drop trailing set

Merge branch 'main' into bugfix/fix-backtick-in-agent-response
clean up thoughts as well
2025-12-24 00:08:29 +00:00 · 2025-02-18 16:02:09 -05:00 · 2025-02-18 15:23:40 -05:00 · 2025-02-18 15:17:59 -05:00 · 2025-02-18 15:10:02 -05:00 · 2025-02-18 14:57:16 -05:00
24 changed files with 913 additions and 773 deletions
--- a/docs/concepts/knowledge.mdx
+++ b/docs/concepts/knowledge.mdx
@@ -91,7 +91,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```


-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
+Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 

 <Note>
  You need to install `docling` for the following example to work: `uv add docling`
@@ -152,10 +152,10 @@ Here are examples of how to use different types of knowledge sources:

 ### Text File Knowledge Source
 ```python
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource

 # Create a text file knowledge source
-text_source = CrewDoclingSource(
+text_source = TextFileKnowledgeSource(
    file_paths=["document.txt", "another.txt"]
 )

--- a/docs/concepts/memory.mdx
+++ b/docs/concepts/memory.mdx
@@ -282,6 +282,19 @@ my_crew = Crew(

 ### Using Google AI embeddings

+#### Prerequisites
+Before using Google AI embeddings, ensure you have:
+- Access to the Gemini API
+- The necessary API keys and permissions
+
+You will need to update your *pyproject.toml* dependencies:
+```YAML
+dependencies = [
+    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
+    "crewai[tools]>=0.100.0,<1.0.0"
+]
+```
+
 ```python Code
 from crewai import Crew, Agent, Task, Process

@@ -434,6 +447,38 @@ my_crew = Crew(
 )
 ```

+### Using Amazon Bedrock embeddings
+
+```python Code
+# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
+
+import os
+import boto3
+from crewai import Crew, Agent, Task, Process
+
+boto3_session = boto3.Session(
+    region_name=os.environ.get("AWS_REGION_NAME"),
+    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
+    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
+)
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    embedder={
+    "provider": "bedrock",
+        "config":{
+            "session": boto3_session,
+            "model": "amazon.titan-embed-text-v2:0",
+            "vector_dimension": 1024
+        }
+    }
+    verbose=True
+)
+```
+
 ### Adding Custom Embedding Function

 ```python Code
--- a/docs/concepts/tasks.mdx
+++ b/docs/concepts/tasks.mdx
@@ -268,7 +268,7 @@ analysis_task = Task(

 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
-efeedback to agents when their output doesn't meet specific criteria.
+feedback to agents when their output doesn't meet specific criteria.

 ### Using Task Guardrails

--- a/docs/how-to/langfuse-observability.mdx
+++ b/docs/how-to/langfuse-observability.mdx
@@ -0,0 +1,98 @@
+---
+title: Agent Monitoring with Langfuse
+description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
+icon: magnifying-glass-chart
+---
+
+# Integrate Langfuse with CrewAI
+
+This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
+
+> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
+
+## Get Started
+
+We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
+
+### Step 1: Install Dependencies
+
+
+```python
+%pip install langfuse openlit crewai crewai_tools
+```
+
+### Step 2: Set Up Environment Variables
+
+Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
+
+
+```python
+import os
+import base64
+
+LANGFUSE_PUBLIC_KEY="pk-lf-..."
+LANGFUSE_SECRET_KEY="sk-lf-..."
+LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
+
+os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
+# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
+os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
+
+# your openai key
+os.environ["OPENAI_API_KEY"] = "sk-..."
+```
+
+### Step 3: Initialize OpenLit
+
+Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
+
+
+```python
+import openlit
+
+openlit.init()
+```
+
+### Step 4: Create a Simple CrewAI Application
+
+We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
+
+
+```python
+from crewai import Agent, Task, Crew
+
+from crewai_tools import (
+    WebsiteSearchTool
+)
+
+web_rag_tool = WebsiteSearchTool()
+
+writer = Agent(
+        role="Writer",
+        goal="You make math engaging and understandable for young children through poetry",
+        backstory="You're an expert in writing haikus but you know nothing of math.",
+        tools=[web_rag_tool],  
+    )
+
+task = Task(description=("What is {multiplication}?"),
+            expected_output=("Compose a haiku that includes the answer."),
+            agent=writer)
+
+crew = Crew(
+  agents=[writer],
+  tasks=[task],
+  share_crew=False
+)
+```
+
+### Step 5: See Traces in Langfuse
+
+After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
+
+![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
+
+_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
+
+## References
+
+- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)
--- a/docs/how-to/portkey-observability-and-guardrails.mdx
+++ b/docs/how-to/portkey-observability-and-guardrails.mdx
@@ -1,211 +0,0 @@
-# Portkey Integration with CrewAI
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-
-
-
-
-## Getting Started
-
-1. **Install Required Packages:**
-
-```bash
-pip install -qU crewai portkey-ai
-```
-
-2. **Configure the LLM Client:**
-
-To build CrewAI Agents with Portkey, you'll need two keys:
- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-```python
-from crewai import LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-gpt_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy", # We are using Virtual key
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-    )
-)
-```
-
-3. **Create and Run Your First Agent:**
-
-```python
-from crewai import Agent, Task, Crew
-
-# Define your agents with roles and goals
-coder = Agent(
-    role='Software developer',
-    goal='Write clear, concise code on demand',
-    backstory='An expert coder with a keen eye for software trends.',
-    llm=gpt_llm
-)
-
-# Create tasks for your agents
-task1 = Task(
-    description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-    expected_output="A clear and concise HTML code",
-    agent=coder
-)
-
-# Instantiate your crew
-crew = Crew(
-    agents=[coder],
-    tasks=[task1],
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-
-## Key Features
-
-| Feature | Description |
-|---------|-------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
- **Simple Cache**: Perfect for exact matches
- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
- **Automatic Retries**: Handle temporary failures gracefully
- **Request Timeouts**: Prevent hanging operations
- **Conditional Routing**: Route requests based on specific conditions
- **Fallbacks**: Set up automatic provider failovers
- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
- Cost per agent interaction
- Response times and latency
- Token usage and efficiency
- Success/failure rates
- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
- Set budget limit and rate limts per Virtual Key (disposable API keys)
- Implement role-based access control
- Track system changes with audit logs
- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
- [📘 Portkey Documentation](https://docs.portkey.ai)
- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
- [🐦 Twitter](https://twitter.com/portkeyai)
- [💬 Discord Community](https://discord.gg/DD7vgKK299)
-
-
-
-
-
-
-
-
-
--- a/docs/how-to/portkey-observability.mdx
+++ b/docs/how-to/portkey-observability.mdx
@@ -1,5 +1,5 @@
 ---
-title: Portkey Observability and Guardrails
+title: Agent Monitoring with Portkey
 description: How to use Portkey with CrewAI
 icon: key
 ---
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -103,7 +103,8 @@
        "how-to/langtrace-observability",
        "how-to/mlflow-observability",
        "how-to/openlit-observability",
-        "how-to/portkey-observability"
+        "how-to/portkey-observability",
+        "how-to/langfuse-observability"
      ]
    },
    {
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.100.1"
+version = "0.102.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
-tools = ["crewai-tools>=0.32.1"]
+tools = ["crewai-tools>=0.36.0"]
 embeddings = [
    "tiktoken~=0.7.0"
 ]
--- a/src/crewai/init.py
+++ b/src/crewai/init.py
@@ -14,7 +14,7 @@ warnings.filterwarnings(
    category=UserWarning,
    module="pydantic.main",
 )
-__version__ = "0.100.1"
+__version__ = "0.102.0"
 __all__ = [
    "Agent",
    "Crew",
--- a/src/crewai/agents/parser.py
+++ b/src/crewai/agents/parser.py
@@ -94,6 +94,13 @@ class CrewAgentParser:

        elif includes_answer:
            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+            # Check whether the final answer ends with triple backticks.
+            if final_answer.endswith("```"):
+                # Count occurrences of triple backticks in the final answer.
+                count = final_answer.count("```")
+                # If count is odd then it's an unmatched trailing set; remove it.
+                if count % 2 != 0:
+                    final_answer = final_answer[:-3].rstrip()
            return AgentFinish(thought, final_answer, text)

        if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
@@ -120,7 +127,10 @@ class CrewAgentParser:
        regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
        thought_match = re.search(regex, text, re.DOTALL)
        if thought_match:
-            return thought_match.group(1).strip()
+            thought = thought_match.group(1).strip()
+            # Remove any triple backticks from the thought string
+            thought = thought.replace("```", "").strip()
+            return thought
        return ""

    def _clean_action(self, text: str) -> str:
--- a/src/crewai/cli/templates/crew/main.py
+++ b/src/crewai/cli/templates/crew/main.py
@@ -56,7 +56,8 @@ def test():
    Test the crew execution and returns the results.
    """
    inputs = {
-        "topic": "AI LLMs"
+        "topic": "AI LLMs",
+        "current_year": str(datetime.now().year)
    }
    try:
        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0"
+    "crewai[tools]>=0.102.0,<1.0.0"
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/flow/pyproject.toml
+++ b/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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
+    "crewai[tools]>=0.102.0,<1.0.0",
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/tool/pyproject.toml
+++ b/src/crewai/cli/templates/tool/pyproject.toml
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1"
+    "crewai[tools]>=0.102.0"
 ]

 [tool.crewai]
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -275,12 +275,26 @@ class Crew(BaseModel):
                if self.entity_memory
                else EntityMemory(crew=self, embedder_config=self.embedder)
            )
-            if hasattr(self, "memory_config") and self.memory_config is not None:
-                self._user_memory = (
-                    self.user_memory if self.user_memory else UserMemory(crew=self)
-                )
+            if (
+                self.memory_config and "user_memory" in self.memory_config
+            ):  # Check for user_memory in config
+                user_memory_config = self.memory_config["user_memory"]
+                if isinstance(
+                    user_memory_config, UserMemory
+                ):  # Check if it is already an instance
+                    self._user_memory = user_memory_config
+                elif isinstance(
+                    user_memory_config, dict
+                ):  # Check if it's a configuration dict
+                    self._user_memory = UserMemory(
+                        crew=self, **user_memory_config
+                    )  # Initialize with config
+                else:
+                    raise TypeError(
+                        "user_memory must be a UserMemory instance or a configuration dictionary"
+                    )
            else:
-                self._user_memory = None
+                self._user_memory = None  # No user memory if not in config
        return self

    @model_validator(mode="after")
@@ -455,8 +469,6 @@ class Crew(BaseModel):
                        )
        return self

-
-
    @property
    def key(self) -> str:
        source = [agent.key for agent in self.agents] + [
@@ -928,13 +940,13 @@ class Crew(BaseModel):
    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
        if not task_outputs:
            raise ValueError("No task outputs available to create crew output.")
-            
+
        # Filter out empty outputs and get the last valid one as the main output
        valid_outputs = [t for t in task_outputs if t.raw]
        if not valid_outputs:
            raise ValueError("No valid task outputs available to create crew output.")
        final_task_output = valid_outputs[-1]
-            
+
        final_string_output = final_task_output.raw
        self._finish_execution(final_string_output)
        token_usage = self.calculate_usage_metrics()
@@ -1148,19 +1160,24 @@ class Crew(BaseModel):
    def test(
        self,
        n_iterations: int,
-        openai_model_name: Optional[str] = None,
+        eval_llm: Union[str, InstanceOf[LLM]],
        inputs: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
        test_crew = self.copy()

+        eval_llm = create_llm(eval_llm)
+
+        if not eval_llm:
+            raise ValueError("Failed to create LLM instance.")
+
        self._test_execution_span = test_crew._telemetry.test_execution_span(
            test_crew,
            n_iterations,
            inputs,
-            openai_model_name,  # type: ignore[arg-type]
+            eval_llm.model,  # type: ignore[arg-type]
        )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(test_crew, eval_llm)  # type: ignore[arg-type]

        for i in range(1, n_iterations + 1):
            evaluator.set_iteration(i)
--- a/src/crewai/flow/flow.py
+++ b/src/crewai/flow/flow.py
@@ -1,4 +1,5 @@
 import asyncio
+import copy
 import inspect
 import logging
 from typing import (
@@ -394,7 +395,6 @@ class FlowMeta(type):
                or hasattr(attr_value, "__trigger_methods__")
                or hasattr(attr_value, "__is_router__")
            ):
-
                # Register start methods
                if hasattr(attr_value, "__is_start_method__"):
                    start_methods.append(attr_name)
@@ -569,6 +569,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
            f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
        )

