ci: trigger test matrix

Co-Authored-By: João <joao@crewai.com>

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which 
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: uv # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.pre-commit-config.yaml

@@ -19,6 +19,7 @@ repos:
         language: system
         pass_filenames: true
         types: [python]
+        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/)
   - repo: https://github.com/astral-sh/uv-pre-commit
     rev: 0.9.3
     hooks:

docs/en/concepts/llms.mdx

@@ -1200,6 +1200,52 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Transport Interceptors">
+    CrewAI provides message interceptors for several providers, allowing you to hook into request/response cycles at the transport layer.
+
+    **Supported Providers:**
+    - ✅ OpenAI
+    - ✅ Anthropic
+
+    **Basic Usage:**
+    ```python
+import httpx
+from crewai import LLM
+from crewai.llms.hooks import BaseInterceptor
+
+class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Custom interceptor to modify requests and responses."""
+
+    def on_outbound(self, request: httpx.Request) -> httpx.Request:
+        """Print request before sending to the LLM provider."""
+        print(request)
+        return request
+
+    def on_inbound(self, response: httpx.Response) -> httpx.Response:
+        """Process response after receiving from the LLM provider."""
+        print(f"Status: {response.status_code}")
+        print(f"Response time: {response.elapsed}")
+        return response
+
+# Use the interceptor with an LLM
+llm = LLM(
+    model="openai/gpt-4o",
+    interceptor=CustomInterceptor()
+)
+    ```
+
+    **Important Notes:**
+    - Both methods must return the received object or type of object.
+    - Modifying received objects may result in unexpected behavior or application crashes.
+    - Not all providers support interceptors - check the supported providers list above
+
+    <Info>
+      Interceptors operate at the transport layer. This is particularly useful for:
+      - Message transformation and filtering
+      - Debugging API interactions
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/en/concepts/tasks.mdx

@@ -60,6 +60,7 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrails** _(optional)_       | `guardrails`       | `Optional[List[Callable] | List[str]]` | List of guardrails to validate task output before proceeding to next task.                                      |
 | **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 <Note type="warning" title="Deprecated: max_retries">
@@ -223,6 +224,7 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+| **Messages**      | `messages`      | `list[LLMMessage]`         | The messages from the last task execution.                                                           |
 
 ### Task Methods and Properties
 
@@ -341,7 +343,11 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+CrewAI supports two types of guardrails:
+
+1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+
+2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
 
 ### Function-Based Guardrails
 
@@ -355,12 +361,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
-        word_count = len(result.split())
+        word_count = len(result.raw.split())
         if word_count > 200:
             return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
-        return (True, result.strip())
+        return (True, result.raw.strip())
     except Exception as e:
         return (False, "Unexpected error during validation")
 
@@ -372,6 +378,147 @@ blog_task = Task(
 )
 ```
 
+### LLM-Based Guardrails (String Descriptions)
+
+Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
+
+**Requirements**:
+- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
+- Provide a clear, descriptive string explaining the validation criteria
+
+```python Code
+from crewai import Task
+
+# Single LLM-based guardrail
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A blog post under 200 words",
+    agent=blog_agent,
+    guardrail="The blog post must be under 200 words and contain no technical jargon"
+)
+```
+
+LLM-based guardrails are particularly useful for:
+- **Complex validation logic** that's difficult to express programmatically
+- **Subjective criteria** like tone, style, or quality assessments
+- **Natural language requirements** that are easier to describe than code
+
+The LLM guardrail will:
+1. Analyze the task output against your description
+2. Return `(True, output)` if the output complies with the criteria
+3. Return `(False, feedback)` with specific feedback if validation fails
+
+**Example with detailed validation criteria**:
+
+```python Code
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="A comprehensive research report",
+    agent=researcher_agent,
+    guardrail="""
+    The research report must:
+    - Be at least 1000 words long
+    - Include at least 5 credible sources
+    - Cover both technical and practical applications
+    - Be written in a professional, academic tone
+    - Avoid speculation or unverified claims
+    """
+)
+```
+
+### Multiple Guardrails
+
+You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
+
+The `guardrails` parameter accepts:
+- A list of guardrail functions or string descriptions
+- A single guardrail function or string (same as `guardrail`)
+
+**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
+    """Check for inappropriate language."""
+    profanity_words = ["badword1", "badword2"]  # Example list
+    content_lower = result.raw.lower()
+    for word in profanity_words:
+        if word in content_lower:
+            return (False, f"Inappropriate language detected: {word}")
+    return (True, result.raw)
+
+def format_output(result: TaskOutput) -> Tuple[bool, Any]:
+    """Format and clean the output."""
+    formatted = result.raw.strip()
+    # Capitalize first letter
+    formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
+    return (True, formatted)
+
+# Apply multiple guardrails sequentially
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,      # First: validate length
+        validate_no_profanity,    # Second: check content
+        format_output             # Third: format the result
+    ],
+    guardrail_max_retries=3
+)
+```
+
+In this example, the guardrails execute in order:
+1. `validate_word_count` checks the word count
+2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
+3. `format_output` formats the final result (using the output from step 2)
+
+If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
+
+**Mixing function-based and LLM-based guardrails**:
+
+You can combine both function-based and string-based guardrails in the same list:
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+# Mix function-based and LLM-based guardrails
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,  # Function-based: precise word count check
+        "The content must be engaging and suitable for a general audience",  # LLM-based: subjective quality check
+        "The writing style should be clear, concise, and free of technical jargon"  # LLM-based: style validation
+    ],
+    guardrail_max_retries=3
+)
+```
+
+This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
+
 ### Guardrail Function Requirements
 
 1. **Function Signature**:

docs/en/mcp/overview.mdx

@@ -11,9 +11,13 @@ The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP)
 
 CrewAI offers **two approaches** for MCP integration:
 
-### Simple DSL Integration** (Recommended)
+### 🚀 **Simple DSL Integration** (Recommended)
 
-Use the `mcps` field directly on agents for seamless MCP tool integration:
+Use the `mcps` field directly on agents for seamless MCP tool integration. The DSL supports both **string references** (for quick setup) and **structured configurations** (for full control).
+
+#### String-Based References (Quick Setup)
+
+Perfect for remote HTTPS servers and CrewAI AMP marketplace:
 
 ```python
 from crewai import Agent
@@ -32,6 +36,46 @@ agent = Agent(
 # MCP tools are now automatically available to your agent!
 ```
 
+#### Structured Configurations (Full Control)
+
+For complete control over connection settings, tool filtering, and all transport types:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
+from crewai.mcp.filters import create_static_tool_filter
+
+agent = Agent(
+    role="Advanced Research Analyst",
+    goal="Research with full control over MCP connections",
+    backstory="Expert researcher with advanced tool access",
+    mcps=[
+        # Stdio transport for local servers
+        MCPServerStdio(
+            command="npx",
+            args=["-y", "@modelcontextprotocol/server-filesystem"],
+            env={"API_KEY": "your_key"},
+            tool_filter=create_static_tool_filter(
+                allowed_tool_names=["read_file", "list_directory"]
+            ),
+            cache_tools_list=True,
+        ),
+        # HTTP/Streamable HTTP transport for remote servers
+        MCPServerHTTP(
+            url="https://api.example.com/mcp",
+            headers={"Authorization": "Bearer your_token"},
+            streamable=True,
+            cache_tools_list=True,
+        ),
+        # SSE transport for real-time streaming
+        MCPServerSSE(
+            url="https://stream.example.com/mcp/sse",
+            headers={"Authorization": "Bearer your_token"},
+        ),
+    ]
+)
+```
+
 ### 🔧 **Advanced: MCPServerAdapter** (For Complex Scenarios)
 
 For advanced use cases requiring manual connection management, the `crewai-tools` library provides the `MCPServerAdapter` class.
@@ -68,12 +112,14 @@ uv pip install 'crewai-tools[mcp]'
 
 ## Quick Start: Simple DSL Integration
 
-The easiest way to integrate MCP servers is using the `mcps` field on your agents:
+The easiest way to integrate MCP servers is using the `mcps` field on your agents. You can use either string references or structured configurations.
+
+### Quick Start with String References
 
 ```python
 from crewai import Agent, Task, Crew
 
-# Create agent with MCP tools
+# Create agent with MCP tools using string references
 research_agent = Agent(
     role="Research Analyst",
     goal="Find and analyze information using advanced search tools",
@@ -96,13 +142,53 @@ crew = Crew(agents=[research_agent], tasks=[research_task])
 result = crew.kickoff()
 ```
 
+### Quick Start with Structured Configurations
+
+```python
+from crewai import Agent, Task, Crew
+from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
+
+# Create agent with structured MCP configurations
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information using advanced search tools",
+    backstory="Expert researcher with access to multiple data sources",
+    mcps=[
+        # Local stdio server
+        MCPServerStdio(
+            command="python",
+            args=["local_server.py"],
+            env={"API_KEY": "your_key"},
+        ),
+        # Remote HTTP server
+        MCPServerHTTP(
+            url="https://api.research.com/mcp",
+            headers={"Authorization": "Bearer your_token"},
+        ),
+    ]
+)
+
+# Create task
+research_task = Task(
+    description="Research the latest developments in AI agent frameworks",
+    expected_output="Comprehensive research report with citations",
+    agent=research_agent
+)
+
+# Create and run crew
+crew = Crew(agents=[research_agent], tasks=[research_task])
+result = crew.kickoff()
+```
+
 That's it! The MCP tools are automatically discovered and available to your agent.
 
 ## MCP Reference Formats
 
-The `mcps` field supports various reference formats for maximum flexibility:
+The `mcps` field supports both **string references** (for quick setup) and **structured configurations** (for full control). You can mix both formats in the same list.
 
-### External MCP Servers
+### String-Based References
+
+#### External MCP Servers
 
 ```python
 mcps=[
@@ -117,7 +203,7 @@ mcps=[
 ]
 ```
 
-### CrewAI AMP Marketplace
+#### CrewAI AMP Marketplace
 
 ```python
 mcps=[
@@ -133,17 +219,166 @@ mcps=[
 ]
 ```
 
-### Mixed References
+### Structured Configurations
+
+#### Stdio Transport (Local Servers)
+
+Perfect for local MCP servers that run as processes:
 
 ```python
+from crewai.mcp import MCPServerStdio
+from crewai.mcp.filters import create_static_tool_filter
+
 mcps=[
-    "https://external-api.com/mcp",              # External server
-    "https://weather.service.com/mcp#forecast",  # Specific external tool
-    "crewai-amp:financial-insights",             # AMP service
-    "crewai-amp:data-analysis#sentiment_tool"    # Specific AMP tool
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+        env={"API_KEY": "your_key"},
+        tool_filter=create_static_tool_filter(
+            allowed_tool_names=["read_file", "write_file"]
+        ),
+        cache_tools_list=True,
+    ),
+    # Python-based server
+    MCPServerStdio(
+        command="python",
+        args=["path/to/server.py"],
+        env={"UV_PYTHON": "3.12", "API_KEY": "your_key"},
+    ),
 ]
 ```
 
+#### HTTP/Streamable HTTP Transport (Remote Servers)
+
+For remote MCP servers over HTTP/HTTPS:
+
+```python
+from crewai.mcp import MCPServerHTTP
+
+mcps=[
+    # Streamable HTTP (default)
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer your_token"},
+        streamable=True,
+        cache_tools_list=True,
+    ),
+    # Standard HTTP
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer your_token"},
+        streamable=False,
+    ),
+]
+```
+
+#### SSE Transport (Real-Time Streaming)
+
+For remote servers using Server-Sent Events:
+
+```python
+from crewai.mcp import MCPServerSSE
+
+mcps=[
+    MCPServerSSE(
+        url="https://stream.example.com/mcp/sse",
+        headers={"Authorization": "Bearer your_token"},
+        cache_tools_list=True,
+    ),
+]
+```
+
+### Mixed References
+
+You can combine string references and structured configurations:
+
+```python
+from crewai.mcp import MCPServerStdio, MCPServerHTTP
+
+mcps=[
+    # String references
+    "https://external-api.com/mcp",              # External server
+    "crewai-amp:financial-insights",             # AMP service
+
+    # Structured configurations
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+    ),
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer token"},
+    ),
+]
+```
+
+### Tool Filtering
+
+Structured configurations support advanced tool filtering:
+
+```python
+from crewai.mcp import MCPServerStdio
+from crewai.mcp.filters import create_static_tool_filter, create_dynamic_tool_filter, ToolFilterContext
+
+# Static filtering (allow/block lists)
+static_filter = create_static_tool_filter(
+    allowed_tool_names=["read_file", "write_file"],
+    blocked_tool_names=["delete_file"],
+)
+
+# Dynamic filtering (context-aware)
+def dynamic_filter(context: ToolFilterContext, tool: dict) -> bool:
+    # Block dangerous tools for certain agent roles
+    if context.agent.role == "Code Reviewer":
+        if "delete" in tool.get("name", "").lower():
+            return False
+    return True
+
+mcps=[
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+        tool_filter=static_filter,  # or dynamic_filter
+    ),
+]
+```
+
+## Configuration Parameters
+
+Each transport type supports specific configuration options:
+
+### MCPServerStdio Parameters
+
+- **`command`** (required): Command to execute (e.g., `"python"`, `"node"`, `"npx"`, `"uvx"`)
+- **`args`** (optional): List of command arguments (e.g., `["server.py"]` or `["-y", "@mcp/server"]`)
+- **`env`** (optional): Dictionary of environment variables to pass to the process
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### MCPServerHTTP Parameters
+
+- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp"`)
+- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
+- **`streamable`** (optional): Whether to use streamable HTTP transport (default: `True`)
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### MCPServerSSE Parameters
+
+- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp/sse"`)
+- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### Common Parameters
+
+All transport types support:
+- **`tool_filter`**: Filter function to control which tools are available. Can be:
+  - `None` (default): All tools are available
+  - Static filter: Created with `create_static_tool_filter()` for allow/block lists
+  - Dynamic filter: Created with `create_dynamic_tool_filter()` for context-aware filtering
+- **`cache_tools_list`**: When `True`, caches the tool list after first discovery to improve performance on subsequent connections
+
 ## Key Features
 
 - 🔄 **Automatic Tool Discovery**: Tools are automatically discovered and integrated
@@ -152,26 +387,47 @@ mcps=[
 - 🛡️ **Error Resilience**: Graceful handling of unavailable servers
 - ⏱️ **Timeout Protection**: Built-in timeouts prevent hanging connections
 - 📊 **Transparent Integration**: Works seamlessly with existing CrewAI features
+- 🔧 **Full Transport Support**: Stdio, HTTP/Streamable HTTP, and SSE transports
+- 🎯 **Advanced Filtering**: Static and dynamic tool filtering capabilities
+- 🔐 **Flexible Authentication**: Support for headers, environment variables, and query parameters
 
 ## Error Handling
 
-The MCP DSL integration is designed to be resilient:
+The MCP DSL integration is designed to be resilient and handles failures gracefully:
 
