fix: Sort imports according to linting rules

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

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)

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)
-
-
-
-
-
-
-
-
-

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
 ---

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"
       ]
     },
     {

docs/tools/faiss_search_tool.mdx

@@ -0,0 +1,142 @@
+# FAISS Search Tool
+
+The FAISS Search Tool enables efficient vector similarity search using Facebook AI Similarity Search (FAISS).
+
+## Usage
+
+```python
+from typing import List, Dict, Any
+from crewai import Agent
+from crewai.tools import FAISSSearchTool
+
+# Initialize tool
+search_tool = FAISSSearchTool(
+    index_type: str = "L2",  # or "IP" for inner product
+    dimension: int = 384,    # Match your embedder's dimension
+    embedder_config: Dict[str, Any] = {
+        "provider": "fastembed",
+        "model": "BAAI/bge-small-en-v1.5"
+    }
+)
+
+# Add documents (with error handling)
+try:
+    search_tool.add_texts([
+        "Document 1 content",
+        "Document 2 content",
+        # ...
+    ])
+except ValueError as e:
+    print(f"Failed to add documents: {e}")
+
+# Add large document sets efficiently
+try:
+    search_tool.add_texts_batch(
+        texts=["Doc 1", "Doc 2", ...],  # Large list of documents
+        batch_size=1000  # Process in batches to manage memory
+    )
+except ValueError as e:
+    print(f"Failed to add documents in batch: {e}")
+
+# Search with error handling
+try:
+    results = search_tool.run(
+        query="search query",
+        k=3,  # Number of results
+        score_threshold=0.6  # Minimum similarity score
+    )
+    for result in results:
+        print(f"Text: {result['text']}, Score: {result['score']}")
+except ValueError as e:
+    print(f"Search failed: {e}")
+
+# Create agent with tool
+agent = Agent(
+    role="researcher",
+    goal="Find relevant information",
+    tools=[search_tool]
+)
+```
+
+## Configuration
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| index_type | str | FAISS index type ("L2" or "IP") |
+| dimension | int | Embedding dimension |
+| embedder_config | dict | Embedder configuration |
+
+## Parameters
+
+### index_type
+- `"L2"`: Euclidean distance (default)
+- `"IP"`: Inner product similarity
+
+### dimension
+Default is 384, which matches the BAAI/bge-small-en-v1.5 model. Adjust this to match your chosen embedder model's output dimension.
+
+### embedder_config
+Configuration for the embedding model. Supports all CrewAI embedder providers:
+- fastembed (default)
+- openai
+- google
+- ollama
+
+## Error Handling
+
+The tool includes comprehensive error handling:
+
+```python
+# Invalid index type
+try:
+    tool = FAISSSearchTool(index_type="INVALID")
+except ValueError as e:
+    print(f"Invalid index type: {e}")
+
+# Empty query
+try:
+    results = tool.run(query="")
+except ValueError as e:
+    print(f"Invalid query: {e}")  # "Query cannot be empty"
+
+# Invalid k value
+try:
+    results = tool.run(query="test", k=0)
+except ValueError as e:
+    print(f"Invalid k: {e}")  # "k must be positive"
+
+# Invalid score threshold
+try:
+    results = tool.run(query="test", score_threshold=1.5)
+except ValueError as e:
+    print(f"Invalid threshold: {e}")  # "score_threshold must be between 0 and 1"
+```
+
+## Performance Considerations
+
+### Memory Management
+For large document sets, use batch processing to manage memory efficiently:
+```python
+# Process documents in batches
+tool.add_texts_batch(texts=large_document_list, batch_size=1000)
+```
+
+### Index Management
+Monitor and manage index size:
+```python
+# Check index size
+print(f"Current index size: {tool.index_size}")
+
+# Check if index is empty
+if tool.is_empty:
+    print("Index is empty")
+
+# Clear index if needed
+tool.clear_index()
+```
+
+### Performance Metrics
+The tool is optimized for performance:
+- Search operations typically complete within 1 second for indices up to 1000 documents
+- Batch processing helps manage memory for large document sets
+- Input sanitization ensures query safety without significant overhead

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"
@@ -37,6 +37,7 @@ dependencies = [
     "tomli>=2.0.2",
     "blinker>=1.9.0",
     "json5>=0.10.0",
+    "faiss-cpu>=1.7.4,<2.0.0",
 ]
 
 [project.urls]
@@ -45,7 +46,7 @@ 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"
 ]

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",

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]

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]

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]

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)
 

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

src/crewai/tools/__init__.py

@@ -1 +1,4 @@
 from .base_tool import BaseTool, tool
+from .faiss_search_tool import FAISSSearchTool
+
+__all__ = ["BaseTool", "tool", "FAISSSearchTool"]

src/crewai/tools/faiss_search_tool.py

@@ -0,0 +1,175 @@
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+import faiss
+import numpy as np
+from pydantic import BaseModel, Field
+
+from crewai.tools import BaseTool
+from crewai.utilities import EmbeddingConfigurator
+
+logger = logging.getLogger(__name__)
+
+class FAISSSearchTool(BaseTool):
+    """FAISS vector similarity search tool for efficient document search."""
+    
+    model_config = {"extra": "allow"}
+    
+    name: str = "FAISS Search Tool"
+    description: str = "Search through documents using FAISS vector similarity search"
+    embedder_config: Optional[Dict[str, Any]] = Field(default=None)
+    dimension: int = Field(default=384)  # Default for BAAI/bge-small-en-v1.5
+    texts: List[str] = Field(default_factory=list)
+    index_type: str = Field(default="L2")
+    index: Any = Field(default=None)  # FAISS index instance
+    embedder: Any = Field(default=None)  # Embedder instance
+    
+    def __init__(
+        self,
+        index_type: str = "L2",
+        dimension: int = 384,
+        embedder_config: Optional[Dict[str, Any]] = None,
+    ):
+        """Initialize FAISS search tool.
+        
+        Args:
+            index_type: Type of FAISS index ("L2" or "IP")
+            dimension: Embedding dimension
+            embedder_config: Configuration for the embedder
+        """
+        super().__init__()
+        self.dimension = dimension
+        self.embedder_config = embedder_config
+        self.index_type = index_type
+        self.index = self._create_index(index_type)
+        self._initialize_embedder()
+
+    def _create_index(self, index_type: str) -> faiss.Index:
+        """Create FAISS index of specified type.
+        
+        Args:
+            index_type: Type of index ("L2" or "IP")
+            
+        Returns:
+            FAISS index instance
+            
+        Raises:
+            ValueError: If index_type is not supported
+        """
+        if index_type == "L2":
+            return faiss.IndexFlatL2(self.dimension)
+        elif index_type == "IP":
+            return faiss.IndexFlatIP(self.dimension)
+        else:
+            raise ValueError(f"Unsupported index type: {index_type}")
+
+    def _initialize_embedder(self):
+        """Initialize the embedder using the provided configuration."""
+        from crewai.knowledge.embedder.fastembed import FastEmbed
+        self.embedder = FastEmbed()
+
+    def _sanitize_query(self, query: str) -> str:
+        """Remove potentially harmful characters from query.
+        
+        Args:
+            query: Input query string
+            
+        Returns:
+            Sanitized query string
+        """
+        return re.sub(r'[^\w\s]', '', query)
+
+    def _run(
+        self,
+        query: str,
+        k: int = 3,
+        score_threshold: float = 0.6
+    ) -> List[Dict[str, Any]]:
+        """Search for similar texts using FAISS.
+        
+        Args:
+            query: Search query
+            k: Number of results to return
+            score_threshold: Minimum similarity score threshold
+            
+        Returns:
+            List of dictionaries containing matched texts and scores
+            
+        Raises:
+            ValueError: If input parameters are invalid
+        """
+        if not query.strip():
+            raise ValueError("Query cannot be empty")
+        if k < 1:
+            raise ValueError("k must be positive")
+        if not 0 <= score_threshold <= 1:
+            raise ValueError("score_threshold must be between 0 and 1")
+            
+        logger.debug(f"Searching for query: {query} with k={k}")
+        query = self._sanitize_query(query)
+        query_embedding = self.embedder.embed_text(query)
+        
+        D, I = self.index.search(
+            np.array([query_embedding], dtype=np.float32),
+            k
+        )
+        
+        results = []
+        for i, (dist, idx) in enumerate(zip(D[0], I[0])):
+            if idx < len(self.texts):
+                score = 1.0 / (1.0 + dist)  # Convert distance to similarity score
+                if score >= score_threshold:
+                    results.append({
+                        "text": self.texts[idx],
+                        "score": score
+                    })
+        return results
+
+    def add_texts(self, texts: List[str]) -> None:
+        """Add texts to the search index.
+        
+        Args:
+            texts: List of texts to add
+            
+        Raises:
+            ValueError: If embedding or indexing fails
+        """
+        try:
+            embeddings = self.embedder.embed_texts(texts)
+            self.index.add(np.array(embeddings, dtype=np.float32))
+            self.texts.extend(texts)
+        except Exception as e:
+            raise ValueError(f"Failed to add texts: {str(e)}")
+            
+    def add_texts_batch(self, texts: List[str], batch_size: int = 1000) -> None:
+        """Add texts in batches to prevent memory issues.
+        
+        Args:
+            texts: List of texts to add
+            batch_size: Size of each batch
+            
+        Raises:
+            ValueError: If batch_size is invalid
+        """
+        if batch_size < 1:
+            raise ValueError("batch_size must be positive")
+            
+        for i in range(0, len(texts), batch_size):
+            batch = texts[i:i + batch_size]
+            self.add_texts(batch)
+            
+    def clear_index(self) -> None:
+        """Clear the index and stored texts."""
+        self.index = self._create_index(self.index_type)
+        self.texts = []
+        
+    @property
+    def index_size(self) -> int:
+        """Return number of vectors in index."""
+        return len(self.texts)
+        
+    @property
+    def is_empty(self) -> bool:
+        """Check if index is empty."""
+        return len(self.texts) == 0

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)

tests/tools/test_faiss_search_tool.py

@@ -0,0 +1,46 @@
+import time
+
+import numpy as np
+import pytest
+
+from crewai.tools import FAISSSearchTool
+
+def test_faiss_search_tool_initialization():
+    tool = FAISSSearchTool()
+    assert tool.name == "FAISS Search Tool"
+    assert tool.dimension == 384
+
+def test_faiss_search_with_texts():
+    tool = FAISSSearchTool()
+    texts = [
+        "The quick brown fox",
+        "jumps over the lazy dog",
+        "A completely different text"
+    ]
+    tool.add_texts(texts)
+    
+    results = tool.run(
+        query="quick fox",
+        k=2,
+        score_threshold=0.5
+    )
+    
+    assert len(results) > 0
+    assert isinstance(results[0]["text"], str)
+    assert isinstance(results[0]["score"], float)
+
+def test_faiss_search_threshold_filtering():
+    tool = FAISSSearchTool()
+    texts = ["Text A", "Text B", "Text C"]
+    tool.add_texts(texts)
+    
+    results = tool.run(
+        query="Something completely different",
+        score_threshold=0.99  # High threshold
+    )
+    
+    assert len(results) == 0  # No results above threshold
+
+def test_invalid_index_type():
+    with pytest.raises(ValueError):
+        FAISSSearchTool(index_type="INVALID")

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 },
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-[[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 = [
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-]
-
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -228,15 +219,6 @@ wheels = [
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-[[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 = [
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-]
-
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -262,18 +244,6 @@ wheels = [
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-[[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 = [
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-]
-
 [[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'" },
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+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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+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
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 [[package]]
@@ -649,7 +619,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.100.1"
+version = "0.102.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -733,7 +703,7 @@ requires-dist = [
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     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.36.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -782,33 +752,24 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.32.1"
+version = "0.36.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "beautifulsoup4" },
     { name = "chromadb" },
+    { name = "click" },
     { name = "crewai" },
     { name = "docker" },
-    { name = "docx2txt" },
     { name = "embedchain" },
     { name = "lancedb" },
-    { name = "linkup-sdk" },
     { name = "openai" },
-    { name = "patronus" },
     { name = "pydantic" },
     { name = "pyright" },
     { name = "pytube" },
     { name = "requests" },
-    { name = "scrapegraph-py" },
-    { name = "selenium" },
-    { name = "serpapi" },
-    { name = "snowflake" },
-    { name = "spider-client" },
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