+    def _copy_state(self) -> T:
+        return copy.deepcopy(self._state)
+
    @property
    def state(self) -> T:
        return self._state
@@ -740,6 +743,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
            event=FlowStartedEvent(
                type="flow_started",
                flow_name=self.__class__.__name__,
+                inputs=inputs,
            ),
        )
        self._log_flow_event(
@@ -803,6 +807,18 @@ class Flow(Generic[T], metaclass=FlowMeta):
    async def _execute_method(
        self, method_name: str, method: Callable, *args: Any, **kwargs: Any
    ) -> Any:
+        dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (kwargs or {})
+        self.event_emitter.send(
+            self,
+            event=MethodExecutionStartedEvent(
+                type="method_execution_started",
+                method_name=method_name,
+                flow_name=self.__class__.__name__,
+                params=dumped_params,
+                state=self._copy_state(),
+            ),
+        )
+
        result = (
            await method(*args, **kwargs)
            if asyncio.iscoroutinefunction(method)
@@ -812,6 +828,18 @@ class Flow(Generic[T], metaclass=FlowMeta):
        self._method_execution_counts[method_name] = (
            self._method_execution_counts.get(method_name, 0) + 1
        )
+
+        self.event_emitter.send(
+            self,
+            event=MethodExecutionFinishedEvent(
+                type="method_execution_finished",
+                method_name=method_name,
+                flow_name=self.__class__.__name__,
+                state=self._copy_state(),
+                result=result,
+            ),
+        )
+
        return result

    async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -950,16 +978,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
        """
        try:
            method = self._methods[listener_name]
-
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionStartedEvent(
-                    type="method_execution_started",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
            sig = inspect.signature(method)
            params = list(sig.parameters.values())
            method_params = [p for p in params if p.name != "self"]
@@ -971,15 +989,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
            else:
                listener_result = await self._execute_method(listener_name, method)

-            self.event_emitter.send(
-                self,
-                event=MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
            # Execute listeners (and possibly routers) of this listener
            await self._execute_listeners(listener_name, listener_result)

--- a/src/crewai/flow/flow_events.py
+++ b/src/crewai/flow/flow_events.py
@@ -1,6 +1,8 @@
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Any, Optional
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel


@dataclass
@@ -15,17 +17,21 @@ class Event:

@dataclass
 class FlowStartedEvent(Event):
-    pass
+    inputs: Optional[Dict[str, Any]] = None


@dataclass
 class MethodExecutionStartedEvent(Event):
    method_name: str
+    state: Union[Dict[str, Any], BaseModel]
+    params: Optional[Dict[str, Any]] = None


@dataclass
 class MethodExecutionFinishedEvent(Event):
    method_name: str
+    state: Union[Dict[str, Any], BaseModel]
+    result: Any = None


@dataclass
--- a/src/crewai/flow/state_utils.py
+++ b/src/crewai/flow/state_utils.py
@@ -0,0 +1,52 @@
+from datetime import date, datetime
+from typing import Any
+
+from pydantic import BaseModel
+
+from crewai.flow import Flow
+
+
+def export_state(flow: Flow) -> dict[str, Any]:
+    """Exports the Flow's internal state as JSON-compatible data structures.
+
+    Performs a one-way transformation of a Flow's state into basic Python types
+    that can be safely serialized to JSON. To prevent infinite recursion with
+    circular references, the conversion is limited to a depth of 5 levels.
+
+    Args:
+        flow: The Flow object whose state needs to be exported
+
+    Returns:
+        dict[str, Any]: The transformed state using JSON-compatible Python
+            types.
+    """
+    return _to_serializable(flow._state)
+
+
+def _to_serializable(obj: Any, max_depth: int = 5, _current_depth: int = 0) -> Any:
+    if _current_depth >= max_depth:
+        return repr(obj)
+
+    if isinstance(obj, (str, int, float, bool, type(None))):
+        return obj
+    elif isinstance(obj, (date, datetime)):
+        return obj.isoformat()
+    elif isinstance(obj, (list, tuple, set)):
+        return [_to_serializable(item, max_depth, _current_depth + 1) for item in obj]
+    elif isinstance(obj, dict):
+        return {
+            _to_serializable_key(key): _to_serializable(
+                value, max_depth, _current_depth + 1
+            )
+            for key, value in obj.items()
+        }
+    elif isinstance(obj, BaseModel):
+        return _to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
+    else:
+        return repr(obj)
+
+
+def _to_serializable_key(key: Any) -> str:
+    if isinstance(key, (str, int)):
+        return str(key)
+    return f"key_{id(key)}_{repr(key)}"
--- a/src/crewai/knowledge/source/excel_knowledge_source.py
+++ b/src/crewai/knowledge/source/excel_knowledge_source.py
@@ -1,28 +1,138 @@
 from pathlib import Path
-from typing import Dict, List
+from typing import Dict, Iterator, List, Optional, Union
+from urllib.parse import urlparse

-from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+from pydantic import Field, field_validator
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.logger import Logger


-class ExcelKnowledgeSource(BaseFileKnowledgeSource):
+class ExcelKnowledgeSource(BaseKnowledgeSource):
    """A knowledge source that stores and queries Excel file content using embeddings."""

-    def load_content(self) -> Dict[Path, str]:
-        """Load and preprocess Excel file content."""
-        pd = self._import_dependencies()
+    # override content to be a dict of file paths to sheet names to csv content

+    _logger: Logger = Logger(verbose=True)
+
+    file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default=None,
+        description="[Deprecated] The path to the file. Use file_paths instead.",
+    )
+    file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default_factory=list, description="The path to the file"
+    )
+    chunks: List[str] = Field(default_factory=list)
+    content: Dict[Path, Dict[str, str]] = Field(default_factory=dict)
+    safe_file_paths: List[Path] = Field(default_factory=list)
+
+    @field_validator("file_path", "file_paths", mode="before")
+    def validate_file_path(cls, v, info):
+        """Validate that at least one of file_path or file_paths is provided."""
+        # Single check if both are None, O(1) instead of nested conditions
+        if (
+            v is None
+            and info.data.get(
+                "file_path" if info.field_name == "file_paths" else "file_paths"
+            )
+            is None
+        ):
+            raise ValueError("Either file_path or file_paths must be provided")
+        return v
+
+    def _process_file_paths(self) -> List[Path]:
+        """Convert file_path to a list of Path objects."""
+
+        if hasattr(self, "file_path") and self.file_path is not None:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            self.file_paths = self.file_path
+
+        if self.file_paths is None:
+            raise ValueError("Your source must be provided with a file_paths: []")
+
+        # Convert single path to list
+        path_list: List[Union[Path, str]] = (
+            [self.file_paths]
+            if isinstance(self.file_paths, (str, Path))
+            else list(self.file_paths)
+            if isinstance(self.file_paths, list)
+            else []
+        )
+
+        if not path_list:
+            raise ValueError(
+                "file_path/file_paths must be a Path, str, or a list of these types"
+            )
+
+        return [self.convert_to_path(path) for path in path_list]
+
+    def validate_content(self):
+        """Validate the paths."""
+        for path in self.safe_file_paths:
+            if not path.exists():
+                self._logger.log(
+                    "error",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
+                    color="red",
+                )
+                raise FileNotFoundError(f"File not found: {path}")
+            if not path.is_file():
+                self._logger.log(
+                    "error",
+                    f"Path is not a file: {path}",
+                    color="red",
+                )
+
+    def model_post_init(self, _) -> None:
+        if self.file_path:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            self.file_paths = self.file_path
+        self.safe_file_paths = self._process_file_paths()
+        self.validate_content()
+        self.content = self._load_content()
+
+    def _load_content(self) -> Dict[Path, Dict[str, str]]:
+        """Load and preprocess Excel file content from multiple sheets.
+
+        Each sheet's content is converted to CSV format and stored.
+
+        Returns:
+            Dict[Path, Dict[str, str]]: A mapping of file paths to their respective sheet contents.
+
+        Raises:
+            ImportError: If required dependencies are missing.
+            FileNotFoundError: If the specified Excel file cannot be opened.
+        """
+        pd = self._import_dependencies()
        content_dict = {}
        for file_path in self.safe_file_paths:
            file_path = self.convert_to_path(file_path)
-            df = pd.read_excel(file_path)
-            content = df.to_csv(index=False)
-            content_dict[file_path] = content
+            with pd.ExcelFile(file_path) as xl:
+                sheet_dict = {
+                    str(sheet_name): str(
+                        pd.read_excel(xl, sheet_name).to_csv(index=False)
+                    )
+                    for sheet_name in xl.sheet_names
+                }
+            content_dict[file_path] = sheet_dict
        return content_dict