 ```python
+from crewai import Agent
+from crewai.mcp import MCPServerStdio, MCPServerHTTP
+
 agent = Agent(
     role="Resilient Agent",
     goal="Continue working despite server issues",
     backstory="Agent that handles failures gracefully",
     mcps=[
+        # String references
         "https://reliable-server.com/mcp",        # Will work
         "https://unreachable-server.com/mcp",     # Will be skipped gracefully
-        "https://slow-server.com/mcp",            # Will timeout gracefully
-        "crewai-amp:working-service"              # Will work
+        "crewai-amp:working-service",             # Will work
+
+        # Structured configs
+        MCPServerStdio(
+            command="python",
+            args=["reliable_server.py"],          # Will work
+        ),
+        MCPServerHTTP(
+            url="https://slow-server.com/mcp",     # Will timeout gracefully
+        ),
     ]
 )
 # Agent will use tools from working servers and log warnings for failing ones
 ```
 
+All connection errors are handled gracefully:
+- **Connection failures**: Logged as warnings, agent continues with available tools
+- **Timeout errors**: Connections timeout after 30 seconds (configurable)
+- **Authentication errors**: Logged clearly for debugging
+- **Invalid configurations**: Validation errors are raised at agent creation time
+
 ## Advanced: MCPServerAdapter
 
 For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.3.0",
+    "crewai==1.4.1",
     "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__ = "1.3.0"
+__version__ = "1.4.1"

lib/crewai-tools/src/crewai_tools/adapters/crewai_rag_adapter.py

@@ -229,6 +229,7 @@ class CrewAIRagAdapter(Adapter):
                             continue
             else:
                 metadata: dict[str, Any] = base_metadata.copy()
+                source_content = SourceContent(source_ref)
 
                 if data_type in [
                     DataType.PDF_FILE,
@@ -239,13 +240,12 @@ class CrewAIRagAdapter(Adapter):
                     DataType.XML,
                     DataType.MDX,
                 ]:
-                    if not os.path.isfile(source_ref):
+                    if not source_content.is_url() and not source_content.path_exists():
                         raise FileNotFoundError(f"File does not exist: {source_ref}")
 
                 loader = data_type.get_loader()
                 chunker = data_type.get_chunker()
 
-                source_content = SourceContent(source_ref)
                 loader_result: LoaderResult = loader.load(source_content)
 
                 chunks = chunker.chunk(loader_result.content)

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

@@ -12,12 +12,16 @@ from pydantic.types import ImportString
 
 
 class QdrantToolSchema(BaseModel):
-    query: str = Field(..., description="Query to search in Qdrant DB")
+    query: str = Field(
+        ..., description="Query to search in Qdrant DB - always required."
+    )
     filter_by: str | None = Field(
-        default=None, description="Parameter to filter the search by."
+        default=None,
+        description="Parameter to filter the search by. When filtering, needs to be used in conjunction with filter_value.",
     )
     filter_value: Any | None = Field(
-        default=None, description="Value to filter the search by."
+        default=None,
+        description="Value to filter the search by. When filtering, needs to be used in conjunction with filter_by.",
     )
 
 

lib/crewai/pyproject.toml

@@ -48,7 +48,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.3.0",
+    "crewai-tools==1.4.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.3.0"
+__version__ = "1.4.1"
 _telemetry_submitted = False
 
 

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

@@ -753,3 +753,99 @@ def get_a2a_agents_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)
+
+
+def extract_agent_identifiers_from_cards(
+    a2a_agents: list[A2AConfig],
+    agent_cards: dict[str, AgentCard],
+) -> tuple[str, ...]:
+    """Extract all valid agent identifiers (endpoints and skill IDs) from agent cards.
+
+    Args:
+        a2a_agents: List of A2A agent configurations
+        agent_cards: Dictionary mapping endpoints to AgentCards
+
+    Returns:
+        Tuple of all valid identifiers (endpoints + skill IDs)
+    """
+    identifiers = set()
+
+    for config in a2a_agents:
+        identifiers.add(config.endpoint)
+
+    for card in agent_cards.values():
+        if card.skills:
+            for skill in card.skills:
+                identifiers.add(skill.id)
+
+    return tuple(sorted(identifiers))
+
+
+def resolve_agent_identifier(
+    identifier: str,
+    a2a_agents: list[A2AConfig],
+    agent_cards: dict[str, AgentCard],
+) -> str:
+    """Resolve an agent identifier (endpoint or skill ID) to a canonical endpoint.
+
+    This function allows both endpoint URLs and skill IDs to be used as agent identifiers.
+    If the identifier is already an endpoint, it's returned as-is. If it's a skill ID,
+    it's resolved to the endpoint of the agent card that contains that skill.
+
+    Args:
+        identifier: Either an endpoint URL or a skill ID
+        a2a_agents: List of A2A agent configurations
+        agent_cards: Dictionary mapping endpoints to AgentCards
+
+    Returns:
+        The canonical endpoint URL
+
+    Raises:
+        ValueError: If the identifier is unknown or ambiguous (matches multiple agents)
+
+    Examples:
+        >>> # Endpoint passthrough
+        >>> resolve_agent_identifier(
+        ...     "http://localhost:10001/.well-known/agent-card.json",
+        ...     a2a_agents,
+        ...     agent_cards
+        ... )
+        'http://localhost:10001/.well-known/agent-card.json'
+
+        >>> # Skill ID resolution
+        >>> resolve_agent_identifier("Research", a2a_agents, agent_cards)
+        'http://localhost:10001/.well-known/agent-card.json'
+    """
+    endpoints = {config.endpoint for config in a2a_agents}
+    if identifier in endpoints:
+        return identifier
+
+    matching_endpoints: list[str] = []
+    for endpoint, card in agent_cards.items():
+        if card.skills:
+            for skill in card.skills:
+                if skill.id == identifier:
+                    matching_endpoints.append(endpoint)
+                    break
+
+    if len(matching_endpoints) == 0:
+        available_endpoints = ", ".join(sorted(endpoints))
+        available_skill_ids = []
+        for card in agent_cards.values():
+            if card.skills:
+                available_skill_ids.extend([skill.id for skill in card.skills])
+        available_skills = ", ".join(sorted(set(available_skill_ids))) if available_skill_ids else "none"
+        raise ValueError(
+            f"Unknown A2A agent identifier '{identifier}'. "
+            f"Available endpoints: {available_endpoints}. "
+            f"Available skill IDs: {available_skills}."
+        )
+
+    if len(matching_endpoints) > 1:
+        endpoints_list = ", ".join(sorted(matching_endpoints))
+        raise ValueError(
+            f"Ambiguous skill ID '{identifier}' found in multiple agents: {endpoints_list}. "
+            f"Please use the specific endpoint URL to disambiguate."
+        )
+
+    return matching_endpoints[0]

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

@@ -23,9 +23,12 @@ from crewai.a2a.templates import (
 )
 from crewai.a2a.types import AgentResponseProtocol
 from crewai.a2a.utils import (
+    create_agent_response_model,
     execute_a2a_delegation,
+    extract_agent_identifiers_from_cards,
     fetch_agent_card,
     get_a2a_agents_and_response_model,
+    resolve_agent_identifier,
 )
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.a2a_events import (
@@ -190,6 +193,9 @@ def _execute_task_with_a2a(
         finally:
             task.description = original_description
 
+    agent_identifiers = extract_agent_identifiers_from_cards(a2a_agents, agent_cards)
+    agent_response_model = create_agent_response_model(agent_identifiers)
+
     task.description = _augment_prompt_with_a2a(
         a2a_agents=a2a_agents,
         task_description=original_description,
@@ -301,6 +307,13 @@ def _augment_prompt_with_a2a(
 IMPORTANT: You have the ability to delegate this task to remote A2A agents.
 
 {agents_text}
+
+AGENT IDENTIFICATION: When setting a2a_ids, you may use either:
+1. The agent's endpoint URL (e.g., "http://localhost:10001/.well-known/agent-card.json")
+2. The exact skill.id from the agent's skills list (e.g., "Research")
+
+Prefer using endpoint URLs when possible to avoid ambiguity. If a skill.id appears on multiple agents, you MUST use the endpoint URL to specify which agent you want.
+
 {history_text}{turn_info}
 
 
@@ -373,6 +386,9 @@ def _handle_agent_response_and_continue(
     if "agent_card" in a2a_result and agent_id not in agent_cards_dict:
         agent_cards_dict[agent_id] = a2a_result["agent_card"]
 
+    agent_identifiers = extract_agent_identifiers_from_cards(a2a_agents, agent_cards_dict)
+    agent_response_model = create_agent_response_model(agent_identifiers)
+
     task.description = _augment_prompt_with_a2a(
         a2a_agents=a2a_agents,
         task_description=original_task_description,
@@ -445,16 +461,20 @@ def _delegate_to_a2a(
         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]
+    agent_identifier = 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_cards_dict = agent_cards or {}
+    try:
+        agent_endpoint = resolve_agent_identifier(
+            agent_identifier, a2a_agents, agent_cards_dict
         )
+    except ValueError as e:
+        raise ValueError(
+            f"Failed to resolve A2A agent identifier '{agent_identifier}': {e}"
+        ) from e
 
-    agent_config = next(filter(lambda x: x.endpoint == agent_id, a2a_agents))
+    agent_config = next(filter(lambda x: x.endpoint == agent_endpoint, a2a_agents))
     task_config = task.config or {}
     context_id = task_config.get("context_id")
     task_id_config = task_config.get("task_id")
@@ -488,7 +508,7 @@ def _delegate_to_a2a(
                 metadata=metadata,
                 extensions=extensions,
                 conversation_history=conversation_history,
-                agent_id=agent_id,
+                agent_id=agent_endpoint,
                 agent_role=Role.user,
                 agent_branch=agent_branch,
                 response_model=agent_config.response_model,
@@ -501,7 +521,7 @@ def _delegate_to_a2a(
                 final_result, next_request = _handle_agent_response_and_continue(
                     self=self,
                     a2a_result=a2a_result,
-                    agent_id=agent_id,
+                    agent_id=agent_endpoint,
                     agent_cards=agent_cards,
                     a2a_agents=a2a_agents,
                     original_task_description=original_task_description,

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

@@ -40,6 +40,16 @@ from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent
 from crewai.llms.base_llm import BaseLLM
+from crewai.mcp import (
+    MCPClient,
+    MCPServerConfig,
+    MCPServerHTTP,
+    MCPServerSSE,
+    MCPServerStdio,
+)
+from crewai.mcp.transports.http import HTTPTransport
+from crewai.mcp.transports.sse import SSETransport
+from crewai.mcp.transports.stdio import StdioTransport
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.fingerprint import Fingerprint
@@ -108,6 +118,8 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
+    _mcp_clients: list[Any] = PrivateAttr(default_factory=list)
+    _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
     max_execution_time: int | None = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -526,6 +538,15 @@ class Agent(BaseAgent):
             self,
             event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
         )
+
+        self._last_messages = (
+            self.agent_executor.messages.copy()
+            if self.agent_executor and hasattr(self.agent_executor, "messages")
+            else []
+        )
+
+        self._cleanup_mcp_clients()
+
         return result
 
     def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> Any:
@@ -649,30 +670,70 @@ class Agent(BaseAgent):
             self._logger.log("error", f"Error getting platform tools: {e!s}")
             return []
 
-    def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]:
-        """Convert MCP server references to CrewAI tools."""
+    def get_mcp_tools(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]:
+        """Convert MCP server references/configs to CrewAI tools.
+
+        Supports both string references (backwards compatible) and structured
+        configuration objects (MCPServerStdio, MCPServerHTTP, MCPServerSSE).
+
+        Args:
+            mcps: List of MCP server references (strings) or configurations.
+
+        Returns:
+            List of BaseTool instances from MCP servers.
+        """
         all_tools = []
+        clients = []
 
-        for mcp_ref in mcps:
-            try:
-                if mcp_ref.startswith("crewai-amp:"):
-                    tools = self._get_amp_mcp_tools(mcp_ref)
-                elif mcp_ref.startswith("https://"):
-                    tools = self._get_external_mcp_tools(mcp_ref)
-                else:
-                    continue
+        for mcp_config in mcps:
+            if isinstance(mcp_config, str):
+                tools = self._get_mcp_tools_from_string(mcp_config)
+            else:
+                tools, client = self._get_native_mcp_tools(mcp_config)
+                if client:
+                    clients.append(client)
 
-                all_tools.extend(tools)
-                self._logger.log(
-                    "info", f"Successfully loaded {len(tools)} tools from {mcp_ref}"
-                )
-
-            except Exception as e:
-                self._logger.log("warning", f"Skipping MCP {mcp_ref} due to error: {e}")
-                continue
+            all_tools.extend(tools)
 
+        # Store clients for cleanup
+        self._mcp_clients.extend(clients)
         return all_tools
 
+    def _cleanup_mcp_clients(self) -> None:
+        """Cleanup MCP client connections after task execution."""
+        if not self._mcp_clients:
+            return
+
+        async def _disconnect_all() -> None:
+            for client in self._mcp_clients:
+                if client and hasattr(client, "connected") and client.connected:
+                    await client.disconnect()
+
+        try:
+            asyncio.run(_disconnect_all())
+        except Exception as e:
+            self._logger.log("error", f"Error during MCP client cleanup: {e}")
+        finally:
+            self._mcp_clients.clear()
+
+    def _get_mcp_tools_from_string(self, mcp_ref: str) -> list[BaseTool]:
+        """Get tools from legacy string-based MCP references.
+
+        This method maintains backwards compatibility with string-based
+        MCP references (https://... and crewai-amp:...).
+
+        Args:
+            mcp_ref: String reference to MCP server.
+
+        Returns:
+            List of BaseTool instances.
+        """
+        if mcp_ref.startswith("crewai-amp:"):
+            return self._get_amp_mcp_tools(mcp_ref)
+        if mcp_ref.startswith("https://"):
+            return self._get_external_mcp_tools(mcp_ref)
+        return []
+
     def _get_external_mcp_tools(self, mcp_ref: str) -> list[BaseTool]:
         """Get tools from external HTTPS MCP server with graceful error handling."""
         from crewai.tools.mcp_tool_wrapper import MCPToolWrapper
@@ -731,6 +792,164 @@ class Agent(BaseAgent):
             )
             return []
 
+    def _get_native_mcp_tools(
+        self, mcp_config: MCPServerConfig
+    ) -> tuple[list[BaseTool], Any | None]:
+        """Get tools from MCP server using structured configuration.
+
+        This method creates an MCP client based on the configuration type,
+        connects to the server, discovers tools, applies filtering, and
+        returns wrapped tools along with the client instance for cleanup.
+
+        Args:
+            mcp_config: MCP server configuration (MCPServerStdio, MCPServerHTTP, or MCPServerSSE).
+
+        Returns:
+            Tuple of (list of BaseTool instances, MCPClient instance for cleanup).
+        """
+        from crewai.tools.base_tool import BaseTool
+        from crewai.tools.mcp_native_tool import MCPNativeTool
+
+        if isinstance(mcp_config, MCPServerStdio):
+            transport = StdioTransport(
+                command=mcp_config.command,
+                args=mcp_config.args,
+                env=mcp_config.env,
+            )
+            server_name = f"{mcp_config.command}_{'_'.join(mcp_config.args)}"
+        elif isinstance(mcp_config, MCPServerHTTP):
+            transport = HTTPTransport(
+                url=mcp_config.url,
+                headers=mcp_config.headers,
+                streamable=mcp_config.streamable,
+            )
+            server_name = self._extract_server_name(mcp_config.url)
+        elif isinstance(mcp_config, MCPServerSSE):
+            transport = SSETransport(
+                url=mcp_config.url,
+                headers=mcp_config.headers,
+            )
+            server_name = self._extract_server_name(mcp_config.url)
+        else:
+            raise ValueError(f"Unsupported MCP server config type: {type(mcp_config)}")
+
+        client = MCPClient(
+            transport=transport,
+            cache_tools_list=mcp_config.cache_tools_list,
+        )
+
+        async def _setup_client_and_list_tools() -> list[dict[str, Any]]:
+            """Async helper to connect and list tools in same event loop."""
+
+            try:
+                if not client.connected:
+                    await client.connect()
+
+                tools_list = await client.list_tools()
+
+                try:
+                    await client.disconnect()
+                    # Small delay to allow background tasks to finish cleanup
+                    # This helps prevent "cancel scope in different task" errors
+                    # when asyncio.run() closes the event loop
+                    await asyncio.sleep(0.1)
+                except Exception as e:
+                    self._logger.log("error", f"Error during disconnect: {e}")
+
+                return tools_list
+            except Exception as e:
+                if client.connected:
+                    await client.disconnect()
+                    await asyncio.sleep(0.1)
+                raise RuntimeError(
+                    f"Error during setup client and list tools: {e}"
+                ) from e
+
+        try:
+            try:
+                asyncio.get_running_loop()
+                import concurrent.futures
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future = executor.submit(
+                        asyncio.run, _setup_client_and_list_tools()
+                    )
+                    tools_list = future.result()
+            except RuntimeError:
+                try:
+                    tools_list = asyncio.run(_setup_client_and_list_tools())
+                except RuntimeError as e:
+                    error_msg = str(e).lower()
+                    if "cancel scope" in error_msg or "task" in error_msg:
+                        raise ConnectionError(
+                            "MCP connection failed due to event loop cleanup issues. "
+                            "This may be due to authentication errors or server unavailability."
+                        ) from e
+                except asyncio.CancelledError as e:
+                    raise ConnectionError(
+                        "MCP connection was cancelled. This may indicate an authentication "
+                        "error or server unavailability."
+                    ) from e
+
+            if mcp_config.tool_filter:
+                filtered_tools = []
+                for tool in tools_list:
+                    if callable(mcp_config.tool_filter):
+                        try:
+                            from crewai.mcp.filters import ToolFilterContext
+
+                            context = ToolFilterContext(
+                                agent=self,
+                                server_name=server_name,
+                                run_context=None,
+                            )
+                            if mcp_config.tool_filter(context, tool):
+                                filtered_tools.append(tool)
+                        except (TypeError, AttributeError):
+                            if mcp_config.tool_filter(tool):
+                                filtered_tools.append(tool)
+                    else:
+                        # Not callable - include tool
+                        filtered_tools.append(tool)
+                tools_list = filtered_tools
+
+            tools = []
+            for tool_def in tools_list:
+                tool_name = tool_def.get("name", "")
+                if not tool_name:
+                    continue
+
+                # Convert inputSchema to Pydantic model if present
+                args_schema = None
+                if tool_def.get("inputSchema"):
+                    args_schema = self._json_schema_to_pydantic(
+                        tool_name, tool_def["inputSchema"]
+                    )
+
+                tool_schema = {
+                    "description": tool_def.get("description", ""),
+                    "args_schema": args_schema,
+                }
+
+                try:
+                    native_tool = MCPNativeTool(
+                        mcp_client=client,
+                        tool_name=tool_name,
+                        tool_schema=tool_schema,
+                        server_name=server_name,
+                    )
+                    tools.append(native_tool)
+                except Exception as e:
+                    self._logger.log("error", f"Failed to create native MCP tool: {e}")
+                    continue
+
+            return cast(list[BaseTool], tools), client
+        except Exception as e:
+            if client.connected:
+                asyncio.run(client.disconnect())
+
+            raise RuntimeError(f"Failed to get native MCP tools: {e}") from e
+
     def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]:
         """Get tools from CrewAI AMP MCP marketplace."""
         # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name"
@@ -1129,6 +1348,15 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint) -> None:
         self.security_config.fingerprint = fingerprint
 
+    @property
+    def last_messages(self) -> list[LLMMessage]:
+        """Get messages from the last task execution.
+
+        Returns:
+            List of LLM messages from the most recent task execution.
+        """
+        return self._last_messages
+
     def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
         """Generate a search query for the knowledge base based on the task description."""
         crewai_event_bus.emit(

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py

@@ -7,7 +7,7 @@ output conversion for OpenAI agents, supporting JSON and Pydantic model formats.
 from typing import Any
 
 from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 
 
 class OpenAIConverterAdapter(BaseConverterAdapter):
@@ -59,7 +59,7 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
             return base_prompt
 
         output_schema: str = (
-            I18N()
+            get_i18n()
             .slice("formatted_task_instructions")
             .format(output_format=self._schema)
         )

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

@@ -25,11 +25,12 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.mcp.config import MCPServerConfig
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities.config import process_config
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.logger import Logger
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.string_utils import interpolate_only
@@ -107,7 +108,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             Set private attributes.
     """
 
-    __hash__ = object.__hash__  # type: ignore
+    __hash__ = object.__hash__
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
     _rpm_controller: RPMController | None = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
@@ -150,7 +151,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings."
+        default_factory=get_i18n, description="Internationalization settings."
     )
     cache_handler: CacheHandler | None = Field(
         default=None, description="An instance of the CacheHandler class."
@@ -180,8 +181,8 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
-    callbacks: list[Callable] = Field(
-        default=[], description="Callbacks to be used for the agent"
+    callbacks: list[Callable[[Any], Any]] = Field(
+        default_factory=list, description="Callbacks to be used for the agent"
     )
     adapted_agent: bool = Field(
         default=False, description="Whether the agent is adapted"
@@ -194,14 +195,14 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=None,
         description="List of applications or application/action combinations that the agent can access through CrewAI Platform. Can contain app names (e.g., 'gmail') or specific actions (e.g., 'gmail/send_email')",
     )
-    mcps: list[str] | None = Field(
+    mcps: list[str | MCPServerConfig] | None = Field(
         default=None,
         description="List of MCP server references. Supports 'https://server.com/path' for external servers and 'crewai-amp:mcp-name' for AMP marketplace. Use '#tool_name' suffix for specific tools.",
     )
 
     @model_validator(mode="before")
     @classmethod
-    def process_model_config(cls, values):
+    def process_model_config(cls, values: Any) -> dict[str, Any]:
         return process_config(values, cls)
 
     @field_validator("tools")
@@ -253,23 +254,39 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
     @field_validator("mcps")
     @classmethod
-    def validate_mcps(cls, mcps: list[str] | None) -> list[str] | None:
+    def validate_mcps(
+        cls, mcps: list[str | MCPServerConfig] | None
+    ) -> list[str | MCPServerConfig] | None:
+        """Validate MCP server references and configurations.
+
+        Supports both string references (for backwards compatibility) and
+        structured configuration objects (MCPServerStdio, MCPServerHTTP, MCPServerSSE).
+        """
         if not mcps:
             return mcps
 
         validated_mcps = []
         for mcp in mcps:
-            if mcp.startswith(("https://", "crewai-amp:")):
+            if isinstance(mcp, str):
+                if mcp.startswith(("https://", "crewai-amp:")):
+                    validated_mcps.append(mcp)
+                else:
+                    raise ValueError(
+                        f"Invalid MCP reference: {mcp}. "
+                        "String references must start with 'https://' or 'crewai-amp:'"
+                    )
+
+            elif isinstance(mcp, (MCPServerConfig)):
                 validated_mcps.append(mcp)
             else:
                 raise ValueError(
-                    f"Invalid MCP reference: {mcp}. Must start with 'https://' or 'crewai-amp:'"
+                    f"Invalid MCP configuration: {type(mcp)}. "
+                    "Must be a string reference or MCPServerConfig instance."
                 )
-
-        return list(set(validated_mcps))
+        return validated_mcps
 
     @model_validator(mode="after")
-    def validate_and_set_attributes(self):
+    def validate_and_set_attributes(self) -> Self:
         # Validate required fields
         for field in ["role", "goal", "backstory"]:
             if getattr(self, field) is None:
@@ -301,7 +318,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             )
 
     @model_validator(mode="after")
-    def set_private_attrs(self):
+    def set_private_attrs(self) -> Self:
         """Set private attributes."""
         self._logger = Logger(verbose=self.verbose)
         if self.max_rpm and not self._rpm_controller:
@@ -313,7 +330,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         return self
 
     @property
-    def key(self):
+    def key(self) -> str:
         source = [
             self._original_role or self.role,
             self._original_goal or self.goal,
@@ -331,7 +348,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         pass
 
     @abstractmethod
-    def create_agent_executor(self, tools=None) -> None:
+    def create_agent_executor(self, tools: list[BaseTool] | None = None) -> None:
         pass
 
     @abstractmethod
@@ -343,7 +360,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         """Get platform tools for the specified list of applications and/or application/action combinations."""
 
     @abstractmethod
-    def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]:
+    def get_mcp_tools(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]:
         """Get MCP tools for the specified list of MCP server references."""
 
     def copy(self) -> Self:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
@@ -443,5 +460,5 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None:
         pass

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

@@ -37,7 +37,11 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.llm_call_hooks import (
+    get_after_llm_call_hooks,
+    get_before_llm_call_hooks,
+)
 from crewai.utilities.printer import Printer
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -65,7 +69,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def __init__(
         self,
-        llm: BaseLLM | Any | None,
+        llm: BaseLLM,
         task: Task,
         crew: Crew,
         agent: Agent,
@@ -106,7 +110,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             callbacks: Optional callbacks list.
             response_model: Optional Pydantic model for structured outputs.
         """
-        self._i18n: I18N = I18N()
+        self._i18n: I18N = get_i18n()
         self.llm = llm
         self.task = task
         self.agent = agent
@@ -130,6 +134,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: list[LLMMessage] = []
         self.iterations = 0
         self.log_error_after = 3
+        self.before_llm_call_hooks: list[Callable] = []
+        self.after_llm_call_hooks: list[Callable] = []
+        self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
+        self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
         if self.llm:
             # This may be mutating the shared llm object and needs further evaluation
             existing_stop = getattr(self.llm, "stop", [])
@@ -214,6 +222,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                     )
+                    break
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
@@ -225,8 +234,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_task=self.task,
                     from_agent=self.agent,
                     response_model=self.response_model,
+                    executor_context=self,
                 )
-                formatted_answer = process_llm_response(answer, self.use_stop_words)
+                formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
 
                 if isinstance(formatted_answer, AgentAction):
                     # Extract agent fingerprint if available
@@ -258,11 +268,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer, tool_result
                     )
 
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text)
+                self._invoke_step_callback(formatted_answer)  # type: ignore[arg-type]
+                self._append_message(formatted_answer.text)  # type: ignore[union-attr,attr-defined]
 
-            except OutputParserError as e:  # noqa: PERF203
-                formatted_answer = handle_output_parser_exception(
+            except OutputParserError as e:
+                formatted_answer = handle_output_parser_exception(  # type: ignore[assignment]
                     e=e,
                     messages=self.messages,
                     iterations=self.iterations,

lib/crewai/src/crewai/agents/parser.py

@@ -18,10 +18,10 @@ from crewai.agents.constants import (
     MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
     UNABLE_TO_REPAIR_JSON_RESULTS,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 
 
-_I18N = I18N()
+_I18N = get_i18n()
 
 
 @dataclass

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]==1.3.0"
+    "crewai[tools]==1.4.1"
 ]
 
 [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]==1.3.0"
+    "crewai[tools]==1.4.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/crew.py

@@ -27,6 +27,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -70,7 +71,7 @@ from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import BaseTool, Tool
+from crewai.tools.base_tool import BaseTool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.crew.models import CrewContext
@@ -81,7 +82,7 @@ from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.logger import Logger
 from crewai.utilities.planning_handler import CrewPlanner
@@ -195,7 +196,7 @@ class Crew(FlowTrackable, BaseModel):
     function_calling_llm: str | InstanceOf[LLM] | Any | None = Field(
         description="Language model that will run the agent.", default=None
     )
-    config: Json | dict[str, Any] | None = Field(default=None)
+    config: Json[dict[str, Any]] | dict[str, Any] | None = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     share_crew: bool | None = Field(default=False)
     step_callback: Any | None = Field(
@@ -294,7 +295,9 @@ class Crew(FlowTrackable, BaseModel):
 
     @field_validator("config", mode="before")
     @classmethod
-    def check_config_type(cls, v: Json | dict[str, Any]) -> Json | dict[str, Any]:
+    def check_config_type(
+        cls, v: Json[dict[str, Any]] | dict[str, Any]
+    ) -> dict[str, Any]:
         """Validates that the config is a valid type.
         Args:
             v: The config to be validated.
@@ -310,7 +313,7 @@ class Crew(FlowTrackable, BaseModel):
         """set private attributes."""
 
         self._cache_handler = CacheHandler()
-        event_listener = EventListener()
+        event_listener = EventListener()  # type: ignore[no-untyped-call]
 
         if (
             is_tracing_enabled()
@@ -330,13 +333,13 @@ class Crew(FlowTrackable, BaseModel):
 
         return self
 
-    def _initialize_default_memories(self):
-        self._long_term_memory = self._long_term_memory or LongTermMemory()
-        self._short_term_memory = self._short_term_memory or ShortTermMemory(
+    def _initialize_default_memories(self) -> None:
+        self._long_term_memory = self._long_term_memory or LongTermMemory()  # type: ignore[no-untyped-call]
+        self._short_term_memory = self._short_term_memory or ShortTermMemory(  # type: ignore[no-untyped-call]
             crew=self,
             embedder_config=self.embedder,
         )
-        self._entity_memory = self.entity_memory or EntityMemory(
+        self._entity_memory = self.entity_memory or EntityMemory(  # type: ignore[no-untyped-call]
             crew=self, embedder_config=self.embedder
         )
 
@@ -380,7 +383,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_manager_llm(self):
+    def check_manager_llm(self) -> Self:
         """Validates that the language model is set when using hierarchical process."""
         if self.process == Process.hierarchical:
             if not self.manager_llm and not self.manager_agent:
@@ -405,7 +408,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_config(self):
+    def check_config(self) -> Self:
         """Validates that the crew is properly configured with agents and tasks."""
         if not self.config and not self.tasks and not self.agents:
             raise PydanticCustomError(
@@ -426,23 +429,20 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_tasks(self):
+    def validate_tasks(self) -> Self:
         if self.process == Process.sequential:
             for task in self.tasks:
                 if task.agent is None:
                     raise PydanticCustomError(
                         "missing_agent_in_task",
-                        (
-                            f"Sequential process error: Agent is missing in the task "
-                            f"with the following description: {task.description}"
-                        ),  # type: ignore # Dynamic string in error message
-                        {},
+                        "Sequential process error: Agent is missing in the task with the following description: {description}",
+                        {"description": task.description},
                     )
 
         return self
 
     @model_validator(mode="after")
-    def validate_end_with_at_most_one_async_task(self):
+    def validate_end_with_at_most_one_async_task(self) -> Self:
         """Validates that the crew ends with at most one asynchronous task."""
         final_async_task_count = 0
 
@@ -505,7 +505,9 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
+    def validate_async_task_cannot_include_sequential_async_tasks_in_context(
+        self,
+    ) -> Self:
         """
         Validates that if a task is set to be executed asynchronously,
         it cannot include other asynchronous tasks in its context unless
@@ -527,7 +529,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_context_no_future_tasks(self):
+    def validate_context_no_future_tasks(self) -> Self:
         """Validates that a task's context does not include future tasks."""
         task_indices = {id(task): i for i, task in enumerate(self.tasks)}
 
@@ -561,7 +563,7 @@ class Crew(FlowTrackable, BaseModel):
         """
         return self.security_config.fingerprint
 
-    def _setup_from_config(self):
+    def _setup_from_config(self) -> None:
         """Initializes agents and tasks from the provided config."""
         if self.config is None:
             raise ValueError("Config should not be None.")
@@ -628,12 +630,12 @@ class Crew(FlowTrackable, BaseModel):
 
             for agent in train_crew.agents:
                 if training_data.get(str(agent.id)):
-                    result = TaskEvaluator(agent).evaluate_training_data(
+                    result = TaskEvaluator(agent).evaluate_training_data(  # type: ignore[arg-type]
                         training_data=training_data, agent_id=str(agent.id)
                     )
                     CrewTrainingHandler(filename).save_trained_data(
                         agent_id=str(agent.role),
-                        trained_data=result.model_dump(),  # type: ignore[arg-type]
+                        trained_data=result.model_dump(),
                     )
 
             crewai_event_bus.emit(
@@ -684,12 +686,8 @@ class Crew(FlowTrackable, BaseModel):
             self._set_tasks_callbacks()
             self._set_allow_crewai_trigger_context_for_first_task()
 
-            i18n = I18N(prompt_file=self.prompt_file)
-
             for agent in self.agents:
-                agent.i18n = i18n
-                # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-                agent.crew = self  # type: ignore[attr-defined]
+                agent.crew = self
                 agent.set_knowledge(crew_embedder=self.embedder)
                 # TODO: Create an AgentFunctionCalling protocol for future refactoring
                 if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
@@ -753,10 +751,12 @@ class Crew(FlowTrackable, BaseModel):
         inputs = inputs or {}
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: list[dict]) -> list[CrewOutput]:
+    async def kickoff_for_each_async(
+        self, inputs: list[dict[str, Any]]
+    ) -> list[CrewOutput]:
         crew_copies = [self.copy() for _ in inputs]
 
-        async def run_crew(crew, input_data):
+        async def run_crew(crew: Self, input_data: Any) -> CrewOutput:
             return await crew.kickoff_async(inputs=input_data)
 
         tasks = [
@@ -775,7 +775,7 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
-    def _handle_crew_planning(self):
+    def _handle_crew_planning(self) -> None:
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")
         result = CrewPlanner(
@@ -793,7 +793,7 @@ class Crew(FlowTrackable, BaseModel):
         output: TaskOutput,
         task_index: int,
         was_replayed: bool = False,
-    ):
+    ) -> None:
         if self._inputs:
             inputs = self._inputs
         else:
@@ -809,6 +809,7 @@ class Crew(FlowTrackable, BaseModel):
                 "json_dict": output.json_dict,
                 "output_format": output.output_format,
                 "agent": output.agent,
+                "messages": output.messages,
             },
             "task_index": task_index,
             "inputs": inputs,
@@ -825,19 +826,21 @@ class Crew(FlowTrackable, BaseModel):
         self._create_manager_agent()
         return self._execute_tasks(self.tasks)
 
-    def _create_manager_agent(self):
-        i18n = I18N(prompt_file=self.prompt_file)
+    def _create_manager_agent(self) -> None:
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
             if manager.tools is not None and len(manager.tools) > 0:
                 self._logger.log(
-                    "warning", "Manager agent should not have tools", color="orange"
+                    "warning",
+                    "Manager agent should not have tools",
+                    color="bold_yellow",
                 )
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
             self.manager_llm = create_llm(self.manager_llm)
+            i18n = get_i18n(prompt_file=self.prompt_file)
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -895,7 +898,7 @@ class Crew(FlowTrackable, BaseModel):
             tools_for_task = self._prepare_tools(
                 agent_to_use,
                 task,
-                cast(list[Tool] | list[BaseTool], tools_for_task),
+                tools_for_task,
             )
 
             self._log_task_start(task, agent_to_use.role)
@@ -915,7 +918,7 @@ class Crew(FlowTrackable, BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -927,7 +930,7 @@ class Crew(FlowTrackable, BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -965,7 +968,7 @@ class Crew(FlowTrackable, BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         # Add delegation tools if agent allows delegation
         if hasattr(agent, "allow_delegation") and getattr(
@@ -1002,21 +1005,21 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._add_mcp_tools(task, tools)
 
         # Return a list[BaseTool] compatible with Task.execute_sync and execute_async
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _get_agent_to_use(self, task: Task) -> BaseAgent | None:
         if self.process == Process.hierarchical:
             return self.manager_agent
         return task.agent
 
+    @staticmethod
     def _merge_tools(
-        self,
-        existing_tools: list[Tool] | list[BaseTool],
-        new_tools: list[Tool] | list[BaseTool],
+        existing_tools: list[BaseTool],
+        new_tools: list[BaseTool],
     ) -> list[BaseTool]:
         """Merge new tools into existing tools list, avoiding duplicates."""
         if not new_tools:
-            return cast(list[BaseTool], existing_tools)
+            return existing_tools
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -1027,63 +1030,62 @@ class Crew(FlowTrackable, BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _inject_delegation_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
         agents: list[BaseAgent],
     ) -> list[BaseTool]:
         if hasattr(task_agent, "get_delegation_tools"):
             delegation_tools = task_agent.get_delegation_tools(agents)
             # Cast delegation_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(list[BaseTool], delegation_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, delegation_tools)
+        return tools
 
     def _inject_platform_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
     ) -> list[BaseTool]:
         apps = getattr(task_agent, "apps", None) or []
 
         if hasattr(task_agent, "get_platform_tools") and apps:
             platform_tools = task_agent.get_platform_tools(apps=apps)
-            return self._merge_tools(tools, cast(list[BaseTool], platform_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, platform_tools)
+        return tools
 
     def _inject_mcp_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
     ) -> list[BaseTool]:
         mcps = getattr(task_agent, "mcps", None) or []
         if hasattr(task_agent, "get_mcp_tools") and mcps:
             mcp_tools = task_agent.get_mcp_tools(mcps=mcps)
-            return self._merge_tools(tools, cast(list[BaseTool], mcp_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, mcp_tools)
+        return tools
 
     def _add_multimodal_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if hasattr(agent, "get_multimodal_tools"):
             multimodal_tools = agent.get_multimodal_tools()
-            # Cast multimodal_tools to the expected type for _merge_tools
             return self._merge_tools(tools, cast(list[BaseTool], multimodal_tools))
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _add_code_execution_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if hasattr(agent, "get_code_execution_tools"):
             code_tools = agent.get_code_execution_tools()
             # Cast code_tools to the expected type for _merge_tools
             return self._merge_tools(tools, cast(list[BaseTool], code_tools))
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _add_delegation_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
+        self, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
@@ -1092,25 +1094,21 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return cast(list[BaseTool], tools)
+        return tools
 
-    def _add_platform_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+    def _add_platform_tools(self, task: Task, tools: list[BaseTool]) -> list[BaseTool]:
         if task.agent:
             tools = self._inject_platform_tools(tools, task.agent)
 
-        return cast(list[BaseTool], tools or [])
+        return tools or []
 
-    def _add_mcp_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+    def _add_mcp_tools(self, task: Task, tools: list[BaseTool]) -> list[BaseTool]:
         if task.agent:
             tools = self._inject_mcp_tools(tools, task.agent)
 
-        return cast(list[BaseTool], tools or [])
+        return tools or []
 
-    def _log_task_start(self, task: Task, role: str = "None"):
+    def _log_task_start(self, task: Task, role: str = "None") -> None:
         if self.output_log_file:
             self._file_handler.log(
                 task_name=task.name,  # type: ignore[arg-type]
@@ -1120,7 +1118,7 @@ class Crew(FlowTrackable, BaseModel):
             )
 
     def _update_manager_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
+        self, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if self.manager_agent:
             if task.agent:
@@ -1129,7 +1127,7 @@ class Crew(FlowTrackable, BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
         if not task.context:
@@ -1239,6 +1237,7 @@ class Crew(FlowTrackable, BaseModel):
                 pydantic=stored_output["pydantic"],
                 json_dict=stored_output["json_dict"],
                 output_format=stored_output["output_format"],
+                messages=stored_output.get("messages", []),
             )
             self.tasks[i].output = task_output
 
@@ -1280,7 +1279,7 @@ class Crew(FlowTrackable, BaseModel):
 
         return required_inputs
 
-    def copy(self):
+    def copy(self) -> Crew:  # type: ignore[override]
         """
         Creates a deep copy of the Crew instance.
 
@@ -1311,7 +1310,7 @@ class Crew(FlowTrackable, BaseModel):
         manager_agent = self.manager_agent.copy() if self.manager_agent else None
         manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
 
-        task_mapping = {}
+        task_mapping: dict[str, Any] = {}
 
         cloned_tasks = []
         existing_knowledge_sources = shallow_copy(self.knowledge_sources)
@@ -1373,7 +1372,6 @@ class Crew(FlowTrackable, BaseModel):
             )
             for task in self.tasks
         ]
-        # type: ignore # "interpolate_inputs" of "Agent" does not return a value (it only ever returns None)
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
@@ -1463,7 +1461,7 @@ class Crew(FlowTrackable, BaseModel):
             )
             raise
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return (
             f"Crew(id={self.id}, process={self.process}, "
             f"number_of_agents={len(self.agents)}, "
@@ -1520,7 +1518,9 @@ class Crew(FlowTrackable, BaseModel):
             if (system := config.get("system")) is not None:
                 name = config.get("name")
                 try:
-                    reset_fn: Callable = cast(Callable, config.get("reset"))
+                    reset_fn: Callable[[Any], Any] = cast(
+                        Callable[[Any], Any], config.get("reset")
+                    )
                     reset_fn(system)
                     self._logger.log(
                         "info",
@@ -1551,7 +1551,9 @@ class Crew(FlowTrackable, BaseModel):
             raise RuntimeError(f"{name} memory system is not initialized")
 
         try:
-            reset_fn: Callable = cast(Callable, config.get("reset"))
+            reset_fn: Callable[[Any], Any] = cast(
+                Callable[[Any], Any], config.get("reset")
+            )
             reset_fn(system)
             self._logger.log(
                 "info",
@@ -1564,7 +1566,7 @@ class Crew(FlowTrackable, BaseModel):
                 f"Failed to reset {name} memory: {e!s}"
             ) from e
 
-    def _get_memory_systems(self):
+    def _get_memory_systems(self) -> dict[str, Any]:
         """Get all available memory systems with their configuration.
 
         Returns:
@@ -1572,10 +1574,10 @@ class Crew(FlowTrackable, BaseModel):
             display names.
         """
 
-        def default_reset(memory):
+        def default_reset(memory: Any) -> Any:
             return memory.reset()
 
-        def knowledge_reset(memory):
+        def knowledge_reset(memory: Any) -> Any:
             return self.reset_knowledge(memory)
 
         # Get knowledge for agents
@@ -1635,7 +1637,7 @@ class Crew(FlowTrackable, BaseModel):
         for ks in knowledges:
             ks.reset()
 
-    def _set_allow_crewai_trigger_context_for_first_task(self):
+    def _set_allow_crewai_trigger_context_for_first_task(self) -> None:
         crewai_trigger_payload = self._inputs and self._inputs.get(
             "crewai_trigger_payload"
         )

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

@@ -16,7 +16,6 @@ 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.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -61,6 +60,14 @@ from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
+from crewai.events.types.mcp_events import (
+    MCPConnectionCompletedEvent,
+    MCPConnectionFailedEvent,
+    MCPConnectionStartedEvent,
+    MCPToolExecutionCompletedEvent,
+    MCPToolExecutionFailedEvent,
+    MCPToolExecutionStartedEvent,
+)
 from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
@@ -153,6 +160,12 @@ __all__ = [
     "LiteAgentExecutionCompletedEvent",
     "LiteAgentExecutionErrorEvent",
     "LiteAgentExecutionStartedEvent",
+    "MCPConnectionCompletedEvent",
+    "MCPConnectionFailedEvent",
+    "MCPConnectionStartedEvent",
+    "MCPToolExecutionCompletedEvent",
+    "MCPToolExecutionFailedEvent",
+    "MCPToolExecutionStartedEvent",
     "MemoryQueryCompletedEvent",
     "MemoryQueryFailedEvent",
     "MemoryQueryStartedEvent",

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

@@ -65,6 +65,14 @@ from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
+from crewai.events.types.mcp_events import (
+    MCPConnectionCompletedEvent,
+    MCPConnectionFailedEvent,
+    MCPConnectionStartedEvent,
+    MCPToolExecutionCompletedEvent,
+    MCPToolExecutionFailedEvent,
+    MCPToolExecutionStartedEvent,
+)
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
@@ -615,5 +623,67 @@ class EventListener(BaseEventListener):
                 event.total_turns,
             )
 
+        # ----------- MCP EVENTS -----------
+
+        @crewai_event_bus.on(MCPConnectionStartedEvent)
+        def on_mcp_connection_started(source, event: MCPConnectionStartedEvent):
+            self.formatter.handle_mcp_connection_started(
+                event.server_name,
+                event.server_url,
+                event.transport_type,
+                event.is_reconnect,
+                event.connect_timeout,
+            )
+
+        @crewai_event_bus.on(MCPConnectionCompletedEvent)
+        def on_mcp_connection_completed(source, event: MCPConnectionCompletedEvent):
+            self.formatter.handle_mcp_connection_completed(
+                event.server_name,
+                event.server_url,
+                event.transport_type,
+                event.connection_duration_ms,
+                event.is_reconnect,
+            )
+
+        @crewai_event_bus.on(MCPConnectionFailedEvent)
+        def on_mcp_connection_failed(source, event: MCPConnectionFailedEvent):
+            self.formatter.handle_mcp_connection_failed(
+                event.server_name,
+                event.server_url,
+                event.transport_type,
+                event.error,
+                event.error_type,
+            )
+
+        @crewai_event_bus.on(MCPToolExecutionStartedEvent)
+        def on_mcp_tool_execution_started(source, event: MCPToolExecutionStartedEvent):
+            self.formatter.handle_mcp_tool_execution_started(
+                event.server_name,
+                event.tool_name,
+                event.tool_args,
+            )
+
+        @crewai_event_bus.on(MCPToolExecutionCompletedEvent)
+        def on_mcp_tool_execution_completed(
+            source, event: MCPToolExecutionCompletedEvent
+        ):
+            self.formatter.handle_mcp_tool_execution_completed(
+                event.server_name,
+                event.tool_name,
+                event.tool_args,
+                event.result,
+                event.execution_duration_ms,
+            )
+
+        @crewai_event_bus.on(MCPToolExecutionFailedEvent)
+        def on_mcp_tool_execution_failed(source, event: MCPToolExecutionFailedEvent):
+            self.formatter.handle_mcp_tool_execution_failed(
+                event.server_name,
+                event.tool_name,
+                event.tool_args,
+                event.error,
+                event.error_type,
+            )
+
 
 event_listener = EventListener()

lib/crewai/src/crewai/events/event_types.py

@@ -40,6 +40,14 @@ from crewai.events.types.llm_guardrail_events import (
     LLMGuardrailCompletedEvent,
     LLMGuardrailStartedEvent,
 )
+from crewai.events.types.mcp_events import (
+    MCPConnectionCompletedEvent,
+    MCPConnectionFailedEvent,
+    MCPConnectionStartedEvent,
+    MCPToolExecutionCompletedEvent,
+    MCPToolExecutionFailedEvent,
+    MCPToolExecutionStartedEvent,
+)
 from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
@@ -115,4 +123,10 @@ EventTypes = (
     | MemoryQueryFailedEvent
     | MemoryRetrievalStartedEvent
     | MemoryRetrievalCompletedEvent
+    | MCPConnectionStartedEvent
+    | MCPConnectionCompletedEvent
+    | MCPConnectionFailedEvent
+    | MCPToolExecutionStartedEvent
+    | MCPToolExecutionCompletedEvent
+    | MCPToolExecutionFailedEvent
 )

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

@@ -0,0 +1,85 @@
+from datetime import datetime
+from typing import Any
+
+from crewai.events.base_events import BaseEvent
+
+
+class MCPEvent(BaseEvent):
+    """Base event for MCP operations."""
+
+    server_name: str
+    server_url: str | None = None
+    transport_type: str | None = None  # "stdio", "http", "sse"
+    agent_id: str | None = None
+    agent_role: str | None = None
+    from_agent: Any | None = None
+    from_task: Any | None = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class MCPConnectionStartedEvent(MCPEvent):
+    """Event emitted when starting to connect to an MCP server."""
+
+    type: str = "mcp_connection_started"
+    connect_timeout: int | None = None
+    is_reconnect: bool = (
+        False  # True if this is a reconnection, False for first connection
+    )
+
+
+class MCPConnectionCompletedEvent(MCPEvent):
+    """Event emitted when successfully connected to an MCP server."""
+
+    type: str = "mcp_connection_completed"
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    connection_duration_ms: float | None = None
+    is_reconnect: bool = (
+        False  # True if this was a reconnection, False for first connection
+    )
+
+
+class MCPConnectionFailedEvent(MCPEvent):
+    """Event emitted when connection to an MCP server fails."""
+
+    type: str = "mcp_connection_failed"
+    error: str
+    error_type: str | None = None  # "timeout", "authentication", "network", etc.
+    started_at: datetime | None = None
+    failed_at: datetime | None = None
+
+
+class MCPToolExecutionStartedEvent(MCPEvent):
+    """Event emitted when starting to execute an MCP tool."""
+
+    type: str = "mcp_tool_execution_started"
+    tool_name: str
+    tool_args: dict[str, Any] | None = None
+
+
+class MCPToolExecutionCompletedEvent(MCPEvent):
+    """Event emitted when MCP tool execution completes."""
+
+    type: str = "mcp_tool_execution_completed"
+    tool_name: str
+    tool_args: dict[str, Any] | None = None
+    result: Any | None = None
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    execution_duration_ms: float | None = None
+
+
+class MCPToolExecutionFailedEvent(MCPEvent):
+    """Event emitted when MCP tool execution fails."""
+
+    type: str = "mcp_tool_execution_failed"
+    tool_name: str
+    tool_args: dict[str, Any] | None = None
+    error: str
+    error_type: str | None = None  # "timeout", "validation", "server_error", etc.
+    started_at: datetime | None = None
+    failed_at: datetime | None = None

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

@@ -2248,3 +2248,203 @@ class ConsoleFormatter:
 
         self.current_a2a_conversation_branch = None
         self.current_a2a_turn_count = 0
+
+    # ----------- MCP EVENTS -----------
+
+    def handle_mcp_connection_started(
+        self,
+        server_name: str,
+        server_url: str | None = None,
+        transport_type: str | None = None,
+        is_reconnect: bool = False,
+        connect_timeout: int | None = None,
+    ) -> None:
+        """Handle MCP connection started event."""
+        if not self.verbose:
+            return
+
+        content = Text()
+        reconnect_text = " (Reconnecting)" if is_reconnect else ""
+        content.append(f"MCP Connection Started{reconnect_text}\n\n", style="cyan bold")
+        content.append("Server: ", style="white")
+        content.append(f"{server_name}\n", style="cyan")
+
+        if server_url:
+            content.append("URL: ", style="white")
+            content.append(f"{server_url}\n", style="cyan dim")
+
+        if transport_type:
+            content.append("Transport: ", style="white")
+            content.append(f"{transport_type}\n", style="cyan")
+
+        if connect_timeout:
+            content.append("Timeout: ", style="white")
+            content.append(f"{connect_timeout}s\n", style="cyan")
+
+        panel = self.create_panel(content, "🔌 MCP Connection", "cyan")
+        self.print(panel)
+        self.print()
+
+    def handle_mcp_connection_completed(
+        self,
+        server_name: str,
+        server_url: str | None = None,
+        transport_type: str | None = None,
+        connection_duration_ms: float | None = None,
+        is_reconnect: bool = False,
+    ) -> None:
+        """Handle MCP connection completed event."""
+        if not self.verbose:
+            return
+
+        content = Text()
+        reconnect_text = " (Reconnected)" if is_reconnect else ""
+        content.append(
+            f"MCP Connection Completed{reconnect_text}\n\n", style="green bold"
+        )
+        content.append("Server: ", style="white")
+        content.append(f"{server_name}\n", style="green")
+
+        if server_url:
+            content.append("URL: ", style="white")
+            content.append(f"{server_url}\n", style="green dim")
+
+        if transport_type:
+            content.append("Transport: ", style="white")
+            content.append(f"{transport_type}\n", style="green")
+
+        if connection_duration_ms is not None:
+            content.append("Duration: ", style="white")
+            content.append(f"{connection_duration_ms:.2f}ms\n", style="green")
+
+        panel = self.create_panel(content, "✅ MCP Connected", "green")
+        self.print(panel)
+        self.print()
+
+    def handle_mcp_connection_failed(
+        self,
+        server_name: str,
+        server_url: str | None = None,
+        transport_type: str | None = None,
+        error: str = "",
+        error_type: str | None = None,
+    ) -> None:
+        """Handle MCP connection failed event."""
+        if not self.verbose:
+            return
+
+        content = Text()
+        content.append("MCP Connection Failed\n\n", style="red bold")
+        content.append("Server: ", style="white")
+        content.append(f"{server_name}\n", style="red")
+
+        if server_url:
+            content.append("URL: ", style="white")
+            content.append(f"{server_url}\n", style="red dim")
+
+        if transport_type:
+            content.append("Transport: ", style="white")
+            content.append(f"{transport_type}\n", style="red")
+
+        if error_type:
+            content.append("Error Type: ", style="white")
+            content.append(f"{error_type}\n", style="red")
+
+        if error:
+            content.append("\nError: ", style="white bold")
+            error_preview = error[:500] + "..." if len(error) > 500 else error
+            content.append(f"{error_preview}\n", style="red")
+
+        panel = self.create_panel(content, "❌ MCP Connection Failed", "red")
+        self.print(panel)
+        self.print()
+
+    def handle_mcp_tool_execution_started(
+        self,
+        server_name: str,
+        tool_name: str,
+        tool_args: dict[str, Any] | None = None,
+    ) -> None:
+        """Handle MCP tool execution started event."""
+        if not self.verbose:
+            return
+
+        content = self.create_status_content(
+            "MCP Tool Execution Started",
+            tool_name,
+            "yellow",
+            tool_args=tool_args or {},
+            Server=server_name,
+        )
+
+        panel = self.create_panel(content, "🔧 MCP Tool", "yellow")
+        self.print(panel)
+        self.print()
+
+    def handle_mcp_tool_execution_completed(
+        self,
+        server_name: str,
+        tool_name: str,
+        tool_args: dict[str, Any] | None = None,
+        result: Any | None = None,
+        execution_duration_ms: float | None = None,
+    ) -> None:
+        """Handle MCP tool execution completed event."""
+        if not self.verbose:
+            return
+
+        content = self.create_status_content(
+            "MCP Tool Execution Completed",
+            tool_name,
+            "green",
+            tool_args=tool_args or {},
+            Server=server_name,
+        )
+
+        if execution_duration_ms is not None:
+            content.append("Duration: ", style="white")
+            content.append(f"{execution_duration_ms:.2f}ms\n", style="green")
+
+        if result is not None:
+            result_str = str(result)
+            if len(result_str) > 500:
+                result_str = result_str[:497] + "..."
+            content.append("\nResult: ", style="white bold")
+            content.append(f"{result_str}\n", style="green")
+
+        panel = self.create_panel(content, "✅ MCP Tool Completed", "green")
+        self.print(panel)
+        self.print()
+
+    def handle_mcp_tool_execution_failed(
+        self,
+        server_name: str,
+        tool_name: str,
+        tool_args: dict[str, Any] | None = None,
+        error: str = "",
+        error_type: str | None = None,
+    ) -> None:
+        """Handle MCP tool execution failed event."""
+        if not self.verbose:
+            return
+
+        content = self.create_status_content(
+            "MCP Tool Execution Failed",
+            tool_name,
+            "red",
+            tool_args=tool_args or {},
+            Server=server_name,
+        )
+
+        if error_type:
+            content.append("Error Type: ", style="white")
+            content.append(f"{error_type}\n", style="red")
+
+        if error:
+            content.append("\nError: ", style="white bold")
+            error_preview = error[:500] + "..." if len(error) > 500 else error
+            content.append(f"{error_preview}\n", style="red")
+
+        panel = self.create_panel(content, "❌ MCP Tool Failed", "red")
+        self.print(panel)
+        self.print()

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

@@ -1,12 +1,10 @@
+from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.persistence import persist
 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__ = [
@@ -17,9 +15,7 @@ __all__ = [
     "listen",
     "or_",
     "persist",
-    "print_structure_summary",
     "router",
     "start",
-    "structure_to_dict",
     "visualize_flow_structure",
 ]

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

@@ -15,7 +15,6 @@ import logging
 from typing import (
     Any,
     ClassVar,
-    Final,
     Generic,
     Literal,
     ParamSpec,
@@ -45,7 +44,7 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.flow.visualization import build_flow_structure, render_interactive
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -58,18 +57,16 @@ from crewai.flow.flow_wrappers import (
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey
 from crewai.flow.utils import (
+    _extract_all_methods,
+    _normalize_condition,
     get_possible_return_constants,
     is_flow_condition_dict,
-    is_flow_condition_list,
     is_flow_method,
     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.flow.visualization import build_flow_structure, render_interactive
 from crewai.utilities.printer import Printer, PrinterColor
 
 
@@ -431,6 +428,8 @@ class FlowMeta(type):
                         possible_returns = get_possible_return_constants(attr_value)
                         if possible_returns:
                             router_paths[attr_name] = possible_returns
+                        else:
+                            router_paths[attr_name] = []
 
         cls._start_methods = start_methods  # type: ignore[attr-defined]
         cls._listeners = listeners  # type: ignore[attr-defined]
@@ -495,7 +494,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             or should_auto_collect_first_time_traces()
         ):
             trace_listener = TraceCollectionListener()
-            trace_listener.setup_listeners(crewai_event_bus)  # type: ignore[no-untyped-call]
+            trace_listener.setup_listeners(crewai_event_bus)
         # Apply any additional kwargs
         if kwargs:
             self._initialize_state(kwargs)
@@ -601,7 +600,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
 
     def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
+        """Create a copy of the current state.
+
+        Returns:
+            A copy of the current state
+        """
+        if isinstance(self._state, BaseModel):
+            try:
+                return self._state.model_copy(deep=True)
+            except (TypeError, AttributeError):
+                try:
+                    state_dict = self._state.model_dump()
+                    model_class = type(self._state)
+                    return model_class(**state_dict)
+                except Exception:
+                    return self._state.model_copy(deep=False)
+        else:
+            try:
+                return copy.deepcopy(self._state)
+            except (TypeError, AttributeError):
+                return cast(T, self._state.copy())
 
     @property
     def state(self) -> T:
@@ -926,8 +944,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 trace_listener = TraceCollectionListener()
                 if trace_listener.batch_manager.batch_owner_type == "flow":
                     if trace_listener.first_time_handler.is_first_time:
-                        trace_listener.first_time_handler.mark_events_collected()  # type: ignore[no-untyped-call]
-                        trace_listener.first_time_handler.handle_execution_completion()  # type: ignore[no-untyped-call]
+                        trace_listener.first_time_handler.mark_events_collected()
+                        trace_listener.first_time_handler.handle_execution_completion()
                     else:
                         trace_listener.batch_manager.finalize_batch()
 

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

@@ -21,6 +21,7 @@ P = ParamSpec("P")
 R = TypeVar("R", covariant=True)
 
 FlowMethodName = NewType("FlowMethodName", str)
+FlowRouteName = NewType("FlowRouteName", str)
 PendingListenerKey = NewType(
     "PendingListenerKey",
     Annotated[str, "nested flow conditions use 'listener_name:object_id'"],

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

@@ -19,11 +19,11 @@ import ast
 from collections import defaultdict, deque
 import inspect
 import textwrap
-from typing import Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from typing_extensions import TypeIs
 
-from crewai.flow.constants import OR_CONDITION, AND_CONDITION
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -33,6 +33,7 @@ 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
 
@@ -40,6 +41,22 @@ _printer = Printer()
 
 
 def get_possible_return_constants(function: Any) -> list[str] | None:
+    """Extract possible string return values from a function using AST parsing.
+
+    This function analyzes the source code of a router method to identify
+    all possible string values it might return. It handles:
+    - Direct string literals: return "value"
+    - Variable assignments: x = "value"; return x
+    - Dictionary lookups: d = {"k": "v"}; return d[key]
+    - Conditional returns: return "a" if cond else "b"
+    - State attributes: return self.state.attr (infers from class context)
+
+    Args:
+        function: The function to analyze.
+
+    Returns:
+        List of possible string return values, or None if analysis fails.
+    """
     try:
         source = inspect.getsource(function)
     except OSError:
@@ -82,6 +99,7 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
     return_values: set[str] = set()
     dict_definitions: dict[str, list[str]] = {}
     variable_values: dict[str, list[str]] = {}
+    state_attribute_values: dict[str, list[str]] = {}
 
     def extract_string_constants(node: ast.expr) -> list[str]:
         """Recursively extract all string constants from an AST node."""
@@ -91,6 +109,17 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
         elif isinstance(node, ast.IfExp):
             strings.extend(extract_string_constants(node.body))
             strings.extend(extract_string_constants(node.orelse))
+        elif isinstance(node, ast.Call):
+            if (
+                isinstance(node.func, ast.Attribute)
+                and node.func.attr == "get"
+                and len(node.args) >= 2
+            ):
+                default_arg = node.args[1]
+                if isinstance(default_arg, ast.Constant) and isinstance(
+                    default_arg.value, str
+                ):
+                    strings.append(default_arg.value)
         return strings
 
     class VariableAssignmentVisitor(ast.NodeVisitor):
@@ -124,6 +153,22 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
 
             self.generic_visit(node)
 
+    def get_attribute_chain(node: ast.expr) -> str | None:
+        """Extract the full attribute chain from an AST node.
+
+        Examples:
+            self.state.run_type -> "self.state.run_type"
+            x.y.z -> "x.y.z"
+            simple_var -> "simple_var"
+        """
+        if isinstance(node, ast.Name):
+            return node.id
+        if isinstance(node, ast.Attribute):
+            base = get_attribute_chain(node.value)
+            if base:
+                return f"{base}.{node.attr}"
+        return None
+
     class ReturnVisitor(ast.NodeVisitor):
         def visit_Return(self, node: ast.Return) -> None:
             if (
@@ -139,21 +184,94 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
                         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}"
+                var_name_ret = get_attribute_chain(node.value)
 
                 if var_name_ret and var_name_ret in variable_values:
                     for v in variable_values[var_name_ret]:
                         return_values.add(v)
+                elif var_name_ret and var_name_ret in state_attribute_values:
+                    for v in state_attribute_values[var_name_ret]:
+                        return_values.add(v)
 
             self.generic_visit(node)
 
         def visit_If(self, node: ast.If) -> None:
             self.generic_visit(node)
 
+    # Try to get the class context to infer state attribute values
+    try:
+        if hasattr(function, "__self__"):
+            # Method is bound, get the class
+            class_obj = function.__self__.__class__
+        elif hasattr(function, "__qualname__") and "." in function.__qualname__:
+            # Method is unbound but we can try to get class from module
+            class_name = function.__qualname__.rsplit(".", 1)[0]
+            if hasattr(function, "__globals__"):
+                class_obj = function.__globals__.get(class_name)
+            else:
+                class_obj = None
+        else:
+            class_obj = None
+
+        if class_obj is not None:
+            try:
+                class_source = inspect.getsource(class_obj)
+                class_source = textwrap.dedent(class_source)
+                class_ast = ast.parse(class_source)
+
+                # Look for comparisons and assignments involving state attributes
+                class StateAttributeVisitor(ast.NodeVisitor):
+                    def visit_Compare(self, node: ast.Compare) -> None:
+                        """Find comparisons like: self.state.attr == "value" """
+                        left_attr = get_attribute_chain(node.left)
+
+                        if left_attr:
+                            for comparator in node.comparators:
+                                if isinstance(comparator, ast.Constant) and isinstance(
+                                    comparator.value, str
+                                ):
+                                    if left_attr not in state_attribute_values:
+                                        state_attribute_values[left_attr] = []
+                                    if (
+                                        comparator.value
+                                        not in state_attribute_values[left_attr]
+                                    ):
+                                        state_attribute_values[left_attr].append(
+                                            comparator.value
+                                        )
+
+                        # Also check right side
+                        for comparator in node.comparators:
+                            right_attr = get_attribute_chain(comparator)
+                            if (
+                                right_attr
+                                and isinstance(node.left, ast.Constant)
+                                and isinstance(node.left.value, str)
+                            ):
+                                if right_attr not in state_attribute_values:
+                                    state_attribute_values[right_attr] = []
+                                if (
+                                    node.left.value
+                                    not in state_attribute_values[right_attr]
+                                ):
+                                    state_attribute_values[right_attr].append(
+                                        node.left.value
+                                    )
+
+                        self.generic_visit(node)
+
+                StateAttributeVisitor().visit(class_ast)
+            except Exception as e:
+                _printer.print(
+                    f"Could not analyze class context for {function.__name__}: {e}",
+                    color="yellow",
+                )
+    except Exception as e:
+        _printer.print(
+            f"Could not introspect class for {function.__name__}: {e}",
+            color="yellow",
+        )
+
     VariableAssignmentVisitor().visit(code_ast)
     ReturnVisitor().visit(code_ast)
 

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

@@ -3,8 +3,6 @@
 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
@@ -18,8 +16,6 @@ __all__ = [
     "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

@@ -6,6 +6,7 @@
     <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://unpkg.com/lucide@latest"></script>
     <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>
@@ -23,93 +24,129 @@
             <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>
+                    <i data-lucide="file-code" style="width: 16px; height: 16px;"></i>
                     Open in IDE
                 </button>
-                <button class="drawer-close" id="drawer-close">×</button>
+                <button class="drawer-close" id="drawer-close">
+                    <i data-lucide="x" style="width: 20px; height: 20px;"></i>
+                </button>
             </div>
         </div>
         <div class="drawer-content" id="drawer-content"></div>
     </div>
 
     <div id="info">
-        <div style="text-align: center; margin-bottom: 20px;">
+        <div style="text-align: center;">
             <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>
+                 style="width: 144px; height: auto;">
         </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 class="nav-button" id="theme-toggle" title="Toggle Dark Mode">
+            <i data-lucide="moon" style="width: 18px; height: 18px;"></i>
+        </div>
+        <div class="nav-button" id="zoom-in" title="Zoom In">
+            <i data-lucide="zoom-in" style="width: 18px; height: 18px;"></i>
+        </div>
+        <div class="nav-button" id="zoom-out" title="Zoom Out">
+            <i data-lucide="zoom-out" style="width: 18px; height: 18px;"></i>
+        </div>
+        <div class="nav-button" id="fit" title="Fit to Screen">
+            <i data-lucide="maximize-2" style="width: 18px; height: 18px;"></i>
+        </div>
+        <div class="nav-button" id="export-png" title="Export to PNG">
+            <i data-lucide="image" style="width: 18px; height: 18px;"></i>
+        </div>
+        <div class="nav-button" id="export-pdf" title="Export to PDF">
+            <i data-lucide="file-text" style="width: 18px; height: 18px;"></i>
+        </div>
+        <!-- <div class="nav-button" id="export-json" title="Export to JSON">
+            <i data-lucide="braces" style="width: 18px; height: 18px;"></i>
+        </div> -->
     </div>
 
     <div id="network-container">
         <div id="network"></div>
     </div>
+
+    <!-- Info panel at bottom -->
+    <div id="legend-panel">
+        <!-- Stats Section -->
+        <div class="legend-section">
+            <div class="legend-stats-row">
+                <div class="legend-stat-item">
+                    <span class="stat-value">'{{ dag_nodes_count }}'</span>
+                    <span class="stat-label">Nodes</span>
+                </div>
+                <div class="legend-stat-item">
+                    <span class="stat-value">'{{ dag_edges_count }}'</span>
+                    <span class="stat-label">Edges</span>
+                </div>
+                <div class="legend-stat-item">
+                    <span class="stat-value">'{{ execution_paths }}'</span>
+                    <span class="stat-label">Paths</span>
+                </div>
+            </div>
+        </div>
+
+        <!-- Node Types Section -->
+        <div class="legend-section">
+            <div class="legend-group">
+                <div class="legend-item-compact">
+                    <div class="legend-color-small" style="background: var(--node-bg-start);"></div>
+                    <span>Start</span>
+                </div>
+                <div class="legend-item-compact">
+                    <div class="legend-color-small" style="background: var(--node-bg-router); border: 2px solid var(--node-border-start);"></div>
+                    <span>Router</span>
+                </div>
+                <div class="legend-item-compact">
+                    <div class="legend-color-small" style="background: var(--node-bg-listen); border: 2px solid var(--node-border-listen);"></div>
+                    <span>Listen</span>
+                </div>
+            </div>
+        </div>
+
+        <!-- Edge Types Section -->
+        <div class="legend-section">
+            <div class="legend-group">
+                <div class="legend-item-compact">
+                    <svg>
+                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-router-color)" stroke-width="2" stroke-dasharray="4,4"/>
+                    </svg>
+                    <span>Router</span>
+                </div>
+                <div class="legend-item-compact">
+                    <svg class="legend-or-line">
+                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-or-color)" stroke-width="2"/>
+                    </svg>
+                    <span>OR</span>
+                </div>
+                <div class="legend-item-compact">
+                    <svg>
+                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-router-color)" stroke-width="2"/>
+                    </svg>
+                    <span>AND</span>
+                </div>
+            </div>
+        </div>
+
+        <!-- IDE Selector Section -->
+        <div class="legend-section">
