding: 0;
+}
+
+.accordion-content .drawer-section {
+    margin-bottom: 0;
+}
+
+.accordion-content .drawer-section:not(:first-child) {
+    margin-top: 0;
+}
+
+.accordion-content .drawer-section-title {
+    display: none;
+}
+
+.accordion-content .class-section-title {
+    display: block;
+    font-size: 12px;
+    text-transform: uppercase;
+    color: '{{ GRAY }}';
+    letter-spacing: 0.5px;
+    margin-bottom: 8px;
+    font-weight: 600;
+}
+
+/*
+ * Synthwave '84 Theme originally by Robb Owen [@Robb0wen] for Visual Studio Code
+ * Demo: https://marc.dev/demo/prism-synthwave84
+ *
+ * Ported for PrismJS by Marc Backes [@themarcba]
+ */
+
+code[class*="language-"],
+pre[class*="language-"] {
+	color: #f92aad;
+	text-shadow: 0 0 2px #100c0f, 0 0 5px #dc078e33, 0 0 10px #fff3;
+	background: none;
+	font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace;
+	font-size: 0.7em;
+	text-align: left;
+	white-space: pre;
+	word-spacing: normal;
+	word-break: normal;
+	word-wrap: normal;
+	line-height: 1.25;
+
+	-moz-tab-size: 4;
+	-o-tab-size: 4;
+	tab-size: 4;
+
+	-webkit-hyphens: none;
+	-moz-hyphens: none;
+	-ms-hyphens: none;
+	hyphens: none;
+}
+
+pre[class*="language-"] {
+	padding-top: 0.4em;
+    padding-bottom: 0.4em;
+	overflow: auto;
+}
+
+:not(pre) > code[class*="language-"],
+pre[class*="language-"] {
+	background-color: transparent !important;
+	background-image: linear-gradient(to bottom, #2a2139 75%, #34294f);
+}
+
+:not(pre) > code[class*="language-"] {
+	padding: .1em;
+	border-radius: .3em;
+	white-space: normal;
+}
+
+.token.comment,
+.token.block-comment,
+.token.prolog,
+.token.doctype,
+.token.cdata {
+	color: #8e8e8e;
+}
+
+.token.punctuation {
+	color: #ccc;
+}
+
+.token.tag,
+.token.attr-name,
+.token.namespace,
+.token.number,
+.token.unit,
+.token.hexcode,
+.token.deleted {
+	color: #e2777a;
+}
+
+.token.property,
+.token.selector {
+	color: #72f1b8;
+	text-shadow: 0 0 2px #100c0f, 0 0 10px #257c5575, 0 0 35px #21272475;
+}
+
+.token.function-name {
+	color: #6196cc;
+}
+
+.token.boolean,
+.token.selector .token.id,
+.token.function {
+	color: #fdfdfd;
+	text-shadow: 0 0 2px #001716, 0 0 3px #03edf975, 0 0 5px #03edf975, 0 0 8px #03edf975;
+}
+
+.token.class-name {
+	color: #fff5f6;
+	text-shadow: 0 0 2px #000, 0 0 10px #fc1f2c75, 0 0 5px #fc1f2c75, 0 0 25px #fc1f2c75;
+}
+
+.token.constant,
+.token.symbol {
+	color: #f92aad;
+	text-shadow: 0 0 2px #100c0f, 0 0 5px #dc078e33, 0 0 10px #fff3;
+}
+
+.token.important,
+.token.atrule,
+.token.keyword,
+.token.selector .token.class,
+.token.builtin {
+	color: #f4eee4;
+	text-shadow: 0 0 2px #393a33, 0 0 8px #f39f0575, 0 0 2px #f39f0575;
+}
+
+.token.string,
+.token.char,
+.token.attr-value,
+.token.regex,
+.token.variable {
+	color: #f87c32;
+}
+
+.token.operator,
+.token.entity,
+.token.url {
+	color: #67cdcc;
+}
+
+.token.important,
+.token.bold {
+	font-weight: bold;
+}
+
+.token.italic {
+	font-style: italic;
+}
+
+.token.entity {
+	cursor: help;
+}
+
+.token.inserted {
+	color: green;
+}