+    def convert_to_path(self, path: Union[Path, str]) -> Path:
+        """Convert a path to a Path object."""
+        return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path
+
    def _import_dependencies(self):
        """Dynamically import dependencies."""
        try:
-            import openpyxl  # noqa
            import pandas as pd

            return pd
@@ -38,10 +148,14 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
        and save the embeddings.
        """
        # Convert dictionary values to a single string if content is a dictionary
-        if isinstance(self.content, dict):
-            content_str = "\n".join(str(value) for value in self.content.values())
-        else:
-            content_str = str(self.content)
+        # Updated to account for .xlsx workbooks with multiple tabs/sheets
+        content_str = ""
+        for value in self.content.values():
+            if isinstance(value, dict):
+                for sheet_value in value.values():
+                    content_str += str(sheet_value) + "\n"
+            else:
+                content_str += str(value) + "\n"

        new_chunks = self._chunk_text(content_str)
        self.chunks.extend(new_chunks)
--- a/src/crewai/utilities/evaluators/crew_evaluator_handler.py
+++ b/src/crewai/utilities/evaluators/crew_evaluator_handler.py
@@ -1,11 +1,12 @@
 from collections import defaultdict

-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, InstanceOf
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table

 from crewai.agent import Agent
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -23,7 +24,7 @@ class CrewEvaluator:

    Attributes:
        crew (Crew): The crew of agents to evaluate.
-        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
+        eval_llm (LLM): Language model instance to use for evaluations
        tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
        iteration (int): The current iteration of the evaluation.
    """
@@ -32,9 +33,9 @@ class CrewEvaluator:
    run_execution_times: defaultdict = defaultdict(list)
    iteration: int = 0

-    def __init__(self, crew, openai_model_name: str):
+    def __init__(self, crew, eval_llm: InstanceOf[LLM]):
        self.crew = crew
-        self.openai_model_name = openai_model_name
+        self.llm = eval_llm
        self._telemetry = Telemetry()
        self._setup_for_evaluating()

@@ -51,7 +52,7 @@ class CrewEvaluator:
            ),
            backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
            verbose=False,
-            llm=self.openai_model_name,
+            llm=self.llm,
        )

    def _evaluation_task(
@@ -181,7 +182,7 @@ class CrewEvaluator:
                self.crew,
                evaluation_result.pydantic.quality,
                current_task.execution_duration,
-                self.openai_model_name,
+                self.llm.model,
            )
            self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
            self.run_execution_times[self.iteration].append(
--- a/tests/crew_test.py
+++ b/tests/crew_test.py
@@ -15,6 +15,7 @@ from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.project import crew
@@ -51,6 +52,7 @@ writer = Agent(

 def test_crew_with_only_conditional_tasks_raises_error():
    """Test that creating a crew with only conditional tasks raises an error."""
+
    def condition_func(task_output: TaskOutput) -> bool:
        return True

@@ -82,6 +84,7 @@ def test_crew_with_only_conditional_tasks_raises_error():
            tasks=[conditional1, conditional2, conditional3],
        )

+
 def test_crew_config_conditional_requirement():
    with pytest.raises(ValueError):
        Crew(process=Process.sequential)
@@ -589,12 +592,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
        _, kwargs = mock_execute_sync.call_args
        tools = kwargs["tools"]

-        assert any(isinstance(tool, TestTool) for tool in tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -653,12 +656,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
        _, kwargs = mock_execute_sync.call_args
        tools = kwargs["tools"]

-        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in new_ceo.tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -782,17 +785,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
        used_tools = kwargs["tools"]

        # Confirm AnotherTestTool is present but TestTool is not
-        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
-            "AnotherTestTool should be present"
-        )
-        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
-            "TestTool should not be present among used tools"
-        )
+        assert any(
+            isinstance(tool, AnotherTestTool) for tool in used_tools
+        ), "AnotherTestTool should be present"
+        assert not any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "TestTool should not be present among used tools"

        # Confirm delegation tool(s) are present
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"

    # Finally, make sure the agent's original tools remain unchanged
    assert len(researcher_with_delegation.tools) == 1
@@ -1593,9 +1596,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():

        # Verify that exactly one tool was used and it was a CodeInterpreterTool
        assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(used_tools[0], CodeInterpreterTool), (
-            "Tool should be CodeInterpreterTool"
-        )
+        assert isinstance(
+            used_tools[0], CodeInterpreterTool
+        ), "Tool should be CodeInterpreterTool"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1952,6 +1955,7 @@ def test_task_callback_on_crew():

 def test_task_callback_both_on_task_and_crew():
    from unittest.mock import MagicMock, patch
+
    mock_callback_on_task = MagicMock()
    mock_callback_on_crew = MagicMock()