+            <div class="legend-ide-column">
+                <label class="legend-ide-label">IDE</label>
+                <select id="ide-selector" class="legend-ide-select">
+                    <option value="auto">Auto-detect</option>
+                    <option value="pycharm">PyCharm</option>
+                    <option value="vscode">VS Code</option>
+                    <option value="jetbrains">JetBrains</option>
+                </select>
+            </div>
+        </div>
+    </div>
 </body>
 </html>

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

@@ -12,6 +12,15 @@
     --shadow-strong: rgba(0, 0, 0, 0.15);
     --edge-label-text: '{{ GRAY }}';
     --edge-label-bg: rgba(255, 255, 255, 0.8);
+    --edge-or-color: #000000;
+    --edge-router-color: '{{ CREWAI_ORANGE }}';
+    --node-border-start: #C94238;
+    --node-border-listen: #3D3D3D;
+    --node-bg-start: #FF7066;
+    --node-bg-router: #FFFFFF;
+    --node-bg-listen: #FFFFFF;
+    --node-text-color: #FFFFFF;
+    --nav-button-hover: #f5f5f5;
 }
 
 [data-theme="dark"] {
@@ -28,6 +37,15 @@
     --shadow-strong: rgba(0, 0, 0, 0.5);
     --edge-label-text: #c9d1d9;
     --edge-label-bg: rgba(22, 27, 34, 0.9);
+    --edge-or-color: #ffffff;
+    --edge-router-color: '{{ CREWAI_ORANGE }}';
+    --node-border-start: #FF5A50;
+    --node-border-listen: #666666;
+    --node-bg-start: #B33830;
+    --node-bg-router: #3D3D3D;
+    --node-bg-listen: #3D3D3D;
+    --node-text-color: #FFFFFF;
+    --nav-button-hover: #30363d;
 }
 
 @keyframes dash {
@@ -70,12 +88,10 @@ body {
     position: absolute;
     top: 20px;
     left: 20px;
-    background: var(--bg-secondary);
+    background: transparent;
     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;
@@ -123,12 +139,16 @@ h3 {
     margin-right: 12px;
     border-radius: 3px;
     box-sizing: border-box;
+    transition: background 0.3s ease, border-color 0.3s ease;
 }
 .legend-item span {
     color: var(--text-secondary);
     font-size: 13px;
     transition: color 0.3s ease;
 }
+.legend-item svg line {
+    transition: stroke 0.3s ease;
+}
 .instructions {
     margin-top: 15px;
     padding-top: 15px;
@@ -153,7 +173,7 @@ h3 {
     bottom: 20px;
     right: auto;
     display: grid;
-    grid-template-columns: repeat(4, 40px);
+    grid-template-columns: repeat(3, 40px);
     gap: 8px;
     z-index: 10002;
     pointer-events: auto;
@@ -163,10 +183,187 @@ h3 {
 .nav-controls.drawer-open {
 }
 
+#legend-panel {
+    position: fixed;
+    left: 164px;
+    bottom: 20px;
+    right: 20px;
+    height: 92px;
+    background: var(--bg-secondary);
+    backdrop-filter: blur(12px) saturate(180%);
+    -webkit-backdrop-filter: blur(12px) saturate(180%);
+    border: 1px solid var(--border-subtle);
+    border-radius: 6px;
+    box-shadow: 0 2px 8px var(--shadow-color);
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    align-items: center;
+    gap: 0;
+    padding: 0 24px;
+    box-sizing: border-box;
+    z-index: 10001;
+    pointer-events: auto;
+    transition: background 0.3s ease, border-color 0.3s ease, box-shadow 0.3s ease, right 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+#legend-panel.drawer-open {
+    right: 405px;
+}
+
+.legend-section {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    min-width: 0;
+    width: -webkit-fill-available;
+    width: -moz-available;
+    width: stretch;
+    position: relative;
+}
+
+.legend-section:not(:last-child)::after {
+    content: '';
+    position: absolute;
+    right: 0;
+    top: 50%;
+    transform: translateY(-50%);
+    width: 1px;
+    height: 48px;
+    background: var(--border-color);
+    transition: background 0.3s ease;
+}
+
+.legend-stats-row {
+    display: flex;
+    gap: 32px;
+    justify-content: center;
+    align-items: center;
+    min-width: 0;
+}
+
+.legend-stat-item {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 4px;
+}
+
+.stat-value {
+    font-size: 19px;
+    font-weight: 700;
+    color: var(--text-primary);
+    line-height: 1;
+    transition: color 0.3s ease;
+}
+
+.stat-label {
+    font-size: 8px;
+    font-weight: 500;
+    text-transform: uppercase;
+    color: var(--text-secondary);
+    letter-spacing: 0.5px;
+    transition: color 0.3s ease;
+}
+
+.legend-items-row {
+    display: flex;
+    gap: 16px;
+    align-items: center;
+    justify-content: center;
+    min-width: 0;
+}
+
+.legend-group {
+    display: flex;
+    gap: 16px;
+    align-items: center;
+}
+
+.legend-item-compact {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+}
+
+.legend-item-compact span {
+    font-size: 12px;
+    font-weight: 500;
+    text-transform: uppercase;
+    color: var(--text-secondary);
+    letter-spacing: 0.5px;
+    white-space: nowrap;
+    font-family: inherit;
+    line-height: 1;
+    transition: color 0.3s ease;
+}
+
+.legend-color-small {
+    width: 17px;
+    height: 17px;
+    border-radius: 2px;
+    box-sizing: border-box;
+    flex-shrink: 0;
+    transition: background 0.3s ease, border-color 0.3s ease;
+}
+
+.legend-item-compact svg {
+    display: block;
+    flex-shrink: 0;
+    width: 29px;
+    height: 14px;
+}
+
+.legend-item-compact svg line {
+    transition: stroke 0.3s ease;
+}
+
+.legend-ide-column {
+    display: flex;
+    flex-direction: row;
+    gap: 8px;
+    align-items: center;
+    justify-content: center;
+    min-width: 0;
+    width: 100%;
+}
+
+.legend-ide-label {
+    font-size: 12px;
+    font-weight: 500;
+    text-transform: uppercase;
+    color: var(--text-secondary);
+    letter-spacing: 0.5px;
+    transition: color 0.3s ease;
+    white-space: nowrap;
+}
+
+.legend-ide-select {
+    width: 120px;
+    padding: 6px 10px;
+    border-radius: 4px;
+    border: 1px solid var(--border-subtle);
+    background: var(--bg-primary);
+    color: var(--text-primary);
+    font-size: 11px;
+    cursor: pointer;
+    transition: all 0.3s ease;
+}
+
+.legend-ide-select:hover {
+    border-color: var(--text-secondary);
+}
+
+.legend-ide-select:focus {
+    outline: none;
+    border-color: '{{ CREWAI_ORANGE }}';
+}
+
 .nav-button {
     width: 40px;
     height: 40px;
     background: var(--bg-secondary);
+    backdrop-filter: blur(12px) saturate(180%);
+    -webkit-backdrop-filter: blur(12px) saturate(180%);
     border: 1px solid var(--border-subtle);
     border-radius: 6px;
     display: flex;
@@ -179,12 +376,12 @@ h3 {
     user-select: none;
     pointer-events: auto;
     position: relative;
-    z-index: 10001;
+    z-index: 10002;
     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);
+    background: var(--nav-button-hover);
 }
 
 #drawer {
@@ -196,9 +393,10 @@ h3 {
     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;
+    z-index: 10003;
+    overflow: hidden;
+    transform: translateZ(0);
+    isolation: isolate;
 }
 
 #drawer.open {
@@ -245,17 +443,22 @@ h3 {
     justify-content: space-between;
     align-items: center;
     margin-bottom: 20px;
-    padding-bottom: 16px;
+    padding: 24px 24px 16px 24px;
     border-bottom: 2px solid '{{ CREWAI_ORANGE }}';
     position: relative;
     z-index: 2001;
 }
 
 .drawer-title {
-    font-size: 20px;
+    font-size: 15px;
     font-weight: 700;
     color: var(--text-primary);
     transition: color 0.3s ease;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    flex: 1;
+    min-width: 0;
 }
 
 .drawer-close {
@@ -267,12 +470,19 @@ h3 {
     padding: 4px 8px;
     line-height: 1;
     transition: color 0.3s ease;
+    display: flex;
+    align-items: center;
+    justify-content: center;
 }
 
 .drawer-close:hover {
     color: '{{ CREWAI_ORANGE }}';
 }
 
+.drawer-close i {
+    display: block;
+}
+
 .drawer-open-ide {
     background: '{{ CREWAI_ORANGE }}';
     border: none;
@@ -290,6 +500,9 @@ h3 {
     position: relative;
     z-index: 9999;
     pointer-events: auto;
+    white-space: nowrap;
+    flex-shrink: 0;
+    min-width: fit-content;
 }
 
 .drawer-open-ide:hover {
@@ -303,14 +516,19 @@ h3 {
     box-shadow: 0 1px 4px rgba(255, 90, 80, 0.2);
 }
 
-.drawer-open-ide svg {
+.drawer-open-ide svg,
+.drawer-open-ide i {
     width: 14px;
     height: 14px;
+    display: block;
 }
 
 .drawer-content {
     color: '{{ DARK_GRAY }}';
     line-height: 1.6;
+    padding: 0 24px 24px 24px;
+    overflow-y: auto;
+    height: calc(100vh - 95px);
 }
 
 .drawer-section {
@@ -326,6 +544,10 @@ h3 {
     position: relative;
 }
 
+.drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1)) {
+    grid-template-columns: 1fr 2fr;
+}
+
 .drawer-metadata-grid::before {
     content: '';
     position: absolute;
@@ -417,20 +639,35 @@ h3 {
     grid-column: 2;
     display: flex;
     flex-direction: column;
-    justify-content: center;
+    justify-content: flex-start;
+    align-items: flex-start;
 }
 
 .drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1))::after {
-    right: 50%;
+    right: 66.666%;
+}
+
+.drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1))::before {
+    left: 33.333%;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(1) .drawer-section-title {
+    align-self: flex-start;
+}
+
+.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(1) > *:not(.drawer-section-title) {
+    width: 100%;
+    align-self: stretch;
 }
 
 .drawer-section-title {
     font-size: 12px;
     text-transform: uppercase;
-    color: '{{ GRAY }}';
+    color: var(--text-secondary);
     letter-spacing: 0.5px;
     margin-bottom: 8px;
     font-weight: 600;
+    transition: color 0.3s ease;
 }
 
 .drawer-badge {
@@ -463,9 +700,44 @@ h3 {
     padding: 3px 0;
 }
 
+.drawer-metadata-grid .drawer-section .drawer-list {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+}
+
+.drawer-metadata-grid .drawer-section .drawer-list li {
+    border-bottom: none;
+    padding: 0;
+}
+
 .drawer-metadata-grid .drawer-section:nth-child(3) .drawer-list li {
     border-bottom: none;
-    padding: 3px 0;
+    padding: 0;
+}
+
+.drawer-metadata-grid .drawer-section {
+    overflow: visible;
+}
+
+.drawer-metadata-grid .drawer-section .condition-group,
+.drawer-metadata-grid .drawer-section .trigger-group {
+    width: 100%;
+    box-sizing: border-box;
+}
+
+.drawer-metadata-grid .drawer-section .condition-children {
+    width: 100%;
+}
+
+.drawer-metadata-grid .drawer-section .trigger-group-items {
+    width: 100%;
+}
+
+.drawer-metadata-grid .drawer-section .drawer-code-link {
+    word-break: break-word;
+    overflow-wrap: break-word;
+    max-width: 100%;
 }
 
 .drawer-code {
@@ -489,6 +761,7 @@ h3 {
     cursor: pointer;
     transition: all 0.2s;
     display: inline-block;
+    margin: 3px 2px;
 }
 
 .drawer-code-link:hover {

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

@@ -1,14 +1,16 @@
 """Flow structure builder for analyzing Flow execution."""
 
 from __future__ import annotations
+
 from collections import defaultdict
+from collections.abc import Iterable
 import inspect
 from typing import TYPE_CHECKING, Any
 
-from crewai.flow.constants import OR_CONDITION
-from crewai.flow.types import FlowMethodName
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
+from crewai.flow.flow_wrappers import FlowCondition
+from crewai.flow.types import FlowMethodName, FlowRouteName
 from crewai.flow.utils import (
-    _extract_all_methods_recursive,
     is_flow_condition_dict,
     is_simple_flow_condition,
 )
@@ -21,7 +23,7 @@ if TYPE_CHECKING:
 
 
 def _extract_direct_or_triggers(
-    condition: str | dict[str, Any] | list[Any],
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
 ) -> list[str]:
     """Extract direct OR-level trigger strings from a condition.
 
@@ -43,16 +45,15 @@ def _extract_direct_or_triggers(
     if isinstance(condition, str):
         return [condition]
     if isinstance(condition, dict):
-        cond_type = condition.get("type", "OR")
+        cond_type = condition.get("type", OR_CONDITION)
         conditions_list = condition.get("conditions", [])
 
-        if cond_type == "OR":
+        if cond_type == OR_CONDITION:
             strings = []
             for sub_cond in conditions_list:
                 strings.extend(_extract_direct_or_triggers(sub_cond))
             return strings
-        else:
-            return []
+        return []
     if isinstance(condition, list):
         strings = []
         for item in condition:
@@ -64,7 +65,7 @@ def _extract_direct_or_triggers(
 
 
 def _extract_all_trigger_names(
-    condition: str | dict[str, Any] | list[Any],
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
 ) -> list[str]:
     """Extract ALL trigger names from a condition for display purposes.
 
@@ -101,6 +102,76 @@ def _extract_all_trigger_names(
     return []
 
 
+def _create_edges_from_condition(
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
+    target: str,
+    nodes: dict[str, NodeMetadata],
+) -> list[StructureEdge]:
+    """Create edges from a condition tree, preserving AND/OR semantics.
+
+    This function recursively processes the condition tree and creates edges
+    with the appropriate condition_type for each trigger.
+
+    For AND conditions, all triggers get edges with condition_type="AND".
+    For OR conditions, triggers get edges with condition_type="OR".
+
+    Args:
+        condition: The condition tree (string, dict, or list).
+        target: The target node name.
+        nodes: Dictionary of all nodes for validation.
+
+    Returns:
+        List of StructureEdge objects representing the condition.
+    """
+    edges: list[StructureEdge] = []
+
+    if isinstance(condition, str):
+        if condition in nodes:
+            edges.append(
+                StructureEdge(
+                    source=condition,
+                    target=target,
+                    condition_type=OR_CONDITION,
+                    is_router_path=False,
+                )
+            )
+    elif callable(condition) and hasattr(condition, "__name__"):
+        method_name = condition.__name__
+        if method_name in nodes:
+            edges.append(
+                StructureEdge(
+                    source=method_name,
+                    target=target,
+                    condition_type=OR_CONDITION,
+                    is_router_path=False,
+                )
+            )
+    elif isinstance(condition, dict):
+        cond_type = condition.get("type", OR_CONDITION)
+        conditions_list = condition.get("conditions", [])
+
+        if cond_type == AND_CONDITION:
+            triggers = _extract_all_trigger_names(condition)
+            edges.extend(
+                StructureEdge(
+                    source=trigger,
+                    target=target,
+                    condition_type=AND_CONDITION,
+                    is_router_path=False,
+                )
+                for trigger in triggers
+                if trigger in nodes
+            )
+        else:
+            for sub_cond in conditions_list:
+                edges.extend(_create_edges_from_condition(sub_cond, target, nodes))
+    elif isinstance(condition, list):
+        for item in condition:
+            edges.extend(_create_edges_from_condition(item, target, nodes))
+
+    return edges
+
+
 def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
     """Build a structure representation of a Flow's execution.
 
@@ -127,8 +198,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
             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]
@@ -140,9 +209,13 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
             ]
 
         if hasattr(method, "__condition_type__") and method.__condition_type__:
+            node_metadata["trigger_condition_type"] = method.__condition_type__
             if "condition_type" not in node_metadata:
                 node_metadata["condition_type"] = method.__condition_type__
 
+        if node_metadata.get("is_router") and "condition_type" not in node_metadata:
+            node_metadata["condition_type"] = "IF"
+
         if (
             hasattr(method, "__trigger_condition__")
             and method.__trigger_condition__ is not None
@@ -228,29 +301,80 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
         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 listener_name in router_methods:
+            continue
 
         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,
+            edges.extend(
+                StructureEdge(
+                    source=str(trigger_method),
+                    target=str(listener_name),
+                    condition_type=cond_type,
+                    is_router_path=False,
+                )
+                for trigger_method in methods
+                if str(trigger_method) in nodes
             )
-            for trigger_method in trigger_methods_list
-            if trigger_method in nodes
+        elif is_flow_condition_dict(condition_data):
+            edges.extend(
+                _create_edges_from_condition(condition_data, str(listener_name), nodes)
+            )
+
+    for method_name, node_metadata in nodes.items():  # type: ignore[assignment]
+        if node_metadata.get("is_router") and "trigger_methods" in node_metadata:
+            trigger_methods = node_metadata["trigger_methods"]
+            condition_type = node_metadata.get("trigger_condition_type", OR_CONDITION)
+
+            if "trigger_condition" in node_metadata:
+                edges.extend(
+                    _create_edges_from_condition(
+                        node_metadata["trigger_condition"],  # type: ignore[arg-type]
+                        method_name,
+                        nodes,
+                    )
+                )
+            else:
+                edges.extend(
+                    StructureEdge(
+                        source=trigger_method,
+                        target=method_name,
+                        condition_type=condition_type,
+                        is_router_path=False,
+                    )
+                    for trigger_method in trigger_methods
+                    if trigger_method in nodes
+                )
+
+    for router_method_name in router_methods:
+        if router_method_name not in flow._router_paths:
+            flow._router_paths[FlowMethodName(router_method_name)] = []
+
+        inferred_paths: Iterable[FlowMethodName | FlowRouteName] = set(
+            flow._router_paths.get(FlowMethodName(router_method_name), [])
         )
 
+        for condition_data in flow._listeners.values():
+            trigger_strings: list[str] = []
+
+            if is_simple_flow_condition(condition_data):
+                _, methods = condition_data
+                trigger_strings = [str(m) for m in methods]
+            elif is_flow_condition_dict(condition_data):
+                trigger_strings = _extract_direct_or_triggers(condition_data)
+
+            for trigger_str in trigger_strings:
+                if trigger_str not in nodes:
+                    # This is likely a router path output
+                    inferred_paths.add(trigger_str)  # type: ignore[attr-defined]
+
+        if inferred_paths:
+            flow._router_paths[FlowMethodName(router_method_name)] = list(
+                inferred_paths  # type: ignore[arg-type]
+            )
+            if router_method_name in nodes:
+                nodes[router_method_name]["router_paths"] = list(inferred_paths)
+
     for router_method_name in router_methods:
         if router_method_name not in flow._router_paths:
             continue
@@ -276,6 +400,7 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
                             target=str(listener_name),
                             condition_type=None,
                             is_router_path=True,
+                            router_path_label=str(path),
                         )
                     )
 
@@ -299,76 +424,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
     )
 
 
-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.
 
@@ -396,6 +451,15 @@ def calculate_execution_paths(structure: FlowStructure) -> int:
         return 0
 
     def count_paths_from(node: str, visited: set[str]) -> int:
+        """Recursively count execution paths from a given node.
+
+        Args:
+            node: Node name to start counting from.
+            visited: Set of already visited nodes to prevent cycles.
+
+        Returns:
+            Number of execution paths from this node to terminal nodes.
+        """
         if node in terminal_nodes:
             return 1
 

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

@@ -20,7 +20,7 @@ class CSSExtension(Extension):
     Provides {% css 'path/to/file.css' %} tag syntax.
     """
 
-    tags: ClassVar[set[str]] = {"css"}  # type: ignore[assignment]
+    tags: ClassVar[set[str]] = {"css"}  # type: ignore[misc]
 
     def parse(self, parser: Parser) -> nodes.Node:
         """Parse {% css 'styles.css' %} tag.
@@ -53,7 +53,7 @@ class JSExtension(Extension):
     Provides {% js 'path/to/file.js' %} tag syntax.
     """
 
-    tags: ClassVar[set[str]] = {"js"}  # type: ignore[assignment]
+    tags: ClassVar[set[str]] = {"js"}  # type: ignore[misc]
 
     def parse(self, parser: Parser) -> nodes.Node:
         """Parse {% js 'script.js' %} tag.
@@ -91,6 +91,116 @@ TEXT_PRIMARY = "#e6edf3"
 TEXT_SECONDARY = "#7d8590"
 
 
+def calculate_node_positions(
+    dag: FlowStructure,
+) -> dict[str, dict[str, int | float]]:
+    """Calculate hierarchical positions (level, x, y) for each node.
+
+    Args:
+        dag: FlowStructure containing nodes and edges.
+
+    Returns:
+        Dictionary mapping node names to their position data (level, x, y).
+    """
+    children: dict[str, list[str]] = {name: [] for name in dag["nodes"]}
+    parents: dict[str, list[str]] = {name: [] for name in dag["nodes"]}
+
+    for edge in dag["edges"]:
+        source = edge["source"]
+        target = edge["target"]
+        if source in children and target in children:
+            children[source].append(target)
+            parents[target].append(source)
+
+    levels: dict[str, int] = {}
+    queue: list[tuple[str, int]] = []
+
+    for start_method in dag["start_methods"]:
+        if start_method in dag["nodes"]:
+            levels[start_method] = 0
+            queue.append((start_method, 0))
+
+    visited: set[str] = set()
+    while queue:
+        node, level = queue.pop(0)
+        if node in visited:
+            continue
+        visited.add(node)
+
+        if node not in levels or levels[node] < level:
+            levels[node] = level
+
+        for child in children.get(node, []):
+            if child not in visited:
+                child_level = level + 1
+                if child not in levels or levels[child] < child_level:
+                    levels[child] = child_level
+                queue.append((child, child_level))
+
+    for name in dag["nodes"]:
+        if name not in levels:
+            levels[name] = 0
+
+    nodes_by_level: dict[int, list[str]] = {}
+    for node, level in levels.items():
+        if level not in nodes_by_level:
+            nodes_by_level[level] = []
+        nodes_by_level[level].append(node)
+
+    positions: dict[str, dict[str, int | float]] = {}
+    level_separation = 300  # Vertical spacing between levels
+    node_spacing = 400  # Horizontal spacing between nodes
+
+    parent_count: dict[str, int] = {}
+    for node, parent_list in parents.items():
+        parent_count[node] = len(parent_list)
+
+    for level, nodes_at_level in sorted(nodes_by_level.items()):
+        y = level * level_separation
+
+        if level == 0:
+            num_nodes = len(nodes_at_level)
+            for i, node in enumerate(nodes_at_level):
+                x = (i - (num_nodes - 1) / 2) * node_spacing
+                positions[node] = {"level": level, "x": x, "y": y}
+        else:
+            for i, node in enumerate(nodes_at_level):
+                parent_list = parents.get(node, [])
+                parent_positions: list[float] = [
+                    positions[parent]["x"]
+                    for parent in parent_list
+                    if parent in positions
+                ]
+
+                if parent_positions:
+                    if len(parent_positions) > 1 and len(set(parent_positions)) == 1:
+                        base_x = parent_positions[0]
+                        avg_x = base_x + node_spacing * 0.4
+                    else:
+                        avg_x = sum(parent_positions) / len(parent_positions)
+                else:
+                    avg_x = i * node_spacing * 0.5
+
+                positions[node] = {"level": level, "x": avg_x, "y": y}
+
+            nodes_at_level_sorted = sorted(
+                nodes_at_level, key=lambda n: positions[n]["x"]
+            )
+            min_spacing = node_spacing * 0.6  # Minimum horizontal distance
+
+            for i in range(len(nodes_at_level_sorted) - 1):
+                current_node = nodes_at_level_sorted[i]
+                next_node = nodes_at_level_sorted[i + 1]
+
+                current_x = positions[current_node]["x"]
+                next_x = positions[next_node]["x"]
+
+                if next_x - current_x < min_spacing:
+                    positions[next_node]["x"] = current_x + min_spacing
+
+    return positions
+
+
 def render_interactive(
     dag: FlowStructure,
     filename: str = "flow_dag.html",
@@ -110,6 +220,8 @@ def render_interactive(
     Returns:
         Absolute path to generated HTML file in temporary directory.
     """
+    node_positions = calculate_node_positions(dag)
+
     nodes_list: list[dict[str, Any]] = []
     for name, metadata in dag["nodes"].items():
         node_type: str = metadata.get("type", "listen")
@@ -120,37 +232,37 @@ def render_interactive(
 
         if node_type == "start":
             color_config = {
-                "background": CREWAI_ORANGE,
-                "border": CREWAI_ORANGE,
+                "background": "var(--node-bg-start)",
+                "border": "var(--node-border-start)",
                 "highlight": {
-                    "background": CREWAI_ORANGE,
-                    "border": CREWAI_ORANGE,
+                    "background": "var(--node-bg-start)",
+                    "border": "var(--node-border-start)",
                 },
             }
-            font_color = WHITE
-            border_width = 2
+            font_color = "var(--node-text-color)"
+            border_width = 3
         elif node_type == "router":
             color_config = {
-                "background": DARK_GRAY,
+                "background": "var(--node-bg-router)",
                 "border": CREWAI_ORANGE,
                 "highlight": {
-                    "background": DARK_GRAY,
+                    "background": "var(--node-bg-router)",
                     "border": CREWAI_ORANGE,
                 },
             }
-            font_color = WHITE
+            font_color = "var(--node-text-color)"
             border_width = 3
         else:
             color_config = {
-                "background": DARK_GRAY,
-                "border": DARK_GRAY,
+                "background": "var(--node-bg-listen)",
+                "border": "var(--node-border-listen)",
                 "highlight": {
-                    "background": DARK_GRAY,
-                    "border": DARK_GRAY,
+                    "background": "var(--node-bg-listen)",
+                    "border": "var(--node-border-listen)",
                 },
             }
-            font_color = WHITE
-            border_width = 2
+            font_color = "var(--node-text-color)"
+            border_width = 3
 
         title_parts: list[str] = []
 
@@ -215,25 +327,34 @@ def render_interactive(
         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,
-            }
-        )
+        position_data = node_positions.get(name, {"level": 0, "x": 0, "y": 0})
+
+        node_data: dict[str, Any] = {
+            "id": name,
+            "label": name,
+            "title": "".join(title_parts),
+            "shape": "custom",
+            "size": 30,
+            "level": position_data["level"],
+            "nodeStyle": {
+                "name": name,
+                "bgColor": bg_color,
+                "borderColor": border_color,
+                "borderWidth": border_width,
+                "fontColor": font_color,
+            },
+            "opacity": 1.0,
+            "glowSize": 0,
+            "glowColor": None,
+        }
+
+        # Add x,y only for graphs with 3-4 nodes
+        total_nodes = len(dag["nodes"])
+        if 3 <= total_nodes <= 4:
+            node_data["x"] = position_data["x"]
+            node_data["y"] = position_data["y"]
+
+        nodes_list.append(node_data)
 
     execution_paths: int = calculate_execution_paths(dag)
 
@@ -246,6 +367,8 @@ def render_interactive(
         if edge["is_router_path"]:
             edge_color = CREWAI_ORANGE
             edge_dashes = [15, 10]
+            if "router_path_label" in edge:
+                edge_label = edge["router_path_label"]
         elif edge["condition_type"] == "AND":
             edge_label = "AND"
             edge_color = CREWAI_ORANGE

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

@@ -10,6 +10,7 @@ class NodeMetadata(TypedDict, total=False):
     is_router: bool
     router_paths: list[str]
     condition_type: str | None
+    trigger_condition_type: str | None
     trigger_methods: list[str]
     trigger_condition: dict[str, Any] | None
     method_signature: dict[str, Any]
@@ -22,13 +23,14 @@ class NodeMetadata(TypedDict, total=False):
     class_line_number: int
 
 
-class StructureEdge(TypedDict):
+class StructureEdge(TypedDict, total=False):
     """Represents a connection in the flow structure."""
 
     source: str
     target: str
     condition_type: str | None
     is_router_path: bool
+    router_path_label: str
 
 
 class FlowStructure(TypedDict):

lib/crewai/src/crewai/lite_agent.py

@@ -1,6 +1,7 @@
 import asyncio
 from collections.abc import Callable
 import inspect
+import json
 from typing import (
     Any,
     Literal,
@@ -58,10 +59,14 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
     render_text_description_and_args,
 )
-from crewai.utilities.converter import generate_model_description
+from crewai.utilities.converter import (
+    Converter,
+    ConverterError,
+    generate_model_description,
+)
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -90,8 +95,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     """
 
     model_config = {"arbitrary_types_allowed": True}
-
-    # Core Agent Properties
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
@@ -102,8 +105,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     tools: list[BaseTool] = Field(
         default_factory=list, description="Tools at agent's disposal"
     )
-
-    # Execution Control Properties
     max_iterations: int = Field(
         default=15, description="Maximum number of iterations for tool usage"
     )
@@ -120,24 +121,17 @@ class LiteAgent(FlowTrackable, BaseModel):
     )
     request_within_rpm_limit: Callable[[], bool] | None = Field(
         default=None,
-        description="Callback to check if the request is within the RPM limit",
+        description="Callback to check if the request is within the RPM8 limit",
     )
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings."
+        default_factory=get_i18n, description="Internationalization settings."
     )
-
-    # Output and Formatting Properties
     response_format: type[BaseModel] | None = Field(
         default=None, description="Pydantic model for structured output"
     )
     verbose: bool = Field(
         default=False, description="Whether to print execution details"
     )
-    callbacks: list[Callable] = Field(
-        default_factory=list, description="Callbacks to be used for the agent"
-    )
-
-    # Guardrail Properties
     guardrail: GuardrailType | None = Field(
         default=None,
         description="Function or string description of a guardrail to validate agent output",
@@ -145,17 +139,12 @@ class LiteAgent(FlowTrackable, BaseModel):
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
     )
-
-    # State and Results
     tools_results: list[dict[str, Any]] = Field(
         default_factory=list, description="Results of the tools used by the agent."
     )
-
-    # Reference of Agent
     original_agent: BaseAgent | None = Field(
         default=None, description="Reference to the agent that created this LiteAgent"
     )
-    # Private Attributes
     _parsed_tools: list[CrewStructuredTool] = PrivateAttr(default_factory=list)
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
@@ -165,6 +154,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     _printer: Printer = PrivateAttr(default_factory=Printer)
     _guardrail: GuardrailCallable | None = PrivateAttr(default=None)
     _guardrail_retry_count: int = PrivateAttr(default=0)
+    _callbacks: list[TokenCalcHandler] = PrivateAttr(default_factory=list)
 
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
@@ -174,15 +164,13 @@ class LiteAgent(FlowTrackable, BaseModel):
             raise ValueError(
                 f"Expected LLM instance of type BaseLLM, got {type(self.llm).__name__}"
             )
-
-        # Initialize callbacks
         token_callback = TokenCalcHandler(token_cost_process=self._token_process)
         self._callbacks = [token_callback]
 
         return self
 
     @model_validator(mode="after")
-    def parse_tools(self):
+    def parse_tools(self) -> Self:
         """Parse the tools and convert them to CrewStructuredTool instances."""
         self._parsed_tools = parse_tools(self.tools)
 
@@ -201,7 +189,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                 )
             self._guardrail = cast(
                 GuardrailCallable,
-                LLMGuardrail(description=self.guardrail, llm=self.llm),
+                cast(object, LLMGuardrail(description=self.guardrail, llm=self.llm)),
             )
 
         return self
@@ -209,8 +197,8 @@ class LiteAgent(FlowTrackable, BaseModel):
     @field_validator("guardrail", mode="before")
     @classmethod
     def validate_guardrail_function(
-        cls, v: Callable | str | None
-    ) -> Callable | str | None:
+        cls, v: GuardrailCallable | str | None
+    ) -> GuardrailCallable | str | None:
         """Validate that the guardrail function has the correct signature.
 
         If v is a callable, validate that it has the correct signature.
@@ -258,7 +246,11 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
+    def kickoff(
+        self,
+        messages: str | list[LLMMessage],
+        response_format: type[BaseModel] | None = None,
+    ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages.
 
@@ -266,6 +258,8 @@ class LiteAgent(FlowTrackable, BaseModel):
             messages: Either a string query or a list of message dictionaries.
                      If a string is provided, it will be converted to a user message.
                      If a list is provided, each dict should have 'role' and 'content' keys.
+            response_format: Optional Pydantic model for structured output. If provided,
+                           overrides self.response_format for this execution.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
@@ -286,9 +280,13 @@ class LiteAgent(FlowTrackable, BaseModel):
             self.tools_results = []
 
             # Format messages for the LLM
-            self._messages = self._format_messages(messages)
+            self._messages = self._format_messages(
+                messages, response_format=response_format
+            )
 
-            return self._execute_core(agent_info=agent_info)
+            return self._execute_core(
+                agent_info=agent_info, response_format=response_format
+            )
 
         except Exception as e:
             self._printer.print(
@@ -306,7 +304,9 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
             raise e
 
-    def _execute_core(self, agent_info: dict[str, Any]) -> LiteAgentOutput:
+    def _execute_core(
+        self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None
+    ) -> LiteAgentOutput:
         # Emit event for agent execution start
         crewai_event_bus.emit(
             self,
@@ -320,15 +320,29 @@ class LiteAgent(FlowTrackable, BaseModel):
         # Execute the agent using invoke loop
         agent_finish = self._invoke_loop()
         formatted_result: BaseModel | None = None
-        if self.response_format:
+
+        active_response_format = response_format or self.response_format
+        if active_response_format:
             try:
-                # Cast to BaseModel to ensure type safety
-                result = self.response_format.model_validate_json(agent_finish.output)
+                model_schema = generate_model_description(active_response_format)
+                schema = json.dumps(model_schema, indent=2)
+                instructions = self.i18n.slice("formatted_task_instructions").format(
+                    output_format=schema
+                )
+
+                converter = Converter(
+                    llm=self.llm,
+                    text=agent_finish.output,
+                    model=active_response_format,
+                    instructions=instructions,
+                )
+
+                result = converter.to_pydantic()
                 if isinstance(result, BaseModel):
                     formatted_result = result
-            except Exception as e:
+            except ConverterError as e:
                 self._printer.print(
-                    content=f"Failed to parse output into response format: {e!s}",
+                    content=f"Failed to parse output into response format after retries: {e.message}",
                     color="yellow",
                 )
 
@@ -344,6 +358,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             pydantic=formatted_result,
             agent_role=self.role,
             usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=self._messages,
         )
 
         # Process guardrail if set
@@ -417,8 +432,14 @@ class LiteAgent(FlowTrackable, BaseModel):
         """
         return await asyncio.to_thread(self.kickoff, messages)
 
-    def _get_default_system_prompt(self) -> str:
-        """Get the default system prompt for the agent."""
+    def _get_default_system_prompt(
+        self, response_format: type[BaseModel] | None = None
+    ) -> str:
+        """Get the default system prompt for the agent.
+
+        Args:
+            response_format: Optional response format to use instead of self.response_format
+        """
         base_prompt = ""
         if self._parsed_tools:
             # Use the prompt template for agents with tools
@@ -439,21 +460,31 @@ class LiteAgent(FlowTrackable, BaseModel):
                 goal=self.goal,
             )
 
-        # Add response format instructions if specified
-        if self.response_format:
-            schema = generate_model_description(self.response_format)
+        active_response_format = response_format or self.response_format
+        if active_response_format:
+            model_description = generate_model_description(active_response_format)
+            schema_json = json.dumps(model_description, indent=2)
             base_prompt += self.i18n.slice("lite_agent_response_format").format(
-                response_format=schema
+                response_format=schema_json
             )
 
         return base_prompt
 
-    def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
-        """Format messages for the LLM."""
+    def _format_messages(
+        self,
+        messages: str | list[LLMMessage],
+        response_format: type[BaseModel] | None = None,
+    ) -> list[LLMMessage]:
+        """Format messages for the LLM.
+
+        Args:
+            messages: Input messages to format
+            response_format: Optional response format to use instead of self.response_format
+        """
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
-        system_prompt = self._get_default_system_prompt()
+        system_prompt = self._get_default_system_prompt(response_format=response_format)
 
         # Add system message at the beginning
         formatted_messages: list[LLMMessage] = [
@@ -523,6 +554,10 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 self._append_message(formatted_answer.text, role="assistant")
             except OutputParserError as e:  # noqa: PERF203
+                self._printer.print(
+                    content="Failed to parse LLM output. Retrying...",
+                    color="yellow",
+                )
                 formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self._messages,
@@ -559,7 +594,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _show_logs(self, formatted_answer: AgentAction | AgentFinish):
+    def _show_logs(self, formatted_answer: AgentAction | AgentFinish) -> None:
         """Show logs for the agent's execution."""
         crewai_event_bus.emit(
             self,
@@ -574,4 +609,4 @@ class LiteAgent(FlowTrackable, BaseModel):
         self, text: str, role: Literal["user", "assistant", "system"] = "assistant"
     ) -> None:
         """Append a message to the message list with the given role."""