lib/crewai/src/crewai/flow/visualization/builder.py

@@ -0,0 +1,432 @@
+"""Flow structure builder for analyzing Flow execution."""
+
+from __future__ import annotations
+from collections import defaultdict
+import inspect
+from typing import TYPE_CHECKING, Any
+
+from crewai.flow.constants import OR_CONDITION
+from crewai.flow.types import FlowMethodName
+from crewai.flow.utils import (
+    _extract_all_methods_recursive,
+    is_flow_condition_dict,
+    is_simple_flow_condition,
+)
+from crewai.flow.visualization.schema import extract_method_signature
+from crewai.flow.visualization.types import FlowStructure, NodeMetadata, StructureEdge
+
+
+if TYPE_CHECKING:
+    from crewai.flow.flow import Flow
+
+
+def _extract_direct_or_triggers(
+    condition: str | dict[str, Any] | list[Any],
+) -> list[str]:
+    """Extract direct OR-level trigger strings from a condition.
+
+    This function extracts strings that would directly trigger a listener,
+    meaning they appear at the top level of an OR condition. Strings nested
+    inside AND conditions are NOT considered direct triggers for router paths.
+
+    For example:
+    - or_("a", "b") -> ["a", "b"] (both are direct triggers)
+    - and_("a", "b") -> [] (neither are direct triggers, both required)
+    - or_(and_("a", "b"), "c") -> ["c"] (only "c" is a direct trigger)
+
+    Args:
+        condition: Can be a string, dict, or list.
+
+    Returns:
+        List of direct OR-level trigger strings.
+    """
+    if isinstance(condition, str):
+        return [condition]
+    if isinstance(condition, dict):
+        cond_type = condition.get("type", "OR")
+        conditions_list = condition.get("conditions", [])
+
+        if cond_type == "OR":
+            strings = []
+            for sub_cond in conditions_list:
+                strings.extend(_extract_direct_or_triggers(sub_cond))
+            return strings
+        else:
+            return []
+    if isinstance(condition, list):
+        strings = []
+        for item in condition:
+            strings.extend(_extract_direct_or_triggers(item))
+        return strings
+    if callable(condition) and hasattr(condition, "__name__"):
+        return [condition.__name__]
+    return []
+
+
+def _extract_all_trigger_names(
+    condition: str | dict[str, Any] | list[Any],
+) -> list[str]:
+    """Extract ALL trigger names from a condition for display purposes.
+
+    Unlike _extract_direct_or_triggers, this extracts ALL strings and method
+    names from the entire condition tree, including those nested in AND conditions.
+    This is used for displaying trigger information in the UI.
+
+    For example:
+    - or_("a", "b") -> ["a", "b"]
+    - and_("a", "b") -> ["a", "b"]
+    - or_(and_("a", method_6), method_4) -> ["a", "method_6", "method_4"]
+
+    Args:
+        condition: Can be a string, dict, or list.
+
+    Returns:
+        List of all trigger names found in the condition.
+    """
+    if isinstance(condition, str):
+        return [condition]
+    if isinstance(condition, dict):
+        conditions_list = condition.get("conditions", [])
+        strings = []
+        for sub_cond in conditions_list:
+            strings.extend(_extract_all_trigger_names(sub_cond))
+        return strings
+    if isinstance(condition, list):
+        strings = []
+        for item in condition:
+            strings.extend(_extract_all_trigger_names(item))
+        return strings
+    if callable(condition) and hasattr(condition, "__name__"):
+        return [condition.__name__]
+    return []
+
+
+def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
+    """Build a structure representation of a Flow's execution.
+
+    Args:
+        flow: Flow instance to analyze.
+
+    Returns:
+        Dictionary with nodes, edges, start_methods, and router_methods.
+    """
+    nodes: dict[str, NodeMetadata] = {}
+    edges: list[StructureEdge] = []
+    start_methods: list[str] = []
+    router_methods: list[str] = []
+
+    for method_name, method in flow._methods.items():
+        node_metadata: NodeMetadata = {"type": "listen"}
+
+        if hasattr(method, "__is_start_method__") and method.__is_start_method__:
+            node_metadata["type"] = "start"
+            start_methods.append(method_name)
+
+        if hasattr(method, "__is_router__") and method.__is_router__:
+            node_metadata["is_router"] = True
+            node_metadata["type"] = "router"
+            router_methods.append(method_name)
+
+            node_metadata["condition_type"] = "IF"
+
+            if method_name in flow._router_paths:
+                node_metadata["router_paths"] = [
+                    str(p) for p in flow._router_paths[method_name]
+                ]
+
+        if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
+            node_metadata["trigger_methods"] = [
+                str(m) for m in method.__trigger_methods__
+            ]
+
+        if hasattr(method, "__condition_type__") and method.__condition_type__:
+            if "condition_type" not in node_metadata:
+                node_metadata["condition_type"] = method.__condition_type__
+
+        if (
+            hasattr(method, "__trigger_condition__")
+            and method.__trigger_condition__ is not None
+        ):
+            node_metadata["trigger_condition"] = method.__trigger_condition__
+
+            if "trigger_methods" not in node_metadata:
+                extracted = _extract_all_trigger_names(method.__trigger_condition__)
+                if extracted:
+                    node_metadata["trigger_methods"] = extracted
+
+        node_metadata["method_signature"] = extract_method_signature(
+            method, method_name
+        )
+
+        try:
+            source_code = inspect.getsource(method)
+            node_metadata["source_code"] = source_code
+
+            try:
+                source_lines, start_line = inspect.getsourcelines(method)
+                node_metadata["source_lines"] = source_lines
+                node_metadata["source_start_line"] = start_line
+            except (OSError, TypeError):
+                pass
+
+            try:
+                source_file = inspect.getsourcefile(method)
+                if source_file:
+                    node_metadata["source_file"] = source_file
+            except (OSError, TypeError):
+                try:
+                    class_file = inspect.getsourcefile(flow.__class__)
+                    if class_file:
+                        node_metadata["source_file"] = class_file
+                except (OSError, TypeError):
+                    pass
+        except (OSError, TypeError):
+            pass
+
+        try:
+            class_obj = flow.__class__
+
+            if class_obj:
+                class_name = class_obj.__name__
+
+                bases = class_obj.__bases__
+                if bases:
+                    base_strs = []
+                    for base in bases:
+                        if hasattr(base, "__name__"):
+                            if hasattr(base, "__origin__"):
+                                base_strs.append(str(base))
+                            else:
+                                base_strs.append(base.__name__)
+                        else:
+                            base_strs.append(str(base))
+
+                    try:
+                        source_lines = inspect.getsource(class_obj).split("\n")
+                        _, class_start_line = inspect.getsourcelines(class_obj)
+
+                        for idx, line in enumerate(source_lines):
+                            stripped = line.strip()
+                            if stripped.startswith("class ") and class_name in stripped:
+                                class_signature = stripped.rstrip(":")
+                                node_metadata["class_signature"] = class_signature
+                                node_metadata["class_line_number"] = (
+                                    class_start_line + idx
+                                )
+                                break
+                    except (OSError, TypeError):
+                        class_signature = f"class {class_name}({', '.join(base_strs)})"
+                        node_metadata["class_signature"] = class_signature
+                else:
+                    class_signature = f"class {class_name}"
+                    node_metadata["class_signature"] = class_signature
+
+                node_metadata["class_name"] = class_name
+        except (OSError, TypeError, AttributeError):
+            pass
+
+        nodes[method_name] = node_metadata
+
+    for listener_name, condition_data in flow._listeners.items():
+        condition_type: str | None = None
+        trigger_methods_list: list[str] = []
+
+        if is_simple_flow_condition(condition_data):
+            cond_type, methods = condition_data
+            condition_type = cond_type
+            trigger_methods_list = [str(m) for m in methods]
+        elif is_flow_condition_dict(condition_data):
+            condition_type = condition_data.get("type", OR_CONDITION)
+            methods_recursive = _extract_all_methods_recursive(condition_data, flow)
+            trigger_methods_list = [str(m) for m in methods_recursive]
+
+        edges.extend(
+            StructureEdge(
+                source=str(trigger_method),
+                target=str(listener_name),
+                condition_type=condition_type,
+                is_router_path=False,
+            )
+            for trigger_method in trigger_methods_list
+            if trigger_method in nodes
+        )
+
+    for router_method_name in router_methods:
+        if router_method_name not in flow._router_paths:
+            continue
+
+        router_paths = flow._router_paths[FlowMethodName(router_method_name)]
+
+        for path in router_paths:
+            for listener_name, condition_data in flow._listeners.items():
+                trigger_strings_from_cond: list[str] = []
+
+                if is_simple_flow_condition(condition_data):
+                    _, methods = condition_data
+                    trigger_strings_from_cond = [str(m) for m in methods]
+                elif is_flow_condition_dict(condition_data):
+                    trigger_strings_from_cond = _extract_direct_or_triggers(
+                        condition_data
+                    )
+
+                if str(path) in trigger_strings_from_cond:
+                    edges.append(
+                        StructureEdge(
+                            source=router_method_name,
+                            target=str(listener_name),
+                            condition_type=None,
+                            is_router_path=True,
+                        )
+                    )
+
+    for start_method in flow._start_methods:
+        if start_method not in nodes and start_method in flow._methods:
+            method = flow._methods[start_method]
+            nodes[str(start_method)] = NodeMetadata(type="start")
+
+            if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
+                nodes[str(start_method)]["trigger_methods"] = [
+                    str(m) for m in method.__trigger_methods__
+                ]
+            if hasattr(method, "__condition_type__") and method.__condition_type__:
+                nodes[str(start_method)]["condition_type"] = method.__condition_type__
+
+    return FlowStructure(
+        nodes=nodes,
+        edges=edges,
+        start_methods=start_methods,
+        router_methods=router_methods,
+    )
+
+
+def structure_to_dict(structure: FlowStructure) -> dict[str, Any]:
+    """Convert FlowStructure to plain dictionary for serialization.
+
+    Args:
+        structure: FlowStructure to convert.
+
+    Returns:
+        Plain dictionary representation.
+    """
+    return {
+        "nodes": dict(structure["nodes"]),
+        "edges": list(structure["edges"]),
+        "start_methods": list(structure["start_methods"]),
+        "router_methods": list(structure["router_methods"]),
+    }
+
+
+def print_structure_summary(structure: FlowStructure) -> str:
+    """Generate human-readable summary of Flow structure.
+
+    Args:
+        structure: FlowStructure to summarize.
+
+    Returns:
+        Formatted string summary.
+    """
+    lines: list[str] = []
+    lines.append("Flow Execution Structure")
+    lines.append("=" * 50)
+    lines.append(f"Total nodes: {len(structure['nodes'])}")
+    lines.append(f"Total edges: {len(structure['edges'])}")
+    lines.append(f"Start methods: {len(structure['start_methods'])}")
+    lines.append(f"Router methods: {len(structure['router_methods'])}")
+    lines.append("")
+
+    if structure["start_methods"]:
+        lines.append("Start Methods:")
+        for method_name in structure["start_methods"]:
+            node = structure["nodes"][method_name]
+            lines.append(f"  - {method_name}")
+            if node.get("condition_type"):
+                lines.append(f"    Condition: {node['condition_type']}")
+            if node.get("trigger_methods"):
+                lines.append(f"    Triggers on: {', '.join(node['trigger_methods'])}")
+        lines.append("")
+
+    if structure["router_methods"]:
+        lines.append("Router Methods:")
+        for method_name in structure["router_methods"]:
+            node = structure["nodes"][method_name]
+            lines.append(f"  - {method_name}")
+            if node.get("router_paths"):
+                lines.append(f"    Paths: {', '.join(node['router_paths'])}")
+        lines.append("")
+
+    if structure["edges"]:
+        lines.append("Connections:")
+        for edge in structure["edges"]:
+            edge_type = ""
+            if edge["is_router_path"]:
+                edge_type = " [Router Path]"
+            elif edge["condition_type"]:
+                edge_type = f" [{edge['condition_type']}]"
+
+            lines.append(f"  {edge['source']} -> {edge['target']}{edge_type}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def calculate_execution_paths(structure: FlowStructure) -> int:
+    """Calculate number of possible execution paths through the flow.
+
+    Args:
+        structure: FlowStructure to analyze.
+
+    Returns:
+        Number of possible execution paths.
+    """
+    graph = defaultdict(list)
+    for edge in structure["edges"]:
+        graph[edge["source"]].append(
+            {
+                "target": edge["target"],
+                "is_router": edge["is_router_path"],
+                "condition": edge["condition_type"],
+            }
+        )
+
+    all_nodes = set(structure["nodes"].keys())
+    nodes_with_outgoing = set(edge["source"] for edge in structure["edges"])
+    terminal_nodes = all_nodes - nodes_with_outgoing
+
+    if not structure["start_methods"] or not terminal_nodes:
+        return 0
+
+    def count_paths_from(node: str, visited: set[str]) -> int:
+        if node in terminal_nodes:
+            return 1
+
+        if node in visited:
+            return 0
+
+        visited.add(node)
+
+        outgoing = graph[node]
+        if not outgoing:
+            visited.remove(node)
+            return 1
+
+        if node in structure["router_methods"]:
+            total = 0
+            for edge_info in outgoing:
+                target = str(edge_info["target"])
+                total += count_paths_from(target, visited.copy())
+            visited.remove(node)
+            return total
+
+        total = 0
+        for edge_info in outgoing:
+            target = str(edge_info["target"])
+            total += count_paths_from(target, visited.copy())
+
+        visited.remove(node)
+        return total if total > 0 else 1
+
+    total_paths = 0
+    for start in structure["start_methods"]:
+        total_paths += count_paths_from(start, set())
+
+    return max(total_paths, 1)