@@ -2101,21 +2105,22 @@ def test_conditional_task_uses_last_output():
        expected_output="First output",
        agent=researcher,
    )
+
    def condition_fails(task_output: TaskOutput) -> bool:
        # This condition will never be met
        return "never matches" in task_output.raw.lower()
-    
+
    def condition_succeeds(task_output: TaskOutput) -> bool:
        # This condition will match first task's output
        return "first success" in task_output.raw.lower()
-    
+
    conditional_task1 = ConditionalTask(
        description="Second task - conditional that fails condition",
        expected_output="Second output",
        agent=researcher,
        condition=condition_fails,
    )
-    
+
    conditional_task2 = ConditionalTask(
        description="Third task - conditional that succeeds using first task output",
        expected_output="Third output",
@@ -2134,35 +2139,37 @@ def test_conditional_task_uses_last_output():
        raw="First success output",  # Will be used by third task's condition
        agent=researcher.role,
    )
-    mock_skipped = TaskOutput(
-        description="Second task output",
-        raw="",  # Empty output since condition fails
-        agent=researcher.role,
-    )
    mock_third = TaskOutput(
        description="Third task output",
        raw="Third task executed",  # Output when condition succeeds using first task output
        agent=writer.role,
    )
-    
+
    # Set up mocks for task execution and conditional logic
    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
        # First conditional fails, second succeeds
        mock_should_execute.side_effect = [False, True]
-        
        with patch.object(Task, "execute_sync") as mock_execute:
            mock_execute.side_effect = [mock_first, mock_third]
            result = crew.kickoff()
-            
+
            # Verify execution behavior
            assert mock_execute.call_count == 2  # Only first and third tasks execute
            assert mock_should_execute.call_count == 2  # Both conditionals checked
-            
-            # Verify outputs collection
+
+            # Verify outputs collection:
+            # First executed task output, followed by an automatically generated (skipped) output, then the conditional execution
            assert len(result.tasks_output) == 3
-            assert result.tasks_output[0].raw == "First success output"  # First task succeeded
-            assert result.tasks_output[1].raw == ""  # Second task skipped (condition failed)
-            assert result.tasks_output[2].raw == "Third task executed"  # Third task used first task's output
+            assert (
+                result.tasks_output[0].raw == "First success output"
+            )  # First task succeeded
+            assert (
+                result.tasks_output[1].raw == ""
+            )  # Second task skipped (condition failed)
+            assert (
+                result.tasks_output[2].raw == "Third task executed"
+            )  # Third task used first task's output
+

@pytest.mark.vcr(filter_headers=["authorization"])
 def test_conditional_tasks_result_collection():
@@ -2172,20 +2179,20 @@ def test_conditional_tasks_result_collection():
        expected_output="First output",
        agent=researcher,
    )
-    
+
    def condition_never_met(task_output: TaskOutput) -> bool:
        return "never matches" in task_output.raw.lower()
-    
+
    def condition_always_met(task_output: TaskOutput) -> bool:
        return "success" in task_output.raw.lower()
-    
+
    task2 = ConditionalTask(
        description="Conditional task that never executes",
        expected_output="Second output",
        agent=researcher,
        condition=condition_never_met,
    )
-    
+
    task3 = ConditionalTask(
        description="Conditional task that always executes",
        expected_output="Third output",
@@ -2204,35 +2211,46 @@ def test_conditional_tasks_result_collection():
        raw="Success output",  # Triggers third task's condition
        agent=researcher.role,
    )
-    mock_skipped = TaskOutput(
-        description="Skipped output",
-        raw="",  # Empty output for skipped task
-        agent=researcher.role,
-    )
    mock_conditional = TaskOutput(
        description="Conditional output",
        raw="Conditional task executed",
        agent=writer.role,
    )
-    
+
    # Set up mocks for task execution and conditional logic
    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
        # First conditional fails, second succeeds
        mock_should_execute.side_effect = [False, True]
-        
        with patch.object(Task, "execute_sync") as mock_execute:
            mock_execute.side_effect = [mock_success, mock_conditional]
            result = crew.kickoff()
-            
+
            # Verify execution behavior
            assert mock_execute.call_count == 2  # Only first and third tasks execute
            assert mock_should_execute.call_count == 2  # Both conditionals checked
-            
+
+            # Verify task output collection:
+            # There should be three outputs: normal task, skipped conditional task (empty output),
+            # and the conditional task that executed.
+            assert len(result.tasks_output) == 3
+            assert (
+                result.tasks_output[0].raw == "Success output"
+            )  # Normal task executed
+            assert result.tasks_output[1].raw == ""  # Second task skipped
+            assert (
+                result.tasks_output[2].raw == "Conditional task executed"
+            )  # Third task executed
+
            # Verify task output collection
            assert len(result.tasks_output) == 3
-            assert result.tasks_output[0].raw == "Success output"      # Normal task executed
-            assert result.tasks_output[1].raw == ""                    # Second task skipped
-            assert result.tasks_output[2].raw == "Conditional task executed"  # Third task executed
+            assert (
+                result.tasks_output[0].raw == "Success output"
+            )  # Normal task executed
+            assert result.tasks_output[1].raw == ""  # Second task skipped
+            assert (
+                result.tasks_output[2].raw == "Conditional task executed"
+            )  # Third task executed
+

@pytest.mark.vcr(filter_headers=["authorization"])
 def test_multiple_conditional_tasks():
@@ -2242,20 +2260,20 @@ def test_multiple_conditional_tasks():
        expected_output="Research output",
        agent=researcher,
    )
-    
+
    def condition1(task_output: TaskOutput) -> bool:
        return "success" in task_output.raw.lower()
-    
+
    def condition2(task_output: TaskOutput) -> bool:
        return "proceed" in task_output.raw.lower()
-    
+
    task2 = ConditionalTask(
        description="First conditional task",
        expected_output="Conditional output 1",
        agent=writer,
        condition=condition1,
    )
-    
+
    task3 = ConditionalTask(
        description="Second conditional task",
        expected_output="Conditional output 2",
@@ -2274,7 +2292,7 @@ def test_multiple_conditional_tasks():
        raw="Success and proceed output",
        agent=researcher.role,
    )
-    
+
    # Set up mocks for task execution
    with patch.object(Task, "execute_sync", return_value=mock_success) as mock_execute:
        result = crew.kickoff()
@@ -2282,6 +2300,7 @@ def test_multiple_conditional_tasks():
        assert mock_execute.call_count == 3
        assert len(result.tasks_output) == 3

+
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_using_contextual_memory():
    from unittest.mock import patch
@@ -3323,7 +3342,8 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
    copy_mock.return_value = crew

    n_iterations = 2
-    crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
+    llm_instance = LLM('gpt-4o-mini')
+    crew.test(n_iterations, llm_instance, inputs={"topic": "AI"})

    # Ensure kickoff is called on the copied crew
    kickoff_mock.assert_has_calls(
@@ -3332,7 +3352,7 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):

    crew_evaluator.assert_has_calls(
        [
-            mock.call(crew, "gpt-4o-mini"),
+            mock.call(crew,llm_instance),
            mock.call().set_iteration(1),
            mock.call().set_iteration(2),
            mock.call().print_crew_evaluation_result(),
@@ -3400,9 +3420,9 @@ def test_fetch_inputs():
    expected_placeholders = {"role_detail", "topic", "field"}
    actual_placeholders = crew.fetch_inputs()

-    assert actual_placeholders == expected_placeholders, (
-        f"Expected {expected_placeholders}, but got {actual_placeholders}"
-    )
+    assert (
+        actual_placeholders == expected_placeholders
+    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"


 def test_task_tools_preserve_code_execution_tools():
@@ -3475,20 +3495,20 @@ def test_task_tools_preserve_code_execution_tools():
        used_tools = kwargs["tools"]