-        self._messages.append(cast(LLMMessage, format_message_for_llm(text, role=role)))
+        self._messages.append(format_message_for_llm(text, role=role))

lib/crewai/src/crewai/lite_agent_output.py

@@ -6,6 +6,8 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from crewai.utilities.types import LLMMessage
+
 
 class LiteAgentOutput(BaseModel):
     """Class that represents the result of a LiteAgent execution."""
@@ -20,6 +22,7 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: dict[str, Any] | None = Field(
         description="Token usage metrics for this execution", default=None
     )
+    messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
 
     def to_dict(self) -> dict[str, Any]:
         """Convert pydantic_output to a dictionary."""

lib/crewai/src/crewai/llm.py

@@ -20,6 +20,7 @@ from typing import (
 )
 
 from dotenv import load_dotenv
+import httpx
 from pydantic import BaseModel, Field
 from typing_extensions import Self
 
@@ -37,6 +38,13 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
 )
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.constants import (
+    ANTHROPIC_MODELS,
+    AZURE_MODELS,
+    BEDROCK_MODELS,
+    GEMINI_MODELS,
+    OPENAI_MODELS,
+)
 from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -53,6 +61,7 @@ if TYPE_CHECKING:
     from litellm.utils import supports_response_schema
 
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -321,19 +330,67 @@ class LLM(BaseLLM):
     completion_cost: float | None = None
 
     def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
-        """Factory method that routes to native SDK or falls back to LiteLLM."""
+        """Factory method that routes to native SDK or falls back to LiteLLM.
+
+        Routing priority:
+            1. If 'provider' kwarg is present, use that provider with constants
+            2. If only 'model' kwarg, use constants to infer provider
+            3. If "/" in model name:
+               - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
+               - If yes, validate model against constants
+               - If valid, route to native SDK; otherwise route to LiteLLM
+        """
         if not model or not isinstance(model, str):
             raise ValueError("Model must be a non-empty string")
 
-        provider = model.partition("/")[0] if "/" in model else "openai"
+        explicit_provider = kwargs.get("provider")
 
-        native_class = cls._get_native_provider(provider)
+        if explicit_provider:
+            provider = explicit_provider
+            use_native = True
+            model_string = model
+        elif "/" in model:
+            prefix, _, model_part = model.partition("/")
+
+            provider_mapping = {
+                "openai": "openai",
+                "anthropic": "anthropic",
+                "claude": "anthropic",
+                "azure": "azure",
+                "azure_openai": "azure",
+                "google": "gemini",
+                "gemini": "gemini",
+                "bedrock": "bedrock",
+                "aws": "bedrock",
+            }
+
+            canonical_provider = provider_mapping.get(prefix.lower())
+
+            if canonical_provider and cls._validate_model_in_constants(
+                model_part, canonical_provider
+            ):
+                provider = canonical_provider
+                use_native = True
+                model_string = model_part
+            else:
+                provider = prefix
+                use_native = False
+                model_string = model_part
+        else:
+            provider = cls._infer_provider_from_model(model)
+            use_native = True
+            model_string = model
+
+        native_class = cls._get_native_provider(provider) if use_native else None
         if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
             try:
-                model_string = model.partition("/")[2] if "/" in model else model
+                # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
+                kwargs_copy = {k: v for k, v in kwargs.items() if k != 'provider'}
                 return cast(
-                    Self, native_class(model=model_string, provider=provider, **kwargs)
+                    Self, native_class(model=model_string, provider=provider, **kwargs_copy)
                 )
+            except NotImplementedError:
+                raise
             except Exception as e:
                 raise ImportError(f"Error importing native provider: {e}") from e
 
@@ -347,6 +404,63 @@ class LLM(BaseLLM):
         instance.is_litellm = True
         return instance
 
+    @classmethod
+    def _validate_model_in_constants(cls, model: str, provider: str) -> bool:
+        """Validate if a model name exists in the provider's constants.
+
+        Args:
+            model: The model name to validate
+            provider: The provider to check against (canonical name)
+
+        Returns:
+            True if the model exists in the provider's constants, False otherwise
+        """
+        if provider == "openai":
+            return model in OPENAI_MODELS
+
+        if provider == "anthropic" or provider == "claude":
+            return model in ANTHROPIC_MODELS
+
+        if provider == "gemini":
+            return model in GEMINI_MODELS
+
+        if provider == "bedrock":
+            return model in BEDROCK_MODELS
+
+        if provider == "azure":
+            # azure does not provide a list of available models, determine a better way to handle this
+            return True
+
+        return False
+
+    @classmethod
+    def _infer_provider_from_model(cls, model: str) -> str:
+        """Infer the provider from the model name.
+
+        Args:
+            model: The model name without provider prefix
+
+        Returns:
+            The inferred provider name, defaults to "openai"
+        """
+
+        if model in OPENAI_MODELS:
+            return "openai"
+
+        if model in ANTHROPIC_MODELS:
+            return "anthropic"
+
+        if model in GEMINI_MODELS:
+            return "gemini"
+
+        if model in BEDROCK_MODELS:
+            return "bedrock"
+
+        if model in AZURE_MODELS:
+            return "azure"
+
+        return "openai"
+
     @classmethod
     def _get_native_provider(cls, provider: str) -> type | None:
         """Get native provider class if available."""
@@ -403,6 +517,7 @@ class LLM(BaseLLM):
         callbacks: list[Any] | None = None,
         reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
         stream: bool = False,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize LLM instance.
@@ -442,6 +557,7 @@ class LLM(BaseLLM):
         self.additional_params = kwargs
         self.is_anthropic = self._is_anthropic_model(model)
         self.stream = stream
+        self.interceptor = interceptor
 
         litellm.drop_params = True
 

lib/crewai/src/crewai/llms/constants.py

@@ -0,0 +1,558 @@
+from typing import Literal, TypeAlias
+
+
+OpenAIModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+OPENAI_MODELS: list[OpenAIModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+
+
+AnthropicModels: TypeAlias = Literal[
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+ANTHROPIC_MODELS: list[AnthropicModels] = [
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+
+GeminiModels: TypeAlias = Literal[
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+GEMINI_MODELS: list[GeminiModels] = [
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+
+
+AzureModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+AZURE_MODELS: list[AzureModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+
+
+BedrockModels: TypeAlias = Literal[
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]
+BEDROCK_MODELS: list[BedrockModels] = [
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]

lib/crewai/src/crewai/llms/hooks/__init__.py

@@ -0,0 +1,6 @@
+"""Interceptor contracts for crewai"""
+
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+__all__ = ["BaseInterceptor"]

lib/crewai/src/crewai/llms/hooks/base.py

@@ -0,0 +1,133 @@
+"""Base classes for LLM transport interceptors.
+
+This module provides abstract base classes for intercepting and modifying
+outbound and inbound messages at the transport level.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Generic, TypeVar
+
+from pydantic_core import core_schema
+
+
+if TYPE_CHECKING:
+    from pydantic import GetCoreSchemaHandler
+    from pydantic_core import CoreSchema
+
+
+T = TypeVar("T")
+U = TypeVar("U")
+
+
+class BaseInterceptor(ABC, Generic[T, U]):
+    """Abstract base class for intercepting transport-level messages.
+
+    Provides hooks to intercept and modify outbound and inbound messages
+    at the transport layer.
+
+    Type parameters:
+        T: Outbound message type (e.g., httpx.Request)
+        U: Inbound message type (e.g., httpx.Response)
+
+    Example:
+        >>> import httpx
+        >>> class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+        ...     def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        ...         message.headers["X-Custom-Header"] = "value"
+        ...         return message
+        ...
+        ...     def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        ...         print(f"Status: {message.status_code}")
+        ...         return message
+    """
+
+    @abstractmethod
+    def on_outbound(self, message: T) -> T:
+        """Intercept outbound message before sending.
+
+        Args:
+            message: Outbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        ...
+
+    @abstractmethod
+    def on_inbound(self, message: U) -> U:
+        """Intercept inbound message after receiving.
+
+        Args:
+            message: Inbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        ...
+
+    async def aon_outbound(self, message: T) -> T:
+        """Async version of on_outbound.
+
+        Args:
+            message: Outbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        raise NotImplementedError
+
+    async def aon_inbound(self, message: U) -> U:
+        """Async version of on_inbound.
+
+        Args:
+            message: Inbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for BaseInterceptor.
+
+        This allows the generic BaseInterceptor to be used in Pydantic models
+        without requiring arbitrary_types_allowed=True. The schema validates
+        that the value is an instance of BaseInterceptor.
+
+        Args:
+            _source_type: The source type being validated (unused).
+            _handler: Handler for generating schemas (unused).
+
+        Returns:
+            A Pydantic core schema that validates BaseInterceptor instances.
+        """
+        return core_schema.no_info_plain_validator_function(
+            _validate_interceptor,
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                lambda x: x, return_schema=core_schema.any_schema()
+            ),
+        )
+
+
+def _validate_interceptor(value: Any) -> BaseInterceptor[T, U]:
+    """Validate that the value is a BaseInterceptor instance.
+
+    Args:
+        value: The value to validate.
+
+    Returns:
+        The validated BaseInterceptor instance.
+
+    Raises:
+        ValueError: If the value is not a BaseInterceptor instance.
+    """
+    if not isinstance(value, BaseInterceptor):
+        raise ValueError(
+            f"Expected BaseInterceptor instance, got {type(value).__name__}"
+        )
+    return value

lib/crewai/src/crewai/llms/hooks/transport.py

@@ -0,0 +1,123 @@
+"""HTTP transport implementations for LLM request/response interception.
+
+This module provides internal transport classes that integrate with BaseInterceptor
+to enable request/response modification at the transport level.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, TypedDict
+
+from httpx import (
+    AsyncHTTPTransport as _AsyncHTTPTransport,
+    HTTPTransport as _HTTPTransport,
+)
+from typing_extensions import NotRequired, Unpack
+
+
+if TYPE_CHECKING:
+    from ssl import SSLContext
+
+    from httpx import Limits, Request, Response
+    from httpx._types import CertTypes, ProxyTypes
+
+    from crewai.llms.hooks.base import BaseInterceptor
+
+
+class HTTPTransportKwargs(TypedDict, total=False):
+    """Typed dictionary for httpx.HTTPTransport initialization parameters.
+
+    These parameters configure the underlying HTTP transport behavior including
+    SSL verification, proxies, connection limits, and low-level socket options.
+    """
+
+    verify: bool | str | SSLContext
+    cert: NotRequired[CertTypes]
+    trust_env: bool
+    http1: bool
+    http2: bool
+    limits: Limits
+    proxy: NotRequired[ProxyTypes]
+    uds: NotRequired[str]
+    local_address: NotRequired[str]
+    retries: int
+    socket_options: NotRequired[
+        Iterable[
+            tuple[int, int, int]
+            | tuple[int, int, bytes | bytearray]
+            | tuple[int, int, None, int]
+        ]
+    ]
+
+
+class HTTPTransport(_HTTPTransport):
+    """HTTP transport that uses an interceptor for request/response modification.
+
+    This transport is used internally when a user provides a BaseInterceptor.
+    Users should not instantiate this class directly - instead, pass an interceptor
+    to the LLM client and this transport will be created automatically.
+    """
+
+    def __init__(
+        self,
+        interceptor: BaseInterceptor[Request, Response],
+        **kwargs: Unpack[HTTPTransportKwargs],
+    ) -> None:
+        """Initialize transport with interceptor.
+
+        Args:
+            interceptor: HTTP interceptor for modifying raw request/response objects.
+            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
+        """
+        super().__init__(**kwargs)
+        self.interceptor = interceptor
+
+    def handle_request(self, request: Request) -> Response:
+        """Handle request with interception.
+
+        Args:
+            request: The HTTP request to handle.
+
+        Returns:
+            The HTTP response.
+        """
+        request = self.interceptor.on_outbound(request)
+        response = super().handle_request(request)
+        return self.interceptor.on_inbound(response)
+
+
+class AsyncHTTPTransport(_AsyncHTTPTransport):
+    """Async HTTP transport that uses an interceptor for request/response modification.
+
+    This transport is used internally when a user provides a BaseInterceptor.
+    Users should not instantiate this class directly - instead, pass an interceptor
+    to the LLM client and this transport will be created automatically.
+    """
+
+    def __init__(
+        self,
+        interceptor: BaseInterceptor[Request, Response],
+        **kwargs: Unpack[HTTPTransportKwargs],
+    ) -> None:
+        """Initialize async transport with interceptor.
+
+        Args:
+            interceptor: HTTP interceptor for modifying raw request/response objects.
+            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
+        """
+        super().__init__(**kwargs)
+        self.interceptor = interceptor
+
+    async def handle_async_request(self, request: Request) -> Response:
+        """Handle async request with interception.
+
+        Args:
+            request: The HTTP request to handle.
+
+        Returns:
+            The HTTP response.
+        """
+        request = await self.interceptor.aon_outbound(request)
+        response = await super().handle_async_request(request)
+        return await self.interceptor.aon_inbound(response)

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

@@ -1,15 +1,15 @@
 from __future__ import annotations
 
-
 import json
 import logging
 import os
-from typing import Any, cast
+from typing import TYPE_CHECKING, Any, cast
 
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -17,10 +17,14 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
 try:
     from anthropic import Anthropic
     from anthropic.types import Message
     from anthropic.types.tool_use_block import ToolUseBlock
+    import httpx
 except ImportError:
     raise ImportError(
         'Anthropic native provider not available, to install: uv add "crewai[anthropic]"'
@@ -47,7 +51,8 @@ class AnthropicCompletion(BaseLLM):
         stop_sequences: list[str] | None = None,
         stream: bool = False,
         client_params: dict[str, Any] | None = None,
-        **kwargs,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
 
@@ -63,6 +68,7 @@ class AnthropicCompletion(BaseLLM):
             stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
             stream: Enable streaming responses
             client_params: Additional parameters for the Anthropic client
+            interceptor: HTTP interceptor for modifying requests/responses at transport level.
             **kwargs: Additional parameters
         """
         super().__init__(
@@ -70,6 +76,7 @@ class AnthropicCompletion(BaseLLM):
         )
 
         # Client params
+        self.interceptor = interceptor
         self.client_params = client_params
         self.base_url = base_url
         self.timeout = timeout
@@ -87,6 +94,30 @@ class AnthropicCompletion(BaseLLM):
         self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = self.is_claude_3  # Claude 3+ supports tool use
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Anthropic API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get client parameters."""
 
@@ -102,6 +133,11 @@ class AnthropicCompletion(BaseLLM):
             "max_retries": self.max_retries,
         }
 
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            client_params["http_client"] = http_client  # type: ignore[assignment]
+
         if self.client_params:
             client_params.update(self.client_params)
 
@@ -110,7 +146,7 @@ class AnthropicCompletion(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -133,7 +169,7 @@ class AnthropicCompletion(BaseLLM):
         try:
             # Emit call started event
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -143,7 +179,7 @@ class AnthropicCompletion(BaseLLM):
 
             # Format messages for Anthropic
             formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
             # Prepare completion parameters
@@ -181,7 +217,7 @@ class AnthropicCompletion(BaseLLM):
         self,
         messages: list[LLMMessage],
         system_message: str | None = None,
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Anthropic messages API.
 
@@ -218,7 +254,9 @@ class AnthropicCompletion(BaseLLM):
 
         return params
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+    def _convert_tools_for_interference(
+        self, tools: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to Anthropic tool use format."""
         anthropic_tools = []
 

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

@@ -3,7 +3,7 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel
 
@@ -13,23 +13,25 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.types import LLMMessage
 
+
 if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.tools.base_tool import BaseTool
 
 
 try:
-    from azure.ai.inference import (  # type: ignore[import-not-found]
+    from azure.ai.inference import (
         ChatCompletionsClient,
     )
-    from azure.ai.inference.models import (  # type: ignore[import-not-found]
+    from azure.ai.inference.models import (
         ChatCompletions,
         ChatCompletionsToolCall,
         StreamingChatCompletionsUpdate,
     )
-    from azure.core.credentials import (  # type: ignore[import-not-found]
+    from azure.core.credentials import (
         AzureKeyCredential,
     )
-    from azure.core.exceptions import (  # type: ignore[import-not-found]
+    from azure.core.exceptions import (
         HttpResponseError,
     )
 
@@ -64,7 +66,8 @@ class AzureCompletion(BaseLLM):
         max_tokens: int | None = None,
         stop: list[str] | None = None,
         stream: bool = False,
-        **kwargs,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Azure AI Inference chat completion client.
 
@@ -82,8 +85,15 @@ class AzureCompletion(BaseLLM):
             max_tokens: Maximum tokens in response
             stop: Stop sequences
             stream: Enable streaming responses
+            interceptor: HTTP interceptor (not yet supported for Azure).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Azure AI Inference provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         super().__init__(
             model=model, temperature=temperature, stop=stop or [], **kwargs
         )
@@ -121,7 +131,7 @@ class AzureCompletion(BaseLLM):
         if self.api_version:
             client_kwargs["api_version"] = self.api_version
 
-        self.client = ChatCompletionsClient(**client_kwargs)
+        self.client = ChatCompletionsClient(**client_kwargs)  # type: ignore[arg-type]
 
         self.top_p = top_p
         self.frequency_penalty = frequency_penalty
@@ -249,7 +259,7 @@ class AzureCompletion(BaseLLM):
     def _prepare_completion_params(
         self,
         messages: list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Azure AI Inference chat completion.
@@ -302,7 +312,9 @@ class AzureCompletion(BaseLLM):
 
         return params
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+    def _convert_tools_for_interference(
+        self, tools: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to Azure OpenAI function calling format."""
 
         from crewai.llms.providers.utils.common import safe_tool_conversion

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

@@ -30,6 +30,8 @@ if TYPE_CHECKING:
         ToolTypeDef,
     )
 
+    from crewai.llms.hooks.base import BaseInterceptor
+
 
 try:
     from boto3.session import Session
@@ -157,8 +159,9 @@ class BedrockCompletion(BaseLLM):
         guardrail_config: dict[str, Any] | None = None,
         additional_model_request_fields: dict[str, Any] | None = None,
         additional_model_response_field_paths: list[str] | None = None,
-        **kwargs,
-    ):
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
+    ) -> None:
         """Initialize AWS Bedrock completion client.
 
         Args:
@@ -176,8 +179,15 @@ class BedrockCompletion(BaseLLM):
             guardrail_config: Guardrail configuration for content filtering
             additional_model_request_fields: Model-specific request parameters
             additional_model_response_field_paths: Custom response field paths
+            interceptor: HTTP interceptor (not yet supported for Bedrock).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for AWS Bedrock provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         # Extract provider from kwargs to avoid duplicate argument
         kwargs.pop("provider", None)
 
@@ -233,6 +243,30 @@ class BedrockCompletion(BaseLLM):
         # Handle inference profiles for newer models
         self.model_id = model
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return list(self.stop_sequences)
+
+    @stop.setter
+    def stop(self, value: Sequence[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Bedrock API.
+
+        Args:
+            value: Stop sequences as a Sequence, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, Sequence):
+            self.stop_sequences = list(value)
+        else:
+            self.stop_sequences = []
+
     def call(
         self,
         messages: str | list[LLMMessage],
@@ -247,7 +281,7 @@ class BedrockCompletion(BaseLLM):
         try:
             # Emit call started event
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -740,7 +774,9 @@ class BedrockCompletion(BaseLLM):
         return converse_messages, system_message
 
     @staticmethod
-    def _format_tools_for_converse(tools: list[dict]) -> list[ConverseToolTypeDef]:
+    def _format_tools_for_converse(
+        tools: list[dict[str, Any]],
+    ) -> list[ConverseToolTypeDef]:
         """Convert CrewAI tools to Converse API format following AWS specification."""
         from crewai.llms.providers.utils.common import safe_tool_conversion
 

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

@@ -1,4 +1,3 @@
-import json
 import logging
 import os
 from typing import Any, cast
@@ -7,6 +6,7 @@ from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -45,7 +45,8 @@ class GeminiCompletion(BaseLLM):
         stream: bool = False,
         safety_settings: dict[str, Any] | None = None,
         client_params: dict[str, Any] | None = None,
-        **kwargs,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
 
@@ -63,8 +64,15 @@ class GeminiCompletion(BaseLLM):
             safety_settings: Safety filter settings
             client_params: Additional parameters to pass to the Google Gen AI Client constructor.
                           Supports parameters like http_options, credentials, debug_config, etc.
+            interceptor: HTTP interceptor (not yet supported for Gemini).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Google Gemini provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         super().__init__(
             model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
         )
@@ -96,7 +104,31 @@ class GeminiCompletion(BaseLLM):
         self.is_gemini_1_5 = "gemini-1.5" in model.lower()
         self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
-    def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Gemini API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
+
+    def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:  # type: ignore[no-any-unimported]
         """Initialize the Google Gen AI client with proper parameter handling.
 
         Args:
@@ -171,7 +203,7 @@ class GeminiCompletion(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -193,7 +225,7 @@ class GeminiCompletion(BaseLLM):
         """
         try:
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -203,7 +235,7 @@ class GeminiCompletion(BaseLLM):
             self.tools = tools
 
             formatted_content, system_instruction = self._format_messages_for_gemini(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
             config = self._prepare_generation_config(
@@ -245,10 +277,10 @@ class GeminiCompletion(BaseLLM):
             )
             raise
 
-    def _prepare_generation_config(
+    def _prepare_generation_config(  # type: ignore[no-any-unimported]
         self,
         system_instruction: str | None = None,
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> types.GenerateContentConfig:
         """Prepare generation config for Google Gemini API.
@@ -297,7 +329,9 @@ class GeminiCompletion(BaseLLM):
 
         return types.GenerateContentConfig(**config_params)
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[types.Tool]:
+    def _convert_tools_for_interference(  # type: ignore[no-any-unimported]
+        self, tools: list[dict[str, Any]]
+    ) -> list[types.Tool]:
         """Convert CrewAI tool format to Gemini function declaration format."""
         gemini_tools = []
 
@@ -320,7 +354,7 @@ class GeminiCompletion(BaseLLM):
 
         return gemini_tools
 
-    def _format_messages_for_gemini(
+    def _format_messages_for_gemini(  # type: ignore[no-any-unimported]
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[types.Content], str | None]:
         """Format messages for Gemini API.
@@ -364,7 +398,7 @@ class GeminiCompletion(BaseLLM):
 
         return contents, system_instruction
 
-    def _handle_completion(
+    def _handle_completion(  # type: ignore[no-any-unimported]
         self,
         contents: list[types.Content],
         system_instruction: str | None,
@@ -431,7 +465,7 @@ class GeminiCompletion(BaseLLM):
 
         return content
 
-    def _handle_streaming_completion(
+    def _handle_streaming_completion(  # type: ignore[no-any-unimported]
         self,
         contents: list[types.Content],
         config: types.GenerateContentConfig,
@@ -560,8 +594,9 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
-    def _convert_contents_to_dict(
-        self, contents: list[types.Content]
+    def _convert_contents_to_dict(  # type: ignore[no-any-unimported]
+        self,
+        contents: list[types.Content],
     ) -> list[dict[str, str]]:
         """Convert contents to dict format."""
         return [

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

@@ -6,6 +6,7 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any
 
+import httpx
 from openai import APIConnectionError, NotFoundError, OpenAI
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
@@ -14,6 +15,7 @@ from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -23,6 +25,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
 
@@ -59,6 +62,7 @@ class OpenAICompletion(BaseLLM):
         top_logprobs: int | None = None,
         reasoning_effort: str | None = None,
         provider: str | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize OpenAI chat completion client."""
@@ -66,6 +70,7 @@ class OpenAICompletion(BaseLLM):
         if provider is None:
             provider = kwargs.pop("provider", "openai")
 
+        self.interceptor = interceptor
         # Client configuration attributes
         self.organization = organization
         self.project = project
@@ -88,6 +93,11 @@ class OpenAICompletion(BaseLLM):
         )
 
         client_config = self._get_client_params()
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            client_config["http_client"] = http_client
+
         self.client = OpenAI(**client_config)
 
         # Completion parameters

lib/crewai/src/crewai/mcp/__init__.py

@@ -0,0 +1,37 @@
+"""MCP (Model Context Protocol) client support for CrewAI agents.
+
+This module provides native MCP client functionality, allowing CrewAI agents
+to connect to any MCP-compliant server using various transport types.
+"""
+
+from crewai.mcp.client import MCPClient
+from crewai.mcp.config import (
+    MCPServerConfig,
+    MCPServerHTTP,
+    MCPServerSSE,
+    MCPServerStdio,
+)
+from crewai.mcp.filters import (
+    StaticToolFilter,
+    ToolFilter,
+    ToolFilterContext,
+    create_dynamic_tool_filter,
+    create_static_tool_filter,
+)
+from crewai.mcp.transports.base import BaseTransport, TransportType
+
+
+__all__ = [
+    "BaseTransport",
+    "MCPClient",
+    "MCPServerConfig",
+    "MCPServerHTTP",
+    "MCPServerSSE",
+    "MCPServerStdio",
+    "StaticToolFilter",
+    "ToolFilter",
+    "ToolFilterContext",
+    "TransportType",
+    "create_dynamic_tool_filter",
+    "create_static_tool_filter",
+]

lib/crewai/src/crewai/mcp/client.py

@@ -0,0 +1,742 @@
+"""MCP client with session management for CrewAI agents."""
+
+import asyncio
+from collections.abc import Callable
+from contextlib import AsyncExitStack
+from datetime import datetime
+import logging
+import time
+from typing import Any
+
+from typing_extensions import Self
+
+
+# BaseExceptionGroup is available in Python 3.11+
+try:
+    from builtins import BaseExceptionGroup
+except ImportError:
+    # Fallback for Python < 3.11 (shouldn't happen in practice)
+    BaseExceptionGroup = Exception
+
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.mcp_events import (
+    MCPConnectionCompletedEvent,
+    MCPConnectionFailedEvent,
+    MCPConnectionStartedEvent,
+    MCPToolExecutionCompletedEvent,
+    MCPToolExecutionFailedEvent,
+    MCPToolExecutionStartedEvent,
+)
+from crewai.mcp.transports.base import BaseTransport
+from crewai.mcp.transports.http import HTTPTransport
+from crewai.mcp.transports.sse import SSETransport
+from crewai.mcp.transports.stdio import StdioTransport
+
+
+# MCP Connection timeout constants (in seconds)
+MCP_CONNECTION_TIMEOUT = 30  # Increased for slow servers
+MCP_TOOL_EXECUTION_TIMEOUT = 30
+MCP_DISCOVERY_TIMEOUT = 30  # Increased for slow servers
+MCP_MAX_RETRIES = 3
+
+# Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
+_mcp_schema_cache: dict[str, tuple[dict[str, Any], float]] = {}
+_cache_ttl = 300  # 5 minutes
+
+
+class MCPClient:
+    """MCP client with session management.
+
+    This client manages connections to MCP servers and provides a high-level
+    interface for interacting with MCP tools, prompts, and resources.
+
+    Example:
+        ```python
+        transport = StdioTransport(command="python", args=["server.py"])
+        client = MCPClient(transport)
+        async with client:
+            tools = await client.list_tools()
+            result = await client.call_tool("tool_name", {"arg": "value"})
+        ```
+    """
+
+    def __init__(
+        self,
+        transport: BaseTransport,
+        connect_timeout: int = MCP_CONNECTION_TIMEOUT,
+        execution_timeout: int = MCP_TOOL_EXECUTION_TIMEOUT,
+        discovery_timeout: int = MCP_DISCOVERY_TIMEOUT,
+        max_retries: int = MCP_MAX_RETRIES,
+        cache_tools_list: bool = False,
+        logger: logging.Logger | None = None,
+    ) -> None:
+        """Initialize MCP client.
+
+        Args:
+            transport: Transport instance for MCP server connection.
+            connect_timeout: Connection timeout in seconds.
+            execution_timeout: Tool execution timeout in seconds.
+            discovery_timeout: Tool discovery timeout in seconds.
+            max_retries: Maximum retry attempts for operations.
+            cache_tools_list: Whether to cache tool list results.
+            logger: Optional logger instance.
+        """
+        self.transport = transport
+        self.connect_timeout = connect_timeout
+        self.execution_timeout = execution_timeout
+        self.discovery_timeout = discovery_timeout
+        self.max_retries = max_retries
+        self.cache_tools_list = cache_tools_list
+        # self._logger = logger or logging.getLogger(__name__)
+        self._session: Any = None
+        self._initialized = False
+        self._exit_stack = AsyncExitStack()
+        self._was_connected = False
+
+    @property
+    def connected(self) -> bool:
+        """Check if client is connected to server."""
+        return self.transport.connected and self._initialized
+
+    @property
+    def session(self) -> Any:
+        """Get the MCP session."""
+        if self._session is None:
+            raise RuntimeError("Client not connected. Call connect() first.")
+        return self._session
+
+    def _get_server_info(self) -> tuple[str, str | None, str | None]:
+        """Get server information for events.
+
+        Returns:
+            Tuple of (server_name, server_url, transport_type).
+        """
+        if isinstance(self.transport, StdioTransport):
+            server_name = f"{self.transport.command} {' '.join(self.transport.args)}"
+            server_url = None
+            transport_type = self.transport.transport_type.value
+        elif isinstance(self.transport, HTTPTransport):
+            server_name = self.transport.url
+            server_url = self.transport.url
+            transport_type = self.transport.transport_type.value
+        elif isinstance(self.transport, SSETransport):
+            server_name = self.transport.url
+            server_url = self.transport.url
+            transport_type = self.transport.transport_type.value
+        else:
+            server_name = "Unknown MCP Server"
+            server_url = None
+            transport_type = (
+                self.transport.transport_type.value
+                if hasattr(self.transport, "transport_type")
+                else None
+            )
+
+        return server_name, server_url, transport_type
+
+    async def connect(self) -> Self:
+        """Connect to MCP server and initialize session.
+
+        Returns:
+            Self for method chaining.
+
+        Raises:
+            ConnectionError: If connection fails.
+            ImportError: If MCP SDK not available.
+        """
+        if self.connected:
+            return self
+
+        # Get server info for events
+        server_name, server_url, transport_type = self._get_server_info()
+        is_reconnect = self._was_connected
+
+        # Emit connection started event
+        started_at = datetime.now()
+        crewai_event_bus.emit(
+            self,
+            MCPConnectionStartedEvent(
+                server_name=server_name,
+                server_url=server_url,
+                transport_type=transport_type,
+                is_reconnect=is_reconnect,
+                connect_timeout=self.connect_timeout,
+            ),
+        )
+
+        try:
+            from mcp import ClientSession
+
+            # Use AsyncExitStack to manage transport and session contexts together
+            # This ensures they're in the same async scope and prevents cancel scope errors
+            # Always enter transport context via exit stack (it handles already-connected state)
+            await self._exit_stack.enter_async_context(self.transport)
+
+            # Create ClientSession with transport streams
+            self._session = ClientSession(
+                self.transport.read_stream,
+                self.transport.write_stream,
+            )
+
+            # Enter the session's async context manager via exit stack
+            await self._exit_stack.enter_async_context(self._session)
+
+            # Initialize the session (required by MCP protocol)
+            try:
+                await asyncio.wait_for(
+                    self._session.initialize(),
+                    timeout=self.connect_timeout,
+                )
+            except asyncio.CancelledError:
+                # If initialization was cancelled (e.g., event loop closing),
+                # cleanup and re-raise - don't suppress cancellation
+                await self._cleanup_on_error()
+                raise
+            except BaseExceptionGroup as eg:
+                # Handle exception groups from anyio task groups
+                # Extract the actual meaningful error (not GeneratorExit)
+                actual_error = None
+                for exc in eg.exceptions:
+                    if isinstance(exc, Exception) and not isinstance(
+                        exc, GeneratorExit
+                    ):
+                        # Check if it's an HTTP error (like 401)
+                        error_msg = str(exc).lower()
+                        if "401" in error_msg or "unauthorized" in error_msg:
+                            actual_error = exc
+                            break
+                        if "cancel scope" not in error_msg and "task" not in error_msg:
+                            actual_error = exc
+                            break
+
+                await self._cleanup_on_error()
+                if actual_error:
+                    raise ConnectionError(
+                        f"Failed to connect to MCP server: {actual_error}"
+                    ) from actual_error
+                raise ConnectionError(f"Failed to connect to MCP server: {eg}") from eg
+
+            self._initialized = True
+            self._was_connected = True
+
+            completed_at = datetime.now()
+            connection_duration_ms = (completed_at - started_at).total_seconds() * 1000
+            crewai_event_bus.emit(
+                self,
+                MCPConnectionCompletedEvent(
+                    server_name=server_name,
+                    server_url=server_url,
+                    transport_type=transport_type,
+                    started_at=started_at,
+                    completed_at=completed_at,
+                    connection_duration_ms=connection_duration_ms,
+                    is_reconnect=is_reconnect,
+                ),
+            )
+
+            return self
+        except ImportError as e:
+            await self._cleanup_on_error()
+            error_msg = (
+                "MCP library not available. Please install with: pip install mcp"
+            )
+            self._emit_connection_failed(
+                server_name,
+                server_url,
+                transport_type,
+                error_msg,
+                "import_error",
+                started_at,
+            )
+            raise ImportError(error_msg) from e
+        except asyncio.TimeoutError as e:
+            await self._cleanup_on_error()
+            error_msg = f"MCP connection timed out after {self.connect_timeout} seconds. The server may be slow or unreachable."
+            self._emit_connection_failed(
+                server_name,
+                server_url,
+                transport_type,
+                error_msg,
+                "timeout",
+                started_at,
+            )
+            raise ConnectionError(error_msg) from e
+        except asyncio.CancelledError:
+            # Re-raise cancellation - don't suppress it
+            await self._cleanup_on_error()
+            self._emit_connection_failed(
+                server_name,
+                server_url,
+                transport_type,
+                "Connection cancelled",
+                "cancelled",
+                started_at,
+            )
+            raise
+        except BaseExceptionGroup as eg:
+            # Handle exception groups from anyio task groups at outer level
+            actual_error = None
+            for exc in eg.exceptions:
+                if isinstance(exc, Exception) and not isinstance(exc, GeneratorExit):
+                    error_msg = str(exc).lower()
+                    if "401" in error_msg or "unauthorized" in error_msg:
+                        actual_error = exc
+                        break
+                    if "cancel scope" not in error_msg and "task" not in error_msg:
+                        actual_error = exc
+                        break
+
+            await self._cleanup_on_error()
+            error_type = (
+                "authentication"
+                if actual_error
+                and (
+                    "401" in str(actual_error).lower()
+                    or "unauthorized" in str(actual_error).lower()
+                )
+                else "network"
+            )
+            error_msg = str(actual_error) if actual_error else str(eg)
+            self._emit_connection_failed(
+                server_name,
+                server_url,
+                transport_type,
+                error_msg,
+                error_type,
+                started_at,
+            )
+            if actual_error:
+                raise ConnectionError(
+                    f"Failed to connect to MCP server: {actual_error}"
+                ) from actual_error
+            raise ConnectionError(f"Failed to connect to MCP server: {eg}") from eg
+        except Exception as e:
+            await self._cleanup_on_error()
+            error_type = (
+                "authentication"
+                if "401" in str(e).lower() or "unauthorized" in str(e).lower()
+                else "network"
+            )
+            self._emit_connection_failed(
+                server_name, server_url, transport_type, str(e), error_type, started_at
+            )
+            raise ConnectionError(f"Failed to connect to MCP server: {e}") from e
+
+    def _emit_connection_failed(
+        self,
+        server_name: str,
+        server_url: str | None,
+        transport_type: str | None,
+        error: str,
+        error_type: str,
+        started_at: datetime,
+    ) -> None:
+        """Emit connection failed event."""
+        failed_at = datetime.now()
+        crewai_event_bus.emit(
+            self,
+            MCPConnectionFailedEvent(
+                server_name=server_name,
+                server_url=server_url,
+                transport_type=transport_type,
+                error=error,
+                error_type=error_type,
+                started_at=started_at,
+                failed_at=failed_at,
+            ),
+        )
+
+    async def _cleanup_on_error(self) -> None:
+        """Cleanup resources when an error occurs during connection."""
+        try:
+            await self._exit_stack.aclose()
+
+        except Exception as e:
+            # Best effort cleanup - ignore all other errors
+            raise RuntimeError(f"Error during MCP client cleanup: {e}") from e
+        finally:
+            self._session = None
+            self._initialized = False
+            self._exit_stack = AsyncExitStack()
+
+    async def disconnect(self) -> None:
+        """Disconnect from MCP server and cleanup resources."""
+        if not self.connected:
+            return
+
+        try:
+            await self._exit_stack.aclose()
+        except Exception as e:
+            raise RuntimeError(f"Error during MCP client disconnect: {e}") from e
+        finally:
+            self._session = None
+            self._initialized = False
+            self._exit_stack = AsyncExitStack()
+
+    async def list_tools(self, use_cache: bool | None = None) -> list[dict[str, Any]]:
+        """List available tools from MCP server.
+
+        Args:
+            use_cache: Whether to use cached results. If None, uses
+                client's cache_tools_list setting.
+
+        Returns:
+            List of tool definitions with name, description, and inputSchema.
+        """
+        if not self.connected:
+            await self.connect()
+
+        # Check cache if enabled
+        use_cache = use_cache if use_cache is not None else self.cache_tools_list
+        if use_cache:
+            cache_key = self._get_cache_key("tools")
+            if cache_key in _mcp_schema_cache:
+                cached_data, cache_time = _mcp_schema_cache[cache_key]
+                if time.time() - cache_time < _cache_ttl:
+                    # Logger removed - return cached data
+                    return cached_data
+
+        # List tools with timeout and retries
+        tools = await self._retry_operation(
+            self._list_tools_impl,
+            timeout=self.discovery_timeout,
+        )
+
+        # Cache results if enabled
+        if use_cache:
+            cache_key = self._get_cache_key("tools")
+            _mcp_schema_cache[cache_key] = (tools, time.time())
+
+        return tools
+
+    async def _list_tools_impl(self) -> list[dict[str, Any]]:
+        """Internal implementation of list_tools."""
+        tools_result = await asyncio.wait_for(
+            self.session.list_tools(),
+            timeout=self.discovery_timeout,
+        )
+
+        return [
+            {
+                "name": tool.name,
+                "description": getattr(tool, "description", ""),
+                "inputSchema": getattr(tool, "inputSchema", {}),
+            }
+            for tool in tools_result.tools
+        ]
+
+    async def call_tool(
+        self, tool_name: str, arguments: dict[str, Any] | None = None
+    ) -> Any:
+        """Call a tool on the MCP server.
+
+        Args:
+            tool_name: Name of the tool to call.
+            arguments: Tool arguments.
+
+        Returns:
+            Tool execution result.
+        """
+        if not self.connected:
+            await self.connect()
+
+        arguments = arguments or {}
+        cleaned_arguments = self._clean_tool_arguments(arguments)
+
+        # Get server info for events
+        server_name, server_url, transport_type = self._get_server_info()
+
+        # Emit tool execution started event
+        started_at = datetime.now()
+        crewai_event_bus.emit(
+            self,
+            MCPToolExecutionStartedEvent(
+                server_name=server_name,
+                server_url=server_url,
+                transport_type=transport_type,
+                tool_name=tool_name,
+                tool_args=cleaned_arguments,
+            ),
+        )
+
+        try:
+            result = await self._retry_operation(
+                lambda: self._call_tool_impl(tool_name, cleaned_arguments),
+                timeout=self.execution_timeout,
+            )
+
+            completed_at = datetime.now()
+            execution_duration_ms = (completed_at - started_at).total_seconds() * 1000
+            crewai_event_bus.emit(
+                self,
+                MCPToolExecutionCompletedEvent(
+                    server_name=server_name,
+                    server_url=server_url,
+                    transport_type=transport_type,
+                    tool_name=tool_name,
+                    tool_args=cleaned_arguments,
+                    result=result,
+                    started_at=started_at,
+                    completed_at=completed_at,
+                    execution_duration_ms=execution_duration_ms,
+                ),
+            )
+
+            return result
+        except Exception as e:
+            failed_at = datetime.now()
+            error_type = (
+                "timeout"
+                if isinstance(e, (asyncio.TimeoutError, ConnectionError))
+                and "timeout" in str(e).lower()
+                else "server_error"
+            )
+            crewai_event_bus.emit(
+                self,
+                MCPToolExecutionFailedEvent(
+                    server_name=server_name,
+                    server_url=server_url,
+                    transport_type=transport_type,
+                    tool_name=tool_name,
+                    tool_args=cleaned_arguments,
+                    error=str(e),
+                    error_type=error_type,
+                    started_at=started_at,
+                    failed_at=failed_at,
+                ),
+            )
+            raise
+
+    def _clean_tool_arguments(self, arguments: dict[str, Any]) -> dict[str, Any]:
+        """Clean tool arguments by removing None values and fixing formats.
+
+        Args:
+            arguments: Raw tool arguments.
+
+        Returns:
+            Cleaned arguments ready for MCP server.
+        """
+        cleaned = {}
+
+        for key, value in arguments.items():
+            # Skip None values
+            if value is None:
+                continue
+
+            # Fix sources array format: convert ["web"] to [{"type": "web"}]
+            if key == "sources" and isinstance(value, list):
+                fixed_sources = []
+                for item in value:
+                    if isinstance(item, str):
+                        # Convert string to object format
+                        fixed_sources.append({"type": item})
+                    elif isinstance(item, dict):
+                        # Already in correct format
+                        fixed_sources.append(item)
+                    else:
+                        # Keep as is if unknown format
+                        fixed_sources.append(item)
+                if fixed_sources:
+                    cleaned[key] = fixed_sources
+                continue
+
+            # Recursively clean nested dictionaries
+            if isinstance(value, dict):
+                nested_cleaned = self._clean_tool_arguments(value)
+                if nested_cleaned:  # Only add if not empty
+                    cleaned[key] = nested_cleaned
+            elif isinstance(value, list):
+                # Clean list items
+                cleaned_list = []
+                for item in value:
+                    if isinstance(item, dict):
+                        cleaned_item = self._clean_tool_arguments(item)
+                        if cleaned_item:
+                            cleaned_list.append(cleaned_item)
+                    elif item is not None:
+                        cleaned_list.append(item)
+                if cleaned_list:
+                    cleaned[key] = cleaned_list
+            else:
+                # Keep primitive values
+                cleaned[key] = value
+
+        return cleaned
+
+    async def _call_tool_impl(self, tool_name: str, arguments: dict[str, Any]) -> Any:
+        """Internal implementation of call_tool."""
+        result = await asyncio.wait_for(
+            self.session.call_tool(tool_name, arguments),
+            timeout=self.execution_timeout,
+        )
+
+        # Extract result content
+        if hasattr(result, "content") and result.content:
+            if isinstance(result.content, list) and len(result.content) > 0:
+                content_item = result.content[0]
+                if hasattr(content_item, "text"):
+                    return str(content_item.text)
+                return str(content_item)
+            return str(result.content)
+
+        return str(result)
+
+    async def list_prompts(self) -> list[dict[str, Any]]:
+        """List available prompts from MCP server.
+
+        Returns:
+            List of prompt definitions.
+        """
+        if not self.connected:
+            await self.connect()
+
+        return await self._retry_operation(
+            self._list_prompts_impl,
+            timeout=self.discovery_timeout,
+        )
+
+    async def _list_prompts_impl(self) -> list[dict[str, Any]]:
+        """Internal implementation of list_prompts."""
+        prompts_result = await asyncio.wait_for(
+            self.session.list_prompts(),
+            timeout=self.discovery_timeout,
+        )
+
+        return [
+            {
+                "name": prompt.name,
+                "description": getattr(prompt, "description", ""),
+                "arguments": getattr(prompt, "arguments", []),
+            }
+            for prompt in prompts_result.prompts
+        ]
+
+    async def get_prompt(
+        self, prompt_name: str, arguments: dict[str, Any] | None = None
+    ) -> dict[str, Any]:
+        """Get a prompt from the MCP server.
+
+        Args:
+            prompt_name: Name of the prompt to get.
+            arguments: Optional prompt arguments.
+
+        Returns:
+            Prompt content and metadata.
+        """
+        if not self.connected:
+            await self.connect()
+
+        arguments = arguments or {}
+
+        return await self._retry_operation(
+            lambda: self._get_prompt_impl(prompt_name, arguments),
+            timeout=self.execution_timeout,
+        )
+
+    async def _get_prompt_impl(
+        self, prompt_name: str, arguments: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Internal implementation of get_prompt."""
+        result = await asyncio.wait_for(
+            self.session.get_prompt(prompt_name, arguments),
+            timeout=self.execution_timeout,
+        )
+
+        return {
+            "name": prompt_name,
+            "messages": [
+                {
+                    "role": msg.role,
+                    "content": msg.content,
+                }
+                for msg in result.messages
+            ],
+            "arguments": arguments,
+        }
+
+    async def _retry_operation(
+        self,
+        operation: Callable[[], Any],
+        timeout: int | None = None,
+    ) -> Any:
+        """Retry an operation with exponential backoff.
+
+        Args:
+            operation: Async operation to retry.
+            timeout: Operation timeout in seconds.
+
+        Returns:
+            Operation result.
+        """
+        last_error = None
+        timeout = timeout or self.execution_timeout
+
+        for attempt in range(self.max_retries):
+            try:
+                if timeout:
+                    return await asyncio.wait_for(operation(), timeout=timeout)
+                return await operation()
+
+            except asyncio.TimeoutError as e:  # noqa: PERF203
+                last_error = f"Operation timed out after {timeout} seconds"
+                if attempt < self.max_retries - 1:
+                    wait_time = 2**attempt
+                    await asyncio.sleep(wait_time)
+                else:
+                    raise ConnectionError(last_error) from e
+
+            except Exception as e:
+                error_str = str(e).lower()
+
+                # Classify errors as retryable or non-retryable
+                if "authentication" in error_str or "unauthorized" in error_str:
+                    raise ConnectionError(f"Authentication failed: {e}") from e
+
+                if "not found" in error_str:
+                    raise ValueError(f"Resource not found: {e}") from e
+
+                # Retryable errors
+                last_error = str(e)
+                if attempt < self.max_retries - 1:
+                    wait_time = 2**attempt
+                    await asyncio.sleep(wait_time)
+                else:
+                    raise ConnectionError(
+                        f"Operation failed after {self.max_retries} attempts: {last_error}"
+                    ) from e
+
+        raise ConnectionError(f"Operation failed: {last_error}")
+
+    def _get_cache_key(self, resource_type: str) -> str:
+        """Generate cache key for resource.
+
+        Args:
+            resource_type: Type of resource (e.g., "tools", "prompts").
+
+        Returns:
+            Cache key string.
+        """
+        # Use transport type and URL/command as cache key
+        if isinstance(self.transport, StdioTransport):
+            key = f"stdio:{self.transport.command}:{':'.join(self.transport.args)}"
+        elif isinstance(self.transport, HTTPTransport):
+            key = f"http:{self.transport.url}"
+        elif isinstance(self.transport, SSETransport):
+            key = f"sse:{self.transport.url}"
+        else:
+            key = f"{self.transport.transport_type}:unknown"
+
+        return f"mcp:{key}:{resource_type}"
+
+    async def __aenter__(self) -> Self:
+        """Async context manager entry."""
+        return await self.connect()
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: Any,
+    ) -> None:
+        """Async context manager exit."""
+        await self.disconnect()

lib/crewai/src/crewai/mcp/config.py

@@ -0,0 +1,124 @@
+"""MCP server configuration models for CrewAI agents.
+
+This module provides Pydantic models for configuring MCP servers with
+various transport types, similar to OpenAI's Agents SDK.
+"""
+
+from pydantic import BaseModel, Field
+
+from crewai.mcp.filters import ToolFilter
+
+
+class MCPServerStdio(BaseModel):
+    """Stdio MCP server configuration.
+
+    This configuration is used for connecting to local MCP servers
+    that run as processes and communicate via standard input/output.
+
+    Example:
+        ```python
+        mcp_server = MCPServerStdio(
+            command="python",
+            args=["path/to/server.py"],
+            env={"API_KEY": "..."},
+            tool_filter=create_static_tool_filter(
+                allowed_tool_names=["read_file", "write_file"]
+            ),
+        )
+        ```
+    """
+
+    command: str = Field(
+        ...,
+        description="Command to execute (e.g., 'python', 'node', 'npx', 'uvx').",
+    )
+    args: list[str] = Field(
+        default_factory=list,
+        description="Command arguments (e.g., ['server.py'] or ['-y', '@mcp/server']).",
+    )
+    env: dict[str, str] | None = Field(
+        default=None,
+        description="Environment variables to pass to the process.",
+    )
+    tool_filter: ToolFilter | None = Field(
+        default=None,
+        description="Optional tool filter for filtering available tools.",
+    )
+    cache_tools_list: bool = Field(
+        default=False,
+        description="Whether to cache the tool list for faster subsequent access.",
+    )
+
+
+class MCPServerHTTP(BaseModel):
+    """HTTP/Streamable HTTP MCP server configuration.
+
+    This configuration is used for connecting to remote MCP servers
+    over HTTP/HTTPS using streamable HTTP transport.
+
+    Example:
+        ```python
+        mcp_server = MCPServerHTTP(
+            url="https://api.example.com/mcp",
+            headers={"Authorization": "Bearer ..."},
+            cache_tools_list=True,
+        )
+        ```
+    """
+
+    url: str = Field(
+        ..., description="Server URL (e.g., 'https://api.example.com/mcp')."
+    )
+    headers: dict[str, str] | None = Field(
+        default=None,
+        description="Optional HTTP headers for authentication or other purposes.",
+    )
+    streamable: bool = Field(
+        default=True,
+        description="Whether to use streamable HTTP transport (default: True).",
+    )
+    tool_filter: ToolFilter | None = Field(
+        default=None,
+        description="Optional tool filter for filtering available tools.",
+    )
+    cache_tools_list: bool = Field(
+        default=False,
+        description="Whether to cache the tool list for faster subsequent access.",
+    )
+
+
+class MCPServerSSE(BaseModel):
+    """Server-Sent Events (SSE) MCP server configuration.
+
+    This configuration is used for connecting to remote MCP servers
+    using Server-Sent Events for real-time streaming communication.
+
+    Example:
+        ```python
+        mcp_server = MCPServerSSE(
+            url="https://api.example.com/mcp/sse",
+            headers={"Authorization": "Bearer ..."},
+        )
+        ```
+    """
+
+    url: str = Field(
+        ...,
+        description="Server URL (e.g., 'https://api.example.com/mcp/sse').",
+    )
+    headers: dict[str, str] | None = Field(
+        default=None,
+        description="Optional HTTP headers for authentication or other purposes.",
+    )
+    tool_filter: ToolFilter | None = Field(
+        default=None,
+        description="Optional tool filter for filtering available tools.",
+    )
+    cache_tools_list: bool = Field(
+        default=False,
+        description="Whether to cache the tool list for faster subsequent access.",
+    )
+
+
+# Type alias for all MCP server configurations
+MCPServerConfig = MCPServerStdio | MCPServerHTTP | MCPServerSSE

lib/crewai/src/crewai/mcp/filters.py

@@ -0,0 +1,166 @@
+"""Tool filtering support for MCP servers.
+
+This module provides utilities for filtering tools from MCP servers,
+including static allow/block lists and dynamic context-aware filtering.
+"""
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from pydantic import BaseModel, Field
+
+
+if TYPE_CHECKING:
+    pass
+
+
+class ToolFilterContext(BaseModel):
+    """Context for dynamic tool filtering.
+
+    This context is passed to dynamic tool filters to provide
+    information about the agent, run context, and server.
+    """
+
+    agent: Any = Field(..., description="The agent requesting tools.")
+    server_name: str = Field(..., description="Name of the MCP server.")
+    run_context: dict[str, Any] | None = Field(
+        default=None,
+        description="Optional run context for additional filtering logic.",
+    )
+
+
+# Type alias for tool filter functions
+ToolFilter = (
+    Callable[[ToolFilterContext, dict[str, Any]], bool]
+    | Callable[[dict[str, Any]], bool]
+)
+
+
+class StaticToolFilter:
+    """Static tool filter with allow/block lists.
+
+    This filter provides simple allow/block list filtering based on
+    tool names. Useful for restricting which tools are available
+    from an MCP server.
+
+    Example:
+        ```python
+        filter = StaticToolFilter(
+            allowed_tool_names=["read_file", "write_file"],
+            blocked_tool_names=["delete_file"],
+        )
+        ```
+    """
+
+    def __init__(
+        self,
+        allowed_tool_names: list[str] | None = None,
+        blocked_tool_names: list[str] | None = None,
+    ) -> None:
+        """Initialize static tool filter.
+
+        Args:
+            allowed_tool_names: List of tool names to allow. If None,
+                all tools are allowed (unless blocked).
+            blocked_tool_names: List of tool names to block. Blocked tools
+                take precedence over allowed tools.
+        """
+        self.allowed_tool_names = set(allowed_tool_names or [])
+        self.blocked_tool_names = set(blocked_tool_names or [])
+
+    def __call__(self, tool: dict[str, Any]) -> bool:
+        """Filter tool based on allow/block lists.
+
+        Args:
+            tool: Tool definition dictionary with at least 'name' key.
+
+        Returns:
+            True if tool should be included, False otherwise.
+        """
+        tool_name = tool.get("name", "")
+
+        # Blocked tools take precedence
+        if self.blocked_tool_names and tool_name in self.blocked_tool_names:
+            return False
+
+        # If allow list exists, tool must be in it
+        if self.allowed_tool_names:
+            return tool_name in self.allowed_tool_names
+
+        # No restrictions - allow all
+        return True
+
+
+def create_static_tool_filter(
+    allowed_tool_names: list[str] | None = None,
+    blocked_tool_names: list[str] | None = None,
+) -> Callable[[dict[str, Any]], bool]:
+    """Create a static tool filter function.
+
+    This is a convenience function for creating static tool filters
+    with allow/block lists.
+
+    Args:
+        allowed_tool_names: List of tool names to allow. If None,
+            all tools are allowed (unless blocked).
+        blocked_tool_names: List of tool names to block. Blocked tools
+            take precedence over allowed tools.
+
+    Returns:
+        Tool filter function that returns True for allowed tools.
+
+    Example:
+        ```python
+        filter_fn = create_static_tool_filter(
+            allowed_tool_names=["read_file", "write_file"],
+            blocked_tool_names=["delete_file"],
+        )
+
+        # Use in MCPServerStdio
+        mcp_server = MCPServerStdio(
+            command="npx",
+            args=["-y", "@modelcontextprotocol/server-filesystem"],
+            tool_filter=filter_fn,
+        )
+        ```
+    """
+    return StaticToolFilter(
+        allowed_tool_names=allowed_tool_names,
+        blocked_tool_names=blocked_tool_names,
+    )
+
+
+def create_dynamic_tool_filter(
+    filter_func: Callable[[ToolFilterContext, dict[str, Any]], bool],
+) -> Callable[[ToolFilterContext, dict[str, Any]], bool]:
+    """Create a dynamic tool filter function.
+
+    This function wraps a dynamic filter function that has access
+    to the tool filter context (agent, server, run context).
+
+    Args:
+        filter_func: Function that takes (context, tool) and returns bool.
+
+    Returns:
+        Tool filter function that can be used with MCP server configs.
+
+    Example:
+        ```python
+        async def context_aware_filter(
+            context: ToolFilterContext, tool: dict[str, Any]
+        ) -> bool:
+            # Block dangerous tools for code reviewers
+            if context.agent.role == "Code Reviewer":
+                if tool["name"].startswith("danger_"):
+                    return False
+            return True
+
+
+        filter_fn = create_dynamic_tool_filter(context_aware_filter)
+
+        mcp_server = MCPServerStdio(
+            command="python", args=["server.py"], tool_filter=filter_fn
+        )
+        ```
+    """
+    return filter_func

lib/crewai/src/crewai/mcp/transports/__init__.py

@@ -0,0 +1,15 @@
+"""MCP transport implementations for various connection types."""
+
+from crewai.mcp.transports.base import BaseTransport, TransportType
+from crewai.mcp.transports.http import HTTPTransport
+from crewai.mcp.transports.sse import SSETransport
+from crewai.mcp.transports.stdio import StdioTransport
+
+
+__all__ = [
+    "BaseTransport",
+    "HTTPTransport",
+    "SSETransport",
+    "StdioTransport",
+    "TransportType",
+]

lib/crewai/src/crewai/mcp/transports/base.py

@@ -0,0 +1,125 @@
+"""Base transport interface for MCP connections."""
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Any, Protocol
+
+from typing_extensions import Self
+
+
+class TransportType(str, Enum):
+    """MCP transport types."""
+
+    STDIO = "stdio"
+    HTTP = "http"
+    STREAMABLE_HTTP = "streamable-http"
+    SSE = "sse"
+
+
+class ReadStream(Protocol):
+    """Protocol for read streams."""
+
+    async def read(self, n: int = -1) -> bytes:
+        """Read bytes from stream."""
+        ...
+
+
+class WriteStream(Protocol):
+    """Protocol for write streams."""
+
+    async def write(self, data: bytes) -> None:
+        """Write bytes to stream."""
+        ...
+
+
+class BaseTransport(ABC):
+    """Base class for MCP transport implementations.
+
+    This abstract base class defines the interface that all transport
+    implementations must follow. Transports handle the low-level communication
+    with MCP servers.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the transport.
+
+        Args:
+            **kwargs: Transport-specific configuration options.
+        """
+        self._read_stream: ReadStream | None = None
+        self._write_stream: WriteStream | None = None
+        self._connected = False
+
+    @property
+    @abstractmethod
+    def transport_type(self) -> TransportType:
+        """Return the transport type."""
+        ...
+
+    @property
+    def connected(self) -> bool:
+        """Check if transport is connected."""
+        return self._connected
+
+    @property
+    def read_stream(self) -> ReadStream:
+        """Get the read stream."""
+        if self._read_stream is None:
+            raise RuntimeError("Transport not connected. Call connect() first.")
+        return self._read_stream
+
+    @property
+    def write_stream(self) -> WriteStream:
+        """Get the write stream."""
+        if self._write_stream is None:
+            raise RuntimeError("Transport not connected. Call connect() first.")
+        return self._write_stream
+
+    @abstractmethod
+    async def connect(self) -> Self:
+        """Establish connection to MCP server.
+
+        Returns:
+            Self for method chaining.
+
+        Raises:
+            ConnectionError: If connection fails.
+        """
+        ...
+
+    @abstractmethod
+    async def disconnect(self) -> None:
+        """Close connection to MCP server."""
+        ...
+
+    @abstractmethod
+    async def __aenter__(self) -> Self:
+        """Async context manager entry."""
+        ...
+
+    @abstractmethod
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: Any,
+    ) -> None:
+        """Async context manager exit."""
+        ...
+
+    def _set_streams(self, read: ReadStream, write: WriteStream) -> None:
+        """Set the read and write streams.
+
+        Args:
+            read: Read stream.
+            write: Write stream.
+        """
+        self._read_stream = read
+        self._write_stream = write
+        self._connected = True
+
+    def _clear_streams(self) -> None:
+        """Clear the read and write streams."""
+        self._read_stream = None
+        self._write_stream = None
+        self._connected = False

lib/crewai/src/crewai/mcp/transports/http.py

@@ -0,0 +1,174 @@
+"""HTTP and Streamable HTTP transport for MCP servers."""
+
+import asyncio
+from typing import Any
+
+from typing_extensions import Self
+
+
+# BaseExceptionGroup is available in Python 3.11+
+try:
+    from builtins import BaseExceptionGroup
+except ImportError:
+    # Fallback for Python < 3.11 (shouldn't happen in practice)
+    BaseExceptionGroup = Exception
+
+from crewai.mcp.transports.base import BaseTransport, TransportType
+
+
+class HTTPTransport(BaseTransport):
+    """HTTP/Streamable HTTP transport for connecting to remote MCP servers.
+
+    This transport connects to MCP servers over HTTP/HTTPS using the
+    streamable HTTP client from the MCP SDK.
+
+    Example:
+        ```python
+        transport = HTTPTransport(
+            url="https://api.example.com/mcp",
+            headers={"Authorization": "Bearer ..."}
+        )
+        async with transport:
+            # Use transport...
+        ```
+    """
+
+    def __init__(
+        self,
+        url: str,
+        headers: dict[str, str] | None = None,
+        streamable: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize HTTP transport.
+
+        Args:
+            url: Server URL (e.g., "https://api.example.com/mcp").
+            headers: Optional HTTP headers.
+            streamable: Whether to use streamable HTTP (default: True).
+            **kwargs: Additional transport options.
+        """
+        super().__init__(**kwargs)
+        self.url = url
+        self.headers = headers or {}
+        self.streamable = streamable
+        self._transport_context: Any = None
+
+    @property
+    def transport_type(self) -> TransportType:
+        """Return the transport type."""
+        return TransportType.STREAMABLE_HTTP if self.streamable else TransportType.HTTP
+
+    async def connect(self) -> Self:
+        """Establish HTTP connection to MCP server.
+
+        Returns:
+            Self for method chaining.
+
+        Raises:
+            ConnectionError: If connection fails.
+            ImportError: If MCP SDK not available.
+        """
+        if self._connected:
+            return self
+
+        try:
+            from mcp.client.streamable_http import streamablehttp_client
+
+            self._transport_context = streamablehttp_client(
+                self.url,
+                headers=self.headers if self.headers else None,
+                terminate_on_close=True,
+            )
+
+            try:
+                read, write, _ = await asyncio.wait_for(
+                    self._transport_context.__aenter__(), timeout=30.0
+                )
+            except asyncio.TimeoutError as e:
+                self._transport_context = None
+                raise ConnectionError(
+                    "Transport context entry timed out after 30 seconds. "
+                    "Server may be slow or unreachable."
+                ) from e
+            except Exception as e:
+                self._transport_context = None
+                raise ConnectionError(f"Failed to enter transport context: {e}") from e
+            self._set_streams(read=read, write=write)
+            return self
+
+        except ImportError as e:
+            raise ImportError(
+                "MCP library not available. Please install with: pip install mcp"
+            ) from e
+        except Exception as e:
+            self._clear_streams()
+            if self._transport_context is not None:
+                self._transport_context = None
+            raise ConnectionError(f"Failed to connect to MCP server: {e}") from e
+
+    async def disconnect(self) -> None:
+        """Close HTTP connection."""
+        if not self._connected:
+            return
+
+        try:
+            # Clear streams first
+            self._clear_streams()
+            # await self._exit_stack.aclose()
+
+            # Exit transport context - this will clean up background tasks
+            # Give a small delay to allow background tasks to complete
+            if self._transport_context is not None:
+                try:
+                    # Wait a tiny bit for any pending operations
+                    await asyncio.sleep(0.1)
+                    await self._transport_context.__aexit__(None, None, None)
+                except (RuntimeError, asyncio.CancelledError) as e:
+                    # Ignore "exit cancel scope in different task" errors and cancellation
+                    # These happen when asyncio.run() closes the event loop
+                    # while background tasks are still running
+                    error_msg = str(e).lower()
+                    if "cancel scope" not in error_msg and "task" not in error_msg:
+                        # Only suppress cancel scope/task errors, re-raise others
+                        if isinstance(e, RuntimeError):
+                            raise
+                        # For CancelledError, just suppress it
+                except BaseExceptionGroup as eg:
+                    # Handle exception groups from anyio task groups
+                    # Suppress if they contain cancel scope errors
+                    should_suppress = False
+                    for exc in eg.exceptions:
+                        error_msg = str(exc).lower()
+                        if "cancel scope" in error_msg or "task" in error_msg:
+                            should_suppress = True
+                            break
+                    if not should_suppress:
+                        raise
+                except Exception as e:
+                    raise RuntimeError(
+                        f"Error during HTTP transport disconnect: {e}"
+                    ) from e
+
+            self._connected = False
+
+        except Exception as e:
+            # Log but don't raise - cleanup should be best effort
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.warning(f"Error during HTTP transport disconnect: {e}")
+
+    async def __aenter__(self) -> Self:
+        """Async context manager entry."""
+        return await self.connect()
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: Any,
+    ) -> None:
+        """Async context manager exit."""
+
+        await self.disconnect()

lib/crewai/src/crewai/mcp/transports/sse.py

@@ -0,0 +1,113 @@
+"""Server-Sent Events (SSE) transport for MCP servers."""
+
+from typing import Any
+
+from typing_extensions import Self
+
+from crewai.mcp.transports.base import BaseTransport, TransportType
+
+
+class SSETransport(BaseTransport):
+    """SSE transport for connecting to remote MCP servers.
+
+    This transport connects to MCP servers using Server-Sent Events (SSE)
+    for real-time streaming communication.
+
+    Example:
+        ```python
+        transport = SSETransport(
+            url="https://api.example.com/mcp/sse",
+            headers={"Authorization": "Bearer ..."}
+        )
+        async with transport:
+            # Use transport...
+        ```
+    """
+
+    def __init__(
+        self,
+        url: str,
+        headers: dict[str, str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize SSE transport.
+
+        Args:
+            url: Server URL (e.g., "https://api.example.com/mcp/sse").
+            headers: Optional HTTP headers.
+            **kwargs: Additional transport options.
+        """
+        super().__init__(**kwargs)
+        self.url = url
+        self.headers = headers or {}
+        self._transport_context: Any = None
+
+    @property
+    def transport_type(self) -> TransportType:
+        """Return the transport type."""
+        return TransportType.SSE
+
+    async def connect(self) -> Self:
+        """Establish SSE connection to MCP server.
+
+        Returns:
+            Self for method chaining.
+
+        Raises:
+            ConnectionError: If connection fails.
+            ImportError: If MCP SDK not available.
+        """
+        if self._connected:
+            return self
+
+        try:
+            from mcp.client.sse import sse_client
+
+            self._transport_context = sse_client(
+                self.url,
+                headers=self.headers if self.headers else None,
+                terminate_on_close=True,
+            )
+
+            read, write = await self._transport_context.__aenter__()
+
+            self._set_streams(read=read, write=write)
+
+            return self
+
+        except ImportError as e:
+            raise ImportError(
+                "MCP library not available. Please install with: pip install mcp"
+            ) from e
+        except Exception as e:
+            self._clear_streams()
+            raise ConnectionError(f"Failed to connect to SSE MCP server: {e}") from e
+
+    async def disconnect(self) -> None:
+        """Close SSE connection."""
+        if not self._connected:
+            return
+
+        try:
+            self._clear_streams()
+            if self._transport_context is not None:
+                await self._transport_context.__aexit__(None, None, None)
+
+        except Exception as e:
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.warning(f"Error during SSE transport disconnect: {e}")
+
+    async def __aenter__(self) -> Self:
+        """Async context manager entry."""
+        return await self.connect()
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: Any,
+    ) -> None:
+        """Async context manager exit."""
+        await self.disconnect()

lib/crewai/src/crewai/mcp/transports/stdio.py

@@ -0,0 +1,153 @@
+"""Stdio transport for MCP servers running as local processes."""
+
+import asyncio
+import os
+import subprocess
+from typing import Any
+
+from typing_extensions import Self
+
+from crewai.mcp.transports.base import BaseTransport, TransportType
+
+
+class StdioTransport(BaseTransport):
+    """Stdio transport for connecting to local MCP servers.
+
+    This transport connects to MCP servers running as local processes,
+    communicating via standard input/output streams. Supports Python,
+    Node.js, and other command-line servers.
+
+    Example:
+        ```python
+        transport = StdioTransport(
+            command="python",
+            args=["path/to/server.py"],
+            env={"API_KEY": "..."}
+        )
+        async with transport:
+            # Use transport...
+        ```
+    """
+
+    def __init__(
+        self,
+        command: str,
+        args: list[str] | None = None,
+        env: dict[str, str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize stdio transport.
+
+        Args:
+            command: Command to execute (e.g., "python", "node", "npx").
+            args: Command arguments (e.g., ["server.py"] or ["-y", "@mcp/server"]).
+            env: Environment variables to pass to the process.
+            **kwargs: Additional transport options.
+        """
+        super().__init__(**kwargs)
+        self.command = command
+        self.args = args or []
+        self.env = env or {}
+        self._process: subprocess.Popen[bytes] | None = None
+        self._transport_context: Any = None
+
+    @property
+    def transport_type(self) -> TransportType:
+        """Return the transport type."""
+        return TransportType.STDIO
+
+    async def connect(self) -> Self:
+        """Start the MCP server process and establish connection.
+
+        Returns:
+            Self for method chaining.
+
+        Raises:
+            ConnectionError: If process fails to start.
+            ImportError: If MCP SDK not available.
+        """
+        if self._connected:
+            return self
+
+        try:
+            from mcp import StdioServerParameters
+            from mcp.client.stdio import stdio_client
+
+            process_env = os.environ.copy()
+            process_env.update(self.env)
+
+            server_params = StdioServerParameters(
+                command=self.command,
+                args=self.args,
+                env=process_env if process_env else None,
+            )
+            self._transport_context = stdio_client(server_params)
+
+            try:
+                read, write = await self._transport_context.__aenter__()
+            except Exception as e:
+                import traceback
+
+                traceback.print_exc()