lib/crewai/src/crewai/flow/visualization/renderers/__init__.py

@@ -0,0 +1,8 @@
+"""Flow structure visualization renderers."""
+
+from crewai.flow.visualization.renderers.interactive import render_interactive
+
+
+__all__ = [
+    "render_interactive",
+]

lib/crewai/src/crewai/flow/visualization/renderers/interactive.py

@@ -0,0 +1,343 @@
+"""Interactive HTML renderer for Flow structure visualization."""
+
+import json
+from pathlib import Path
+import tempfile
+from typing import Any, ClassVar
+import webbrowser
+
+from jinja2 import Environment, FileSystemLoader, nodes, select_autoescape
+from jinja2.ext import Extension
+from jinja2.parser import Parser
+
+from crewai.flow.visualization.builder import calculate_execution_paths
+from crewai.flow.visualization.types import FlowStructure
+
+
+class CSSExtension(Extension):
+    """Jinja2 extension for rendering CSS link tags.
+
+    Provides {% css 'path/to/file.css' %} tag syntax.
+    """
+
+    tags: ClassVar[set[str]] = {"css"}  # type: ignore[assignment]
+
+    def parse(self, parser: Parser) -> nodes.Node:
+        """Parse {% css 'styles.css' %} tag.
+
+        Args:
+            parser: Jinja2 parser instance.
+
+        Returns:
+            Output node with rendered CSS link tag.
+        """
+        lineno: int = next(parser.stream).lineno
+        args: list[nodes.Expr] = [parser.parse_expression()]
+        return nodes.Output([self.call_method("_render_css", args)]).set_lineno(lineno)
+
+    def _render_css(self, href: str) -> str:
+        """Render CSS link tag.
+
+        Args:
+            href: Path to CSS file.
+
+        Returns:
+            HTML link tag string.
+        """
+        return f'<link rel="stylesheet" href="{href}">'
+
+
+class JSExtension(Extension):
+    """Jinja2 extension for rendering script tags.
+
+    Provides {% js 'path/to/file.js' %} tag syntax.
+    """
+
+    tags: ClassVar[set[str]] = {"js"}  # type: ignore[assignment]
+
+    def parse(self, parser: Parser) -> nodes.Node:
+        """Parse {% js 'script.js' %} tag.
+
+        Args:
+            parser: Jinja2 parser instance.
+
+        Returns:
+            Output node with rendered script tag.
+        """
+        lineno: int = next(parser.stream).lineno
+        args: list[nodes.Expr] = [parser.parse_expression()]
+        return nodes.Output([self.call_method("_render_js", args)]).set_lineno(lineno)
+
+    def _render_js(self, src: str) -> str:
+        """Render script tag.
+
+        Args:
+            src: Path to JavaScript file.
+
+        Returns:
+            HTML script tag string.
+        """
+        return f'<script src="{src}"></script>'
+
+
+CREWAI_ORANGE = "#FF5A50"
+DARK_GRAY = "#333333"
+WHITE = "#FFFFFF"
+GRAY = "#666666"
+BG_DARK = "#0d1117"
+BG_CARD = "#161b22"
+BORDER_SUBTLE = "#30363d"
+TEXT_PRIMARY = "#e6edf3"
+TEXT_SECONDARY = "#7d8590"
+
+
+def render_interactive(
+    dag: FlowStructure,
+    filename: str = "flow_dag.html",
+    show: bool = True,
+) -> str:
+    """Create interactive HTML visualization of Flow structure.
+
+    Generates three output files in a temporary directory: HTML template,
+    CSS stylesheet, and JavaScript. Optionally opens the visualization in
+    default browser.
+
+    Args:
+        dag: FlowStructure to visualize.
+        filename: Output HTML filename (basename only, no path).
+        show: Whether to open in browser.
+
+    Returns:
+        Absolute path to generated HTML file in temporary directory.
+    """
+    nodes_list: list[dict[str, Any]] = []
+    for name, metadata in dag["nodes"].items():
+        node_type: str = metadata.get("type", "listen")
+
+        color_config: dict[str, Any]
+        font_color: str
+        border_width: int
+
+        if node_type == "start":
+            color_config = {
+                "background": CREWAI_ORANGE,
+                "border": CREWAI_ORANGE,
+                "highlight": {
+                    "background": CREWAI_ORANGE,
+                    "border": CREWAI_ORANGE,
+                },
+            }
+            font_color = WHITE
+            border_width = 2
+        elif node_type == "router":
+            color_config = {
+                "background": DARK_GRAY,
+                "border": CREWAI_ORANGE,
+                "highlight": {
+                    "background": DARK_GRAY,
+                    "border": CREWAI_ORANGE,
+                },
+            }
+            font_color = WHITE
+            border_width = 3
+        else:
+            color_config = {
+                "background": DARK_GRAY,
+                "border": DARK_GRAY,
+                "highlight": {
+                    "background": DARK_GRAY,
+                    "border": DARK_GRAY,
+                },
+            }
+            font_color = WHITE
+            border_width = 2
+
+        title_parts: list[str] = []
+
+        type_badge_bg: str = (
+            CREWAI_ORANGE if node_type in ["start", "router"] else DARK_GRAY
+        )
+        title_parts.append(f"""
+            <div style="border-bottom: 1px solid rgba(102,102,102,0.15); padding-bottom: 8px; margin-bottom: 10px;">
+                <div style="font-size: 13px; font-weight: 700; color: {DARK_GRAY}; margin-bottom: 6px;">{name}</div>
+                <span style="display: inline-block; background: {type_badge_bg}; color: white; padding: 2px 8px; border-radius: 4px; font-size: 10px; font-weight: 600; text-transform: uppercase; letter-spacing: 0.5px;">{node_type}</span>
+            </div>
+        """)
+
+        if metadata.get("condition_type"):
+            condition = metadata["condition_type"]
+            if condition == "AND":
+                condition_badge_bg = "rgba(255,90,80,0.12)"
+                condition_color = CREWAI_ORANGE
+            elif condition == "IF":
+                condition_badge_bg = "rgba(255,90,80,0.18)"
+                condition_color = CREWAI_ORANGE
+            else:
+                condition_badge_bg = "rgba(102,102,102,0.12)"
+                condition_color = GRAY
+            title_parts.append(f"""
+                <div style="margin-bottom: 8px;">
+                    <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 3px; font-weight: 600;">Condition</div>
+                    <span style="display: inline-block; background: {condition_badge_bg}; color: {condition_color}; padding: 3px 8px; border-radius: 4px; font-size: 11px; font-weight: 700;">{condition}</span>
+                </div>
+            """)
+
+        if metadata.get("trigger_methods"):
+            triggers = metadata["trigger_methods"]
+            triggers_items = "".join(
+                [
+                    f'<li style="margin: 3px 0;"><code style="background: rgba(102,102,102,0.08); padding: 2px 6px; border-radius: 3px; font-size: 10px; color: {DARK_GRAY}; border: 1px solid rgba(102,102,102,0.12);">{t}</code></li>'
+                    for t in triggers
+                ]
+            )
+            title_parts.append(f"""
+                <div style="margin-bottom: 8px;">
+                    <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">Triggers</div>
+                    <ul style="list-style: none; padding: 0; margin: 0;">{triggers_items}</ul>
+                </div>
+            """)
+
+        if metadata.get("router_paths"):
+            paths = metadata["router_paths"]
+            paths_items = "".join(
+                [
+                    f'<li style="margin: 3px 0;"><code style="background: rgba(255,90,80,0.08); padding: 2px 6px; border-radius: 3px; font-size: 10px; color: {CREWAI_ORANGE}; border: 1px solid rgba(255,90,80,0.2); font-weight: 600;">{p}</code></li>'
+                    for p in paths
+                ]
+            )
+            title_parts.append(f"""
+                <div>
+                    <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">Router Paths</div>
+                    <ul style="list-style: none; padding: 0; margin: 0;">{paths_items}</ul>
+                </div>
+            """)
+
+        bg_color = color_config["background"]
+        border_color = color_config["border"]
+
+        nodes_list.append(
+            {
+                "id": name,
+                "label": name,
+                "title": "".join(title_parts),
+                "shape": "custom",
+                "size": 30,
+                "nodeStyle": {
+                    "name": name,
+                    "bgColor": bg_color,
+                    "borderColor": border_color,
+                    "borderWidth": border_width,
+                    "fontColor": font_color,
+                },
+                "opacity": 1.0,
+                "glowSize": 0,
+                "glowColor": None,
+            }
+        )
+
+    execution_paths: int = calculate_execution_paths(dag)
+
+    edges_list: list[dict[str, Any]] = []
+    for edge in dag["edges"]:
+        edge_label: str = ""
+        edge_color: str = GRAY
+        edge_dashes: bool | list[int] = False
+
+        if edge["is_router_path"]:
+            edge_color = CREWAI_ORANGE
+            edge_dashes = [15, 10]
+        elif edge["condition_type"] == "AND":
+            edge_label = "AND"
+            edge_color = CREWAI_ORANGE
+        elif edge["condition_type"] == "OR":
+            edge_label = "OR"
+            edge_color = GRAY
+
+        edge_data: dict[str, Any] = {
+            "from": edge["source"],
+            "to": edge["target"],
+            "label": edge_label,
+            "arrows": "to",
+            "width": 2,
+            "selectionWidth": 0,
+            "color": {
+                "color": edge_color,
+                "highlight": edge_color,
+            },
+        }
+
+        if edge_dashes is not False:
+            edge_data["dashes"] = edge_dashes
+
+        edges_list.append(edge_data)
+
+    template_dir = Path(__file__).parent.parent / "assets"
+    env = Environment(
+        loader=FileSystemLoader(template_dir),
+        autoescape=select_autoescape(["html", "xml", "css", "js"]),
+        variable_start_string="'{{",
+        variable_end_string="}}'",
+        extensions=[CSSExtension, JSExtension],
+    )
+
+    temp_dir = Path(tempfile.mkdtemp(prefix="crewai_flow_"))
+    output_path = temp_dir / Path(filename).name
+    css_filename = output_path.stem + "_style.css"
+    css_output_path = temp_dir / css_filename
+    js_filename = output_path.stem + "_script.js"
+    js_output_path = temp_dir / js_filename
+
+    css_file = template_dir / "style.css"
+    css_content = css_file.read_text(encoding="utf-8")
+
+    css_content = css_content.replace("'{{ WHITE }}'", WHITE)
+    css_content = css_content.replace("'{{ DARK_GRAY }}'", DARK_GRAY)
+    css_content = css_content.replace("'{{ GRAY }}'", GRAY)
+    css_content = css_content.replace("'{{ CREWAI_ORANGE }}'", CREWAI_ORANGE)
+
+    css_output_path.write_text(css_content, encoding="utf-8")
+
+    js_file = template_dir / "interactive.js"
+    js_content = js_file.read_text(encoding="utf-8")
+
+    dag_nodes_json = json.dumps(dag["nodes"])
+    dag_full_json = json.dumps(dag)
+
+    js_content = js_content.replace("{{ WHITE }}", WHITE)
+    js_content = js_content.replace("{{ DARK_GRAY }}", DARK_GRAY)
+    js_content = js_content.replace("{{ GRAY }}", GRAY)
+    js_content = js_content.replace("{{ CREWAI_ORANGE }}", CREWAI_ORANGE)
+    js_content = js_content.replace("'{{ nodeData }}'", dag_nodes_json)
+    js_content = js_content.replace("'{{ dagData }}'", dag_full_json)
+    js_content = js_content.replace("'{{ nodes_list_json }}'", json.dumps(nodes_list))
+    js_content = js_content.replace("'{{ edges_list_json }}'", json.dumps(edges_list))
+
+    js_output_path.write_text(js_content, encoding="utf-8")
+
+    template = env.get_template("interactive_flow.html.j2")
+
+    html_content = template.render(
+        CREWAI_ORANGE=CREWAI_ORANGE,
+        DARK_GRAY=DARK_GRAY,
+        WHITE=WHITE,
+        GRAY=GRAY,
+        BG_DARK=BG_DARK,
+        BG_CARD=BG_CARD,
+        BORDER_SUBTLE=BORDER_SUBTLE,
+        TEXT_PRIMARY=TEXT_PRIMARY,
+        TEXT_SECONDARY=TEXT_SECONDARY,
+        nodes_list_json=json.dumps(nodes_list),
+        edges_list_json=json.dumps(edges_list),
+        dag_nodes_count=len(dag["nodes"]),
+        dag_edges_count=len(dag["edges"]),
+        execution_paths=execution_paths,
+        css_path=css_filename,
+        js_path=js_filename,
+    )
+
+    output_path.write_text(html_content, encoding="utf-8")
+
+    if show:
+        webbrowser.open(f"file://{output_path.absolute()}")
+
+    return str(output_path.absolute())