        # Verify all expected tools are present
-        assert any(isinstance(tool, TestTool) for tool in used_tools), (
-            "Task's TestTool should be present"
-        )
-        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
-            "CodeInterpreterTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "Task's TestTool should be present"
+        assert any(
+            isinstance(tool, CodeInterpreterTool) for tool in used_tools
+        ), "CodeInterpreterTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"

        # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert len(used_tools) == 4, (
-            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-        )
+        assert (
+            len(used_tools) == 4
+        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -3532,9 +3552,9 @@ def test_multimodal_flag_adds_multimodal_tools():
        used_tools = kwargs["tools"]

        # Check that the multimodal tool was added
-        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
-            "AddImageTool should be present when agent is multimodal"
-        )
+        assert any(
+            isinstance(tool, AddImageTool) for tool in used_tools
+        ), "AddImageTool should be present when agent is multimodal"

        # Verify we have exactly one tool (just the AddImageTool)
        assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -3760,9 +3780,9 @@ def test_crew_guardrail_feedback_in_context():
    assert len(execution_contexts) > 1, "Task should have been executed multiple times"

    # Verify that the second execution included the guardrail feedback
-    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
-        "Guardrail feedback should be included in retry context"
-    )
+    assert (
+        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
+    ), "Guardrail feedback should be included in retry context"

    # Verify final output meets guardrail requirements
    assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
--- a/tests/flow/test_state_utils.py
+++ b/tests/flow/test_state_utils.py
@@ -0,0 +1,156 @@
+from datetime import date, datetime
+from typing import List
+from unittest.mock import Mock
+
+import pytest
+from pydantic import BaseModel
+
+from crewai.flow import Flow
+from crewai.flow.state_utils import export_state
+
+
+class Address(BaseModel):
+    street: str
+    city: str
+    country: str
+
+
+class Person(BaseModel):
+    name: str
+    age: int
+    address: Address
+    birthday: date
+    skills: List[str]
+
+
+@pytest.fixture
+def mock_flow():
+    def create_flow(state):
+        flow = Mock(spec=Flow)
+        flow._state = state
+        return flow
+
+    return create_flow
+
+
+@pytest.mark.parametrize(
+    "test_input,expected",
+    [
+        ({"text": "hello world"}, {"text": "hello world"}),
+        ({"number": 42}, {"number": 42}),
+        ({"decimal": 3.14}, {"decimal": 3.14}),
+        ({"flag": True}, {"flag": True}),
+        ({"empty": None}, {"empty": None}),
+        ({"list": [1, 2, 3]}, {"list": [1, 2, 3]}),
+        ({"tuple": (1, 2, 3)}, {"tuple": [1, 2, 3]}),
+        ({"set": {1, 2, 3}}, {"set": [1, 2, 3]}),
+        ({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
+    ],
+)
+def test_basic_serialization(mock_flow, test_input, expected):
+    flow = mock_flow(test_input)
+    result = export_state(flow)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "input_date,expected",
+    [
+        (date(2024, 1, 1), "2024-01-01"),
+        (datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
+    ],
+)
+def test_temporal_serialization(mock_flow, input_date, expected):
+    flow = mock_flow({"date": input_date})
+    result = export_state(flow)
+    assert result["date"] == expected
+
+
+@pytest.mark.parametrize(
+    "key,value,expected_key_type",
+    [
+        (("tuple", "key"), "value", str),
+        (None, "value", str),
+        (123, "value", str),
+        ("normal", "value", str),
+    ],
+)
+def test_dictionary_key_serialization(mock_flow, key, value, expected_key_type):
+    flow = mock_flow({key: value})
+    result = export_state(flow)
+    assert len(result) == 1
+    result_key = next(iter(result.keys()))
+    assert isinstance(result_key, expected_key_type)
+    assert result[result_key] == value
+
+
+@pytest.mark.parametrize(
+    "callable_obj,expected_in_result",
+    [
+        (lambda x: x * 2, "lambda"),
+        (str.upper, "upper"),
+    ],
+)
+def test_callable_serialization(mock_flow, callable_obj, expected_in_result):
+    flow = mock_flow({"func": callable_obj})
+    result = export_state(flow)
+    assert isinstance(result["func"], str)
+    assert expected_in_result in result["func"].lower()
+
+
+def test_pydantic_model_serialization(mock_flow):
+    address = Address(street="123 Main St", city="Tech City", country="Pythonia")
+
+    person = Person(
+        name="John Doe",
+        age=30,
+        address=address,
+        birthday=date(1994, 1, 1),
+        skills=["Python", "Testing"],
+    )
+
+    flow = mock_flow(
+        {
+            "single_model": address,
+            "nested_model": person,
+            "model_list": [address, address],
+            "model_dict": {"home": address},
+        }
+    )
+
+    result = export_state(flow)
+
+    assert result["single_model"]["street"] == "123 Main St"
+
+    assert result["nested_model"]["name"] == "John Doe"
+    assert result["nested_model"]["address"]["city"] == "Tech City"
+    assert result["nested_model"]["birthday"] == "1994-01-01"
+
+    assert len(result["model_list"]) == 2
+    assert all(m["street"] == "123 Main St" for m in result["model_list"])
+    assert result["model_dict"]["home"]["city"] == "Tech City"
+
+
+def test_depth_limit(mock_flow):
+    """Test max depth handling with a deeply nested structure"""
+
+    def create_nested(depth):
+        if depth == 0:
+            return "value"
+        return {"next": create_nested(depth - 1)}
+
+    deep_structure = create_nested(10)
+    flow = mock_flow(deep_structure)
+    result = export_state(flow)
+
+    assert result == {
+        "next": {
+            "next": {
+                "next": {
+                    "next": {
+                        "next": "{'next': {'next': {'next': {'next': {'next': 'value'}}}}}"
+                    }
+                }
+            }
+        }
+    }
--- a/tests/flow_test.py
+++ b/tests/flow_test.py
@@ -1,11 +1,18 @@
 """Test Flow creation and execution basic functionality."""

 import asyncio
+from datetime import datetime

 import pytest
 from pydantic import BaseModel

 from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.flow_events import (
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)


 def test_simple_sequential_flow():
@@ -398,3 +405,218 @@ def test_router_with_multiple_conditions():

    # final_step should run after router_and
    assert execution_order.index("log_final_step") > execution_order.index("router_and")
+
+
+def test_unstructured_flow_event_emission():
+    """Test that the correct events are emitted during unstructured flow
+    execution with all fields validated."""
+
+    class PoemFlow(Flow):
+        @start()
+        def prepare_flower(self):
+            self.state["flower"] = "roses"
+            return "foo"
+
+        @start()
+        def prepare_color(self):
+            self.state["color"] = "red"
+            return "bar"
+
+        @listen(prepare_color)
+        def write_first_sentence(self):
+            return f"{self.state['flower']} are {self.state['color']}"
+
+        @listen(write_first_sentence)
+        def finish_poem(self, first_sentence):
+            separator = self.state.get("separator", "\n")
+            return separator.join([first_sentence, "violets are blue"])
+
+        @listen(finish_poem)
+        def save_poem_to_database(self):
+            # A method without args/kwargs to ensure events are sent correctly
+            pass
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = PoemFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff(inputs={"separator": ", "})
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "PoemFlow"
+    assert event_log[0].inputs == {"separator": ", "}
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    # Asserting for concurrent start method executions in a for loop as you
+    # can't guarantee ordering in asynchronous executions
+    for i in range(1, 5):
+        event = event_log[i]
+        assert isinstance(event.state, dict)
+        assert isinstance(event.state["id"], str)
+
+        if event.method_name == "prepare_flower":
+            if isinstance(event, MethodExecutionStartedEvent):
+                assert event.params == {}
+                assert event.state["separator"] == ", "
+            elif isinstance(event, MethodExecutionFinishedEvent):
+                assert event.result == "foo"
+                assert event.state["flower"] == "roses"
+                assert event.state["separator"] == ", "
+            else:
+                assert False, "Unexpected event type for prepare_flower"
+        elif event.method_name == "prepare_color":
+            if isinstance(event, MethodExecutionStartedEvent):
+                assert event.params == {}
+                assert event.state["separator"] == ", "
+            elif isinstance(event, MethodExecutionFinishedEvent):
+                assert event.result == "bar"
+                assert event.state["color"] == "red"
+                assert event.state["separator"] == ", "
+            else:
+                assert False, "Unexpected event type for prepare_color"
+        else:
+            assert False, f"Unexpected method {event.method_name} in prepare events"
+
+    assert isinstance(event_log[5], MethodExecutionStartedEvent)
+    assert event_log[5].method_name == "write_first_sentence"
+    assert event_log[5].params == {}
+    assert isinstance(event_log[5].state, dict)
+    assert event_log[5].state["flower"] == "roses"
+    assert event_log[5].state["color"] == "red"
+    assert event_log[5].state["separator"] == ", "
+
+    assert isinstance(event_log[6], MethodExecutionFinishedEvent)
+    assert event_log[6].method_name == "write_first_sentence"
+    assert event_log[6].result == "roses are red"
+
+    assert isinstance(event_log[7], MethodExecutionStartedEvent)
+    assert event_log[7].method_name == "finish_poem"
+    assert event_log[7].params == {"_0": "roses are red"}
+    assert isinstance(event_log[7].state, dict)
+    assert event_log[7].state["flower"] == "roses"
+    assert event_log[7].state["color"] == "red"
+
+    assert isinstance(event_log[8], MethodExecutionFinishedEvent)
+    assert event_log[8].method_name == "finish_poem"
+    assert event_log[8].result == "roses are red, violets are blue"
+
+    assert isinstance(event_log[9], MethodExecutionStartedEvent)
+    assert event_log[9].method_name == "save_poem_to_database"
+    assert event_log[9].params == {}
+    assert isinstance(event_log[9].state, dict)
+    assert event_log[9].state["flower"] == "roses"
+    assert event_log[9].state["color"] == "red"
+
+    assert isinstance(event_log[10], MethodExecutionFinishedEvent)
+    assert event_log[10].method_name == "save_poem_to_database"
+    assert event_log[10].result is None
+
+    assert isinstance(event_log[11], FlowFinishedEvent)
+    assert event_log[11].flow_name == "PoemFlow"
+    assert event_log[11].result is None
+    assert isinstance(event_log[11].timestamp, datetime)
+
+
+def test_structured_flow_event_emission():
+    """Test that the correct events are emitted during structured flow
+    execution with all fields validated."""
+
+    class OnboardingState(BaseModel):
+        name: str = ""
+        sent: bool = False
+
+    class OnboardingFlow(Flow[OnboardingState]):
+        @start()
+        def user_signs_up(self):
+            self.state.sent = False
+
+        @listen(user_signs_up)
+        def send_welcome_message(self):
+            self.state.sent = True
+            return f"Welcome, {self.state.name}!"
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = OnboardingFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff(inputs={"name": "Anakin"})
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "OnboardingFlow"
+    assert event_log[0].inputs == {"name": "Anakin"}
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    assert isinstance(event_log[1], MethodExecutionStartedEvent)
+    assert event_log[1].method_name == "user_signs_up"
+
+    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
+    assert event_log[2].method_name == "user_signs_up"
+
+    assert isinstance(event_log[3], MethodExecutionStartedEvent)
+    assert event_log[3].method_name == "send_welcome_message"
+    assert event_log[3].params == {}
+    assert getattr(event_log[3].state, "sent") is False
+
+    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
+    assert event_log[4].method_name == "send_welcome_message"
+    assert getattr(event_log[4].state, "sent") is True
+    assert event_log[4].result == "Welcome, Anakin!"
+
+    assert isinstance(event_log[5], FlowFinishedEvent)
+    assert event_log[5].flow_name == "OnboardingFlow"
+    assert event_log[5].result == "Welcome, Anakin!"
+    assert isinstance(event_log[5].timestamp, datetime)
+
+
+def test_stateless_flow_event_emission():
+    """Test that the correct events are emitted stateless during flow execution
+    with all fields validated."""
+
+    class StatelessFlow(Flow):
+        @start()
+        def init(self):
+            pass
+
+        @listen(init)
+        def process(self):
+            return "Deeds will not be less valiant because they are unpraised."
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = StatelessFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff()
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "StatelessFlow"
+    assert event_log[0].inputs is None
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    assert isinstance(event_log[1], MethodExecutionStartedEvent)
+    assert event_log[1].method_name == "init"
+
+    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
+    assert event_log[2].method_name == "init"
+
+    assert isinstance(event_log[3], MethodExecutionStartedEvent)
+    assert event_log[3].method_name == "process"
+
+    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
+    assert event_log[4].method_name == "process"
+
+    assert isinstance(event_log[5], FlowFinishedEvent)
+    assert event_log[5].flow_name == "StatelessFlow"
+    assert (
+        event_log[5].result
+        == "Deeds will not be less valiant because they are unpraised."
+    )
+    assert isinstance(event_log[5].timestamp, datetime)
--- a/uv.lock
+++ b/uv.lock
@@ -198,15 +198,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/39/e3/893e8757be2612e6c266d9bb58ad2e3651524b5b40cf56761e985a28b13e/asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47", size = 23828 },
 ]

-[[package]]
-name = "asn1crypto"
-version = "1.5.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c9/7f/09065fd9e27da0eda08b4d6897f1c13535066174cc023af248fc2a8d5e5a/asn1crypto-1.5.1-py2.py3-none-any.whl", hash = "sha256:db4e40728b728508912cbb3d44f19ce188f218e9eba635821bb4b68564f8fd67", size = 105045 },
-]
-
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -228,15 +219,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721 },
 ]

-[[package]]
-name = "atpublic"
-version = "5.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/5d/18/b1d247792440378abeeb0853f9daa2a127284b68776af6815990be7fcdb0/atpublic-5.0.tar.gz", hash = "sha256:d5cb6cbabf00ec1d34e282e8ce7cbc9b74ba4cb732e766c24e2d78d1ad7f723f", size = 14646 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/6b/03/2cb0e5326e19b7d877bc9c3a7ef436a30a06835b638580d1f5e21a0409ed/atpublic-5.0-py3-none-any.whl", hash = "sha256:b651dcd886666b1042d1e38158a22a4f2c267748f4e97fde94bc492a4a28a3f3", size = 5207 },
-]
-
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -262,18 +244,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/e4/0e/38cb7b781371e79e9c697fb78f3ccd18fda8bd547d0a2e76e616561a3792/auth0_python-4.7.2-py3-none-any.whl", hash = "sha256:df2224f9b1e170b3aa12d8bc7ff02eadb7cc229307a09ec6b8a55fd1e0e05dc8", size = 131834 },
 ]

-[[package]]
-name = "authlib"
-version = "1.3.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cryptography" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/09/47/df70ecd34fbf86d69833fe4e25bb9ecbaab995c8e49df726dd416f6bb822/authlib-1.3.1.tar.gz", hash = "sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917", size = 146074 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/1f/bc95e43ffb57c05b8efcc376dd55a0240bf58f47ddf5a0f92452b6457b75/Authlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377", size = 223827 },
-]
-
 [[package]]
 name = "autoflake"
 version = "2.3.1"
@@ -595,14 +565,14 @@ wheels = [

 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
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Author	SHA1	Message	Date
Brandon Hancock	b043eb89aa	drop trailing set	2025-02-18 16:02:09 -05:00
Brandon Hancock (bhancock_ai)	bc7b142aa8	Merge branch 'main' into bugfix/fix-backtick-in-agent-response	2025-02-18 15:23:40 -05:00
Brandon Hancock	66eaf2744a	clean up thoughts as well	2025-02-18 15:17:59 -05:00
Brandon Hancock	091713b070	fix issue	2025-02-18 15:10:02 -05:00
Brandon Hancock	d240034570	updating prompts	2025-02-18 14:57:16 -05:00
sharmasundip	7dc47adb5c	fix user memory config issue (#2086 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-02-18 11:59:29 -05:00
Vidit Ostwal	ac819bcb6e	Added functionality to have any llm run test functionality (#2071 ) * Added functionality to have any llm run test functionality * Fixed lint issues * Fixed Linting issues * Fixed unit test case * Fixed unit test * Fixed test case * Fixed unit test case --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-02-18 11:45:26 -05:00
Vini Brasil	b6d668fc66	Implement Flow state export method (#2134 ) This commit implements a method for exporting the state of a flow into a JSON-serializable dictionary. The idea is producing a human-readable version of state that can be inspected or consumed by other systems, hence JSON and not pickling or marshalling. I consider it an export because it's a one-way process, meaning it cannot be loaded back into Python because of complex types.	2025-02-18 08:47:01 -05:00
luctrate	1b488b6da7	fix: Missing required template variable 'current_year' in description (#2085 )	2025-02-13 10:19:52 -03:00
João Moura	d3b398ed52	preparring new version	2025-02-12 18:16:48 -05:00
Vini Brasil	d52fd09602	Fix linting issues (#2115 )	2025-02-12 15:33:16 -05:00
Vini Brasil	d6800d8957	Ensure `@start` methods emit `MethodExecutionStartedEvent` (#2114 ) Previously, `@start` methods triggered a `FlowStartedEvent` but did not emit a `MethodExecutionStartedEvent`. This was fine for a single entry point but caused ambiguity when multiple `@start` methods existed. This commit (1) emits events for starting points, (2) adds tests ensuring ordering, (3) adds more fields to events.	2025-02-12 14:19:41 -06:00
Tony Kipkemboi	2fd7506ed9	Merge pull request #2109 from tonykipkemboi/main docs: update observability documentation and mint configuration	2025-02-12 11:43:28 -05:00
Tony Kipkemboi	161084aff2	Update observability documentation and mint configuration	2025-02-12 10:17:36 -05:00
Tony Kipkemboi	b145cb3247	Merge pull request #2100 from jannikmaierhoefer/main docs: add Langfuse guide	2025-02-12 10:05:07 -05:00
Jannik Maierhöfer	1adbcf697d	fix openlit typo	2025-02-11 13:11:08 +01:00
Jannik Maierhöfer	e51355200a	docs: add Langfuse guide	2025-02-11 12:52:49 +01:00
Brandon Hancock (bhancock_ai)	47818f4f41	updating bedrock docs (#2088 ) Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-02-10 12:48:12 -05:00
Brandon Hancock (bhancock_ai)	9b10fd47b0	incorporate Small update in memory.mdx, fixing Google AI parameters #2008 (#2087 )	2025-02-10 12:17:41 -05:00
Brandon Hancock (bhancock_ai)	c408368267	fix linting issues in new tests (#2089 ) Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-02-10 12:10:53 -05:00
Kevin King	90b3145e92	Updated excel_knowledge_source.py to account for excel files with multiple tabs. (#1921 ) * Updated excel_knowledge_source.py to account for excel sheets that have multiple tabs. The old implementation contained a single df=pd.read_excel(excel_file_path), which only reads the first or most recently used excel sheet. The updated functionality reads all sheets in the excel workbook. * updated load_content() function in excel_knowledge_source.py to reduce memory usage and provide better documentation * accidentally didn't delete the old load_content() function in last commit - corrected this * Added an override for the content field from the inheritted BaseFileKnowledgeSource to account for the change in the load_content method to support excel files with multiple tabs/sheets. This change should ensure it passes the type check test, as it failed before since content was assigned a different type in BaseFileKnowledgeSource * Now removed the commented out imports in _import_dependencies, as requested * Updated excel_knowledge_source to fix linter errors and type errors. Changed inheritence from basefileknowledgesource to baseknowledgesource because basefileknowledgesource's types conflicted (in particular the load_content function and the content class variable. --------- Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-02-10 08:56:32 -08:00
Nicolas Lorin	fbd0e015d5	doc: use the corresponding source depending on filetype (#2038 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-02-09 20:25:33 -03:00
Bradley Goodyear	17e25fb842	Fix a typo in the Task Guardrails section (#2043 ) Co-authored-by: João Moura <joaomdmoura@gmail.com>	2025-02-09 20:23:52 -03:00