+                self._transport_context = None
+                raise ConnectionError(
+                    f"Failed to enter stdio transport context: {e}"
+                ) from e
+
+            self._set_streams(read=read, write=write)
+
+            return self
+
+        except ImportError as e:
+            raise ImportError(
+                "MCP library not available. Please install with: pip install mcp"
+            ) from e
+        except Exception as e:
+            self._clear_streams()
+            if self._transport_context is not None:
+                self._transport_context = None
+            raise ConnectionError(f"Failed to start MCP server process: {e}") from e
+
+    async def disconnect(self) -> None:
+        """Terminate the MCP server process and close connection."""
+        if not self._connected:
+            return
+
+        try:
+            self._clear_streams()
+
+            if self._transport_context is not None:
+                await self._transport_context.__aexit__(None, None, None)
+
+            if self._process is not None:
+                try:
+                    self._process.terminate()
+                    try:
+                        await asyncio.wait_for(self._process.wait(), timeout=5.0)
+                    except asyncio.TimeoutError:
+                        self._process.kill()
+                        await self._process.wait()
+                # except ProcessLookupError:
+                #     pass
+                finally:
+                    self._process = None
+
+        except Exception as e:
+            # Log but don't raise - cleanup should be best effort
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.warning(f"Error during stdio transport disconnect: {e}")
+
+    async def __aenter__(self) -> Self:
+        """Async context manager entry."""
+        return await self.connect()
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: Any,
+    ) -> None:
+        """Async context manager exit."""
+        await self.disconnect()

lib/crewai/src/crewai/project/utils.py

@@ -1,21 +1,75 @@
 """Utility functions for the crewai project module."""
 
 from collections.abc import Callable
-from functools import lru_cache
-from typing import ParamSpec, TypeVar, cast
+from functools import wraps
+from typing import Any, ParamSpec, TypeVar, cast
+
+from pydantic import BaseModel
+
+from crewai.agents.cache.cache_handler import CacheHandler
 
 
 P = ParamSpec("P")
 R = TypeVar("R")
+cache = CacheHandler()
+
+
+def _make_hashable(arg: Any) -> Any:
+    """Convert argument to hashable form for caching.
+
+    Args:
+        arg: The argument to convert.
+
+    Returns:
+        Hashable representation of the argument.
+    """
+    if isinstance(arg, BaseModel):
+        return arg.model_dump_json()
+    if isinstance(arg, dict):
+        return tuple(sorted((k, _make_hashable(v)) for k, v in arg.items()))
+    if isinstance(arg, list):
+        return tuple(_make_hashable(item) for item in arg)
+    if hasattr(arg, "__dict__"):
+        return ("__instance__", id(arg))
+    return arg
 
 
 def memoize(meth: Callable[P, R]) -> Callable[P, R]:
     """Memoize a method by caching its results based on arguments.
 
+    Handles Pydantic BaseModel instances by converting them to JSON strings
+    before hashing for cache lookup.
+
     Args:
         meth: The method to memoize.
 
     Returns:
         A memoized version of the method that caches results.
     """
-    return cast(Callable[P, R], lru_cache(typed=True)(meth))
+
+    @wraps(meth)
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        """Wrapper that converts arguments to hashable form before caching.
+
+        Args:
+            *args: Positional arguments to the memoized method.
+            **kwargs: Keyword arguments to the memoized method.
+
+        Returns:
+            The result of the memoized method call.
+        """
+        hashable_args = tuple(_make_hashable(arg) for arg in args)
+        hashable_kwargs = tuple(
+            sorted((k, _make_hashable(v)) for k, v in kwargs.items())
+        )
+        cache_key = str((hashable_args, hashable_kwargs))
+
+        cached_result: R | None = cache.read(tool=meth.__name__, input=cache_key)
+        if cached_result is not None:
+            return cached_result
+
+        result = meth(*args, **kwargs)
+        cache.add(tool=meth.__name__, input=cache_key, output=result)
+        return result
+
+    return cast(Callable[P, R], wrapper)

lib/crewai/src/crewai/rag/chromadb/utils.py

@@ -67,31 +67,44 @@ def _prepare_documents_for_chromadb(
     ids: list[str] = []
     texts: list[str] = []
     metadatas: list[Mapping[str, str | int | float | bool]] = []
+    seen_ids: dict[str, int] = {}
+
+    try:
+        for doc in documents:
+            if "doc_id" in doc:
+                doc_id = str(doc["doc_id"])
+            else:
+                metadata = doc.get("metadata")
+                if metadata and isinstance(metadata, dict) and "doc_id" in metadata:
+                    doc_id = str(metadata["doc_id"])
+                else:
+                    content_for_hash = doc["content"]
+                    if metadata:
+                        metadata_str = json.dumps(metadata, sort_keys=True)
+                        content_for_hash = f"{content_for_hash}|{metadata_str}"
+                    doc_id = hashlib.sha256(content_for_hash.encode()).hexdigest()
 
-    for doc in documents:
-        if "doc_id" in doc:
-            ids.append(doc["doc_id"])
-        else:
-            content_for_hash = doc["content"]
             metadata = doc.get("metadata")
             if metadata:
-                metadata_str = json.dumps(metadata, sort_keys=True)
-                content_for_hash = f"{content_for_hash}|{metadata_str}"
-
-            content_hash = hashlib.blake2b(
-                content_for_hash.encode(), digest_size=32
-            ).hexdigest()
-            ids.append(content_hash)
-
-        texts.append(doc["content"])
-        metadata = doc.get("metadata")
-        if metadata:
-            if isinstance(metadata, list):
-                metadatas.append(metadata[0] if metadata and metadata[0] else {})
+                if isinstance(metadata, list):
+                    processed_metadata = metadata[0] if metadata and metadata[0] else {}
+                else:
+                    processed_metadata = metadata
             else:
-                metadatas.append(metadata)
-        else:
-            metadatas.append({})
+                processed_metadata = {}
+
+            if doc_id in seen_ids:
+                idx = seen_ids[doc_id]
+                texts[idx] = doc["content"]
+                metadatas[idx] = processed_metadata
+            else:
+                idx = len(ids)
+                ids.append(doc_id)
+                texts.append(doc["content"])
+                metadatas.append(processed_metadata)
+                seen_ids[doc_id] = idx
+    except Exception as e:
+        raise ValueError(f"Error preparing documents for ChromaDB: {e}") from e
 
     return PreparedDocuments(ids, texts, metadatas)
 

lib/crewai/src/crewai/task.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from collections.abc import Callable
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 import datetime
@@ -29,6 +28,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.events.event_bus import crewai_event_bus
@@ -52,7 +52,7 @@ from crewai.utilities.guardrail_types import (
     GuardrailType,
     GuardrailsType,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
 from crewai.utilities.string_utils import interpolate_only
 
@@ -90,7 +90,7 @@ class Task(BaseModel):
     used_tools: int = 0
     tools_errors: int = 0
     delegations: int = 0
-    i18n: I18N = Field(default_factory=I18N)
+    i18n: I18N = Field(default_factory=get_i18n)
     name: str | None = Field(default=None)
     prompt_context: str | None = None
     description: str = Field(description="Description of the actual task.")
@@ -207,8 +207,8 @@ class Task(BaseModel):
     @field_validator("guardrail")
     @classmethod
     def validate_guardrail_function(
-        cls, v: str | Callable | None
-    ) -> str | Callable | None:
+        cls, v: str | GuardrailCallable | None
+    ) -> str | GuardrailCallable | None:
         """
         If v is a callable, validate that the guardrail function has the correct signature and behavior.
         If v is a string, return it as is.
@@ -265,11 +265,11 @@ class Task(BaseModel):
 
     @model_validator(mode="before")
     @classmethod
-    def process_model_config(cls, values):
+    def process_model_config(cls, values: dict[str, Any]) -> dict[str, Any]:
         return process_config(values, cls)
 
     @model_validator(mode="after")
-    def validate_required_fields(self):
+    def validate_required_fields(self) -> Self:
         required_fields = ["description", "expected_output"]
         for field in required_fields:
             if getattr(self, field) is None:
@@ -418,14 +418,14 @@ class Task(BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_tools(self):
+    def check_tools(self) -> Self:
         """Check if the tools are set."""
         if not self.tools and self.agent and self.agent.tools:
-            self.tools.extend(self.agent.tools)
+            self.tools = self.agent.tools
         return self
 
     @model_validator(mode="after")
-    def check_output(self):
+    def check_output(self) -> Self:
         """Check if an output type is set."""
         output_types = [self.output_json, self.output_pydantic]
         if len([type for type in output_types if type]) > 1:
@@ -437,7 +437,7 @@ class Task(BaseModel):
         return self
 
     @model_validator(mode="after")
-    def handle_max_retries_deprecation(self):
+    def handle_max_retries_deprecation(self) -> Self:
         if self.max_retries is not None:
             warnings.warn(
                 "The 'max_retries' parameter is deprecated and will be removed in CrewAI v1.0.0. "
@@ -518,14 +518,18 @@ class Task(BaseModel):
             tools = tools or self.tools or []
 
             self.processed_by_agents.add(agent.role)
-            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))  # type: ignore[no-untyped-call]
             result = agent.execute_task(
                 task=self,
                 context=context,
                 tools=tools,
             )
 
-            pydantic_output, json_output = self._export_output(result)
+            if not self._guardrails and not self._guardrail:
+                pydantic_output, json_output = self._export_output(result)
+            else:
+                pydantic_output, json_output = None, None
+
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
@@ -535,6 +539,7 @@ class Task(BaseModel):
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
+                messages=agent.last_messages,
             )
 
             if self._guardrails:
@@ -576,12 +581,13 @@ class Task(BaseModel):
                 )
                 self._save_file(content)
             crewai_event_bus.emit(
-                self, TaskCompletedEvent(output=task_output, task=self)
+                self,
+                TaskCompletedEvent(output=task_output, task=self),  # type: ignore[no-untyped-call]
             )
             return task_output
         except Exception as e:
             self.end_time = datetime.datetime.now()
-            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))  # type: ignore[no-untyped-call]
             raise e  # Re-raise the exception after emitting the event
 
     def prompt(self) -> str:
@@ -786,7 +792,7 @@ Follow these guidelines:
             return OutputFormat.PYDANTIC
         return OutputFormat.RAW
 
-    def _save_file(self, result: dict | str | Any) -> None:
+    def _save_file(self, result: dict[str, Any] | str | Any) -> None:
         """Save task output to a file.
 
         Note:
@@ -838,7 +844,7 @@ Follow these guidelines:
             ) from e
         return
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return f"Task(description={self.description}, expected_output={self.expected_output})"
 
     @property
@@ -944,6 +950,7 @@ Follow these guidelines:
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
+                messages=agent.last_messages,
             )
 
         return task_output

lib/crewai/src/crewai/tasks/task_output.py

@@ -6,6 +6,7 @@ from typing import Any
 from pydantic import BaseModel, Field, model_validator
 
 from crewai.tasks.output_format import OutputFormat
+from crewai.utilities.types import LLMMessage
 
 
 class TaskOutput(BaseModel):
@@ -40,6 +41,7 @@ class TaskOutput(BaseModel):
     output_format: OutputFormat = Field(
         description="Output format of the task", default=OutputFormat.RAW
     )
+    messages: list[LLMMessage] = Field(description="Messages of the task", default=[])
 
     @model_validator(mode="after")
     def set_summary(self):

lib/crewai/src/crewai/tools/agent_tools/agent_tools.py

@@ -1,8 +1,16 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
 from crewai.tools.agent_tools.ask_question_tool import AskQuestionTool
 from crewai.tools.agent_tools.delegate_work_tool import DelegateWorkTool
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
+
+
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.tools.base_tool import BaseTool
+    from crewai.utilities.i18n import I18N
 
 
 class AgentTools:
@@ -10,7 +18,7 @@ class AgentTools:
 
     def __init__(self, agents: list[BaseAgent], i18n: I18N | None = None) -> None:
         self.agents = agents
-        self.i18n = i18n if i18n is not None else I18N()
+        self.i18n = i18n if i18n is not None else get_i18n()
 
     def tools(self) -> list[BaseTool]:
         """Get all available agent tools"""

lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,12 +1,12 @@
 import logging
+from typing import Any
 
 from pydantic import Field
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import I18N
-
+from crewai.utilities.i18n import I18N, get_i18n
 
 
 logger = logging.getLogger(__name__)
@@ -17,7 +17,7 @@ class BaseAgentTool(BaseTool):
 
     agents: list[BaseAgent] = Field(description="List of available agents")
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings"
+        default_factory=get_i18n, description="Internationalization settings"
     )
 
     def sanitize_agent_name(self, name: str) -> str:
@@ -40,7 +40,7 @@ class BaseAgentTool(BaseTool):
         return normalized.replace('"', "").casefold()
 
     @staticmethod
-    def _get_coworker(coworker: str | None, **kwargs) -> str | None:
+    def _get_coworker(coworker: str | None, **kwargs: Any) -> str | None:
         coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
         if coworker:
             is_list = coworker.startswith("[") and coworker.endswith("]")
@@ -83,7 +83,7 @@ class BaseAgentTool(BaseTool):
             available_agents = [agent.role for agent in self.agents]
             logger.debug(f"Available agents: {available_agents}")
 
-            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
+            agent = [
                 available_agent
                 for available_agent in self.agents
                 if self.sanitize_agent_name(available_agent.role) == sanitized_name

lib/crewai/src/crewai/tools/mcp_native_tool.py

@@ -0,0 +1,162 @@
+"""Native MCP tool wrapper for CrewAI agents.
+
+This module provides a tool wrapper that reuses existing MCP client sessions
+for better performance and connection management.
+"""
+
+import asyncio
+from typing import Any
+
+from crewai.tools import BaseTool
+
+
+class MCPNativeTool(BaseTool):
+    """Native MCP tool that reuses client sessions.
+
+    This tool wrapper is used when agents connect to MCP servers using
+    structured configurations. It reuses existing client sessions for
+    better performance and proper connection lifecycle management.
+
+    Unlike MCPToolWrapper which connects on-demand, this tool uses
+    a shared MCP client instance that maintains a persistent connection.
+    """
+
+    def __init__(
+        self,
+        mcp_client: Any,
+        tool_name: str,
+        tool_schema: dict[str, Any],
+        server_name: str,
+    ) -> None:
+        """Initialize native MCP tool.
+
+        Args:
+            mcp_client: MCPClient instance with active session.
+            tool_name: Original name of the tool on the MCP server.
+            tool_schema: Schema information for the tool.
+            server_name: Name of the MCP server for prefixing.
+        """
+        # Create tool name with server prefix to avoid conflicts
+        prefixed_name = f"{server_name}_{tool_name}"
+
+        # Handle args_schema properly - BaseTool expects a BaseModel subclass
+        args_schema = tool_schema.get("args_schema")
+
+        # Only pass args_schema if it's provided
+        kwargs = {
+            "name": prefixed_name,
+            "description": tool_schema.get(
+                "description", f"Tool {tool_name} from {server_name}"
+            ),
+        }
+
+        if args_schema is not None:
+            kwargs["args_schema"] = args_schema
+
+        super().__init__(**kwargs)
+
+        # Set instance attributes after super().__init__
+        self._mcp_client = mcp_client
+        self._original_tool_name = tool_name
+        self._server_name = server_name
+        # self._logger = logging.getLogger(__name__)
+
+    @property
+    def mcp_client(self) -> Any:
+        """Get the MCP client instance."""
+        return self._mcp_client
+
+    @property
+    def original_tool_name(self) -> str:
+        """Get the original tool name."""
+        return self._original_tool_name
+
+    @property
+    def server_name(self) -> str:
+        """Get the server name."""
+        return self._server_name
+
+    def _run(self, **kwargs) -> str:
+        """Execute tool using the MCP client session.
+
+        Args:
+            **kwargs: Arguments to pass to the MCP tool.
+
+        Returns:
+            Result from the MCP tool execution.
+        """
+        try:
+            try:
+                asyncio.get_running_loop()
+
+                import concurrent.futures
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    coro = self._run_async(**kwargs)
+                    future = executor.submit(asyncio.run, coro)
+                    return future.result()
+            except RuntimeError:
+                return asyncio.run(self._run_async(**kwargs))
+
+        except Exception as e:
+            raise RuntimeError(
+                f"Error executing MCP tool {self.original_tool_name}: {e!s}"
+            ) from e
+
+    async def _run_async(self, **kwargs) -> str:
+        """Async implementation of tool execution.
+
+        Args:
+            **kwargs: Arguments to pass to the MCP tool.
+
+        Returns:
+            Result from the MCP tool execution.
+        """
+        # Note: Since we use asyncio.run() which creates a new event loop each time,
+        # Always reconnect on-demand because asyncio.run() creates new event loops per call
+        # All MCP transport context managers (stdio, streamablehttp_client, sse_client)
+        # use anyio.create_task_group() which can't span different event loops
+        if self._mcp_client.connected:
+            await self._mcp_client.disconnect()
+
+        await self._mcp_client.connect()
+
+        try:
+            result = await self._mcp_client.call_tool(self.original_tool_name, kwargs)
+
+        except Exception as e:
+            error_str = str(e).lower()
+            if (
+                "not connected" in error_str
+                or "connection" in error_str
+                or "send" in error_str
+            ):
+                await self._mcp_client.disconnect()
+                await self._mcp_client.connect()
+                # Retry the call
+                result = await self._mcp_client.call_tool(
+                    self.original_tool_name, kwargs
+                )
+            else:
+                raise
+
+        finally:
+            # Always disconnect after tool call to ensure clean context manager lifecycle
+            # This prevents "exit cancel scope in different task" errors
+            # All transport context managers must be exited in the same event loop they were entered
+            await self._mcp_client.disconnect()
+
+        # Extract result content
+        if isinstance(result, str):
+            return result
+
+        # Handle various result formats
+        if hasattr(result, "content") and result.content:
+            if isinstance(result.content, list) and len(result.content) > 0:
+                content_item = result.content[0]
+                if hasattr(content_item, "text"):
+                    return str(content_item.text)
+                return str(content_item)
+            return str(result.content)
+
+        return str(result)

lib/crewai/src/crewai/tools/tool_usage.py

@@ -7,10 +7,10 @@ import json
 from json import JSONDecodeError
 from textwrap import dedent
 import time
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 import json5
-from json_repair import repair_json  # type: ignore[import-untyped,import-error]
+from json_repair import repair_json  # type: ignore[import-untyped]
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import (
@@ -28,7 +28,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
 )
 from crewai.utilities.converter import Converter
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
 
 
@@ -39,7 +39,18 @@ if TYPE_CHECKING:
     from crewai.llm import LLM
     from crewai.task import Task
 
-OPENAI_BIGGER_MODELS = [
+
+OPENAI_BIGGER_MODELS: list[
+    Literal[
+        "gpt-4",
+        "gpt-4o",
+        "o1-preview",
+        "o1-mini",
+        "o1",
+        "o3",
+        "o3-mini",
+    ]
+] = [
     "gpt-4",
     "gpt-4o",
     "o1-preview",
@@ -81,7 +92,7 @@ class ToolUsage:
         action: Any = None,
         fingerprint_context: dict[str, str] | None = None,
     ) -> None:
-        self._i18n: I18N = agent.i18n if agent else I18N()
+        self._i18n: I18N = agent.i18n if agent else get_i18n()
         self._printer: Printer = Printer()
         self._telemetry: Telemetry = Telemetry()
         self._run_attempts: int = 1
@@ -100,12 +111,14 @@ class ToolUsage:
         # Set the maximum parsing attempts for bigger models
         if (
             self.function_calling_llm
-            and self.function_calling_llm in OPENAI_BIGGER_MODELS
+            and self.function_calling_llm.model in OPENAI_BIGGER_MODELS
         ):
             self._max_parsing_attempts = 2
             self._remember_format_after_usages = 4
 
-    def parse_tool_calling(self, tool_string: str):
+    def parse_tool_calling(
+        self, tool_string: str
+    ) -> ToolCalling | InstructorToolCalling | ToolUsageError:
         """Parse the tool string and return the tool calling."""
         return self._tool_calling(tool_string)
 
@@ -153,7 +166,7 @@ class ToolUsage:
         tool: CrewStructuredTool,
         calling: ToolCalling | InstructorToolCalling,
     ) -> str:
-        if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
+        if self._check_tool_repeated_usage(calling=calling):
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
@@ -163,7 +176,7 @@ class ToolUsage:
                     tool_name=tool.name,
                     attempts=self._run_attempts,
                 )
-                return self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+                return self._format_result(result=result)
 
             except Exception:
                 if self.task:
@@ -241,7 +254,7 @@ class ToolUsage:
                     try:
                         acceptable_args = tool.args_schema.model_json_schema()[
                             "properties"
-                        ].keys()  # type: ignore
+                        ].keys()
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
@@ -276,19 +289,19 @@ class ToolUsage:
                         self._printer.print(
                             content=f"\n\n{error_message}\n", color="red"
                         )
-                    return error  # type: ignore # No return value expected
+                    return error
 
                 if self.task:
                     self.task.increment_tools_errors()
-                return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
+                return self.use(calling=calling, tool_string=tool_string)
 
             if self.tools_handler:
                 should_cache = True
                 if (
                     hasattr(available_tool, "cache_function")
-                    and available_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    and available_tool.cache_function
                 ):
-                    should_cache = available_tool.cache_function(  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    should_cache = available_tool.cache_function(
                         calling.arguments, result
                     )
 
@@ -300,7 +313,7 @@ class ToolUsage:
             tool_name=tool.name,
             attempts=self._run_attempts,
         )
-        result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+        result = self._format_result(result=result)
         data = {
             "result": result,
             "tool_name": tool.name,
@@ -508,7 +521,7 @@ class ToolUsage:
                     self.task.increment_tools_errors()
                 if self.agent and self.agent.verbose:
                     self._printer.print(content=f"\n\n{e}\n", color="red")
-                return ToolUsageError(  # type: ignore # Incompatible return value type (got "ToolUsageError", expected "ToolCalling | InstructorToolCalling")
+                return ToolUsageError(
                     f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                 )
             return self._tool_calling(tool_string)
@@ -567,7 +580,7 @@ class ToolUsage:
         # If all parsing attempts fail, raise an error
         raise Exception(error_message)
 
-    def _emit_validate_input_error(self, final_error: str):
+    def _emit_validate_input_error(self, final_error: str) -> None:
         tool_selection_data = {
             "agent_key": getattr(self.agent, "key", None) if self.agent else None,
             "agent_role": getattr(self.agent, "role", None) if self.agent else None,
@@ -636,7 +649,7 @@ class ToolUsage:
 
     def _prepare_event_data(
         self, tool: Any, tool_calling: ToolCalling | InstructorToolCalling
-    ) -> dict:
+    ) -> dict[str, Any]:
         event_data = {
             "run_attempts": self._run_attempts,
             "delegations": self.task.delegations if self.task else 0,
@@ -660,7 +673,7 @@ class ToolUsage:
 
         return event_data
 
-    def _add_fingerprint_metadata(self, arguments: dict) -> dict:
+    def _add_fingerprint_metadata(self, arguments: dict[str, Any]) -> dict[str, Any]:
         """Add fingerprint metadata to tool arguments if available.
 
         Args:

lib/crewai/src/crewai/translations/en.json

@@ -22,12 +22,12 @@
     "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
-    "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
+    "formatted_task_instructions": "Ensure your final answer strictly adheres to the following OpenAPI schema: {output_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
     "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
+    "lite_agent_response_format": "Ensure your final answer strictly adheres to the following OpenAPI schema: {response_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
     "knowledge_search_query": "The original query is: {task_prompt}.",
     "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
   },

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -33,6 +33,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.agents.crew_agent_executor import CrewAgentExecutor
     from crewai.lite_agent import LiteAgent
     from crewai.llm import LLM
     from crewai.task import Task
@@ -127,7 +128,7 @@ def handle_max_iterations_exceeded(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-) -> AgentAction | AgentFinish:
+) -> AgentFinish:
     """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.
 
     Args:
@@ -139,7 +140,7 @@ def handle_max_iterations_exceeded(
         callbacks: List of callbacks for the LLM call.
 
     Returns:
-        The final formatted answer after exceeding max iterations.
+        AgentFinish with the final answer after exceeding max iterations.
     """
     printer.print(
         content="Maximum iterations reached. Requesting final answer.",
@@ -157,7 +158,7 @@ def handle_max_iterations_exceeded(
 
     # Perform one more LLM call to get the final answer
     answer = llm.call(
-        messages,  # type: ignore[arg-type]
+        messages,
         callbacks=callbacks,
     )
 
@@ -168,8 +169,16 @@ def handle_max_iterations_exceeded(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    # Return the formatted answer, regardless of its type
-    return format_answer(answer=answer)
+    formatted = format_answer(answer=answer)
+
+    # If format_answer returned an AgentAction, convert it to AgentFinish
+    if isinstance(formatted, AgentFinish):
+        return formatted
+    return AgentFinish(
+        thought=formatted.thought,
+        output=formatted.text,
+        text=formatted.text,
+    )
 
 
 def format_message_for_llm(
@@ -228,6 +237,7 @@ def get_llm_response(
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
+    executor_context: CrewAgentExecutor | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
@@ -239,6 +249,7 @@ def get_llm_response(
         from_task: Optional task context for the LLM call
         from_agent: Optional agent context for the LLM call
         response_model: Optional Pydantic model for structured outputs
+        executor_context: Optional executor context for hook invocation
 
     Returns:
         The response from the LLM as a string
@@ -247,12 +258,17 @@ def get_llm_response(
         Exception: If an error occurs.
         ValueError: If the response is None or empty.
     """
+
+    if executor_context is not None:
+        _setup_before_llm_call_hooks(executor_context, printer)
+        messages = executor_context.messages
+
     try:
         answer = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
             callbacks=callbacks,
             from_task=from_task,
-            from_agent=from_agent,
+            from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,
         )
     except Exception as e:
@@ -264,7 +280,7 @@ def get_llm_response(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return answer
+    return _setup_after_llm_call_hooks(executor_context, answer, printer)
 
 
 def process_llm_response(
@@ -294,8 +310,8 @@ def handle_agent_action_core(
     formatted_answer: AgentAction,
     tool_result: ToolResult,
     messages: list[LLMMessage] | None = None,
-    step_callback: Callable | None = None,
-    show_logs: Callable | None = None,
+    step_callback: Callable | None = None,  # type: ignore[type-arg]
+    show_logs: Callable | None = None,  # type: ignore[type-arg]
 ) -> AgentAction | AgentFinish:
     """Core logic for handling agent actions and tool results.
 
@@ -481,7 +497,7 @@ def summarize_messages(
             ),
         ]
         summary = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
             callbacks=callbacks,
         )
         summarized_contents.append({"content": str(summary)})
@@ -653,3 +669,92 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
             else:
                 attributes[key] = value
     return attributes
+
+
+def _setup_before_llm_call_hooks(
+    executor_context: CrewAgentExecutor | None, printer: Printer
+) -> None:
+    """Setup and invoke before_llm_call hooks for the executor context.
+
+    Args:
+        executor_context: The executor context to setup the hooks for.
+        printer: Printer instance for error logging.
+    """
+    if executor_context and executor_context.before_llm_call_hooks:
+        from crewai.utilities.llm_call_hooks import LLMCallHookContext
+
+        original_messages = executor_context.messages
+
+        hook_context = LLMCallHookContext(executor_context)
+        try:
+            for hook in executor_context.before_llm_call_hooks:
+                hook(hook_context)
+        except Exception as e:
+            printer.print(
+                content=f"Error in before_llm_call hook: {e}",
+                color="yellow",
+            )
+
+        if not isinstance(executor_context.messages, list):
+            printer.print(
+                content=(
+                    "Warning: before_llm_call hook replaced messages with non-list. "
+                    "Restoring original messages list. Hooks should modify messages in-place, "
+                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
+                ),
+                color="yellow",
+            )
+            if isinstance(original_messages, list):
+                executor_context.messages = original_messages
+            else:
+                executor_context.messages = []
+
+
+def _setup_after_llm_call_hooks(
+    executor_context: CrewAgentExecutor | None,
+    answer: str,
+    printer: Printer,
+) -> str:
+    """Setup and invoke after_llm_call hooks for the executor context.
+
+    Args:
+        executor_context: The executor context to setup the hooks for.
+        answer: The LLM response string.
+        printer: Printer instance for error logging.
+
+    Returns:
+        The potentially modified response string.
+    """
+    if executor_context and executor_context.after_llm_call_hooks:
+        from crewai.utilities.llm_call_hooks import LLMCallHookContext
+
+        original_messages = executor_context.messages
+
+        hook_context = LLMCallHookContext(executor_context, response=answer)
+        try:
+            for hook in executor_context.after_llm_call_hooks:
+                modified_response = hook(hook_context)
+                if modified_response is not None and isinstance(modified_response, str):
+                    answer = modified_response
+
+        except Exception as e:
+            printer.print(
+                content=f"Error in after_llm_call hook: {e}",
+                color="yellow",
+            )
+
+        if not isinstance(executor_context.messages, list):
+            printer.print(
+                content=(
+                    "Warning: after_llm_call hook replaced messages with non-list. "
+                    "Restoring original messages list. Hooks should modify messages in-place, "
+                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
+                ),
+                color="yellow",
+            )
+            if isinstance(original_messages, list):
+                executor_context.messages = original_messages
+            else:
+                executor_context.messages = []
+
+    return answer

lib/crewai/src/crewai/utilities/converter.py

@@ -10,9 +10,9 @@ from pydantic import BaseModel, ValidationError
 from typing_extensions import Unpack
 
 from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
+from crewai.utilities.i18n import get_i18n
 from crewai.utilities.internal_instructor import InternalInstructor
 from crewai.utilities.printer import Printer
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 
 if TYPE_CHECKING:
@@ -22,6 +22,7 @@ if TYPE_CHECKING:
     from crewai.llms.base_llm import BaseLLM
 
 _JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
+_I18N = get_i18n()
 
 
 class ConverterError(Exception):
@@ -87,8 +88,7 @@ class Converter(OutputConverter):
                             result = self.model.model_validate(result)
                         elif isinstance(result, str):
                             try:
-                                parsed = json.loads(result)
-                                result = self.model.model_validate(parsed)
+                                result = self.model.model_validate_json(result)
                             except Exception as parse_err:
                                 raise ConverterError(
                                     f"Failed to convert partial JSON result into Pydantic: {parse_err}"
@@ -172,6 +172,16 @@ def convert_to_model(
     model = output_pydantic or output_json
     if model is None:
         return result
+
+    if converter_cls:
+        return convert_with_instructions(
+            result=result,
+            model=model,
+            is_json_output=bool(output_json),
+            agent=agent,
+            converter_cls=converter_cls,
+        )
+
     try:
         escaped_result = json.dumps(json.loads(result, strict=False))
         return validate_model(
@@ -251,7 +261,7 @@ def handle_partial_json(
         except json.JSONDecodeError:
             pass
         except ValidationError:
-            pass
+            raise
         except Exception as e:
             Printer().print(
                 content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
@@ -335,25 +345,26 @@ def get_conversion_instructions(
     Returns:
 
     """
-    instructions = "Please convert the following text into valid JSON."
+    instructions = ""
     if (
         llm
         and not isinstance(llm, str)
         and hasattr(llm, "supports_function_calling")
         and llm.supports_function_calling()
     ):
-        model_schema = PydanticSchemaParser(model=model).get_schema()
-        instructions += (
-            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
-            f"Use this format exactly:\n```json\n{model_schema}\n```"
+        schema_dict = generate_model_description(model)
+        schema = json.dumps(schema_dict, indent=2)
+        formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
+            output_format=schema
         )
+        instructions += formatted_task_instructions
     else:
         model_description = generate_model_description(model)
-        schema_json = json.dumps(model_description["json_schema"]["schema"], indent=2)
-        instructions += (
-            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
-            f"Use this format exactly:\n```json\n{schema_json}\n```"
+        schema_json = json.dumps(model_description, indent=2)
+        formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
+            output_format=schema_json
         )
+        instructions += formatted_task_instructions
     return instructions
 
 

lib/crewai/src/crewai/utilities/i18n.py

@@ -1,5 +1,6 @@
 """Internationalization support for CrewAI prompts and messages."""
 
+from functools import lru_cache
 import json
 import os
 from typing import Literal
@@ -108,3 +109,19 @@ class I18N(BaseModel):
             return self._prompts[kind][key]
         except Exception as e:
             raise Exception(f"Prompt for '{kind}':'{key}'  not found.") from e
+
+
+@lru_cache(maxsize=None)
+def get_i18n(prompt_file: str | None = None) -> I18N:
+    """Get a cached I18N instance.
+
+    This function caches I18N instances to avoid redundant file I/O and JSON parsing.
+    Each unique prompt_file path gets its own cached instance.
+
+    Args:
+        prompt_file: Optional custom prompt file path. Defaults to None (uses built-in prompts).
+
+    Returns:
+        Cached I18N instance.
+    """
+    return I18N(prompt_file=prompt_file)

lib/crewai/src/crewai/utilities/llm_call_hooks.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from crewai.agents.crew_agent_executor import CrewAgentExecutor
+
+
+class LLMCallHookContext:
+    """Context object passed to LLM call hooks with full executor access.
+
+    Provides hooks with complete access to the executor state, allowing
+    modification of messages, responses, and executor attributes.
+
+    Attributes:
+        executor: Full reference to the CrewAgentExecutor instance
+        messages: Direct reference to executor.messages (mutable list).
+            Can be modified in both before_llm_call and after_llm_call hooks.
+            Modifications in after_llm_call hooks persist to the next iteration,
+            allowing hooks to modify conversation history for subsequent LLM calls.
+            IMPORTANT: Modify messages in-place (e.g., append, extend, remove items).
+            Do NOT replace the list (e.g., context.messages = []), as this will break
+            the executor. Use context.messages.append() or context.messages.extend()
+            instead of assignment.
+        agent: Reference to the agent executing the task
+        task: Reference to the task being executed
+        crew: Reference to the crew instance
+        llm: Reference to the LLM instance
+        iterations: Current iteration count
+        response: LLM response string (only set for after_llm_call hooks).
+            Can be modified by returning a new string from after_llm_call hook.
+    """
+
+    def __init__(
+        self,
+        executor: CrewAgentExecutor,
+        response: str | None = None,
+    ) -> None:
+        """Initialize hook context with executor reference.
+
+        Args:
+            executor: The CrewAgentExecutor instance
+            response: Optional response string (for after_llm_call hooks)
+        """
+        self.executor = executor
+        self.messages = executor.messages
+        self.agent = executor.agent
+        self.task = executor.task
+        self.crew = executor.crew
+        self.llm = executor.llm
+        self.iterations = executor.iterations
+        self.response = response
+
+
+# Global hook registries (optional convenience feature)
+_before_llm_call_hooks: list[Callable[[LLMCallHookContext], None]] = []
+_after_llm_call_hooks: list[Callable[[LLMCallHookContext], str | None]] = []
+
+
+def register_before_llm_call_hook(
+    hook: Callable[[LLMCallHookContext], None],
+) -> None:
+    """Register a global before_llm_call hook.
+
+    Global hooks are added to all executors automatically.
+    This is a convenience function for registering hooks that should
+    apply to all LLM calls across all executors.
+
+    Args:
+        hook: Function that receives LLMCallHookContext and can modify
+            context.messages directly. Should return None.
+            IMPORTANT: Modify messages in-place (append, extend, remove items).
+            Do NOT replace the list (context.messages = []), as this will break execution.
+    """
+    _before_llm_call_hooks.append(hook)
+
+
+def register_after_llm_call_hook(
+    hook: Callable[[LLMCallHookContext], str | None],
+) -> None:
+    """Register a global after_llm_call hook.
+
+    Global hooks are added to all executors automatically.
+    This is a convenience function for registering hooks that should
+    apply to all LLM calls across all executors.
+
+    Args:
+        hook: Function that receives LLMCallHookContext and can modify:
+            - The response: Return modified response string or None to keep original
+            - The messages: Modify context.messages directly (mutable reference)
+            Both modifications are supported and can be used together.
+            IMPORTANT: Modify messages in-place (append, extend, remove items).
+            Do NOT replace the list (context.messages = []), as this will break execution.
+    """
+    _after_llm_call_hooks.append(hook)
+
+
+def get_before_llm_call_hooks() -> list[Callable[[LLMCallHookContext], None]]:
+    """Get all registered global before_llm_call hooks.
+
+    Returns:
+        List of registered before hooks
+    """
+    return _before_llm_call_hooks.copy()
+
+
+def get_after_llm_call_hooks() -> list[Callable[[LLMCallHookContext], str | None]]:
+    """Get all registered global after_llm_call hooks.
+
+    Returns:
+        List of registered after hooks
+    """
+    return _after_llm_call_hooks.copy()

lib/crewai/src/crewai/utilities/prompts.py

@@ -1,29 +1,38 @@
+"""Prompt generation and management utilities for CrewAI agents."""
+
 from __future__ import annotations
 
-from typing import Any, TypedDict
+from typing import Annotated, Any, Literal, TypedDict
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 
 
 class StandardPromptResult(TypedDict):
     """Result with only prompt field for standard mode."""
 
-    prompt: str
+    prompt: Annotated[str, "The generated prompt string"]
 
 
 class SystemPromptResult(StandardPromptResult):
     """Result with system, user, and prompt fields for system prompt mode."""
 
-    system: str
-    user: str
+    system: Annotated[str, "The system prompt component"]
+    user: Annotated[str, "The user prompt component"]
+
+
+COMPONENTS = Literal["role_playing", "tools", "no_tools", "task"]
 
 
 class Prompts(BaseModel):
-    """Manages and generates prompts for a generic agent."""
+    """Manages and generates prompts for a generic agent.
 
-    i18n: I18N = Field(default_factory=I18N)
+    Notes:
+        - Need to refactor so that prompt is not tightly coupled to agent.
+    """
+
+    i18n: I18N = Field(default_factory=get_i18n)
     has_tools: bool = Field(
         default=False, description="Indicates if the agent has access to tools"
     )
@@ -36,7 +45,7 @@ class Prompts(BaseModel):
     response_template: str | None = Field(
         default=None, description="Custom response prompt template"
     )
-    use_system_prompt: bool | None = Field(
+    use_system_prompt: bool = Field(
         default=False,
         description="Whether to use the system prompt when no custom templates are provided",
     )
@@ -48,7 +57,7 @@ class Prompts(BaseModel):
         Returns:
             A dictionary containing the constructed prompt(s).
         """
-        slices: list[str] = ["role_playing"]
+        slices: list[COMPONENTS] = ["role_playing"]
         if self.has_tools:
             slices.append("tools")
         else:
@@ -77,7 +86,7 @@ class Prompts(BaseModel):
 
     def _build_prompt(
         self,
-        components: list[str],
+        components: list[COMPONENTS],
         system_template: str | None = None,
         prompt_template: str | None = None,
         response_template: str | None = None,

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -13,7 +13,6 @@ from crewai.events.types.reasoning_events import (
 )
 from crewai.llm import LLM
 from crewai.task import Task
-from crewai.utilities.i18n import I18N
 
 
 class ReasoningPlan(BaseModel):
@@ -62,7 +61,6 @@ class AgentReasoning:
         agent: The agent performing the reasoning.
         llm: The language model used for reasoning.
         logger: Logger for logging events and errors.
-        i18n: Internationalization utility for retrieving prompts.
     """
 
     def __init__(self, task: Task, agent: Agent) -> None:
@@ -76,7 +74,6 @@ class AgentReasoning:
         self.agent = agent
         self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
-        self.i18n = I18N()
 
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
         """Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
@@ -163,8 +160,7 @@ class AgentReasoning:
             llm=self.llm,
             prompt=reasoning_prompt,
             task=self.task,
-            agent=self.agent,
-            i18n=self.i18n,
+            reasoning_agent=self.agent,
             backstory=self.__get_agent_backstory(),
             plan_type="initial_plan",
         )
@@ -208,8 +204,7 @@ class AgentReasoning:
                     llm=self.llm,
                     prompt=refine_prompt,
                     task=self.task,
-                    agent=self.agent,
-                    i18n=self.i18n,
+                    reasoning_agent=self.agent,
                     backstory=self.__get_agent_backstory(),
                     plan_type="refine_plan",
                 )
@@ -238,14 +233,14 @@ class AgentReasoning:
         self.logger.debug(f"Using function calling for {prompt_type} reasoning")
 
         try:
-            system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+            system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
                 role=self.agent.role,
                 goal=self.agent.goal,
                 backstory=self.__get_agent_backstory(),
             )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(plan: str, ready: bool = True):
+            def _create_reasoning_plan(plan: str, ready: bool = True) -> str:
                 """Return the reasoning plan result in JSON string form."""
                 return json.dumps({"plan": plan, "ready": ready})
 
@@ -281,7 +276,9 @@ class AgentReasoning:
             )
 
             try:
-                system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+                system_prompt = self.agent.i18n.retrieve(
+                    "reasoning", prompt_type
+                ).format(
                     role=self.agent.role,
                     goal=self.agent.goal,
                     backstory=self.__get_agent_backstory(),
@@ -326,7 +323,7 @@ class AgentReasoning:
         """
         available_tools = self.__format_available_tools()
 
-        return self.i18n.retrieve("reasoning", "create_plan_prompt").format(
+        return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
@@ -357,7 +354,7 @@ class AgentReasoning:
         Returns:
             str: The refine prompt.
         """
-        return self.i18n.retrieve("reasoning", "refine_plan_prompt").format(
+        return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
@@ -405,8 +402,7 @@ def _call_llm_with_reasoning_prompt(
     llm: LLM,
     prompt: str,
     task: Task,
-    agent: Agent,
-    i18n: I18N,
+    reasoning_agent: Agent,
     backstory: str,
     plan_type: Literal["initial_plan", "refine_plan"],
 ) -> str:
@@ -416,17 +412,16 @@ def _call_llm_with_reasoning_prompt(
         llm: The language model to use.
         prompt: The prompt to send to the LLM.
         task: The task for which the agent is reasoning.
-        agent: The agent performing the reasoning.
-        i18n: Internationalization utility for retrieving prompts.
+        reasoning_agent: The agent performing the reasoning.
         backstory: The agent's backstory.
         plan_type: The type of plan being created ("initial_plan" or "refine_plan").
 
     Returns:
         The LLM response.
     """
-    system_prompt = i18n.retrieve("reasoning", plan_type).format(
-        role=agent.role,
-        goal=agent.goal,
+    system_prompt = reasoning_agent.i18n.retrieve("reasoning", plan_type).format(
+        role=reasoning_agent.role,
+        goal=reasoning_agent.goal,
         backstory=backstory,
     )
 
@@ -436,6 +431,6 @@ def _call_llm_with_reasoning_prompt(
             {"role": "user", "content": prompt},
         ],
         from_task=task,
-        from_agent=agent,
+        from_agent=reasoning_agent,
     )
     return str(response)

lib/crewai/src/crewai/utilities/types.py

@@ -1,6 +1,8 @@
 """Types for CrewAI utilities."""
 
-from typing import Any, Literal, TypedDict
+from typing import Any, Literal
+
+from typing_extensions import TypedDict
 
 
 class LLMMessage(TypedDict):

lib/crewai/tests/a2a/test_resolve_agent_identifier.py

@@ -0,0 +1,245 @@
+"""Test resolve_agent_identifier function for A2A skill ID resolution."""
+
+import pytest
+from a2a.types import AgentCapabilities, AgentCard, AgentSkill
+
+from crewai.a2a.config import A2AConfig
+from crewai.a2a.utils import resolve_agent_identifier
+
+
+@pytest.fixture
+def sample_agent_configs():
+    """Create sample A2A agent configurations."""
+    return [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+        A2AConfig(endpoint="http://localhost:10002/.well-known/agent-card.json"),
+    ]
+
+
+@pytest.fixture
+def sample_agent_cards():
+    """Create sample AgentCards with skills."""
+    card1 = AgentCard(
+        name="Research Agent",
+        description="An expert research agent",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct comprehensive research",
+                tags=["research", "analysis"],
+                examples=["Research quantum computing"],
+            )
+        ],
+    )
+
+    card2 = AgentCard(
+        name="Writing Agent",
+        description="An expert writing agent",
+        url="http://localhost:10002",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Writing",
+                name="Writing",
+                description="Write high-quality content",
+                tags=["writing", "content"],
+                examples=["Write a blog post"],
+            )
+        ],
+    )
+
+    return {
+        "http://localhost:10001/.well-known/agent-card.json": card1,
+        "http://localhost:10002/.well-known/agent-card.json": card2,
+    }
+
+
+def test_resolve_endpoint_passthrough(sample_agent_configs, sample_agent_cards):
+    """Test that endpoint URLs are returned as-is."""
+    endpoint = "http://localhost:10001/.well-known/agent-card.json"
+    result = resolve_agent_identifier(endpoint, sample_agent_configs, sample_agent_cards)
+    assert result == endpoint
+
+
+def test_resolve_unique_skill_id(sample_agent_configs, sample_agent_cards):
+    """Test that a unique skill ID resolves to the correct endpoint."""
+    result = resolve_agent_identifier("Research", sample_agent_configs, sample_agent_cards)
+    assert result == "http://localhost:10001/.well-known/agent-card.json"
+
+    result = resolve_agent_identifier("Writing", sample_agent_configs, sample_agent_cards)
+    assert result == "http://localhost:10002/.well-known/agent-card.json"
+
+
+def test_resolve_unknown_identifier(sample_agent_configs, sample_agent_cards):
+    """Test that unknown identifiers raise a descriptive error."""
+    with pytest.raises(ValueError) as exc_info:
+        resolve_agent_identifier("UnknownSkill", sample_agent_configs, sample_agent_cards)
+
+    error_msg = str(exc_info.value)
+    assert "Unknown A2A agent identifier 'UnknownSkill'" in error_msg
+    assert "Available endpoints:" in error_msg
+    assert "Available skill IDs:" in error_msg
+    assert "Research" in error_msg
+    assert "Writing" in error_msg
+
+
+def test_resolve_ambiguous_skill_id():
+    """Test that ambiguous skill IDs raise a descriptive error."""
+    configs = [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+        A2AConfig(endpoint="http://localhost:10002/.well-known/agent-card.json"),
+    ]
+
+    card1 = AgentCard(
+        name="Research Agent 1",
+        description="First research agent",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct research",
+                tags=["research"],
+                examples=["Research topic"],
+            )
+        ],
+    )
+
+    card2 = AgentCard(
+        name="Research Agent 2",
+        description="Second research agent",
+        url="http://localhost:10002",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct research",
+                tags=["research"],
+                examples=["Research topic"],
+            )
+        ],
+    )
+
+    cards = {
+        "http://localhost:10001/.well-known/agent-card.json": card1,
+        "http://localhost:10002/.well-known/agent-card.json": card2,
+    }
+
+    with pytest.raises(ValueError) as exc_info:
+        resolve_agent_identifier("Research", configs, cards)
+
+    error_msg = str(exc_info.value)
+    assert "Ambiguous skill ID 'Research'" in error_msg
+    assert "found in multiple agents" in error_msg
+    assert "http://localhost:10001/.well-known/agent-card.json" in error_msg
+    assert "http://localhost:10002/.well-known/agent-card.json" in error_msg
+    assert "Please use the specific endpoint URL to disambiguate" in error_msg
+
+
+def test_resolve_with_no_skills():
+    """Test resolution when agent cards have no skills."""
+    configs = [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+    ]
+
+    card = AgentCard(
+        name="Agent Without Skills",
+        description="An agent without skills",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[],
+    )
+
+    cards = {
+        "http://localhost:10001/.well-known/agent-card.json": card,
+    }
+
+    result = resolve_agent_identifier(
+        "http://localhost:10001/.well-known/agent-card.json", configs, cards
+    )
+    assert result == "http://localhost:10001/.well-known/agent-card.json"
+
+    with pytest.raises(ValueError) as exc_info:
+        resolve_agent_identifier("SomeSkill", configs, cards)
+
+    error_msg = str(exc_info.value)
+    assert "Unknown A2A agent identifier 'SomeSkill'" in error_msg
+    assert "Available skill IDs: none" in error_msg
+
+
+def test_resolve_with_multiple_skills_same_card(sample_agent_configs):
+    """Test resolution when a card has multiple skills."""
+    card = AgentCard(
+        name="Multi-Skill Agent",
+        description="An agent with multiple skills",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct research",
+                tags=["research"],
+                examples=["Research topic"],
+            ),
+            AgentSkill(
+                id="Analysis",
+                name="Analysis",
+                description="Analyze data",
+                tags=["analysis"],
+                examples=["Analyze data"],
+            ),
+        ],
+    )
+
+    cards = {
+        "http://localhost:10001/.well-known/agent-card.json": card,
+    }
+
+    result1 = resolve_agent_identifier("Research", sample_agent_configs[:1], cards)
+    assert result1 == "http://localhost:10001/.well-known/agent-card.json"
+
+    result2 = resolve_agent_identifier("Analysis", sample_agent_configs[:1], cards)
+    assert result2 == "http://localhost:10001/.well-known/agent-card.json"
+
+
+def test_resolve_empty_agent_cards():
+    """Test resolution with empty agent cards dictionary."""
+    configs = [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+    ]
+    cards = {}
+
+    result = resolve_agent_identifier(
+        "http://localhost:10001/.well-known/agent-card.json", configs, cards
+    )
+    assert result == "http://localhost:10001/.well-known/agent-card.json"
+
+    with pytest.raises(ValueError) as exc_info:
+        resolve_agent_identifier("SomeSkill", configs, cards)
+
+    error_msg = str(exc_info.value)
+    assert "Unknown A2A agent identifier 'SomeSkill'" in error_msg

lib/crewai/tests/a2a/test_skill_id_integration.py

@@ -0,0 +1,287 @@
+"""Integration test for A2A skill ID resolution (issue #3897)."""
+
+import pytest
+from a2a.types import AgentCapabilities, AgentCard, AgentSkill
+from pydantic import BaseModel
+
+from crewai.a2a.config import A2AConfig
+from crewai.a2a.utils import (
+    create_agent_response_model,
+    extract_agent_identifiers_from_cards,
+    resolve_agent_identifier,
+)
+
+
+def test_skill_id_resolution_integration():
+    """Test the complete flow of skill ID resolution as described in issue #3897.
+    
+    This test replicates the exact scenario from the bug report:
+    1. User creates A2A config with endpoint URL
+    2. Remote agent has AgentCard with skill.id="Research"
+    3. LLM returns a2a_ids=["Research"] instead of the endpoint URL
+    4. System should resolve "Research" to the endpoint and proceed successfully
+    """
+    a2a_config = A2AConfig(
+        endpoint="http://localhost:10001/.well-known/agent-card.json"
+    )
+    a2a_agents = [a2a_config]
+    
+    agent_card = AgentCard(
+        name="Research Agent",
+        description="An expert research agent that can conduct thorough research",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct comprehensive research on any topic",
+                tags=["research", "analysis", "information-gathering"],
+                examples=[
+                    "Research the latest developments in quantum computing",
+                    "What are the current trends in renewable energy?",
+                ],
+            )
+        ],
+    )
+    
+    agent_cards = {
+        "http://localhost:10001/.well-known/agent-card.json": agent_card
+    }
+    
+    identifiers = extract_agent_identifiers_from_cards(a2a_agents, agent_cards)
+    
+    assert "http://localhost:10001/.well-known/agent-card.json" in identifiers
+    assert "Research" in identifiers
+    
+    agent_response_model = create_agent_response_model(identifiers)
+    
+    agent_response_data = {
+        "a2a_ids": ["Research"],  # LLM uses skill ID instead of endpoint
+        "message": "Please research quantum computing developments",
+        "is_a2a": True,
+    }
+    
+    agent_response = agent_response_model.model_validate(agent_response_data)
+    assert agent_response.a2a_ids == ("Research",)
+    assert agent_response.message == "Please research quantum computing developments"
+    assert agent_response.is_a2a is True
+    
+    resolved_endpoint = resolve_agent_identifier(
+        "Research", a2a_agents, agent_cards
+    )
+    assert resolved_endpoint == "http://localhost:10001/.well-known/agent-card.json"
+    
+    resolved_endpoint_direct = resolve_agent_identifier(
+        "http://localhost:10001/.well-known/agent-card.json",
+        a2a_agents,
+        agent_cards,
+    )
+    assert resolved_endpoint_direct == "http://localhost:10001/.well-known/agent-card.json"
+
+
+def test_skill_id_validation_error_before_fix():
+    """Test that demonstrates the original bug (for documentation purposes).
+    
+    Before the fix, creating an AgentResponse model with only endpoints
+    would cause a validation error when the LLM returned a skill ID.
+    """
+    endpoints_only = ("http://localhost:10001/.well-known/agent-card.json",)
+    agent_response_model_old = create_agent_response_model(endpoints_only)
+    
+    agent_response_data = {
+        "a2a_ids": ["Research"],
+        "message": "Please research quantum computing",
+        "is_a2a": True,
+    }
+    
+    with pytest.raises(Exception) as exc_info:
+        agent_response_model_old.model_validate(agent_response_data)
+    
+    error_msg = str(exc_info.value)
+    assert "validation error" in error_msg.lower() or "literal" in error_msg.lower()
+
+
+def test_multiple_agents_with_unique_skill_ids():
+    """Test that multiple agents with unique skill IDs work correctly."""
+    a2a_agents = [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+        A2AConfig(endpoint="http://localhost:10002/.well-known/agent-card.json"),
+    ]
+    
+    card1 = AgentCard(
+        name="Research Agent",
+        description="Research agent",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct research",
+                tags=["research"],
+            )
+        ],
+    )
+    
+    card2 = AgentCard(
+        name="Writing Agent",
+        description="Writing agent",
+        url="http://localhost:10002",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Writing",
+                name="Writing",
+                description="Write content",
+                tags=["writing"],
+            )
+        ],
+    )
+    
+    agent_cards = {
+        "http://localhost:10001/.well-known/agent-card.json": card1,
+        "http://localhost:10002/.well-known/agent-card.json": card2,
+    }
+    
+    identifiers = extract_agent_identifiers_from_cards(a2a_agents, agent_cards)
+    
+    assert len(identifiers) == 4
+    assert "http://localhost:10001/.well-known/agent-card.json" in identifiers
+    assert "http://localhost:10002/.well-known/agent-card.json" in identifiers
+    assert "Research" in identifiers
+    assert "Writing" in identifiers
+    
+    agent_response_model = create_agent_response_model(identifiers)
+    
+    response1 = agent_response_model.model_validate({
+        "a2a_ids": ["Research"],
+        "message": "Do research",
+        "is_a2a": True,
+    })
+    assert response1.a2a_ids == ("Research",)
+    
+    response2 = agent_response_model.model_validate({
+        "a2a_ids": ["Writing"],
+        "message": "Write content",
+        "is_a2a": True,
+    })
+    assert response2.a2a_ids == ("Writing",)
+    
+    endpoint1 = resolve_agent_identifier("Research", a2a_agents, agent_cards)
+    assert endpoint1 == "http://localhost:10001/.well-known/agent-card.json"
+    
+    endpoint2 = resolve_agent_identifier("Writing", a2a_agents, agent_cards)
+    assert endpoint2 == "http://localhost:10002/.well-known/agent-card.json"
+
+
+def test_multi_turn_skill_id_resolution():
+    """Test that skill IDs work in multi-turn A2A conversations.
+    
+    This test verifies the fix in _handle_agent_response_and_continue()
+    that rebuilds the AgentResponse model with both endpoints and skill IDs
+    for subsequent turns in multi-turn conversations.
+    
+    Scenario:
+    1. First turn: LLM returns skill ID "Research"
+    2. A2A agent responds
+    3. Second turn: LLM returns skill ID "Writing" (different agent)
+    4. Both turns should accept skill IDs without validation errors
+    """
+    a2a_agents = [
+        A2AConfig(endpoint="http://localhost:10001/.well-known/agent-card.json"),
+        A2AConfig(endpoint="http://localhost:10002/.well-known/agent-card.json"),
+    ]
+    
+    card1 = AgentCard(
+        name="Research Agent",
+        description="Research agent",
+        url="http://localhost:10001",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Research",
+                name="Research",
+                description="Conduct research",
+                tags=["research"],
+            )
+        ],
+    )
+    
+    card2 = AgentCard(
+        name="Writing Agent",
+        description="Writing agent",
+        url="http://localhost:10002",
+        version="1.0.0",
+        capabilities=AgentCapabilities(),
+        default_input_modes=["text/plain"],
+        default_output_modes=["text/plain"],
+        skills=[
+            AgentSkill(
+                id="Writing",
+                name="Writing",
+                description="Write content",
+                tags=["writing"],
+            )
+        ],
+    )
+    
+    agent_cards_turn1 = {
+        "http://localhost:10001/.well-known/agent-card.json": card1,
+    }
+    
+    identifiers_turn1 = extract_agent_identifiers_from_cards(a2a_agents, agent_cards_turn1)
+    model_turn1 = create_agent_response_model(identifiers_turn1)
+    
+    response_turn1 = model_turn1.model_validate({
+        "a2a_ids": ["Research"],
+        "message": "Please research quantum computing",
+        "is_a2a": True,
+    })
+    assert response_turn1.a2a_ids == ("Research",)
+    
+    endpoint_turn1 = resolve_agent_identifier("Research", a2a_agents, agent_cards_turn1)
+    assert endpoint_turn1 == "http://localhost:10001/.well-known/agent-card.json"
+    
+    agent_cards_turn2 = {
+        "http://localhost:10001/.well-known/agent-card.json": card1,
+        "http://localhost:10002/.well-known/agent-card.json": card2,
+    }
+    
+    identifiers_turn2 = extract_agent_identifiers_from_cards(a2a_agents, agent_cards_turn2)
+    model_turn2 = create_agent_response_model(identifiers_turn2)
+    
+    assert "Research" in identifiers_turn2
+    assert "Writing" in identifiers_turn2
+    
+    response_turn2 = model_turn2.model_validate({
+        "a2a_ids": ["Writing"],
+        "message": "Now write a report based on the research",
+        "is_a2a": True,
+    })
+    assert response_turn2.a2a_ids == ("Writing",)
+    
+    endpoint_turn2 = resolve_agent_identifier("Writing", a2a_agents, agent_cards_turn2)
+    assert endpoint_turn2 == "http://localhost:10002/.well-known/agent-card.json"
+    
+    response_turn3 = model_turn2.model_validate({
+        "a2a_ids": ["Research"],
+        "message": "Research more details",
+        "is_a2a": True,
+    })
+    assert response_turn3.a2a_ids == ("Research",)
+    
+    endpoint_turn3 = resolve_agent_identifier("Research", a2a_agents, agent_cards_turn2)
+    assert endpoint_turn3 == "http://localhost:10001/.well-known/agent-card.json"

lib/crewai/tests/agents/test_agent.py

@@ -508,7 +508,47 @@ def test_agent_custom_max_iterations():
     assert isinstance(result, str)
     assert len(result) > 0
     assert call_count > 0
-    assert call_count == 3
+    # With max_iter=1, expect 2 calls:
+    # - Call 1: iteration 0
+    # - Call 2: iteration 1 (max reached, handle_max_iterations_exceeded called, then loop breaks)
+    assert call_count == 2
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.timeout(30)
+def test_agent_max_iterations_stops_loop():
+    """Test that agent execution terminates when max_iter is reached."""
+
+    @tool
+    def get_data(step: str) -> str:
+        """Get data for a step. Always returns data requiring more steps."""
+        return f"Data for {step}: incomplete, need to query more steps."
+
+    agent = Agent(
+        role="data collector",
+        goal="collect data using the get_data tool",
+        backstory="You must use the get_data tool extensively",
+        max_iter=2,
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Use get_data tool for step1, step2, step3, step4, step5, step6, step7, step8, step9, and step10. Do NOT stop until you've called it for ALL steps.",
+        expected_output="A summary of all data collected",
+    )
+
+    result = agent.execute_task(
+        task=task,
+        tools=[get_data],
+    )
+
+    assert result is not None
+    assert isinstance(result, str)
+
+    assert agent.agent_executor.iterations <= agent.max_iter + 2, (
+        f"Agent ran {agent.agent_executor.iterations} iterations "
+        f"but should stop around {agent.max_iter + 1}. "
+    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -2117,15 +2157,14 @@ def test_agent_with_only_crewai_knowledge():
         goal="Provide information based on knowledge sources",
         backstory="You have access to specific knowledge sources.",
         llm=LLM(
-            model="openrouter/openai/gpt-4o-mini",
-            api_key=os.getenv("OPENROUTER_API_KEY"),
+            model="gpt-4o-mini",
         ),
     )
 
     # Create a task that requires the agent to use the knowledge
     task = Task(
         description="What is Vidit's favorite color?",
-        expected_output="Vidit's favorclearite color.",
+        expected_output="Vidit's favorite color.",
         agent=agent,
     )
 
@@ -2675,3 +2714,293 @@ def test_agent_without_apps_no_platform_tools():
 
     tools = crew._prepare_tools(agent, task, [])
     assert tools == []
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_before_llm_call_hook_modifies_messages():
+    """Test that before_llm_call hooks can modify messages."""
+    from crewai.utilities.llm_call_hooks import LLMCallHookContext, register_before_llm_call_hook
+
+    hook_called = False
+    original_message_count = 0
+
+    def before_hook(context: LLMCallHookContext) -> None:
+        nonlocal hook_called, original_message_count
+        hook_called = True
+        original_message_count = len(context.messages)
+        context.messages.append({
+            "role": "user",
+            "content": "Additional context: This is a test modification."
+        })
+
+    register_before_llm_call_hook(before_hook)
+
+    try:
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            allow_delegation=False,
+        )
+
+        task = Task(
+            description="Say hello",
+            expected_output="A greeting",
+            agent=agent,
+        )
+
+        result = agent.execute_task(task)
+
+        assert hook_called, "before_llm_call hook should have been called"
+        assert len(agent.agent_executor.messages) > original_message_count
+        assert result is not None
+    finally:
+        pass
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_after_llm_call_hook_modifies_messages_for_next_iteration():
+    """Test that after_llm_call hooks can modify messages for the next iteration."""
+    from crewai.utilities.llm_call_hooks import LLMCallHookContext, register_after_llm_call_hook
+
+    hook_call_count = 0
+    hook_iterations = []
+    messages_added_in_iteration_0 = False
+    test_message_content = "HOOK_ADDED_MESSAGE_FOR_NEXT_ITERATION"
+
+    def after_hook(context: LLMCallHookContext) -> str | None:
+        nonlocal hook_call_count, hook_iterations, messages_added_in_iteration_0
+        hook_call_count += 1
+        current_iteration = context.iterations
+        hook_iterations.append(current_iteration)
+
+        if current_iteration == 0:
+            messages_before = len(context.messages)
+            context.messages.append({
+                "role": "user",
+                "content": test_message_content
+            })
+            messages_added_in_iteration_0 = True
+            assert len(context.messages) == messages_before + 1
+
+        return None
+
+    register_after_llm_call_hook(after_hook)
+
+    try:
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            allow_delegation=False,
+            max_iter=3,
+        )
+
+        task = Task(
+            description="Count to 3, taking your time",
+            expected_output="A count",
+            agent=agent,
+        )
+
+        result = agent.execute_task(task)
+
+        assert hook_call_count > 0, "after_llm_call hook should have been called"
+        assert messages_added_in_iteration_0, "Message should have been added in iteration 0"
+
+        executor_messages = agent.agent_executor.messages
+        message_contents = [msg.get("content", "") for msg in executor_messages if isinstance(msg, dict)]
+        assert any(test_message_content in content for content in message_contents), (
+            f"Message added by hook in iteration 0 should be present in executor messages. "
+            f"Messages: {message_contents}"
+        )
+
+        assert len(executor_messages) > 2, "Executor should have more than initial messages"
+        assert result is not None
+    finally:
+        pass
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_after_llm_call_hook_modifies_messages():
+    """Test that after_llm_call hooks can modify messages for next iteration."""
+    from crewai.utilities.llm_call_hooks import LLMCallHookContext, register_after_llm_call_hook
+
+    hook_called = False
+    messages_before_hook = 0
+
+    def after_hook(context: LLMCallHookContext) -> str | None:
+        nonlocal hook_called, messages_before_hook
+        hook_called = True
+        messages_before_hook = len(context.messages)
+        context.messages.append({
+            "role": "user",
+            "content": "Remember: This is iteration 2 context."
+        })
+        return None  # Don't modify response
+
+    register_after_llm_call_hook(after_hook)
+
+    try:
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            allow_delegation=False,
+            max_iter=2,
+        )
+
+        task = Task(
+            description="Count to 2",
+            expected_output="A count",
+            agent=agent,
+        )
+
+        result = agent.execute_task(task)
+
+        assert hook_called, "after_llm_call hook should have been called"
+        assert len(agent.agent_executor.messages) > messages_before_hook
+        assert result is not None
+    finally:
+        pass
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_hooks_with_crew():
+    """Test that LLM call hooks work with crew execution."""
+    from crewai.utilities.llm_call_hooks import (
+        LLMCallHookContext,
+        register_after_llm_call_hook,
+        register_before_llm_call_hook,
+    )
+
+    before_hook_called = False
+    after_hook_called = False
+
+    def before_hook(context: LLMCallHookContext) -> None:
+        nonlocal before_hook_called
+        before_hook_called = True
+        assert context.executor is not None
+        assert context.agent is not None
+        assert context.task is not None
+        context.messages.append({
+            "role": "system",
+            "content": "Additional system context from hook."
+        })
+
+    def after_hook(context: LLMCallHookContext) -> str | None:
+        nonlocal after_hook_called
+        after_hook_called = True
+        assert context.response is not None
+        assert len(context.messages) > 0
+        return None
+
+    register_before_llm_call_hook(before_hook)
+    register_after_llm_call_hook(after_hook)
+
+    try:
+        agent = Agent(
+            role="Researcher",
+            goal="Research topics",
+            backstory="You are a researcher",
+            allow_delegation=False,
+        )
+
+        task = Task(
+            description="Research AI frameworks",
+            expected_output="A research summary",
+            agent=agent,
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        result = crew.kickoff()
+
+        assert before_hook_called, "before_llm_call hook should have been called"
+        assert after_hook_called, "after_llm_call hook should have been called"
+        assert result is not None
+        assert result.raw is not None
+    finally:
+        pass
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_hooks_can_modify_executor_attributes():
+    """Test that hooks can access and modify executor attributes like tools."""
+    from crewai.utilities.llm_call_hooks import LLMCallHookContext, register_before_llm_call_hook
+    from crewai.tools import tool
+
+    @tool
+    def test_tool() -> str:
+        """A test tool."""
+        return "test result"
+
+    hook_called = False
+    original_tools_count = 0
+
+    def before_hook(context: LLMCallHookContext) -> None:
+        nonlocal hook_called, original_tools_count
+        hook_called = True
+        original_tools_count = len(context.executor.tools)
+        assert context.executor.max_iter > 0
+        assert context.executor.iterations >= 0
+        assert context.executor.tools is not None
+
+    register_before_llm_call_hook(before_hook)
+
+    try:
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            tools=[test_tool],
+            allow_delegation=False,
+        )
+
+        task = Task(
+            description="Use the test tool",
+            expected_output="Tool result",
+            agent=agent,
+        )
+
+        result = agent.execute_task(task)
+
+        assert hook_called, "before_llm_call hook should have been called"
+        assert original_tools_count >= 0
+        assert result is not None
+    finally:
+        pass
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_hooks_error_handling():
+    """Test that hook errors don't break execution."""
+    from crewai.utilities.llm_call_hooks import LLMCallHookContext, register_before_llm_call_hook
+
+    hook_called = False
+
+    def error_hook(context: LLMCallHookContext) -> None:
+        nonlocal hook_called
+        hook_called = True
+        raise ValueError("Test hook error")
+
+    register_before_llm_call_hook(error_hook)
+
+    try:
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            allow_delegation=False,
+        )
+
+        task = Task(
+            description="Say hello",
+            expected_output="A greeting",
+            agent=agent,
+        )
+
+        result = agent.execute_task(task)
+
+        assert hook_called, "before_llm_call hook should have been called"
+        assert result is not None
+    finally:
+        pass

lib/crewai/tests/agents/test_lite_agent.py

@@ -238,6 +238,27 @@ def test_lite_agent_returns_usage_metrics():
     assert result.usage_metrics["total_tokens"] > 0
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_output_includes_messages():
+    """Test that LiteAgentOutput includes messages from agent execution."""
+    llm = LLM(model="gpt-4o-mini")
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff("What is the population of Tokyo?")
+
+    assert isinstance(result, LiteAgentOutput)
+    assert hasattr(result, "messages")
+    assert isinstance(result.messages, list)
+    assert len(result.messages) > 0
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_lite_agent_returns_usage_metrics_async():
@@ -382,8 +403,8 @@ def test_guardrail_is_called_using_string():
     assert not guardrail_events["completed"][0].success
     assert guardrail_events["completed"][1].success
     assert (
-        "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
-        in result.raw
+        "top 10 best Brazilian soccer players" in result.raw or
+        "Brazilian players" in result.raw
     )
 
 

lib/crewai/tests/cassettes/TestAnthropicHeaderInterceptor.test_header_interceptor_with_real_call.yaml

@@ -0,0 +1,106 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Reply with just
+      the word: SUCCESS"}],"model":"claude-3-5-haiku-20241022","stream":false}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '145'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - Anthropic/Python 0.71.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 0.71.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAAwAAAP//dJDLasMwEEX/5a5lsF1nUe1KCARCu6gpFEoRgzTEamzJ1qNNCf734tDQ
+        F10N3HNmBu4JgzfcQ0L3lA0XV8Wq6MgeclGXdVOVdQ0BayAxxL0qq9rcXm/vb2g37ehtc+xeHu+m
+        4xYC6X3kxeIYac8QCL5fAorRxkQuQUB7l9glyKfTxU98XMh5SLQP6/WmbTE/C8TkRxWYoneQYGdU
+        ysHhE0SeMjvNkC73vUA+f5UnWDfmpJI/sIuQVSOgSXesdGBK1jv1UygvPDCZ/9hld7nPY8cDB+rV
+        avjrf9Gq+01nAZ/T96gRiBxerWaVLAdILE0ZCgbz/AEAAP//AwA4VVIcmwEAAA==
+    headers:
+      CF-RAY:
+      - 9997ac4cbfb443fa-EWR
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Nov 2025 22:50:55 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - 87faf353-e074-4658-b885-bfac7aa5a7b5
+      anthropic-ratelimit-input-tokens-limit:
+      - '400000'
+      anthropic-ratelimit-input-tokens-remaining:
+      - '400000'
+      anthropic-ratelimit-input-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      anthropic-ratelimit-output-tokens-limit:
+      - '80000'
+      anthropic-ratelimit-output-tokens-remaining:
+      - '80000'
+      anthropic-ratelimit-output-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2025-11-04T22:50:54Z'
+      anthropic-ratelimit-tokens-limit:
+      - '480000'
+      anthropic-ratelimit-tokens-remaining:
+      - '480000'
+      anthropic-ratelimit-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - req_011CUofgQ1jTrjQ2sveXU1cC
+      retry-after:
+      - '5'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-envoy-upstream-service-time:
+      - '461'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/TestAnthropicInterceptorIntegration.test_anthropic_call_with_interceptor_tracks_requests.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say ''Hello World''
+      and nothing else"}],"model":"claude-3-5-haiku-20241022","stream":false}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      anthropic-version:
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