lib/crewai/src/crewai/flow/visualization/schema.py

@@ -0,0 +1,104 @@
+"""OpenAPI schema conversion utilities for Flow methods."""
+
+import inspect
+from typing import Any, get_args, get_origin
+
+
+def type_to_openapi_schema(type_hint: Any) -> dict[str, Any]:
+    """Convert Python type hint to OpenAPI schema.
+
+    Args:
+        type_hint: Python type hint to convert.
+
+    Returns:
+        OpenAPI schema dictionary.
+    """
+    if type_hint is inspect.Parameter.empty:
+        return {}
+
+    if type_hint is None or type_hint is type(None):
+        return {"type": "null"}
+
+    if hasattr(type_hint, "__module__") and hasattr(type_hint, "__name__"):
+        if type_hint.__module__ == "typing" and type_hint.__name__ == "Any":
+            return {}
+
+    type_str = str(type_hint)
+    if type_str == "typing.Any" or type_str == "<class 'typing.Any'>":
+        return {}
+
+    if isinstance(type_hint, str):
+        return {"type": type_hint}
+
+    origin = get_origin(type_hint)
+    args = get_args(type_hint)
+
+    if type_hint is str:
+        return {"type": "string"}
+    if type_hint is int:
+        return {"type": "integer"}
+    if type_hint is float:
+        return {"type": "number"}
+    if type_hint is bool:
+        return {"type": "boolean"}
+    if type_hint is dict or origin is dict:
+        if args and len(args) > 1:
+            return {
+                "type": "object",
+                "additionalProperties": type_to_openapi_schema(args[1]),
+            }
+        return {"type": "object"}
+    if type_hint is list or origin is list:
+        if args:
+            return {"type": "array", "items": type_to_openapi_schema(args[0])}
+        return {"type": "array"}
+    if hasattr(type_hint, "__name__"):
+        return {"type": "object", "className": type_hint.__name__}
+
+    return {}
+
+
+def extract_method_signature(method: Any, method_name: str) -> dict[str, Any]:
+    """Extract method signature as OpenAPI schema with documentation.
+
+    Args:
+        method: Method to analyze.
+        method_name: Method name.
+
+    Returns:
+        Dictionary with operationId, parameters, returns, summary, and description.
+    """
+    try:
+        sig = inspect.signature(method)
+
+        parameters = {}
+        for param_name, param in sig.parameters.items():
+            if param_name == "self":
+                continue
+            parameters[param_name] = type_to_openapi_schema(param.annotation)
+
+        return_type = type_to_openapi_schema(sig.return_annotation)
+
+        docstring = inspect.getdoc(method)
+
+        result: dict[str, Any] = {
+            "operationId": method_name,
+            "parameters": parameters,
+            "returns": return_type,
+        }
+
+        if docstring:
+            lines = docstring.strip().split("\n")
+            summary = lines[0].strip()
+
+            if summary:
+                result["summary"] = summary
+
+            if len(lines) > 1:
+                description = "\n".join(line.strip() for line in lines[1:]).strip()
+                if description:
+                    result["description"] = description
+
+        return result
+    except Exception:
+        return {"operationId": method_name, "parameters": {}, "returns": {}}

lib/crewai/src/crewai/flow/visualization/types.py

@@ -0,0 +1,40 @@
+"""Type definitions for Flow structure visualization."""
+
+from typing import Any, TypedDict
+
+
+class NodeMetadata(TypedDict, total=False):
+    """Metadata for a single node in the flow structure."""
+
+    type: str
+    is_router: bool
+    router_paths: list[str]
+    condition_type: str | None
+    trigger_methods: list[str]
+    trigger_condition: dict[str, Any] | None
+    method_signature: dict[str, Any]
+    source_code: str
+    source_lines: list[str]
+    source_start_line: int
+    source_file: str
+    class_signature: str
+    class_name: str
+    class_line_number: int
+
+
+class StructureEdge(TypedDict):
+    """Represents a connection in the flow structure."""
+
+    source: str
+    target: str
+    condition_type: str | None
+    is_router_path: bool
+
+
+class FlowStructure(TypedDict):
+    """Complete structure representation of a Flow."""
+
+    nodes: dict[str, NodeMetadata]
+    edges: list[StructureEdge]
+    start_methods: list[str]
+    router_methods: list[str]

lib/crewai/src/crewai/flow/visualization_utils.py

@@ -1,342 +0,0 @@
-"""
-Utilities for creating visual representations of flow structures.
-
-This module provides functions for generating network visualizations of flows,
-including node placement, edge creation, and visual styling. It handles the
-conversion of flow structures into visual network graphs with appropriate
-styling and layout.
-
-Example
--------
->>> flow = Flow()
->>> net = Network(directed=True)
->>> node_positions = compute_positions(flow, node_levels)
->>> add_nodes_to_network(net, flow, node_positions, node_styles)
->>> add_edges(net, flow, node_positions, colors)
-"""
-
-import ast
-import inspect
-from typing import Any
-
-from crewai.flow.config import (
-    CrewNodeStyle,
-    FlowColors,
-    MethodNodeStyle,
-    NodeStyles,
-    RouterNodeStyle,
-    StartNodeStyle,
-)
-from crewai.flow.utils import (
-    build_ancestor_dict,
-    build_parent_children_dict,
-    get_child_index,
-    is_ancestor,
-)
-from crewai.utilities.printer import Printer
-
-
-_printer = Printer()
-
-
-def method_calls_crew(method: Any) -> bool:
-    """
-    Check if the method contains a call to `.crew()`, `.kickoff()`, or `.kickoff_async()`.
-
-    Parameters
-    ----------
-    method : Any
-        The method to analyze for crew or agent execution calls.
-
-    Returns
-    -------
-    bool
-        True if the method calls .crew(), .kickoff(), or .kickoff_async(), False otherwise.
-
-    Notes
-    -----
-    Uses AST analysis to detect method calls, specifically looking for
-    attribute access of 'crew', 'kickoff', or 'kickoff_async'.
-    This includes both traditional Crew execution (.crew()) and Agent/LiteAgent
-    execution (.kickoff() or .kickoff_async()).
-    """
-    try:
-        source = inspect.getsource(method)
-        source = inspect.cleandoc(source)
-        tree = ast.parse(source)
-    except Exception as e:
-        _printer.print(f"Could not parse method {method.__name__}: {e}", color="red")
-        return False
-
-    class CrewCallVisitor(ast.NodeVisitor):
-        """AST visitor to detect .crew(), .kickoff(), or .kickoff_async() method calls."""
-
-        def __init__(self) -> None:
-            self.found = False
-
-        def visit_Call(self, node: ast.Call) -> None:
-            if isinstance(node.func, ast.Attribute):
-                if node.func.attr in ("crew", "kickoff", "kickoff_async"):
-                    self.found = True
-            self.generic_visit(node)
-
-    visitor = CrewCallVisitor()
-    visitor.visit(tree)
-    return visitor.found
-
-
-def add_nodes_to_network(
-    net: Any,
-    flow: Any,
-    node_positions: dict[str, tuple[float, float]],
-    node_styles: NodeStyles,
-) -> None:
-    """
-    Add nodes to the network visualization with appropriate styling.
-
-    Parameters
-    ----------
-    net : Any
-        The pyvis Network instance to add nodes to.
-    flow : Any
-        The flow instance containing method information.
-    node_positions : Dict[str, Tuple[float, float]]
-        Dictionary mapping node names to their (x, y) positions.
-    node_styles : Dict[str, Dict[str, Any]]
-        Dictionary containing style configurations for different node types.
-
-    Notes
-    -----
-    Node types include:
-    - Start methods
-    - Router methods
-    - Crew methods
-    - Regular methods
-    """
-
-    def human_friendly_label(method_name: str) -> str:
-        return method_name.replace("_", " ").title()
-
-    node_style: (
-        StartNodeStyle | RouterNodeStyle | CrewNodeStyle | MethodNodeStyle | None
-    )
-    for method_name, (x, y) in node_positions.items():
-        method = flow._methods.get(method_name)
-        if hasattr(method, "__is_start_method__"):
-            node_style = node_styles["start"]
-        elif hasattr(method, "__is_router__"):
-            node_style = node_styles["router"]
-        elif method_calls_crew(method):
-            node_style = node_styles["crew"]
-        else:
-            node_style = node_styles["method"]
-
-        node_style = node_style.copy()
-        label = human_friendly_label(method_name)
-
-        node_style.update(
-            {
-                "label": label,
-                "shape": "box",
-                "font": {
-                    "multi": "html",
-                    "color": node_style.get("font", {}).get("color", "#FFFFFF"),
-                },
-            }
-        )
-
-        net.add_node(
-            method_name,
-            x=x,
-            y=y,
-            fixed=True,
-            physics=False,
-            **node_style,
-        )
-
-
-def compute_positions(
-    flow: Any,
-    node_levels: dict[str, int],
-    y_spacing: float = 150,
-    x_spacing: float = 300,
-) -> dict[str, tuple[float, float]]:
-    """
-    Compute the (x, y) positions for each node in the flow graph.
-
-    Parameters
-    ----------
-    flow : Any
-        The flow instance to compute positions for.
-    node_levels : Dict[str, int]
-        Dictionary mapping node names to their hierarchical levels.
-    y_spacing : float, optional
-        Vertical spacing between levels, by default 150.
-    x_spacing : float, optional
-        Horizontal spacing between nodes, by default 300.
-
-    Returns
-    -------
-    Dict[str, Tuple[float, float]]
-        Dictionary mapping node names to their (x, y) coordinates.
-    """
-    level_nodes: dict[int, list[str]] = {}
-    node_positions: dict[str, tuple[float, float]] = {}
-
-    for method_name, level in node_levels.items():
-        level_nodes.setdefault(level, []).append(method_name)
-
-    for level, nodes in level_nodes.items():
-        x_offset = -(len(nodes) - 1) * x_spacing / 2  # Center nodes horizontally
-        for i, method_name in enumerate(nodes):
-            x = x_offset + i * x_spacing
-            y = level * y_spacing
-            node_positions[method_name] = (x, y)
-
-    return node_positions
-
-
-def add_edges(
-    net: Any,
-    flow: Any,
-    node_positions: dict[str, tuple[float, float]],
-    colors: FlowColors,
-) -> None:
-    edge_smooth: dict[str, str | float] = {"type": "continuous"}  # Default value
-    """
-    Add edges to the network visualization with appropriate styling.
-
-    Parameters
-    ----------
-    net : Any
-        The pyvis Network instance to add edges to.
-    flow : Any
-        The flow instance containing edge information.
-    node_positions : Dict[str, Tuple[float, float]]
-        Dictionary mapping node names to their positions.
-    colors : Dict[str, str]
-        Dictionary mapping edge types to their colors.
-
-    Notes
-    -----
-    - Handles both normal listener edges and router edges
-    - Applies appropriate styling (color, dashes) based on edge type
-    - Adds curvature to edges when needed (cycles or multiple children)
-    """
-    ancestors = build_ancestor_dict(flow)
-    parent_children = build_parent_children_dict(flow)
-
-    # Edges for normal listeners
-    for method_name in flow._listeners:
-        condition_type, trigger_methods = flow._listeners[method_name]
-        is_and_condition = condition_type == "AND"
-
-        for trigger in trigger_methods:
-            # Check if nodes exist before adding edges
-            if trigger in node_positions and method_name in node_positions:
-                is_router_edge = any(
-                    trigger in paths for paths in flow._router_paths.values()
-                )
-                edge_color = colors["router_edge"] if is_router_edge else colors["edge"]
-
-                is_cycle_edge = is_ancestor(trigger, method_name, ancestors)
-                parent_has_multiple_children = len(parent_children.get(trigger, [])) > 1
-                needs_curvature = is_cycle_edge or parent_has_multiple_children
-
-                if needs_curvature:
-                    source_pos = node_positions.get(trigger)
-                    target_pos = node_positions.get(method_name)
-
-                    if source_pos and target_pos:
-                        dx = target_pos[0] - source_pos[0]
-                        smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
-                        index = get_child_index(trigger, method_name, parent_children)
-                        edge_smooth = {
-                            "type": smooth_type,
-                            "roundness": 0.2 + (0.1 * index),
-                        }
-                    else:
-                        edge_smooth = {"type": "cubicBezier"}
-                else:
-                    edge_smooth.update({"type": "continuous"})
-
-                edge_style = {
-                    "color": edge_color,
-                    "width": 2,
-                    "arrows": "to",
-                    "dashes": True if is_router_edge or is_and_condition else False,
-                    "smooth": edge_smooth,
-                }
-
-                net.add_edge(trigger, method_name, **edge_style)
-            else:
-                # Nodes not found in node_positions. Check if it's a known router outcome and a known method.
-                is_router_edge = any(
-                    trigger in paths for paths in flow._router_paths.values()
-                )
-                # Check if method_name is a known method
-                method_known = method_name in flow._methods
-
-                # If it's a known router edge and the method is known, don't warn.
-                # This means the path is legitimate, just not reflected as nodes here.
-                if not (is_router_edge and method_known):
-                    _printer.print(
-                        f"Warning: No node found for '{trigger}' or '{method_name}'. Skipping edge.",
-                        color="yellow",
-                    )
-
-    # Edges for router return paths
-    for router_method_name, paths in flow._router_paths.items():
-        for path in paths:
-            for listener_name, (
-                _condition_type,
-                trigger_methods,
-            ) in flow._listeners.items():
-                if path in trigger_methods:
-                    if (
-                        router_method_name in node_positions
-                        and listener_name in node_positions
-                    ):
-                        is_cycle_edge = is_ancestor(
-                            router_method_name, listener_name, ancestors
-                        )
-                        parent_has_multiple_children = (
-                            len(parent_children.get(router_method_name, [])) > 1
-                        )
-                        needs_curvature = is_cycle_edge or parent_has_multiple_children
-
-                        if needs_curvature:
-                            source_pos = node_positions.get(router_method_name)
-                            target_pos = node_positions.get(listener_name)
-
-                            if source_pos and target_pos:
-                                dx = target_pos[0] - source_pos[0]
-                                smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
-                                index = get_child_index(
-                                    router_method_name, listener_name, parent_children
-                                )
-                                edge_smooth = {
-                                    "type": smooth_type,
-                                    "roundness": 0.2 + (0.1 * index),
-                                }
-                            else:
-                                edge_smooth = {"type": "cubicBezier"}
-                        else:
-                            edge_smooth.update({"type": "continuous"})
-
-                        edge_style = {
-                            "color": colors["router_edge"],
-                            "width": 2,
-                            "arrows": "to",
-                            "dashes": True,
-                            "smooth": edge_smooth,
-                        }
-                        net.add_edge(router_method_name, listener_name, **edge_style)
-                    else:
-                        # Same check here: known router edge and known method?
-                        method_known = listener_name in flow._methods
-                        if not method_known:
-                            _printer.print(
-                                f"Warning: No node found for '{router_method_name}' or '{listener_name}'. Skipping edge.",
-                                color="yellow",
-                            )

lib/crewai/src/crewai/llm.py

@@ -21,6 +21,7 @@ from typing import (
 
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field
+from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
@@ -36,30 +37,34 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
 )
 from crewai.llms.base_llm import BaseLLM
+from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
 from crewai.utilities.logger_utils import suppress_warnings
-from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from litellm import Choices
     from litellm.exceptions import ContextWindowExceededError
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
-    from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Choices, ModelResponse
     from litellm.utils import supports_response_schema
 
+    from crewai.agent.core import Agent
+    from crewai.task import Task
+    from crewai.tools.base_tool import BaseTool
+    from crewai.utilities.types import LLMMessage
+
 try:
     import litellm
-    from litellm import Choices, CustomLogger
     from litellm.exceptions import ContextWindowExceededError
+    from litellm.integrations.custom_logger import CustomLogger
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
-    from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Choices, ModelResponse
     from litellm.utils import supports_response_schema
 
     LITELLM_AVAILABLE = True
@@ -72,6 +77,7 @@ except ImportError:
     ChatCompletionDeltaToolCall = None  # type: ignore
     ModelResponse = None  # type: ignore
     supports_response_schema = None  # type: ignore
+    CustomLogger = None  # type: ignore
 
 
 load_dotenv()
@@ -104,11 +110,13 @@ class FilteredStream(io.TextIOBase):
 
             return self._original_stream.write(s)
 
-    def flush(self):
-        with self._lock:
-            return self._original_stream.flush()
+    def flush(self) -> None:
+        if self._lock:
+            with self._lock:
+                return self._original_stream.flush()
+        return None
 
-    def __getattr__(self, name):
+    def __getattr__(self, name: str) -> Any:
         """Delegate attribute access to the wrapped original stream.
 
         This ensures compatibility with libraries (e.g., Rich) that rely on
@@ -122,16 +130,16 @@ class FilteredStream(io.TextIOBase):
     # confuses Rich). These explicit pass-throughs ensure the wrapped Console
     # still sees a fully-featured stream.
     @property
-    def encoding(self):
+    def encoding(self) -> str | Any:  # type: ignore[override]
         return getattr(self._original_stream, "encoding", "utf-8")
 
-    def isatty(self):
+    def isatty(self) -> bool:
         return self._original_stream.isatty()
 
-    def fileno(self):
+    def fileno(self) -> int:
         return self._original_stream.fileno()
 
-    def writable(self):
+    def writable(self) -> bool:
         return True
 
 
@@ -312,7 +320,7 @@ class AccumulatedToolArgs(BaseModel):
 class LLM(BaseLLM):
     completion_cost: float | None = None
 
-    def __new__(cls, model: str, is_litellm: bool = False, **kwargs) -> LLM:
+    def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
         """Factory method that routes to native SDK or falls back to LiteLLM."""
         if not model or not isinstance(model, str):
             raise ValueError("Model must be a non-empty string")
@@ -323,7 +331,9 @@ class LLM(BaseLLM):
         if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
             try:
                 model_string = model.partition("/")[2] if "/" in model else model
-                return native_class(model=model_string, provider=provider, **kwargs)
+                return cast(
+                    Self, native_class(model=model_string, provider=provider, **kwargs)
+                )
             except Exception as e:
                 raise ImportError(f"Error importing native provider: {e}") from e
 
@@ -393,13 +403,21 @@ class LLM(BaseLLM):
         callbacks: list[Any] | None = None,
         reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
         stream: bool = False,
-        **kwargs,
-    ):
+        **kwargs: Any,
+    ) -> None:
         """Initialize LLM instance.
 
         Note: This __init__ method is only called for fallback instances.
         Native provider instances handle their own initialization in their respective classes.
         """
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            base_url=base_url,
+            timeout=timeout,
+            **kwargs,
+        )
         self.model = model
         self.timeout = timeout
         self.temperature = temperature
@@ -454,7 +472,7 @@ class LLM(BaseLLM):
     def _prepare_completion_params(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, BaseTool]] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for the completion call.
 
@@ -505,9 +523,10 @@ class LLM(BaseLLM):
         params: dict[str, Any],
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-    ) -> str:
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> Any:
         """Handle a streaming response from the LLM.
 
         Args:
@@ -516,6 +535,7 @@ class LLM(BaseLLM):
             available_functions: Dict of available functions
             from_task: Optional task object
             from_agent: Optional agent object
+            response_model: Optional response model
 
         Returns:
             str: The complete response text
@@ -716,14 +736,30 @@ class LLM(BaseLLM):
                                 tool_calls = message.tool_calls
             except Exception as e:
                 logging.debug(f"Error checking for tool calls: {e}")
-            # --- 8) If no tool calls or no available functions, return the text response directly
 
             if not tool_calls or not available_functions:
                 # Track token usage and log callbacks if available in streaming mode
                 if usage_info:
                     self._track_token_usage_internal(usage_info)
                 self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
-                # Emit completion event and return response
+
+                if response_model and self.is_litellm:
+                    instructor_instance = InternalInstructor(
+                        content=full_response,
+                        model=response_model,
+                        llm=self,
+                    )
+                    result = instructor_instance.to_pydantic()
+                    structured_response = result.model_dump_json()
+                    self._handle_emit_call_events(
+                        response=structured_response,
+                        call_type=LLMCallType.LLM_CALL,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        messages=params["messages"],
+                    )
+                    return structured_response
+
                 self._handle_emit_call_events(
                     response=full_response,
                     call_type=LLMCallType.LLM_CALL,
@@ -784,9 +820,9 @@ class LLM(BaseLLM):
         tool_calls: list[ChatCompletionDeltaToolCall],
         accumulated_tool_args: defaultdict[int, AccumulatedToolArgs],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-    ) -> None | str:
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+    ) -> Any:
         for tool_call in tool_calls:
             current_tool_accumulator = accumulated_tool_args[tool_call.index]
 
@@ -869,8 +905,9 @@ class LLM(BaseLLM):
         params: dict[str, Any],
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle a non-streaming response from the LLM.
 
@@ -880,23 +917,69 @@ class LLM(BaseLLM):
             available_functions: Dict of available functions
             from_task: Optional Task that invoked the LLM
             from_agent: Optional Agent that invoked the LLM
+            response_model: Optional Response model
 
         Returns:
             str: The response text
         """
-        # --- 1) Make the completion call
+        # --- 1) Handle response_model with InternalInstructor for LiteLLM
+        if response_model and self.is_litellm:
+            from crewai.utilities.internal_instructor import InternalInstructor
+
+            messages = params.get("messages", [])
+            if not messages:
+                raise ValueError("Messages are required when using response_model")
+
+            # Combine all message content for InternalInstructor
+            combined_content = "\n\n".join(
+                f"{msg['role'].upper()}: {msg['content']}" for msg in messages
+            )
+
+            instructor_instance = InternalInstructor(
+                content=combined_content,
+                model=response_model,
+                llm=self,
+            )
+            result = instructor_instance.to_pydantic()
+            structured_response = result.model_dump_json()
+            self._handle_emit_call_events(
+                response=structured_response,
+                call_type=LLMCallType.LLM_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+                messages=params["messages"],
+            )
+            return structured_response
+
         try:
             # Attempt to make the completion call, but catch context window errors
             # and convert them to our own exception type for consistent handling
             # across the codebase. This allows CrewAgentExecutor to handle context
             # length issues appropriately.
+            if response_model:
+                params["response_model"] = response_model
             response = litellm.completion(**params)
 
         except ContextWindowExceededError as e:
             # Convert litellm's context window error to our own exception type
             # for consistent handling in the rest of the codebase
             raise LLMContextLengthExceededError(str(e)) from e
-        # --- 2) Extract response message and content
+
+        # --- 2) Handle structured output response (when response_model is provided)
+        if response_model is not None:
+            # When using instructor/response_model, litellm returns a Pydantic model instance
+            if isinstance(response, BaseModel):
+                structured_response = response.model_dump_json()
+                self._handle_emit_call_events(
+                    response=structured_response,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+                return structured_response
+
+        # --- 3) Extract response message and content (standard response)
         response_message = cast(Choices, cast(ModelResponse, response).choices)[
             0
         ].message
@@ -951,9 +1034,9 @@ class LLM(BaseLLM):
         self,
         tool_calls: list[Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-    ) -> str | None:
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+    ) -> Any:
         """Handle a tool call from the LLM.
 
         Args:
@@ -1039,11 +1122,12 @@ class LLM(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, BaseTool]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """High-level LLM call method.
 
@@ -1060,6 +1144,7 @@ class LLM(BaseLLM):
                                that can be invoked by the LLM.
             from_task: Optional Task that invoked the LLM
             from_agent: Optional Agent that invoked the LLM
+            response_model: Optional Model that contains a pydantic response model.
 
         Returns:
             Union[str, Any]: Either a text response from the LLM (str) or
@@ -1105,11 +1190,21 @@ class LLM(BaseLLM):
                 # --- 7) Make the completion call and handle response
                 if self.stream:
                     return self._handle_streaming_response(
-                        params, callbacks, available_functions, from_task, from_agent
+                        params=params,
+                        callbacks=callbacks,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        response_model=response_model,
                     )
 
                 return self._handle_non_streaming_response(
-                    params, callbacks, available_functions, from_task, from_agent
+                    params=params,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    response_model=response_model,
                 )
             except LLMContextLengthExceededError:
                 # Re-raise LLMContextLengthExceededError as it should be handled
@@ -1141,6 +1236,7 @@ class LLM(BaseLLM):
                         available_functions=available_functions,
                         from_task=from_task,
                         from_agent=from_agent,
+                        response_model=response_model,
                     )
 
                 crewai_event_bus.emit(
@@ -1155,10 +1251,10 @@ class LLM(BaseLLM):
         self,
         response: Any,
         call_type: LLMCallType,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-        messages: str | list[dict[str, Any]] | None = None,
-    ):
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        messages: str | list[LLMMessage] | None = None,
+    ) -> None:
         """Handle the events for the LLM call.
 
         Args:
@@ -1324,7 +1420,7 @@ class LLM(BaseLLM):
         return self.context_window_size
 
     @staticmethod
-    def set_callbacks(callbacks: list[Any]):
+    def set_callbacks(callbacks: list[Any]) -> None:
         """
         Attempt to keep a single set of callbacks in litellm by removing old
         duplicates and adding new ones.
@@ -1377,7 +1473,7 @@ class LLM(BaseLLM):
                 litellm.success_callback = success_callbacks
                 litellm.failure_callback = failure_callbacks
 
-    def __copy__(self):
+    def __copy__(self) -> LLM:
         """Create a shallow copy of the LLM instance."""
         # Filter out parameters that are already explicitly passed to avoid conflicts
         filtered_params = {
@@ -1437,7 +1533,7 @@ class LLM(BaseLLM):
             **filtered_params,
         )
 
-    def __deepcopy__(self, memo):
+    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:
         """Create a deep copy of the LLM instance."""
         import copy
 

lib/crewai/src/crewai/llms/base_llm.py

@@ -10,6 +10,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 import json
 import logging
+import re
 from typing import TYPE_CHECKING, Any, Final
 
 from pydantic import BaseModel
@@ -31,11 +32,15 @@ from crewai.types.usage_metrics import UsageMetrics
 
 
 if TYPE_CHECKING:
+    from crewai.agent.core import Agent
+    from crewai.task import Task
+    from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
 
 
 DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096
 DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
+_JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
 
 
 class BaseLLM(ABC):
@@ -65,9 +70,8 @@ class BaseLLM(ABC):
         temperature: float | None = None,
         api_key: str | None = None,
         base_url: str | None = None,
-        timeout: float | None = None,
         provider: str | None = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> None:
         """Initialize the BaseLLM with default attributes.
 
@@ -93,8 +97,10 @@ class BaseLLM(ABC):
             self.stop: list[str] = []
         elif isinstance(stop, str):
             self.stop = [stop]
-        else:
+        elif isinstance(stop, list):
             self.stop = stop
+        else:
+            self.stop = []
 
         self._token_usage = {
             "total_tokens": 0,
@@ -118,11 +124,12 @@ class BaseLLM(ABC):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, BaseTool]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call the LLM with the given messages.
 
@@ -139,6 +146,7 @@ class BaseLLM(ABC):
                                that can be invoked by the LLM.
             from_task: Optional task caller to be used for the LLM call.
             from_agent: Optional agent caller to be used for the LLM call.
+            response_model: Optional response model to be used for the LLM call.
 
         Returns:
             Either a text response from the LLM (str) or
@@ -150,7 +158,9 @@ class BaseLLM(ABC):
             RuntimeError: If the LLM request fails for other reasons.
         """
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+    def _convert_tools_for_interference(
+        self, tools: list[dict[str, BaseTool]]
+    ) -> list[dict[str, BaseTool]]:
         """Convert tools to a format that can be used for interference.
 
         Args:
@@ -237,11 +247,11 @@ class BaseLLM(ABC):
     def _emit_call_started_event(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, BaseTool]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
     ) -> None:
         """Emit LLM call started event."""
         if not hasattr(crewai_event_bus, "emit"):
@@ -264,8 +274,8 @@ class BaseLLM(ABC):
         self,
         response: Any,
         call_type: LLMCallType,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         messages: str | list[dict[str, Any]] | None = None,
     ) -> None:
         """Emit LLM call completed event."""
@@ -284,8 +294,8 @@ class BaseLLM(ABC):
     def _emit_call_failed_event(
         self,
         error: str,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
     ) -> None:
         """Emit LLM call failed event."""
         if not hasattr(crewai_event_bus, "emit"):
@@ -303,8 +313,8 @@ class BaseLLM(ABC):
     def _emit_stream_chunk_event(
         self,
         chunk: str,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         tool_call: dict[str, Any] | None = None,
     ) -> None:
         """Emit stream chunk event."""
@@ -326,8 +336,8 @@ class BaseLLM(ABC):
         function_name: str,
         function_args: dict[str, Any],
         available_functions: dict[str, Any],
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
     ) -> str | None:
         """Handle tool execution with proper event emission.
 
@@ -443,10 +453,10 @@ class BaseLLM(ABC):
                     f"Message at index {i} must have 'role' and 'content' keys"
                 )
 
-        return messages  # type: ignore[return-value]
+        return messages
 
+    @staticmethod
     def _validate_structured_output(
-        self,
         response: str,
         response_format: type[BaseModel] | None,
     ) -> str | BaseModel:
@@ -471,10 +481,7 @@ class BaseLLM(ABC):
                 data = json.loads(response)
                 return response_format.model_validate(data)
 
-            # Try to extract JSON from response
-            import re
-
-            json_match = re.search(r"\{.*\}", response, re.DOTALL)
+            json_match = _JSON_EXTRACTION_PATTERN.search(response)
             if json_match:
                 data = json.loads(json_match.group())
                 return response_format.model_validate(data)
@@ -487,7 +494,8 @@ class BaseLLM(ABC):
                 f"Failed to parse response into {response_format.__name__}: {e}"
             ) from e
 
-    def _extract_provider(self, model: str) -> str:
+    @staticmethod
+    def _extract_provider(model: str) -> str:
         """Extract provider from model string.
 
         Args:

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -1,7 +1,13 @@
+from __future__ import annotations
+
+
+import json
 import logging
 import os
 from typing import Any, cast
 
+from pydantic import BaseModel
+
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.agent_utils import is_context_length_exceeded
@@ -109,6 +115,7 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Anthropic messages API.
 
@@ -147,11 +154,19 @@ class AnthropicCompletion(BaseLLM):
             # Handle streaming vs non-streaming
             if self.stream:
                 return self._handle_streaming_completion(
-                    completion_params, available_functions, from_task, from_agent
+                    completion_params,
+                    available_functions,
+                    from_task,
+                    from_agent,
+                    response_model,
                 )
 
             return self._handle_completion(
-                completion_params, available_functions, from_task, from_agent
+                completion_params,
+                available_functions,
+                from_task,
+                from_agent,
+                response_model,
             )
 
         except Exception as e:
@@ -290,8 +305,19 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
+        if response_model:
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
+
+            params["tools"] = [structured_tool]
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
+
         try:
             response: Message = self.client.messages.create(**params)
 
@@ -304,6 +330,24 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(response)
         self._track_token_usage_internal(usage)
 
+        if response_model and response.content:
+            tool_uses = [
+                block for block in response.content if isinstance(block, ToolUseBlock)
+            ]
+            if tool_uses and tool_uses[0].name == "structured_output":
+                structured_data = tool_uses[0].input
+                structured_json = json.dumps(structured_data)
+
+                self._emit_call_completed_event(
+                    response=structured_json,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+
+                return structured_json
+
         # Check if Claude wants to use tools
         if response.content and available_functions:
             tool_uses = [
@@ -349,8 +393,19 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming message completion."""
+        if response_model:
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
+
+            params["tools"] = [structured_tool]
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
+
         full_response = ""
 
         # Remove 'stream' parameter as messages.stream() doesn't accept it
@@ -374,6 +429,26 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
+        if response_model and final_message.content:
+            tool_uses = [
+                block
+                for block in final_message.content
+                if isinstance(block, ToolUseBlock)
+            ]
+            if tool_uses and tool_uses[0].name == "structured_output":
+                structured_data = tool_uses[0].input
+                structured_json = json.dumps(structured_data)
+
+                self._emit_call_completed_event(
+                    response=structured_json,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+
+                return structured_json
+
         if final_message.content and available_functions:
             tool_uses = [
                 block

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -1,7 +1,11 @@
+from __future__ import annotations
+
 import json
 import logging
 import os
-from typing import Any
+from typing import Any, TYPE_CHECKING
+
+from pydantic import BaseModel
 
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -9,6 +13,9 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.types import LLMMessage
 
+if TYPE_CHECKING:
+    from crewai.tools.base_tool import BaseTool
+
 
 try:
     from azure.ai.inference import (  # type: ignore[import-not-found]
@@ -157,11 +164,12 @@ class AzureCompletion(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, BaseTool]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Azure AI Inference chat completions API.
 
@@ -192,17 +200,25 @@ class AzureCompletion(BaseLLM):
 
             # Prepare completion parameters
             completion_params = self._prepare_completion_params(
-                formatted_messages, tools
+                formatted_messages, tools, response_model
             )
 
             # Handle streaming vs non-streaming
             if self.stream:
                 return self._handle_streaming_completion(
-                    completion_params, available_functions, from_task, from_agent
+                    completion_params,
+                    available_functions,
+                    from_task,
+                    from_agent,
+                    response_model,
                 )
 
             return self._handle_completion(
-                completion_params, available_functions, from_task, from_agent
+                completion_params,
+                available_functions,
+                from_task,
+                from_agent,
+                response_model,
             )
 
         except HttpResponseError as e:
@@ -234,12 +250,14 @@ class AzureCompletion(BaseLLM):
         self,
         messages: list[LLMMessage],
         tools: list[dict] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Azure AI Inference chat completion.
 
         Args:
             messages: Formatted messages for Azure
             tools: Tool definitions
+            response_model: Pydantic model for structured output
 
         Returns:
             Parameters dictionary for Azure API
@@ -249,6 +267,15 @@ class AzureCompletion(BaseLLM):
             "stream": self.stream,
         }
 
+        if response_model and self.is_openai_model:
+            params["response_format"] = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": response_model.__name__,
+                    "schema": response_model.model_json_schema(),
+                },
+            }
+
         # Only include model parameter for non-Azure OpenAI endpoints
         # Azure OpenAI endpoints have the deployment name in the URL
         if not self.is_azure_openai_endpoint:
@@ -334,6 +361,7 @@ class AzureCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         # Make API call
@@ -350,6 +378,26 @@ class AzureCompletion(BaseLLM):
             usage = self._extract_azure_token_usage(response)
             self._track_token_usage_internal(usage)
 
+            if response_model and self.is_openai_model:
+                content = message.content or ""
+                try:
+                    structured_data = response_model.model_validate_json(content)
+                    structured_json = structured_data.model_dump_json()
+
+                    self._emit_call_completed_event(
+                        response=structured_json,
+                        call_type=LLMCallType.LLM_CALL,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        messages=params["messages"],
+                    )
+
+                    return structured_json
+                except Exception as e:
+                    error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
+                    logging.error(error_msg)
+                    raise ValueError(error_msg) from e
+
             # Handle tool calls
             if message.tool_calls and available_functions:
                 tool_call = message.tool_calls[0]  # Handle first tool call
@@ -409,6 +457,7 @@ class AzureCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming chat completion."""
         full_response = ""

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -5,6 +5,7 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any, TypedDict, cast
 
+from pydantic import BaseModel
 from typing_extensions import Required
 
 from crewai.events.types.llm_events import LLMCallType
@@ -240,6 +241,7 @@ class BedrockCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call AWS Bedrock Converse API."""
         try: