Support multi org in CLI (#2969)

* feat: support to list, switch and see your current organization

* feat: store the current org after logged in

* feat: filtering agents, tools and their actions by organization_uuid if present

* fix linter offenses

* refactor: propagate the current org thought Header instead of params

* refactor: rename org column name to ID instead of Handle

---------

Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>

.github/workflows/linter.yml

@@ -30,4 +30,7 @@ jobs:
       - name: Run Ruff on Changed Files
         if: ${{ steps.changed-files.outputs.files != '' }}
         run: |
-          echo "${{ steps.changed-files.outputs.files }}" | tr " " "\n" | xargs -I{} ruff check "{}"
+          echo "${{ steps.changed-files.outputs.files }}" \
+            | tr ' ' '\n' \
+            | grep -v 'src/crewai/cli/templates/' \
+            | xargs -I{} ruff check "{}"

.github/workflows/tests.yml

@@ -14,7 +14,7 @@ jobs:
     timeout-minutes: 15
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12']
+        python-version: ['3.10', '3.11', '3.12', '3.13']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4

README.md

@@ -1,27 +1,70 @@
-<div align="center">
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework">
+  </a>
+</p>
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11239" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
+</p>
 
-![Logo of CrewAI](./docs/images/crewai_logo.png)
+<p align="center">
+  <a href="https://crewai.com">Homepage</a>
+  ·
+  <a href="https://docs.crewai.com">Docs</a>
+  ·
+  <a href="https://app.crewai.com">Start Cloud Trial</a>
+  ·
+  <a href="https://blog.crewai.com">Blog</a>
+  ·
+  <a href="https://community.crewai.com">Forum</a>
+</p>
 
-</div>
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/network/members">
+    <img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/issues">
+    <img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/pulls">
+    <img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests">
+  </a>
+  <a href="https://opensource.org/licenses/MIT">
+    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
+  </a>
+</p>
+
+<p align="center">
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version">
+  </a>
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads">
+  </a>
+  <a href="https://twitter.com/crewAIInc">
+    <img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow">
+  </a>
+</p>
 
 ### Fast and Flexible Multi-Agent Automation Framework
 
-CrewAI is a lean, lightning-fast Python framework built entirely from
-scratch—completely **independent of LangChain or other agent frameworks**.
-It empowers developers with both high-level simplicity and precise low-level
-control, ideal for creating autonomous AI agents tailored to any scenario.
+> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**.
+> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
 
 - **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
 - **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
 
-With over 100,000 developers certified through our community courses at
-[learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
+With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
 standard for enterprise-ready AI automation.
 
 # CrewAI Enterprise Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
-that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -35,21 +78,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
 - **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful,
-reliable solution to transform complex business processes into efficient,
+CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
-<h3>
-
-[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Discourse](https://community.crewai.com)
-
-</h3>
-
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-
-</div>
-
 ## Table of contents
 
 - [Why CrewAI?](#why-crewai)
@@ -88,7 +119,12 @@ CrewAI empowers developers and enterprises to confidently build intelligent auto
 
 ## Getting Started
 
-### Learning Resources
+Setup and run your first CrewAI agents by following this tutorial.
+
+[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
+
+###
+ Learning Resources
 
 Learn CrewAI through our comprehensive courses:
 
@@ -125,7 +161,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 
@@ -367,7 +403,7 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-CrewAI stands apart as a lean, standalone, high-performance framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
+CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
 
 - **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
 - **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.

docs/common-room-tracking.js

@@ -0,0 +1,18 @@
+(function() {
+  if (typeof window === 'undefined') return;
+  if (typeof window.signals !== 'undefined') return;
+  var script = document.createElement('script');
+  script.src = 'https://cdn.cr-relay.com/v1/site/883520f4-c431-44be-80e7-e123a1ee7a2b/signals.js';
+  script.async = true;
+  window.signals = Object.assign(
+    [],
+    ['page', 'identify', 'form'].reduce(function (acc, method){
+      acc[method] = function () {
+        signals.push([method, arguments]);
+        return signals;
+      };
+     return acc;
+    }, {})
+  );
+  document.head.appendChild(script);
+})(); 

docs/concepts/agents.mdx

@@ -43,7 +43,6 @@ The Visual Agent Builder enables:
 | **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
 | **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
 | **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
-| **Memory** _(optional)_                 | `memory`                 | `bool`                        | Whether the agent should maintain memory of interactions. Default is True.                                            |
 | **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
 | **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
 | **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
@@ -156,7 +155,6 @@ agent = Agent(
               "you excel at finding patterns in complex datasets.",
     llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
     function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    memory=True,  # Default: True
     verbose=False,  # Default: False
     allow_delegation=False,  # Default: False
     max_iter=20,  # Default: 20 iterations
@@ -537,7 +535,6 @@ The context window management feature works automatically in the background. You
 - Adjust `max_iter` and `max_retry_limit` based on task complexity
 
 ### Memory and Context Management
-- Use `memory: true` for tasks requiring historical context
 - Leverage `knowledge_sources` for domain-specific information
 - Configure `embedder` when using custom embedding models
 - Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
@@ -585,7 +582,6 @@ The context window management feature works automatically in the background. You
    - Review code sandbox settings
 
 4. **Memory Issues**: If agent responses seem inconsistent:
-   - Verify memory is enabled
    - Check knowledge source configuration
    - Review conversation history management
 

docs/concepts/crews.mdx

@@ -325,12 +325,12 @@ for result in results:
 
 # Example of using kickoff_async
 inputs = {'topic': 'AI in healthcare'}
-async_result = my_crew.kickoff_async(inputs=inputs)
+async_result = await my_crew.kickoff_async(inputs=inputs)
 print(async_result)
 
 # Example of using kickoff_for_each_async
 inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
-async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
 for async_result in async_results:
     print(async_result)
 ```

docs/concepts/memory.mdx

@@ -46,22 +46,96 @@ crew = Crew(
 - **Storage Location**: Platform-specific location via `appdirs` package
 - **Custom Storage Directory**: Set `CREWAI_STORAGE_DIR` environment variable
 
-### Custom Embedder Configuration
+## Storage Location Transparency
+
+<Info>
+**Understanding Storage Locations**: CrewAI uses platform-specific directories to store memory and knowledge files following OS conventions. Understanding these locations helps with production deployments, backups, and debugging.
+</Info>
+
+### Where CrewAI Stores Files
+
+By default, CrewAI uses the `appdirs` library to determine storage locations following platform conventions. Here's exactly where your files are stored:
+
+#### Default Storage Locations by Platform
+
+**macOS:**
+```
+~/Library/Application Support/CrewAI/{project_name}/
+├── knowledge/           # Knowledge base ChromaDB files
+├── short_term_memory/   # Short-term memory ChromaDB files  
+├── long_term_memory/    # Long-term memory ChromaDB files
+├── entities/            # Entity memory ChromaDB files
+└── long_term_memory_storage.db  # SQLite database
+```
+
+**Linux:**
+```
+~/.local/share/CrewAI/{project_name}/
+├── knowledge/
+├── short_term_memory/
+├── long_term_memory/
+├── entities/
+└── long_term_memory_storage.db
+```
+
+**Windows:**
+```
+C:\Users\{username}\AppData\Local\CrewAI\{project_name}\
+├── knowledge\
+├── short_term_memory\
+├── long_term_memory\
+├── entities\
+└── long_term_memory_storage.db
+```
+
+### Finding Your Storage Location
+
+To see exactly where CrewAI is storing files on your system:
+
 ```python
+from crewai.utilities.paths import db_storage_path
+import os
+
+# Get the base storage path
+storage_path = db_storage_path()
+print(f"CrewAI storage location: {storage_path}")
+
+# List all CrewAI storage directories
+if os.path.exists(storage_path):
+    print("\nStored files and directories:")
+    for item in os.listdir(storage_path):
+        item_path = os.path.join(storage_path, item)
+        if os.path.isdir(item_path):
+            print(f"📁 {item}/")
+            # Show ChromaDB collections
+            if os.path.exists(item_path):
+                for subitem in os.listdir(item_path):
+                    print(f"   └── {subitem}")
+        else:
+            print(f"📄 {item}")
+else:
+    print("No CrewAI storage directory found yet.")
+```
+
+### Controlling Storage Locations
+
+#### Option 1: Environment Variable (Recommended)
+```python
+import os
+from crewai import Crew
+
+# Set custom storage location
+os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage"
+
+# All memory and knowledge will now be stored in ./my_project_storage/
 crew = Crew(
     agents=[...],
     tasks=[...],
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-3-small"
-        }
-    }
+    memory=True
 )
 ```
 
-### Custom Storage Paths
+#### Option 2: Custom Storage Paths
 ```python
 import os
 from crewai import Crew
@@ -69,16 +143,547 @@ from crewai.memory import LongTermMemory
 from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
 
 # Configure custom storage location
+custom_storage_path = "./storage"
+os.makedirs(custom_storage_path, exist_ok=True)
+
 crew = Crew(
     memory=True,
     long_term_memory=LongTermMemory(
         storage=LTMSQLiteStorage(
-            db_path=os.getenv("CREWAI_STORAGE_DIR", "./storage") + "/memory.db"
+            db_path=f"{custom_storage_path}/memory.db"
         )
     )
 )
 ```
 
+#### Option 3: Project-Specific Storage
+```python
+import os
+from pathlib import Path
+
+# Store in project directory
+project_root = Path(__file__).parent
+storage_dir = project_root / "crewai_storage"
+
+os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
+
+# Now all storage will be in your project directory
+```
+
+### Embedding Provider Defaults
+
+<Info>
+**Default Embedding Provider**: CrewAI defaults to OpenAI embeddings for consistency and reliability. You can easily customize this to match your LLM provider or use local embeddings.
+</Info>
+
+#### Understanding Default Behavior
+```python
+# When using Claude as your LLM...
+from crewai import Agent, LLM
+
+agent = Agent(
+    role="Analyst",
+    goal="Analyze data",
+    backstory="Expert analyst",
+    llm=LLM(provider="anthropic", model="claude-3-sonnet")  # Using Claude
+)
+
+# CrewAI will use OpenAI embeddings by default for consistency
+# You can easily customize this to match your preferred provider
+```
+
+#### Customizing Embedding Providers
+```python
+from crewai import Crew
+
+# Option 1: Match your LLM provider
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    embedder={
+        "provider": "anthropic",  # Match your LLM provider
+        "config": {
+            "api_key": "your-anthropic-key",
+            "model": "text-embedding-3-small"
+        }
+    }
+)
+
+# Option 2: Use local embeddings (no external API calls)
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {"model": "mxbai-embed-large"}
+    }
+)
+```
+
+### Debugging Storage Issues
+
+#### Check Storage Permissions
+```python
+import os
+from crewai.utilities.paths import db_storage_path
+
+storage_path = db_storage_path()
+print(f"Storage path: {storage_path}")
+print(f"Path exists: {os.path.exists(storage_path)}")
+print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}")
+
+# Create with proper permissions
+if not os.path.exists(storage_path):
+    os.makedirs(storage_path, mode=0o755, exist_ok=True)
+    print(f"Created storage directory: {storage_path}")
+```
+
+#### Inspect ChromaDB Collections
+```python
+import chromadb
+from crewai.utilities.paths import db_storage_path
+
+# Connect to CrewAI's ChromaDB
+storage_path = db_storage_path()
+chroma_path = os.path.join(storage_path, "knowledge")
+
+if os.path.exists(chroma_path):
+    client = chromadb.PersistentClient(path=chroma_path)
+    collections = client.list_collections()
+    
+    print("ChromaDB Collections:")
+    for collection in collections:
+        print(f"  - {collection.name}: {collection.count()} documents")
+else:
+    print("No ChromaDB storage found")
+```
+
+#### Reset Storage (Debugging)
+```python
+from crewai import Crew
+
+# Reset all memory storage
+crew = Crew(agents=[...], tasks=[...], memory=True)
+
+# Reset specific memory types
+crew.reset_memories(command_type='short')     # Short-term memory
+crew.reset_memories(command_type='long')      # Long-term memory  
+crew.reset_memories(command_type='entity')    # Entity memory
+crew.reset_memories(command_type='knowledge') # Knowledge storage
+```
+
+### Production Best Practices
+
+1. **Set `CREWAI_STORAGE_DIR`** to a known location in production for better control
+2. **Choose explicit embedding providers** to match your LLM setup
+3. **Monitor storage directory size** for large-scale deployments
+4. **Include storage directories** in your backup strategy
+5. **Set appropriate file permissions** (0o755 for directories, 0o644 for files)
+6. **Use project-relative paths** for containerized deployments
+
+### Common Storage Issues
+
+**"ChromaDB permission denied" errors:**
+```bash
+# Fix permissions
+chmod -R 755 ~/.local/share/CrewAI/
+```
+
+**"Database is locked" errors:**
+```python
+# Ensure only one CrewAI instance accesses storage
+import fcntl
+import os
+
+storage_path = db_storage_path()
+lock_file = os.path.join(storage_path, ".crewai.lock")
+
+with open(lock_file, 'w') as f:
+    fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+    # Your CrewAI code here
+```
+
+**Storage not persisting between runs:**
+```python
+# Verify storage location is consistent
+import os
+print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR"))
+print("Current working directory:", os.getcwd())
+print("Computed storage path:", db_storage_path())
+```
+
+## Custom Embedder Configuration
+
+CrewAI supports multiple embedding providers to give you flexibility in choosing the best option for your use case. Here's a comprehensive guide to configuring different embedding providers for your memory system.
+
+### Why Choose Different Embedding Providers?
+
+- **Cost Optimization**: Local embeddings (Ollama) are free after initial setup
+- **Privacy**: Keep your data local with Ollama or use your preferred cloud provider
+- **Performance**: Some models work better for specific domains or languages
+- **Consistency**: Match your embedding provider with your LLM provider
+- **Compliance**: Meet specific regulatory or organizational requirements
+
+### OpenAI Embeddings (Default)
+
+OpenAI provides reliable, high-quality embeddings that work well for most use cases.
+
+```python
+from crewai import Crew
+
+# Basic OpenAI configuration (uses environment OPENAI_API_KEY)
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small"  # or "text-embedding-3-large"
+        }
+    }
+)
+
+# Advanced OpenAI configuration
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": "your-openai-api-key",  # Optional: override env var
+            "model": "text-embedding-3-large",
+            "dimensions": 1536,  # Optional: reduce dimensions for smaller storage
+            "organization_id": "your-org-id"  # Optional: for organization accounts
+        }
+    }
+)
+```
+
+### Azure OpenAI Embeddings
+
+For enterprise users with Azure OpenAI deployments.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",  # Use openai provider for Azure
+        "config": {
+            "api_key": "your-azure-api-key",
+            "api_base": "https://your-resource.openai.azure.com/",
+            "api_type": "azure",
+            "api_version": "2023-05-15",
+            "model": "text-embedding-3-small",
+            "deployment_id": "your-deployment-name"  # Azure deployment name
+        }
+    }
+)
+```
+
+### Google AI Embeddings
+
+Use Google's text embedding models for integration with Google Cloud services.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "google",
+        "config": {
+            "api_key": "your-google-api-key",
+            "model": "text-embedding-004"  # or "text-embedding-preview-0409"
+        }
+    }
+)
+```
+
+### Vertex AI Embeddings
+
+For Google Cloud users with Vertex AI access.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "project_id": "your-gcp-project-id",
+            "region": "us-central1",  # or your preferred region
+            "api_key": "your-service-account-key",
+            "model_name": "textembedding-gecko"
+        }
+    }
+)
+```
+
+### Ollama Embeddings (Local)
+
+Run embeddings locally for privacy and cost savings.
+
+```python
+# First, install and run Ollama locally, then pull an embedding model:
+# ollama pull mxbai-embed-large
+
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large",  # or "nomic-embed-text"
+            "url": "http://localhost:11434/api/embeddings"  # Default Ollama URL
+        }
+    }
+)
+
+# For custom Ollama installations
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large",
+            "url": "http://your-ollama-server:11434/api/embeddings"
+        }
+    }
+)
+```
+
+### Cohere Embeddings
+
+Use Cohere's embedding models for multilingual support.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "cohere",
+        "config": {
+            "api_key": "your-cohere-api-key",
+            "model": "embed-english-v3.0"  # or "embed-multilingual-v3.0"
+        }
+    }
+)
+```
+
+### VoyageAI Embeddings
+
+High-performance embeddings optimized for retrieval tasks.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "voyageai",
+        "config": {
+            "api_key": "your-voyage-api-key",
+            "model": "voyage-large-2",  # or "voyage-code-2" for code
+            "input_type": "document"  # or "query"
+        }
+    }
+)
+```
+
+### AWS Bedrock Embeddings
+
+For AWS users with Bedrock access.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "bedrock",
+        "config": {
+            "aws_access_key_id": "your-access-key",
+            "aws_secret_access_key": "your-secret-key",
+            "region_name": "us-east-1",
+            "model": "amazon.titan-embed-text-v1"
+        }
+    }
+)
+```
+
+### Hugging Face Embeddings
+
+Use open-source models from Hugging Face.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "huggingface",
+        "config": {
+            "api_key": "your-hf-token",  # Optional for public models
+            "model": "sentence-transformers/all-MiniLM-L6-v2",
+            "api_url": "https://api-inference.huggingface.co"  # or your custom endpoint
+        }
+    }
+)
+```
+
+### IBM Watson Embeddings
+
+For IBM Cloud users.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "api_key": "your-watson-api-key",
+            "url": "your-watson-instance-url",
+            "model": "ibm/slate-125m-english-rtrvr"
+        }
+    }
+)
+```
+
+### Choosing the Right Embedding Provider
+
+| Provider | Best For | Pros | Cons |
+|:---------|:----------|:------|:------|
+| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
+| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
+| **Google AI** | Google ecosystem | Good performance | Requires Google account |
+| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
+| **Cohere** | Multilingual content | Great language support | Specialized use case |
+| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
+
+### Environment Variable Configuration
+
+For security, store API keys in environment variables:
+
+```python
+import os
+
+# Set environment variables
+os.environ["OPENAI_API_KEY"] = "your-openai-key"
+os.environ["GOOGLE_API_KEY"] = "your-google-key"
+os.environ["COHERE_API_KEY"] = "your-cohere-key"
+
+# Use without exposing keys in code
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small"
+            # API key automatically loaded from environment
+        }
+    }
+)
+```
+
+### Testing Different Embedding Providers
+
+Compare embedding providers for your specific use case:
+
+```python
+from crewai import Crew
+from crewai.utilities.paths import db_storage_path
+
+# Test different providers with the same data
+providers_to_test = [
+    {
+        "name": "OpenAI",
+        "config": {
+            "provider": "openai",
+            "config": {"model": "text-embedding-3-small"}
+        }
+    },
+    {
+        "name": "Ollama",
+        "config": {
+            "provider": "ollama", 
+            "config": {"model": "mxbai-embed-large"}
+        }
+    }
+]
+
+for provider in providers_to_test:
+    print(f"\nTesting {provider['name']} embeddings...")
+    
+    # Create crew with specific embedder
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        memory=True,
+        embedder=provider['config']
+    )
+    
+    # Run your test and measure performance
+    result = crew.kickoff()
+    print(f"{provider['name']} completed successfully")
+```
+
+### Troubleshooting Embedding Issues
+
+**Model not found errors:**
+```python
+# Verify model availability
+from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+
+configurator = EmbeddingConfigurator()
+try:
+    embedder = configurator.configure_embedder({
+        "provider": "ollama",
+        "config": {"model": "mxbai-embed-large"}
+    })
+    print("Embedder configured successfully")
+except Exception as e:
+    print(f"Configuration error: {e}")
+```
+
+**API key issues:**
+```python
+import os
+
+# Check if API keys are set
+required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"]
+for key in required_keys:
+    if os.getenv(key):
+        print(f"✅ {key} is set")
+    else:
+        print(f"❌ {key} is not set")
+```
+
+**Performance comparison:**
+```python
+import time
+
+def test_embedding_performance(embedder_config, test_text="This is a test document"):
+    start_time = time.time()
+    
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        memory=True,
+        embedder=embedder_config
+    )
+    
+    # Simulate memory operation
+    crew.kickoff()
+    
+    end_time = time.time()
+    return end_time - start_time
+
+# Compare performance
+openai_time = test_embedding_performance({
+    "provider": "openai",
+    "config": {"model": "text-embedding-3-small"}
+})
+
+ollama_time = test_embedding_performance({
+    "provider": "ollama", 
+    "config": {"model": "mxbai-embed-large"}
+})
+
+print(f"OpenAI: {openai_time:.2f}s")
+print(f"Ollama: {ollama_time:.2f}s")
+```
+
 ## 2. User Memory with Mem0 (Legacy)
 
 <Warning>

docs/concepts/reasoning.mdx

@@ -6,11 +6,11 @@ icon: brain
 
 ## Overview
 
-Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
+Agent reasoning is a feature that allows agents to reflect on a task and create a plan before and during execution. This helps agents approach tasks more methodically and adapt their strategy as they progress through complex tasks.
 
 ## Usage
 
-To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
+To enable reasoning for an agent, set `reasoning=True` when creating the agent:
 
 ```python
 from crewai import Agent
@@ -19,13 +19,43 @@ agent = Agent(
     role="Data Analyst",
     goal="Analyze complex datasets and provide insights",
     backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
+    reasoning=True,  # Enable basic reasoning
     max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
 )
 ```
 
+### Interval-based Reasoning
+
+To enable periodic reasoning during task execution, set `reasoning_interval` to specify how often the agent should re-evaluate its plan:
+
+```python
+agent = Agent(
+    role="Research Analyst",
+    goal="Find comprehensive information about a topic",
+    backstory="You are a skilled research analyst who methodically approaches information gathering.",
+    reasoning=True,
+    reasoning_interval=3,  # Re-evaluate plan every 3 steps
+)
+```
+
+### Adaptive Reasoning
+
+For more dynamic reasoning that adapts to the execution context, enable `adaptive_reasoning`:
+
+```python
+agent = Agent(
+    role="Strategic Advisor",
+    goal="Provide strategic advice based on market research",
+    backstory="You are an experienced strategic advisor who adapts your approach based on the information you discover.",
+    reasoning=True,
+    adaptive_reasoning=True,  # Agent decides when to reason based on context
+)
+```
+
 ## How It Works
 
+### Initial Reasoning
+
 When reasoning is enabled, before executing a task, the agent will:
 
 1. Reflect on the task and create a detailed plan
@@ -33,7 +63,17 @@ When reasoning is enabled, before executing a task, the agent will:
 3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
 4. Inject the reasoning plan into the task description before execution
 
-This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
+### Mid-execution Reasoning
+
+During task execution, the agent can re-evaluate and adjust its plan based on:
+
+1. **Interval-based reasoning**: The agent reasons after a fixed number of steps (specified by `reasoning_interval`)
+2. **Adaptive reasoning**: The agent uses its LLM to intelligently decide when reasoning is needed based on:
+   - Current execution context (task description, expected output, steps taken)
+   - The agent's own judgment about whether strategic reassessment would be beneficial
+   - Automatic fallback when recent errors or failures are detected in the execution
+
+This mid-execution reasoning helps agents adapt to new information, overcome obstacles, and optimize their approach as they work through complex tasks.
 
 ## Configuration Options
 
@@ -45,35 +85,44 @@ This process helps the agent break down complex tasks into manageable steps and
     Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
 </ParamField>
 
-## Example
+<ParamField body="reasoning_interval" type="int" default="None"> 
+    Interval of steps after which the agent should reason again during execution. If None, reasoning only happens before execution.
+</ParamField>
 
-Here's a complete example:
+<ParamField body="adaptive_reasoning" type="bool" default="False"> 
+    Whether the agent should adaptively decide when to reason during execution based on context.
+</ParamField>
 
-```python
-from crewai import Agent, Task, Crew
+## Technical Implementation
 
-# Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
-    reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
-)
+### Interval-based Reasoning
 
-# Create a task
-analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
-)
+The interval-based reasoning feature works by:
 
-# Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
-result = crew.kickoff()
+1. Tracking the number of steps since the last reasoning event
+2. Triggering reasoning when `steps_since_reasoning >= reasoning_interval`
+3. Resetting the counter after each reasoning event
+4. Generating an updated plan based on current progress
 
-print(result)
-```
+This creates a predictable pattern of reflection during task execution, which is useful for complex tasks where periodic reassessment is beneficial.
+
+### Adaptive Reasoning
+
+The adaptive reasoning feature uses LLM function calling to determine when reasoning should occur:
+
+1. **LLM-based decision**: The agent's LLM evaluates the current execution context (task description, expected output, steps taken so far) to decide if reasoning is needed
+2. **Error detection fallback**: When recent messages contain error indicators like "error", "exception", "failed", etc., reasoning is automatically triggered
+
+This creates an intelligent reasoning pattern where the agent uses its own judgment to determine when strategic reassessment would be most beneficial, while maintaining automatic error recovery.
+
+### Mid-execution Reasoning Process
+
+When mid-execution reasoning is triggered, the agent:
+
+1. Summarizes current progress (steps taken, tools used, recent actions)
+2. Evaluates the effectiveness of the current approach
+3. Adjusts the plan based on new information and challenges encountered
+4. Continues execution with the updated plan
 
 ## Error Handling
 
@@ -93,7 +142,7 @@ agent = Agent(
     role="Data Analyst",
     goal="Analyze data and provide insights",
     reasoning=True,
-    max_reasoning_attempts=3
+    reasoning_interval=5  # Re-evaluate plan every 5 steps
 )
 
 # Create a task
@@ -144,4 +193,33 @@ I'll analyze the sales data to identify the top 3 trends.
 READY: I am ready to execute the task.
 ```
 
-This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.
+During execution, the agent might generate an updated plan:
+
+```
+Based on progress so far (3 steps completed):
+
+Updated Reasoning Plan:
+After examining the data structure and initial exploratory analysis, I need to adjust my approach:
+
+1. Current findings:
+   - The data shows seasonal patterns that need deeper investigation
+   - Customer segments show varying purchasing behaviors
+   - There are outliers in the luxury product category
+
+2. Adjusted approach:
+   - Focus more on seasonal analysis with year-over-year comparisons
+   - Segment analysis by both demographics and purchasing frequency
+   - Investigate the luxury product category anomalies
+
+3. Next steps:
+   - Apply time series analysis to better quantify seasonal patterns
+   - Create customer cohorts for more precise segmentation
+   - Perform statistical tests on the luxury category data
+
+4. Expected outcome:
+   Still on track to deliver the top 3 sales trends, but with more precise quantification and actionable insights.
+
+READY: I am ready to continue executing the task.
+```
+
+This mid-execution reasoning helps the agent adapt its approach based on what it has learned during the initial steps of the task.

docs/docs.json

@@ -85,7 +85,12 @@
           {
             "group": "MCP Integration",
             "pages": [
-              "mcp/crewai-mcp-integration"
+              "mcp/overview",
+              "mcp/stdio",
+              "mcp/sse",
+              "mcp/streamable-http",
+              "mcp/multiple-servers",
+              "mcp/security"
             ]
           },
           {
@@ -164,8 +169,7 @@
                   "tools/ai-ml/llamaindextool",
                   "tools/ai-ml/langchaintool",
                   "tools/ai-ml/ragtool",
-                  "tools/ai-ml/codeinterpretertool",
-                  "tools/ai-ml/patronustools"
+                  "tools/ai-ml/codeinterpretertool"
                 ]
               },
               {
@@ -190,40 +194,43 @@
             ]
           },
           {
-            "group": "Agent Monitoring & Observability",
+            "group": "Observability",
             "pages": [
-              "how-to/agentops-observability",
-              "how-to/arize-phoenix-observability",
-              "how-to/langfuse-observability",
-              "how-to/langtrace-observability",
-              "how-to/mlflow-observability",
-              "how-to/openlit-observability",
-              "how-to/opik-observability",
-              "how-to/portkey-observability",
-              "how-to/weave-integration"
+              "observability/overview",
+              "observability/agentops",
+              "observability/arize-phoenix",
+              "observability/langfuse",
+              "observability/langtrace",
+              "observability/mlflow",
+              "observability/openlit",
+              "observability/opik",
+              "observability/patronus-evaluation",
+              "observability/portkey",
+              "observability/weave"
             ]
           },
           {
             "group": "Learn",
             "pages": [
-              "how-to/conditional-tasks",
-              "how-to/coding-agents",
-              "how-to/create-custom-tools",
-              "how-to/custom-llm",
-              "how-to/custom-manager-agent",
-              "how-to/customizing-agents",
-              "how-to/dalle-image-generation",
-              "how-to/force-tool-output-as-result",
-              "how-to/hierarchical-process",
-              "how-to/human-in-the-loop",
-              "how-to/human-input-on-execution",
-              "how-to/kickoff-async",
-              "how-to/kickoff-for-each",
-              "how-to/llm-connections",
-              "how-to/multimodal-agents",
-              "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/sequential-process",
-              "how-to/using-annotations"
+              "learn/overview",
+              "learn/llm-selection-guide",
+              "learn/conditional-tasks",
+              "learn/coding-agents",
+              "learn/create-custom-tools",
+              "learn/custom-llm",
+              "learn/custom-manager-agent",
+              "learn/customizing-agents",
+              "learn/dalle-image-generation",
+              "learn/force-tool-output-as-result",
+              "learn/hierarchical-process",
+              "learn/human-input-on-execution",
+              "learn/kickoff-async",
+              "learn/kickoff-for-each",
+              "learn/llm-connections",
+              "learn/multimodal-agents",
+              "learn/replay-tasks-from-latest-crew-kickoff",
+              "learn/sequential-process",
+              "learn/using-annotations"
             ]
           },
           {
@@ -267,6 +274,7 @@
               "enterprise/guides/slack-trigger",
               "enterprise/guides/team-management",
               "enterprise/guides/webhook-automation",
+              "enterprise/guides/human-in-the-loop",
               "enterprise/guides/zapier-trigger"
             ]
           },
@@ -352,7 +360,7 @@
   "navbar": {
     "links": [
       {
-        "label": "Start Free Trial",
+        "label": "Start Cloud Trial",
         "href": "https://app.crewai.com"
       }
     ],

docs/enterprise/features/hallucination-guardrail.mdx

@@ -25,8 +25,13 @@ AI hallucinations occur when language models generate content that appears plaus
 from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 from crewai import LLM
 
-# Initialize the guardrail with reference context
+# Basic usage - will use task's expected_output as context
 guardrail = HallucinationGuardrail(
+    llm=LLM(model="gpt-4o-mini")
+)
+
+# With explicit reference context
+context_guardrail = HallucinationGuardrail(
     context="AI helps with various tasks including analysis and generation.",
     llm=LLM(model="gpt-4o-mini")
 )

docs/enterprise/features/tool-repository.mdx

@@ -21,6 +21,7 @@ Before using the Tool Repository, ensure you have:
 
 - A [CrewAI Enterprise](https://app.crewai.com) account
 - [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
+- uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
 - [Git](https://git-scm.com) installed and configured
 - Access permissions to publish or install tools in your CrewAI Enterprise organization
 

docs/enterprise/guides/human-in-the-loop.mdx

@@ -0,0 +1,78 @@
+---
+title: "HITL Workflows"
+description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
+icon: "user-check"
+---
+
+Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
+
+## Setting Up HITL Workflows
+
+<Steps>
+    <Step title="Configure Your Task">
+        Set up your task with human input enabled:
+        <Frame>
+            <img src="/images/enterprise/crew-human-input.png" alt="Crew Human Input" />
+        </Frame>
+    </Step>
+
+    <Step title="Provide Webhook URL">
+        When kicking off your crew, include a webhook URL for human input:
+        <Frame>
+            <img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
+        </Frame>
+    </Step>
+
+    <Step title="Receive Webhook Notification">
+        Once the crew completes the task requiring human input, you'll receive a webhook notification containing:
+            - **Execution ID**
+            - **Task ID**
+            - **Task output**
+    </Step>
+
+    <Step title="Review Task Output">
+        The system will pause in the `Pending Human Input` state. Review the task output carefully.
+    </Step>
+
+    <Step title="Submit Human Feedback">
+        Call the resume endpoint of your crew with the following information:
+        <Frame>
+            <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
+        </Frame>
+        <Warning>
+            **Feedback Impact on Task Execution**:
+            It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
+        </Warning>
+        This means:
+        - All information in your feedback becomes part of the task's context.
+        - Irrelevant details may negatively influence it.
+        - Concise, relevant feedback helps maintain task focus and efficiency.
+        - Always review your feedback carefully before submission to ensure it contains only pertinent information that will positively guide the task's execution.
+    </Step>
+    <Step title="Handle Negative Feedback">
+        If you provide negative feedback:
+        - The crew will retry the task with added context from your feedback.
+        - You'll receive another webhook notification for further review.
+        - Repeat steps 4-6 until satisfied.
+    </Step>
+
+    <Step title="Execution Continuation">
+        When you submit positive feedback, the execution will proceed to the next steps.
+    </Step>
+</Steps>
+
+## Best Practices
+
+- **Be Specific**: Provide clear, actionable feedback that directly addresses the task at hand
+- **Stay Relevant**: Only include information that will help improve the task execution
+- **Be Timely**: Respond to HITL prompts promptly to avoid workflow delays
+- **Review Carefully**: Double-check your feedback before submitting to ensure accuracy
+
+## Common Use Cases
+
+HITL workflows are particularly valuable for:
+- Quality assurance and validation
+- Complex decision-making scenarios
+- Sensitive or high-stakes operations
+- Creative tasks requiring human judgment
+- Compliance and regulatory reviews 

docs/guides/advanced/customizing-prompts.mdx

@@ -6,7 +6,7 @@ icon: message-pen
 
 ## Why Customize Prompts?
 
-Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here’s why you might want to take advantage of this deeper control:
+Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here's why you might want to take advantage of this deeper control:
 
 1. **Optimize for specific LLMs** – Different models (such as GPT-4, Claude, or Llama) thrive with prompt formats tailored to their unique architectures.
 2. **Change the language** – Build agents that operate exclusively in languages beyond English, handling nuances with precision.
@@ -20,13 +20,174 @@ This guide explores how to tap into CrewAI's prompts at a lower level, giving yo
 
 Under the hood, CrewAI employs a modular prompt system that you can customize extensively:
 
-- **Agent templates** – Govern each agent’s approach to their assigned role.
+- **Agent templates** – Govern each agent's approach to their assigned role.
 - **Prompt slices** – Control specialized behaviors such as tasks, tool usage, and output structure.
 - **Error handling** – Direct how agents respond to failures, exceptions, or timeouts.
 - **Tool-specific prompts** – Define detailed instructions for how tools are invoked or utilized.
 
 Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
 
+## Understanding Default System Instructions
+
+<Warning>
+**Production Transparency Issue**: CrewAI automatically injects default instructions into your prompts that you might not be aware of. This section explains what's happening under the hood and how to gain full control.
+</Warning>
+
+When you define an agent with `role`, `goal`, and `backstory`, CrewAI automatically adds additional system instructions that control formatting and behavior. Understanding these default injections is crucial for production systems where you need full prompt transparency.
+
+### What CrewAI Automatically Injects
+
+Based on your agent configuration, CrewAI adds different default instructions:
+
+#### For Agents Without Tools
+```text
+"I MUST use these formats, my job depends on it!"
+```
+
+#### For Agents With Tools  
+```text
+"IMPORTANT: Use the following format in your response:
+
+Thought: you should always think about what to do
+Action: the action to take, only one name of [tool_names]
+Action Input: the input to the action, just a simple JSON object...
+```
+
+#### For Structured Outputs (JSON/Pydantic)
+```text
+"Ensure your final answer contains only the content in the following format: {output_format}
+Ensure the final output does not include any code block markers like ```json or ```python."
+```
+
+### Viewing the Complete System Prompt
+
+To see exactly what prompt is being sent to your LLM, you can inspect the generated prompt:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai.utilities.prompts import Prompts
+
+# Create your agent
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst with 10 years of experience.",
+    verbose=True
+)
+
+# Create a sample task
+task = Task(
+    description="Analyze the sales data and identify trends",
+    expected_output="A detailed analysis with key insights and trends",
+    agent=agent
+)
+
+# Create the prompt generator
+prompt_generator = Prompts(
+    agent=agent,
+    has_tools=len(agent.tools) > 0,
+    use_system_prompt=agent.use_system_prompt
+)
+
+# Generate and inspect the actual prompt
+generated_prompt = prompt_generator.task_execution()
+
+# Print the complete system prompt that will be sent to the LLM
+if "system" in generated_prompt:
+    print("=== SYSTEM PROMPT ===")
+    print(generated_prompt["system"])
+    print("\n=== USER PROMPT ===")
+    print(generated_prompt["user"])
+else:
+    print("=== COMPLETE PROMPT ===")
+    print(generated_prompt["prompt"])
+
+# You can also see how the task description gets formatted
+print("\n=== TASK CONTEXT ===")
+print(f"Task Description: {task.description}")
+print(f"Expected Output: {task.expected_output}")
+```
+
+### Overriding Default Instructions
+
+You have several options to gain full control over the prompts:
+
+#### Option 1: Custom Templates (Recommended)
+```python
+from crewai import Agent
+
+# Define your own system template without default instructions
+custom_system_template = """You are {role}. {backstory}
+Your goal is: {goal}
+
+Respond naturally and conversationally. Focus on providing helpful, accurate information."""
+
+custom_prompt_template = """Task: {input}
+
+Please complete this task thoughtfully."""
+
+agent = Agent(
+    role="Research Assistant", 
+    goal="Help users find accurate information",
+    backstory="You are a helpful research assistant.",
+    system_template=custom_system_template,
+    prompt_template=custom_prompt_template,
+    use_system_prompt=True  # Use separate system/user messages
+)
+```
+
+#### Option 2: Custom Prompt File
+Create a `custom_prompts.json` file to override specific prompt slices:
+
+```json
+{
+  "slices": {
+    "no_tools": "\nProvide your best answer in a natural, conversational way.",
+    "tools": "\nYou have access to these tools: {tools}\n\nUse them when helpful, but respond naturally.",
+    "formatted_task_instructions": "Format your response as: {output_format}"
+  }
+}
+```
+
+Then use it in your crew:
+
+```python
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    prompt_file="custom_prompts.json",
+    verbose=True
+)
+```
+
+#### Option 3: Disable System Prompts for o1 Models
+```python
+agent = Agent(
+    role="Analyst",
+    goal="Analyze data", 
+    backstory="Expert analyst",
+    use_system_prompt=False  # Disables system prompt separation
+)
+```
+
+### Debugging with Observability Tools
+
+For production transparency, integrate with observability platforms to monitor all prompts and LLM interactions. This allows you to see exactly what prompts (including default instructions) are being sent to your LLMs.
+
+See our [Observability documentation](/how-to/observability) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
+
+### Best Practices for Production
+
+1. **Always inspect generated prompts** before deploying to production
+2. **Use custom templates** when you need full control over prompt content
+3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/how-to/observability))
+4. **Test with different LLMs** as default instructions may work differently across models
+5. **Document your prompt customizations** for team transparency
+
+<Tip>
+The default instructions exist to ensure consistent agent behavior, but they can interfere with domain-specific requirements. Use the customization options above to maintain full control over your agent's behavior in production systems.
+</Tip>
+
 ## Best Practices for Managing Prompt Files
 
 When engaging in low-level prompt customization, follow these guidelines to keep things organized and maintainable:
@@ -44,7 +205,7 @@ One straightforward approach is to create a JSON file for the prompts you want t
 1. Craft a JSON file with your updated prompt slices.
 2. Reference that file via the `prompt_file` parameter in your Crew.
 
-CrewAI then merges your customizations with the defaults, so you don’t have to redefine every prompt. Here’s how:
+CrewAI then merges your customizations with the defaults, so you don't have to redefine every prompt. Here's how:
 
 ### Example: Basic Prompt Customization
 
@@ -93,14 +254,14 @@ With these few edits, you gain low-level control over how your agents communicat
 
 ## Optimizing for Specific Models
 
-Different models thrive on differently structured prompts. Making deeper adjustments can significantly boost performance by aligning your prompts with a model’s nuances.
+Different models thrive on differently structured prompts. Making deeper adjustments can significantly boost performance by aligning your prompts with a model's nuances.
 
 ### Example: Llama 3.3 Prompting Template
 
-For instance, when dealing with Meta’s Llama 3.3, deeper-level customization may reflect the recommended structure described at:
+For instance, when dealing with Meta's Llama 3.3, deeper-level customization may reflect the recommended structure described at:
 https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#prompt-template
 
-Here’s an example to highlight how you might fine-tune an Agent to leverage Llama 3.3 in code:
+Here's an example to highlight how you might fine-tune an Agent to leverage Llama 3.3 in code:
 
 ```python
 from crewai import Agent, Crew, Task, Process
@@ -148,8 +309,8 @@ Through this deeper configuration, you can exercise comprehensive, low-level con
 
 ## Conclusion
 
-Low-level prompt customization in CrewAI opens the door to super custom, complex use cases. By establishing well-organized prompt files (or direct inline templates), you can accommodate various models, languages, and specialized domains. This level of flexibility ensures you can craft precisely the AI behavior you need, all while knowing CrewAI still provides reliable defaults when you don’t override them.
+Low-level prompt customization in CrewAI opens the door to super custom, complex use cases. By establishing well-organized prompt files (or direct inline templates), you can accommodate various models, languages, and specialized domains. This level of flexibility ensures you can craft precisely the AI behavior you need, all while knowing CrewAI still provides reliable defaults when you don't override them.
 
 <Check>
-You now have the foundation for advanced prompt customizations in CrewAI. Whether you’re adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
+You now have the foundation for advanced prompt customizations in CrewAI. Whether you're adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
 </Check>

docs/how-to/custom-llm.mdx

@@ -1,646 +0,0 @@
----
-title: Custom LLM Implementation
-description: Learn how to create custom LLM implementations in CrewAI.
-icon: code
----
-
-## Custom LLM Implementations
-
-CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
-
-To create a custom LLM implementation, you need to:
-
-1. Inherit from the `BaseLLM` abstract base class
-2. Implement the required methods:
-   - `call()`: The main method to call the LLM with messages
-   - `supports_function_calling()`: Whether the LLM supports function calling
-   - `supports_stop_words()`: Whether the LLM supports stop words
-   - `get_context_window_size()`: The context window size of the LLM
-
-## Example: Basic Custom LLM
-
-```python
-from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
-
-class CustomLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not api_key or not isinstance(api_key, str):
-            raise ValueError("Invalid API key: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with the given messages.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM
-        # For example, using requests:
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint, 
-                headers=headers, 
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        # Return True if your LLM supports function calling
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        # Return True if your LLM supports stop words
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        # Return the context window size of your LLM
-        return 8192
-```
-
-## Error Handling Best Practices
-
-When implementing custom LLMs, it's important to handle errors properly to ensure robustness and reliability. Here are some best practices:
-
-### 1. Implement Try-Except Blocks for API Calls
-
-Always wrap API calls in try-except blocks to handle different types of errors:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        # API call implementation
-        response = requests.post(
-            self.endpoint,
-            headers=self.headers,
-            json=self.prepare_payload(messages),
-            timeout=30  # Set a reasonable timeout
-        )
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        return response.json()["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-### 2. Implement Retry Logic for Transient Failures
-
-For transient failures like network issues or rate limiting, implement retry logic with exponential backoff:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import time
-    
-    max_retries = 3
-    retry_delay = 1  # seconds
-    
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(
-                self.endpoint,
-                headers=self.headers,
-                json=self.prepare_payload(messages),
-                timeout=30
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except (requests.Timeout, requests.ConnectionError) as e:
-            if attempt < max_retries - 1:
-                time.sleep(retry_delay * (2 ** attempt))  # Exponential backoff
-                continue
-            raise TimeoutError(f"LLM request failed after {max_retries} attempts: {str(e)}")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-```
-
-### 3. Validate Input Parameters
-
-Always validate input parameters to prevent runtime errors:
-
-```python
-def __init__(self, api_key: str, endpoint: str):
-    super().__init__()
-    if not api_key or not isinstance(api_key, str):
-        raise ValueError("Invalid API key: must be a non-empty string")
-    if not endpoint or not isinstance(endpoint, str):
-        raise ValueError("Invalid endpoint URL: must be a non-empty string")
-    self.api_key = api_key
-    self.endpoint = endpoint
-```
-
-### 4. Handle Authentication Errors Gracefully
-
-Provide clear error messages for authentication failures:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        response = requests.post(self.endpoint, headers=self.headers, json=data)
-        if response.status_code == 401:
-            raise ValueError("Authentication failed: Invalid API key or token")
-        elif response.status_code == 403:
-            raise ValueError("Authorization failed: Insufficient permissions")
-        response.raise_for_status()
-        # Process response
-    except Exception as e:
-        # Handle error
-        raise
-```
-
-## Example: JWT-based Authentication
-
-For services that use JWT-based authentication instead of API keys, you can implement a custom LLM like this:
-
-```python
-from crewai import BaseLLM, Agent, Task
-from typing import Any, Dict, List, Optional, Union
-
-class JWTAuthLLM(BaseLLM):
-    def __init__(self, jwt_token: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not jwt_token or not isinstance(jwt_token, str):
-            raise ValueError("Invalid JWT token: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.jwt_token = jwt_token
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with JWT authentication.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM with JWT authentication
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.jwt_token}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint,
-                headers=headers,
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            
-            if response.status_code == 401:
-                raise ValueError("Authentication failed: Invalid JWT token")
-            elif response.status_code == 403:
-                raise ValueError("Authorization failed: Insufficient permissions")
-                
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        return 8192
-```
-
-## Troubleshooting
-
-Here are some common issues you might encounter when implementing custom LLMs and how to resolve them:
-
-### 1. Authentication Failures
-
-**Symptoms**: 401 Unauthorized or 403 Forbidden errors
-
-**Solutions**:
-- Verify that your API key or JWT token is valid and not expired
-- Check that you're using the correct authentication header format
-- Ensure that your token has the necessary permissions
-
-### 2. Timeout Issues
-
-**Symptoms**: Requests taking too long or timing out
-
-**Solutions**:
-- Implement timeout handling as shown in the examples
-- Use retry logic with exponential backoff
-- Consider using a more reliable network connection
-
-### 3. Response Parsing Errors
-
-**Symptoms**: KeyError, IndexError, or ValueError when processing responses
-
-**Solutions**:
-- Validate the response format before accessing nested fields
-- Implement proper error handling for malformed responses
-- Check the API documentation for the expected response format
-
-### 4. Rate Limiting
-
-**Symptoms**: 429 Too Many Requests errors
-
-**Solutions**:
-- Implement rate limiting in your custom LLM
-- Add exponential backoff for retries
-- Consider using a token bucket algorithm for more precise rate control
-
-## Advanced Features
-
-### Logging
-
-Adding logging to your custom LLM can help with debugging and monitoring:
-
-```python
-import logging
-from typing import Any, Dict, List, Optional, Union
-
-class LoggingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.logger = logging.getLogger("crewai.llm.custom")
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self.logger.info(f"Calling LLM with {len(messages) if isinstance(messages, list) else 1} messages")
-        try:
-            # API call implementation
-            response = self._make_api_call(messages, tools)
-            self.logger.debug(f"LLM response received: {response[:100]}...")
-            return response
-        except Exception as e:
-            self.logger.error(f"LLM call failed: {str(e)}")
-            raise
-```
-
-### Rate Limiting
-
-Implementing rate limiting can help avoid overwhelming the LLM API:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class RateLimitedLLM(BaseLLM):
-    def __init__(
-        self, 
-        api_key: str, 
-        endpoint: str, 
-        requests_per_minute: int = 60
-    ):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.requests_per_minute = requests_per_minute
-        self.request_times: List[float] = []
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self._enforce_rate_limit()
-        # Record this request time
-        self.request_times.append(time.time())
-        # Make the actual API call
-        return self._make_api_call(messages, tools)
-        
-    def _enforce_rate_limit(self) -> None:
-        """Enforce the rate limit by waiting if necessary."""
-        now = time.time()
-        # Remove request times older than 1 minute
-        self.request_times = [t for t in self.request_times if now - t < 60]
-        
-        if len(self.request_times) >= self.requests_per_minute:
-            # Calculate how long to wait
-            oldest_request = min(self.request_times)
-            wait_time = 60 - (now - oldest_request)
-            if wait_time > 0:
-                time.sleep(wait_time)
-```
-
-### Metrics Collection
-
-Collecting metrics can help you monitor your LLM usage:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class MetricsCollectingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.metrics: Dict[str, Any] = {
-            "total_calls": 0,
-            "total_tokens": 0,
-            "errors": 0,
-            "latency": []
-        }
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        start_time = time.time()
-        self.metrics["total_calls"] += 1
-        
-        try:
-            response = self._make_api_call(messages, tools)
-            # Estimate tokens (simplified)
-            if isinstance(messages, str):
-                token_estimate = len(messages) // 4
-            else:
-                token_estimate = sum(len(m.get("content", "")) // 4 for m in messages)
-            self.metrics["total_tokens"] += token_estimate
-            return response
-        except Exception as e:
-            self.metrics["errors"] += 1
-            raise
-        finally:
-            latency = time.time() - start_time
-            self.metrics["latency"].append(latency)
-            
-    def get_metrics(self) -> Dict[str, Any]:
-        """Return the collected metrics."""
-        avg_latency = sum(self.metrics["latency"]) / len(self.metrics["latency"]) if self.metrics["latency"] else 0
-        return {
-            **self.metrics,
-            "avg_latency": avg_latency
-        }
-```
-
-## Advanced Usage: Function Calling
-
-If your LLM supports function calling, you can implement the function calling logic in your custom LLM:
-
-```python
-import json
-from typing import Any, Dict, List, Optional, Union
-
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import requests
-    
-    try:
-        headers = {
-            "Authorization": f"Bearer {self.jwt_token}",
-            "Content-Type": "application/json"
-        }
-        
-        # Convert string message to proper format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        
-        data = {
-            "messages": messages,
-            "tools": tools
-        }
-        
-        response = requests.post(
-            self.endpoint,
-            headers=headers,
-            json=data,
-            timeout=30
-        )
-        response.raise_for_status()
-        response_data = response.json()
-        
-        # Check if the LLM wants to call a function
-        if response_data["choices"][0]["message"].get("tool_calls"):
-            tool_calls = response_data["choices"][0]["message"]["tool_calls"]
-            
-            # Process each tool call
-            for tool_call in tool_calls:
-                function_name = tool_call["function"]["name"]
-                function_args = json.loads(tool_call["function"]["arguments"])
-                
-                if available_functions and function_name in available_functions:
-                    function_to_call = available_functions[function_name]
-                    function_response = function_to_call(**function_args)
-                    
-                    # Add the function response to the messages
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "name": function_name,
-                        "content": str(function_response)
-                    })
-            
-            # Call the LLM again with the updated messages
-            return self.call(messages, tools, callbacks, available_functions)
-        
-        # Return the text response if no function call
-        return response_data["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-## Using Your Custom LLM with CrewAI
-
-Once you've implemented your custom LLM, you can use it with CrewAI agents and crews:
-
-```python
-from crewai import Agent, Task, Crew
-from typing import Dict, Any
-
-# Create your custom LLM instance
-jwt_llm = JWTAuthLLM(
-    jwt_token="your.jwt.token", 
-    endpoint="https://your-llm-endpoint.com/v1/chat/completions"
-)
-
-# Use it with an agent
-agent = Agent(
-    role="Research Assistant",
-    goal="Find information on a topic",
-    backstory="You are a research assistant tasked with finding information.",
-    llm=jwt_llm,
-)
-
-# Create a task for the agent
-task = Task(
-    description="Research the benefits of exercise",
-    agent=agent,
-    expected_output="A summary of the benefits of exercise",
-)
-
-# Execute the task
-result = agent.execute_task(task)
-print(result)
-
-# Or use it with a crew
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    manager_llm=jwt_llm,  # Use your custom LLM for the manager
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Implementing Your Own Authentication Mechanism
-
-The `BaseLLM` class allows you to implement any authentication mechanism you need, not just JWT or API keys. You can use:
-
-- OAuth tokens
-- Client certificates
-- Custom headers
-- Session-based authentication
-- Any other authentication method required by your LLM provider
-
-Simply implement the appropriate authentication logic in your custom LLM class.

docs/how-to/portkey-observability.mdx

@@ -1,202 +0,0 @@
----
-title: Portkey Integration
-description: How to use Portkey with CrewAI
-icon: key
----
-
-<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
-
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="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
-        )
-    )
-    ```
-    </Step>
-    <Step title="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)
-    ```
-    </Step>
-</Steps>
-
-## 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/installation.mdx

@@ -22,7 +22,7 @@ Watch this video tutorial for a step-by-step demonstration of the installation p
 <Note>
   **Python Version Requirements**
 
-  CrewAI requires `Python >=3.10 and <3.13`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <=3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```

docs/learn/custom-llm.mdx

@@ -0,0 +1,350 @@
+---
+title: Custom LLM Implementation
+description: Learn how to create custom LLM implementations in CrewAI.
+icon: code
+---
+
+## Overview
+
+CrewAI supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to integrate any LLM provider that doesn't have built-in support in LiteLLM, or implement custom authentication mechanisms.
+
+## Quick Start
+
+Here's a minimal custom LLM implementation:
+
+```python
+from crewai import BaseLLM
+from typing import Any, Dict, List, Optional, Union
+import requests
+
+class CustomLLM(BaseLLM):
+    def __init__(self, model: str, api_key: str, endpoint: str, temperature: Optional[float] = None):
+        # IMPORTANT: Call super().__init__() with required parameters
+        super().__init__(model=model, temperature=temperature)
+        
+        self.api_key = api_key
+        self.endpoint = endpoint
+        
+    def call(
+        self,
+        messages: Union[str, List[Dict[str, str]]],
+        tools: Optional[List[dict]] = None,
+        callbacks: Optional[List[Any]] = None,
+        available_functions: Optional[Dict[str, Any]] = None,
+    ) -> Union[str, Any]:
+        """Call the LLM with the given messages."""
+        # Convert string to message format if needed
+        if isinstance(messages, str):
+            messages = [{"role": "user", "content": messages}]
+        
+        # Prepare request
+        payload = {
+            "model": self.model,
+            "messages": messages,
+            "temperature": self.temperature,
+        }
+        
+        # Add tools if provided and supported
+        if tools and self.supports_function_calling():
+            payload["tools"] = tools
+        
+        # Make API call
+        response = requests.post(
+            self.endpoint,
+            headers={
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json"
+            },
+            json=payload,
+            timeout=30
+        )
+        response.raise_for_status()
+        
+        result = response.json()
+        return result["choices"][0]["message"]["content"]
+        
+    def supports_function_calling(self) -> bool:
+        """Override if your LLM supports function calling."""
+        return True  # Change to False if your LLM doesn't support tools
+        
+    def get_context_window_size(self) -> int:
+        """Return the context window size of your LLM."""
+        return 8192  # Adjust based on your model's actual context window
+```
+
+## Using Your Custom LLM
+
+```python
+from crewai import Agent, Task, Crew
+
+# Assuming you have the CustomLLM class defined above
+# Create your custom LLM
+custom_llm = CustomLLM(
+    model="my-custom-model",
+    api_key="your-api-key",
+    endpoint="https://api.example.com/v1/chat/completions",
+    temperature=0.7
+)
+
+# Use with an agent
+agent = Agent(
+    role="Research Assistant",
+    goal="Find and analyze information",
+    backstory="You are a research assistant.",
+    llm=custom_llm
+)
+
+# Create and execute tasks
+task = Task(
+    description="Research the latest developments in AI",
+    expected_output="A comprehensive summary",
+    agent=agent
+)
+
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+```
+
+## Required Methods
+
+### Constructor: `__init__()`
+
+**Critical**: You must call `super().__init__(model, temperature)` with the required parameters:
+
+```python
+def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
+    # REQUIRED: Call parent constructor with model and temperature
+    super().__init__(model=model, temperature=temperature)
+    
+    # Your custom initialization
+    self.api_key = api_key
+```
+
+### Abstract Method: `call()`
+
+The `call()` method is the heart of your LLM implementation. It must:
+
+- Accept messages (string or list of dicts with 'role' and 'content')
+- Return a string response
+- Handle tools and function calling if supported
+- Raise appropriate exceptions for errors
+
+### Optional Methods
+
+```python
+def supports_function_calling(self) -> bool:
+    """Return True if your LLM supports function calling."""
+    return True  # Default is True
+
+def supports_stop_words(self) -> bool:
+    """Return True if your LLM supports stop sequences."""
+    return True  # Default is True
+
+def get_context_window_size(self) -> int:
+    """Return the context window size."""
+    return 4096  # Default is 4096
+```
+
+## Common Patterns
+
+### Error Handling
+
+```python
+import requests
+
+def call(self, messages, tools=None, callbacks=None, available_functions=None):
+    try:
+        response = requests.post(
+            self.endpoint,
+            headers={"Authorization": f"Bearer {self.api_key}"},
+            json=payload,
+            timeout=30
+        )
+        response.raise_for_status()
+        return response.json()["choices"][0]["message"]["content"]
+        
+    except requests.Timeout:
+        raise TimeoutError("LLM request timed out")
+    except requests.RequestException as e:
+        raise RuntimeError(f"LLM request failed: {str(e)}")
+    except (KeyError, IndexError) as e:
+        raise ValueError(f"Invalid response format: {str(e)}")
+```
+
+### Custom Authentication
+
+```python
+from crewai import BaseLLM
+from typing import Optional
+
+class CustomAuthLLM(BaseLLM):
+    def __init__(self, model: str, auth_token: str, endpoint: str, temperature: Optional[float] = None):
+        super().__init__(model=model, temperature=temperature)
+        self.auth_token = auth_token
+        self.endpoint = endpoint
+    
+    def call(self, messages, tools=None, callbacks=None, available_functions=None):
+        headers = {
+            "Authorization": f"Custom {self.auth_token}",  # Custom auth format
+            "Content-Type": "application/json"
+        }
+        # Rest of implementation...
+```
+
+### Stop Words Support
+
+CrewAI automatically adds `"\nObservation:"` as a stop word to control agent behavior. If your LLM supports stop words:
+
+```python
+def call(self, messages, tools=None, callbacks=None, available_functions=None):
+    payload = {
+        "model": self.model,
+        "messages": messages,
+        "stop": self.stop  # Include stop words in API call
+    }
+    # Make API call...
+
+def supports_stop_words(self) -> bool:
+    return True  # Your LLM supports stop sequences
+```
+
+If your LLM doesn't support stop words natively:
+
+```python
+def call(self, messages, tools=None, callbacks=None, available_functions=None):
+    response = self._make_api_call(messages, tools)
+    content = response["choices"][0]["message"]["content"]
+    
+    # Manually truncate at stop words
+    if self.stop:
+        for stop_word in self.stop:
+            if stop_word in content:
+                content = content.split(stop_word)[0]
+                break
+    
+    return content
+
+def supports_stop_words(self) -> bool:
+    return False  # Tell CrewAI we handle stop words manually
+```
+
+## Function Calling
+
+If your LLM supports function calling, implement the complete flow:
+
+```python
+import json
+
+def call(self, messages, tools=None, callbacks=None, available_functions=None):
+    # Convert string to message format
+    if isinstance(messages, str):
+        messages = [{"role": "user", "content": messages}]
+    
+    # Make API call
+    response = self._make_api_call(messages, tools)
+    message = response["choices"][0]["message"]
+    
+    # Check for function calls
+    if "tool_calls" in message and available_functions:
+        return self._handle_function_calls(
+            message["tool_calls"], messages, tools, available_functions
+        )
+    
+    return message["content"]
+
+def _handle_function_calls(self, tool_calls, messages, tools, available_functions):
+    """Handle function calling with proper message flow."""
+    for tool_call in tool_calls:
+        function_name = tool_call["function"]["name"]
+        
+        if function_name in available_functions:
+            # Parse and execute function
+            function_args = json.loads(tool_call["function"]["arguments"])
+            function_result = available_functions[function_name](**function_args)
+            
+            # Add function call and result to message history
+            messages.append({
+                "role": "assistant",
+                "content": None,
+                "tool_calls": [tool_call]
+            })
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call["id"],
+                "name": function_name,
+                "content": str(function_result)
+            })
+            
+            # Call LLM again with updated context
+            return self.call(messages, tools, None, available_functions)
+    
+    return "Function call failed"
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Constructor Errors**
+```python
+# ❌ Wrong - missing required parameters
+def __init__(self, api_key: str):
+    super().__init__()
+
+# ✅ Correct
+def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
+    super().__init__(model=model, temperature=temperature)
+```
+
+**Function Calling Not Working**
+- Ensure `supports_function_calling()` returns `True`
+- Check that you handle `tool_calls` in the response
+- Verify `available_functions` parameter is used correctly
+
+**Authentication Failures**
+- Verify API key format and permissions
+- Check authentication header format
+- Ensure endpoint URLs are correct
+
+**Response Parsing Errors**
+- Validate response structure before accessing nested fields
+- Handle cases where content might be None
+- Add proper error handling for malformed responses
+
+## Testing Your Custom LLM
+
+```python
+from crewai import Agent, Task, Crew
+
+def test_custom_llm():
+    llm = CustomLLM(
+        model="test-model",
+        api_key="test-key",
+        endpoint="https://api.test.com"
+    )
+    
+    # Test basic call
+    result = llm.call("Hello, world!")
+    assert isinstance(result, str)
+    assert len(result) > 0
+    
+    # Test with CrewAI agent
+    agent = Agent(
+        role="Test Agent",
+        goal="Test custom LLM",
+        backstory="A test agent.",
+        llm=llm
+    )
+    
+    task = Task(
+        description="Say hello",
+        expected_output="A greeting",
+        agent=agent
+    )
+    
+    crew = Crew(agents=[agent], tasks=[task])
+    result = crew.kickoff()
+    assert "hello" in result.raw.lower()
+```
+
+This guide covers the essentials of implementing custom LLMs in CrewAI.

docs/learn/kickoff-async.mdx

@@ -108,6 +108,7 @@ crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
 
 # Async function to kickoff multiple crews asynchronously and wait for all to finish
 async def async_multiple_crews():
+    # Create coroutines for concurrent execution
     result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
     result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
 

docs/learn/llm-connections.mdx

@@ -9,7 +9,7 @@ icon: brain-circuit
 CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
 
 <Note>
-    By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set. 
+    By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set.
     You can easily configure your agents to use a different model or provider as described in this guide.
 </Note>
 
@@ -117,18 +117,27 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
 <Tabs>
     <Tab title="Using Environment Variables">
         <CodeGroup>
-        ```python Code
+        ```python Generic
         import os
 
         os.environ["OPENAI_API_KEY"] = "your-api-key"
         os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
         os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
         ```
+
+        ```python Google
+        import os
+
+        # Example using Gemini's OpenAI-compatible API.
+        os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
+        os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Add your Gemini model here, under openai/
+        ```
         </CodeGroup>
     </Tab>
     <Tab title="Using LLM Class Attributes">
         <CodeGroup>
-            ```python Code
+            ```python Generic
             llm = LLM(
                 model="custom-model-name",
                 api_key="your-api-key",
@@ -136,6 +145,16 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
             )
             agent = Agent(llm=llm, ...)
             ```
+
+            ```python Google
+            # Example using Gemini's OpenAI-compatible API
+            llm = LLM(
+                model="openai/gemini-2.0-flash",
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                api_key="your-gemini-key",  # Should start with AIza...
+            )
+            agent = Agent(llm=llm, ...)
+            ```
         </CodeGroup>
     </Tab>
 </Tabs>
@@ -169,7 +188,7 @@ For local models like those provided by Ollama:
 
 You can change the base API URL for any LLM provider by setting the `base_url` parameter:
 
-```python Code  
+```python Code
 llm = LLM(
     model="custom-model-name",
     base_url="https://api.your-provider.com/v1",

docs/learn/llm-selection-guide.mdx

@@ -0,0 +1,729 @@
+---
+title: 'Strategic LLM Selection Guide'
+description: 'Strategic framework for choosing the right LLM for your CrewAI AI agents and writing effective task and agent definitions'
+icon: 'brain-circuit'
+---
+
+## The CrewAI Approach to LLM Selection
+
+Rather than prescriptive model recommendations, we advocate for a **thinking framework** that helps you make informed decisions based on your specific use case, constraints, and requirements. The LLM landscape evolves rapidly, with new models emerging regularly and existing ones being updated frequently. What matters most is developing a systematic approach to evaluation that remains relevant regardless of which specific models are available.
+
+<Note>
+This guide focuses on strategic thinking rather than specific model recommendations, as the LLM landscape evolves rapidly.
+</Note>
+
+## Quick Decision Framework
+
+<Steps>
+  <Step title="Analyze Your Tasks">
+    Begin by deeply understanding what your tasks actually require. Consider the cognitive complexity involved, the depth of reasoning needed, the format of expected outputs, and the amount of context the model will need to process. This foundational analysis will guide every subsequent decision.
+  </Step>
+  <Step title="Map Model Capabilities">
+    Once you understand your requirements, map them to model strengths. Different model families excel at different types of work; some are optimized for reasoning and analysis, others for creativity and content generation, and others for speed and efficiency.
+  </Step>
+  <Step title="Consider Constraints">
+    Factor in your real-world operational constraints including budget limitations, latency requirements, data privacy needs, and infrastructure capabilities. The theoretically best model may not be the practically best choice for your situation.
+  </Step>
+  <Step title="Test and Iterate">
+    Start with reliable, well-understood models and optimize based on actual performance in your specific use case. Real-world results often differ from theoretical benchmarks, so empirical testing is crucial.
+  </Step>
+</Steps>
+
+## Core Selection Framework
+
+### a. Task-First Thinking
+
+The most critical step in LLM selection is understanding what your task actually demands. Too often, teams select models based on general reputation or benchmark scores without carefully analyzing their specific requirements. This approach leads to either over-engineering simple tasks with expensive, complex models, or under-powering sophisticated work with models that lack the necessary capabilities.
+
+<Tabs>
+  <Tab title="Reasoning Complexity">
+    - **Simple Tasks** represent the majority of everyday AI work and include basic instruction following, straightforward data processing, and simple formatting operations. These tasks typically have clear inputs and outputs with minimal ambiguity. The cognitive load is low, and the model primarily needs to follow explicit instructions rather than engage in complex reasoning.
+
+    - **Complex Tasks** require multi-step reasoning, strategic thinking, and the ability to handle ambiguous or incomplete information. These might involve analyzing multiple data sources, developing comprehensive strategies, or solving problems that require breaking down into smaller components. The model needs to maintain context across multiple reasoning steps and often must make inferences that aren't explicitly stated.
+
+    - **Creative Tasks** demand a different type of cognitive capability focused on generating novel, engaging, and contextually appropriate content. This includes storytelling, marketing copy creation, and creative problem-solving. The model needs to understand nuance, tone, and audience while producing content that feels authentic and engaging rather than formulaic.
+  </Tab>
+  
+  <Tab title="Output Requirements">
+    - **Structured Data** tasks require precision and consistency in format adherence. When working with JSON, XML, or database formats, the model must reliably produce syntactically correct output that can be programmatically processed. These tasks often have strict validation requirements and little tolerance for format errors, making reliability more important than creativity.
+
+    - **Creative Content** outputs demand a balance of technical competence and creative flair. The model needs to understand audience, tone, and brand voice while producing content that engages readers and achieves specific communication goals. Quality here is often subjective and requires models that can adapt their writing style to different contexts and purposes.
+
+    - **Technical Content** sits between structured data and creative content, requiring both precision and clarity. Documentation, code generation, and technical analysis need to be accurate and comprehensive while remaining accessible to the intended audience. The model must understand complex technical concepts and communicate them effectively.
+  </Tab>
+  
+  <Tab title="Context Needs">
+    - **Short Context** scenarios involve focused, immediate tasks where the model needs to process limited information quickly. These are often transactional interactions where speed and efficiency matter more than deep understanding. The model doesn't need to maintain extensive conversation history or process large documents.
+
+    - **Long Context** requirements emerge when working with substantial documents, extended conversations, or complex multi-part tasks. The model needs to maintain coherence across thousands of tokens while referencing earlier information accurately. This capability becomes crucial for document analysis, comprehensive research, and sophisticated dialogue systems.
+
+    - **Very Long Context** scenarios push the boundaries of what's currently possible, involving massive document processing, extensive research synthesis, or complex multi-session interactions. These use cases require models specifically designed for extended context handling and often involve trade-offs between context length and processing speed.
+  </Tab>
+</Tabs>
+
+### b. Model Capability Mapping
+
+Understanding model capabilities requires looking beyond marketing claims and benchmark scores to understand the fundamental strengths and limitations of different model architectures and training approaches.
+
+<AccordionGroup>
+  <Accordion title="Reasoning Models" icon="brain">
+    Reasoning models represent a specialized category designed specifically for complex, multi-step thinking tasks. These models excel when problems require careful analysis, strategic planning, or systematic problem decomposition. They typically employ techniques like chain-of-thought reasoning or tree-of-thought processing to work through complex problems step by step.
+
+    The strength of reasoning models lies in their ability to maintain logical consistency across extended reasoning chains and to break down complex problems into manageable components. They're particularly valuable for strategic planning, complex analysis, and situations where the quality of reasoning matters more than speed of response.
+
+    However, reasoning models often come with trade-offs in terms of speed and cost. They may also be less suitable for creative tasks or simple operations where their sophisticated reasoning capabilities aren't needed. Consider these models when your tasks involve genuine complexity that benefits from systematic, step-by-step analysis.
+  </Accordion>
+  
+  <Accordion title="General Purpose Models" icon="microchip">
+    General purpose models offer the most balanced approach to LLM selection, providing solid performance across a wide range of tasks without extreme specialization in any particular area. These models are trained on diverse datasets and optimized for versatility rather than peak performance in specific domains.
+
+    The primary advantage of general purpose models is their reliability and predictability across different types of work. They handle most standard business tasks competently, from research and analysis to content creation and data processing. This makes them excellent choices for teams that need consistent performance across varied workflows.
+
+    While general purpose models may not achieve the peak performance of specialized alternatives in specific domains, they offer operational simplicity and reduced complexity in model management. They're often the best starting point for new projects, allowing teams to understand their specific needs before potentially optimizing with more specialized models.
+  </Accordion>
+  
+  <Accordion title="Fast & Efficient Models" icon="bolt">
+    Fast and efficient models prioritize speed, cost-effectiveness, and resource efficiency over sophisticated reasoning capabilities. These models are optimized for high-throughput scenarios where quick responses and low operational costs are more important than nuanced understanding or complex reasoning.
+
+    These models excel in scenarios involving routine operations, simple data processing, function calling, and high-volume tasks where the cognitive requirements are relatively straightforward. They're particularly valuable for applications that need to process many requests quickly or operate within tight budget constraints.
+
+    The key consideration with efficient models is ensuring that their capabilities align with your task requirements. While they can handle many routine operations effectively, they may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation. They're best used for well-defined, routine operations where speed and cost matter more than sophistication.
+  </Accordion>
+  
+  <Accordion title="Creative Models" icon="pen">
+    Creative models are specifically optimized for content generation, writing quality, and creative thinking tasks. These models typically excel at understanding nuance, tone, and style while producing engaging, contextually appropriate content that feels natural and authentic.
+
+    The strength of creative models lies in their ability to adapt writing style to different audiences, maintain consistent voice and tone, and generate content that engages readers effectively. They often perform better on tasks involving storytelling, marketing copy, brand communications, and other content where creativity and engagement are primary goals.
+
+    When selecting creative models, consider not just their ability to generate text, but their understanding of audience, context, and purpose. The best creative models can adapt their output to match specific brand voices, target different audience segments, and maintain consistency across extended content pieces.
+  </Accordion>
+  
+  <Accordion title="Open Source Models" icon="code">
+    Open source models offer unique advantages in terms of cost control, customization potential, data privacy, and deployment flexibility. These models can be run locally or on private infrastructure, providing complete control over data handling and model behavior.
+
+    The primary benefits of open source models include elimination of per-token costs, ability to fine-tune for specific use cases, complete data privacy, and independence from external API providers. They're particularly valuable for organizations with strict data privacy requirements, budget constraints, or specific customization needs.
+
+    However, open source models require more technical expertise to deploy and maintain effectively. Teams need to consider infrastructure costs, model management complexity, and the ongoing effort required to keep models updated and optimized. The total cost of ownership may be higher than cloud-based alternatives when factoring in technical overhead.
+  </Accordion>
+</AccordionGroup>
+
+## Strategic Configuration Patterns
+
+### a. Multi-Model Approach
+
+<Tip>
+Use different models for different purposes within the same crew to optimize both performance and cost.
+</Tip>
+
+The most sophisticated CrewAI implementations often employ multiple models strategically, assigning different models to different agents based on their specific roles and requirements. This approach allows teams to optimize for both performance and cost by using the most appropriate model for each type of work.
+
+Planning agents benefit from reasoning models that can handle complex strategic thinking and multi-step analysis. These agents often serve as the "brain" of the operation, developing strategies and coordinating other agents' work. Content agents, on the other hand, perform best with creative models that excel at writing quality and audience engagement. Processing agents handling routine operations can use efficient models that prioritize speed and cost-effectiveness.
+
+**Example: Research and Analysis Crew**
+
+```python
+from crewai import Agent, Task, Crew, LLM
+
+# High-capability reasoning model for strategic planning
+manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
+
+# Creative model for content generation
+content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
+
+# Efficient model for data processing
+processing_llm = LLM(model="gpt-4o-mini", temperature=0)
+
+research_manager = Agent(
+    role="Research Strategy Manager",
+    goal="Develop comprehensive research strategies and coordinate team efforts",
+    backstory="Expert research strategist with deep analytical capabilities",
+    llm=manager_llm,  # High-capability model for complex reasoning
+    verbose=True
+)
+
+content_writer = Agent(
+    role="Research Content Writer",
+    goal="Transform research findings into compelling, well-structured reports",
+    backstory="Skilled writer who excels at making complex topics accessible",
+    llm=content_llm,  # Creative model for engaging content
+    verbose=True
+)
+
+data_processor = Agent(
+    role="Data Analysis Specialist", 
+    goal="Extract and organize key data points from research sources",
+    backstory="Detail-oriented analyst focused on accuracy and efficiency",
+    llm=processing_llm,  # Fast, cost-effective model for routine tasks
+    verbose=True
+)
+
+crew = Crew(
+    agents=[research_manager, content_writer, data_processor],
+    tasks=[...],  # Your specific tasks
+    manager_llm=manager_llm,  # Manager uses the reasoning model
+    verbose=True
+)
+```
+
+The key to successful multi-model implementation is understanding how different agents interact and ensuring that model capabilities align with agent responsibilities. This requires careful planning but can result in significant improvements in both output quality and operational efficiency.
+
+### b. Component-Specific Selection
+
+<Tabs>
+  <Tab title="Manager LLM">
+    The manager LLM plays a crucial role in hierarchical CrewAI processes, serving as the coordination point for multiple agents and tasks. This model needs to excel at delegation, task prioritization, and maintaining context across multiple concurrent operations.
+
+    Effective manager LLMs require strong reasoning capabilities to make good delegation decisions, consistent performance to ensure predictable coordination, and excellent context management to track the state of multiple agents simultaneously. The model needs to understand the capabilities and limitations of different agents while optimizing task allocation for efficiency and quality.
+
+    Cost considerations are particularly important for manager LLMs since they're involved in every operation. The model needs to provide sufficient capability for effective coordination while remaining cost-effective for frequent use. This often means finding models that offer good reasoning capabilities without the premium pricing of the most sophisticated options.
+  </Tab>
+  
+  <Tab title="Function Calling LLM">
+    Function calling LLMs handle tool usage across all agents, making them critical for crews that rely heavily on external tools and APIs. These models need to excel at understanding tool capabilities, extracting parameters accurately, and handling tool responses effectively.
+
+    The most important characteristics for function calling LLMs are precision and reliability rather than creativity or sophisticated reasoning. The model needs to consistently extract the correct parameters from natural language requests and handle tool responses appropriately. Speed is also important since tool usage often involves multiple round trips that can impact overall performance.
+
+    Many teams find that specialized function calling models or general purpose models with strong tool support work better than creative or reasoning-focused models for this role. The key is ensuring that the model can reliably bridge the gap between natural language instructions and structured tool calls.
+  </Tab>
+  
+  <Tab title="Agent-Specific Overrides">
+    Individual agents can override crew-level LLM settings when their specific needs differ significantly from the general crew requirements. This capability allows for fine-tuned optimization while maintaining operational simplicity for most agents.
+
+    Consider agent-specific overrides when an agent's role requires capabilities that differ substantially from other crew members. For example, a creative writing agent might benefit from a model optimized for content generation, while a data analysis agent might perform better with a reasoning-focused model.
+
+    The challenge with agent-specific overrides is balancing optimization with operational complexity. Each additional model adds complexity to deployment, monitoring, and cost management. Teams should focus overrides on agents where the performance improvement justifies the additional complexity.
+  </Tab>
+</Tabs>
+
+## Task Definition Framework
+
+### a. Focus on Clarity Over Complexity
+
+Effective task definition is often more important than model selection in determining the quality of CrewAI outputs. Well-defined tasks provide clear direction and context that enable even modest models to perform well, while poorly defined tasks can cause even sophisticated models to produce unsatisfactory results.
+
+<AccordionGroup>
+  <Accordion title="Effective Task Descriptions" icon="list-check">
+    The best task descriptions strike a balance between providing sufficient detail and maintaining clarity. They should define the specific objective clearly enough that there's no ambiguity about what success looks like, while explaining the approach or methodology in enough detail that the agent understands how to proceed.
+
+    Effective task descriptions include relevant context and constraints that help the agent understand the broader purpose and any limitations they need to work within. They break complex work into focused steps that can be executed systematically, rather than presenting overwhelming, multi-faceted objectives that are difficult to approach systematically.
+
+    Common mistakes include being too vague about objectives, failing to provide necessary context, setting unclear success criteria, or combining multiple unrelated tasks into a single description. The goal is to provide enough information for the agent to succeed while maintaining focus on a single, clear objective.
+  </Accordion>
+  
+  <Accordion title="Expected Output Guidelines" icon="bullseye">
+    Expected output guidelines serve as a contract between the task definition and the agent, clearly specifying what the deliverable should look like and how it will be evaluated. These guidelines should describe both the format and structure needed, as well as the key elements that must be included for the output to be considered complete.
+
+    The best output guidelines provide concrete examples of quality indicators and define completion criteria clearly enough that both the agent and human reviewers can assess whether the task has been completed successfully. This reduces ambiguity and helps ensure consistent results across multiple task executions.
+
+    Avoid generic output descriptions that could apply to any task, missing format specifications that leave agents guessing about structure, unclear quality standards that make evaluation difficult, or failing to provide examples or templates that help agents understand expectations.
+  </Accordion>
+</AccordionGroup>
+
+### b. Task Sequencing Strategy
+
+<Tabs>
+  <Tab title="Sequential Dependencies">
+    Sequential task dependencies are essential when tasks build upon previous outputs, information flows from one task to another, or quality depends on the completion of prerequisite work. This approach ensures that each task has access to the information and context it needs to succeed.
+
+    Implementing sequential dependencies effectively requires using the context parameter to chain related tasks, building complexity gradually through task progression, and ensuring that each task produces outputs that serve as meaningful inputs for subsequent tasks. The goal is to maintain logical flow between dependent tasks while avoiding unnecessary bottlenecks.
+
+    Sequential dependencies work best when there's a clear logical progression from one task to another and when the output of one task genuinely improves the quality or feasibility of subsequent tasks. However, they can create bottlenecks if not managed carefully, so it's important to identify which dependencies are truly necessary versus those that are merely convenient.
+  </Tab>
+  
+  <Tab title="Parallel Execution">
+    Parallel execution becomes valuable when tasks are independent of each other, time efficiency is important, or different expertise areas are involved that don't require coordination. This approach can significantly reduce overall execution time while allowing specialized agents to work on their areas of strength simultaneously.
+
+    Successful parallel execution requires identifying tasks that can truly run independently, grouping related but separate work streams effectively, and planning for result integration when parallel tasks need to be combined into a final deliverable. The key is ensuring that parallel tasks don't create conflicts or redundancies that reduce overall quality.
+
+    Consider parallel execution when you have multiple independent research streams, different types of analysis that don't depend on each other, or content creation tasks that can be developed simultaneously. However, be mindful of resource allocation and ensure that parallel execution doesn't overwhelm your available model capacity or budget.
+  </Tab>
+</Tabs>
+
+## Optimizing Agent Configuration for LLM Performance
+
+### a. Role-Driven LLM Selection
+
+<Warning>
+Generic agent roles make it impossible to select the right LLM. Specific roles enable targeted model optimization.
+</Warning>
+
+The specificity of your agent roles directly determines which LLM capabilities matter most for optimal performance. This creates a strategic opportunity to match precise model strengths with agent responsibilities.
+
+**Generic vs. Specific Role Impact on LLM Choice:**
+
+When defining roles, think about the specific domain knowledge, working style, and decision-making frameworks that would be most valuable for the tasks the agent will handle. The more specific and contextual the role definition, the better the model can embody that role effectively.
+```python
+# ✅ Specific role - clear LLM requirements
+specific_agent = Agent(
+    role="SaaS Revenue Operations Analyst",  # Clear domain expertise needed
+    goal="Analyze recurring revenue metrics and identify growth opportunities",
+    backstory="Specialist in SaaS business models with deep understanding of ARR, churn, and expansion revenue",
+    llm=LLM(model="gpt-4o")  # Reasoning model justified for complex analysis
+)
+```
+
+**Role-to-Model Mapping Strategy:**
+
+- **"Research Analyst"** → Reasoning model (GPT-4o, Claude Sonnet) for complex analysis
+- **"Content Editor"** → Creative model (Claude, GPT-4o) for writing quality
+- **"Data Processor"** → Efficient model (GPT-4o-mini, Gemini Flash) for structured tasks
+- **"API Coordinator"** → Function-calling optimized model (GPT-4o, Claude) for tool usage
+
+### b. Backstory as Model Context Amplifier
+
+<Info>
+Strategic backstories multiply your chosen LLM's effectiveness by providing domain-specific context that generic prompting cannot achieve.
+</Info>
+
+A well-crafted backstory transforms your LLM choice from generic capability to specialized expertise. This is especially crucial for cost optimization - a well-contextualized efficient model can outperform a premium model without proper context.
+
+**Context-Driven Performance Example:**
+
+```python
+# Context amplifies model effectiveness
+domain_expert = Agent(
+    role="B2B SaaS Marketing Strategist",
+    goal="Develop comprehensive go-to-market strategies for enterprise software",
+    backstory="""
+    You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO. 
+    You understand the nuances of enterprise sales cycles, the importance of product-market 
+    fit in different verticals, and how to balance growth metrics with unit economics. 
+    You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving 
+    you perspective on both established and disruptive go-to-market strategies.
+    """,
+    llm=LLM(model="claude-3-5-sonnet", temperature=0.3)  # Balanced creativity with domain knowledge
+)
+
+# This context enables Claude to perform like a domain expert
+# Without it, even it would produce generic marketing advice
+```
+
+**Backstory Elements That Enhance LLM Performance:**
+- **Domain Experience**: "10+ years in enterprise SaaS sales"
+- **Specific Expertise**: "Specializes in technical due diligence for Series B+ rounds"
+- **Working Style**: "Prefers data-driven decisions with clear documentation"
+- **Quality Standards**: "Insists on citing sources and showing analytical work"
+
+### c. Holistic Agent-LLM Optimization
+
+The most effective agent configurations create synergy between role specificity, backstory depth, and LLM selection. Each element reinforces the others to maximize model performance.
+
+**Optimization Framework:**
+
+```python
+# Example: Technical Documentation Agent
+tech_writer = Agent(
+    role="API Documentation Specialist",  # Specific role for clear LLM requirements
+    goal="Create comprehensive, developer-friendly API documentation",
+    backstory="""
+    You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints, 
+    and SDK integration guides. You've worked with developer tools companies and 
+    understand what developers need: clear examples, comprehensive error handling, 
+    and practical use cases. You prioritize accuracy and usability over marketing fluff.
+    """,
+    llm=LLM(
+        model="claude-3-5-sonnet",  # Excellent for technical writing
+        temperature=0.1  # Low temperature for accuracy
+    ),
+    tools=[code_analyzer_tool, api_scanner_tool],
+    verbose=True 
+)
+```
+
+**Alignment Checklist:**
+- ✅ **Role Specificity**: Clear domain and responsibilities
+- ✅ **LLM Match**: Model strengths align with role requirements 
+- ✅ **Backstory Depth**: Provides domain context the LLM can leverage
+- ✅ **Tool Integration**: Tools support the agent's specialized function
+- ✅ **Parameter Tuning**: Temperature and settings optimize for role needs
+
+The key is creating agents where every configuration choice reinforces your LLM selection strategy, maximizing performance while optimizing costs.
+
+## Practical Implementation Checklist
+
+Rather than repeating the strategic framework, here's a tactical checklist for implementing your LLM selection decisions in CrewAI:
+
+<Steps>
+  <Step title="Audit Your Current Setup" icon="clipboard-check">
+    **What to Review:**
+    - Are all agents using the same LLM by default?
+    - Which agents handle the most complex reasoning tasks?
+    - Which agents primarily do data processing or formatting?
+    - Are any agents heavily tool-dependent?
+    
+    **Action**: Document current agent roles and identify optimization opportunities.
+  </Step>
+  
+  <Step title="Implement Crew-Level Strategy" icon="users-gear">
+    **Set Your Baseline:**
+    ```python
+    # Start with a reliable default for the crew
+    default_crew_llm = LLM(model="gpt-4o-mini")  # Cost-effective baseline
+    
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        memory=True
+    )
+    ```
+    
+    **Action**: Establish your crew's default LLM before optimizing individual agents.
+  </Step>
+  
+  <Step title="Optimize High-Impact Agents" icon="star">
+    **Identify and Upgrade Key Agents:**
+    ```python
+    # Manager or coordination agents
+    manager_agent = Agent(
+        role="Project Manager",
+        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
+        # ... rest of config
+    )
+    
+    # Creative or customer-facing agents  
+    content_agent = Agent(
+        role="Content Creator",
+        llm=LLM(model="claude-3-5-sonnet"),  # Best for writing
+        # ... rest of config
+    )
+    ```
+    
+    **Action**: Upgrade 20% of your agents that handle 80% of the complexity.
+  </Step>
+  
+  <Step title="Validate with Enterprise Testing" icon="test-tube">
+    **Once you deploy your agents to production:**
+    - Use [CrewAI Enterprise platform](https://app.crewai.com) to A/B test your model selections
+    - Run multiple iterations with real inputs to measure consistency and performance
+    - Compare cost vs. performance across your optimized setup
+    - Share results with your team for collaborative decision-making
+    
+    **Action**: Replace guesswork with data-driven validation using the testing platform.
+  </Step>
+</Steps>
+
+### When to Use Different Model Types
+
+<Tabs>
+  <Tab title="Reasoning Models">
+    Reasoning models become essential when tasks require genuine multi-step logical thinking, strategic planning, or high-level decision making that benefits from systematic analysis. These models excel when problems need to be broken down into components and analyzed systematically rather than handled through pattern matching or simple instruction following.
+
+    Consider reasoning models for business strategy development, complex data analysis that requires drawing insights from multiple sources, multi-step problem solving where each step depends on previous analysis, and strategic planning tasks that require considering multiple variables and their interactions.
+
+    However, reasoning models often come with higher costs and slower response times, so they're best reserved for tasks where their sophisticated capabilities provide genuine value rather than being used for simple operations that don't require complex reasoning.
+  </Tab>
+  
+  <Tab title="Creative Models">
+    Creative models become valuable when content generation is the primary output and the quality, style, and engagement level of that content directly impact success. These models excel when writing quality and style matter significantly, creative ideation or brainstorming is needed, or brand voice and tone are important considerations.
+
+    Use creative models for blog post writing and article creation, marketing copy that needs to engage and persuade, creative storytelling and narrative development, and brand communications where voice and tone are crucial. These models often understand nuance and context better than general purpose alternatives.
+
+    Creative models may be less suitable for technical or analytical tasks where precision and factual accuracy are more important than engagement and style. They're best used when the creative and communicative aspects of the output are primary success factors.
+  </Tab>
+  
+  <Tab title="Efficient Models">
+    Efficient models are ideal for high-frequency, routine operations where speed and cost optimization are priorities. These models work best when tasks have clear, well-defined parameters and don't require sophisticated reasoning or creative capabilities.
+
+    Consider efficient models for data processing and transformation tasks, simple formatting and organization operations, function calling and tool usage where precision matters more than sophistication, and high-volume operations where cost per operation is a significant factor.
+
+    The key with efficient models is ensuring that their capabilities align with task requirements. They can handle many routine operations effectively but may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation.
+  </Tab>
+  
+  <Tab title="Open Source Models">
+    Open source models become attractive when budget constraints are significant, data privacy requirements exist, customization needs are important, or local deployment is required for operational or compliance reasons.
+
+    Consider open source models for internal company tools where data privacy is paramount, privacy-sensitive applications that can't use external APIs, cost-optimized deployments where per-token pricing is prohibitive, and situations requiring custom model modifications or fine-tuning.
+
+    However, open source models require more technical expertise to deploy and maintain effectively. Consider the total cost of ownership including infrastructure, technical overhead, and ongoing maintenance when evaluating open source options.
+  </Tab>
+</Tabs>
+
+## Common CrewAI Model Selection Pitfalls
+
+<AccordionGroup>
+  <Accordion title="The 'One Model Fits All' Trap" icon="triangle-exclamation">
+    **The Problem**: Using the same LLM for all agents in a crew, regardless of their specific roles and responsibilities. This is often the default approach but rarely optimal.
+
+    **Real Example**: Using GPT-4o for both a strategic planning manager and a data extraction agent. The manager needs reasoning capabilities worth the premium cost, but the data extractor could perform just as well with GPT-4o-mini at a fraction of the price.
+
+    **CrewAI Solution**: Leverage agent-specific LLM configuration to match model capabilities with agent roles:
+    ```python
+    # Strategic agent gets premium model
+    manager = Agent(role="Strategy Manager", llm=LLM(model="gpt-4o"))
+    
+    # Processing agent gets efficient model  
+    processor = Agent(role="Data Processor", llm=LLM(model="gpt-4o-mini"))
+    ```
+  </Accordion>
+  
+  <Accordion title="Ignoring Crew-Level vs Agent-Level LLM Hierarchy" icon="shuffle">
+    **The Problem**: Not understanding how CrewAI's LLM hierarchy works - crew LLM, manager LLM, and agent LLM settings can conflict or be poorly coordinated.
+
+    **Real Example**: Setting a crew to use Claude, but having agents configured with GPT models, creating inconsistent behavior and unnecessary model switching overhead.
+
+    **CrewAI Solution**: Plan your LLM hierarchy strategically:
+    ```python
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[task1, task2],
+        manager_llm=LLM(model="gpt-4o"),  # For crew coordination
+        process=Process.hierarchical  # When using manager_llm
+    )
+    
+    # Agents inherit crew LLM unless specifically overridden
+    agent1 = Agent(llm=LLM(model="claude-3-5-sonnet"))  # Override for specific needs
+    ```
+  </Accordion>
+  
+  <Accordion title="Function Calling Model Mismatch" icon="screwdriver-wrench">
+    **The Problem**: Choosing models based on general capabilities while ignoring function calling performance for tool-heavy CrewAI workflows.
+
+    **Real Example**: Selecting a creative-focused model for an agent that primarily needs to call APIs, search tools, or process structured data. The agent struggles with tool parameter extraction and reliable function calls.
+
+    **CrewAI Solution**: Prioritize function calling capabilities for tool-heavy agents:
+    ```python
+    # For agents that use many tools
+    tool_agent = Agent(
+        role="API Integration Specialist",
+        tools=[search_tool, api_tool, data_tool],
+        llm=LLM(model="gpt-4o"),  # Excellent function calling
+        # OR
+        llm=LLM(model="claude-3-5-sonnet")  # Also strong with tools
+    )
+    ```
+  </Accordion>
+  
+  <Accordion title="Premature Optimization Without Testing" icon="gear">
+    **The Problem**: Making complex model selection decisions based on theoretical performance without validating with actual CrewAI workflows and tasks.
+
+    **Real Example**: Implementing elaborate model switching logic based on task types without testing if the performance gains justify the operational complexity.
+
+    **CrewAI Solution**: Start simple, then optimize based on real performance data:
+    ```python
+    # Start with this
+    crew = Crew(agents=[...], tasks=[...], llm=LLM(model="gpt-4o-mini"))
+    
+    # Test performance, then optimize specific agents as needed
+    # Use Enterprise platform testing to validate improvements
+    ```
+  </Accordion>
+
+  <Accordion title="Overlooking Context and Memory Limitations" icon="brain">
+    **The Problem**: Not considering how model context windows interact with CrewAI's memory and context sharing between agents.
+
+    **Real Example**: Using a short-context model for agents that need to maintain conversation history across multiple task iterations, or in crews with extensive agent-to-agent communication.
+
+    **CrewAI Solution**: Match context capabilities to crew communication patterns.
+  </Accordion>
+</AccordionGroup>
+
+## Testing and Iteration Strategy
+
+<Steps>
+  <Step title="Start Simple" icon="play">
+    Begin with reliable, general-purpose models that are well-understood and widely supported. This provides a stable foundation for understanding your specific requirements and performance expectations before optimizing for specialized needs.
+  </Step>
+  <Step title="Measure What Matters" icon="chart-line">
+    Develop metrics that align with your specific use case and business requirements rather than relying solely on general benchmarks. Focus on measuring outcomes that directly impact your success rather than theoretical performance indicators.
+  </Step>
+  <Step title="Iterate Based on Results" icon="arrows-rotate">
+    Make model changes based on observed performance in your specific context rather than theoretical considerations or general recommendations. Real-world performance often differs significantly from benchmark results or general reputation.
+  </Step>
+  <Step title="Consider Total Cost" icon="calculator">
+    Evaluate the complete cost of ownership including model costs, development time, maintenance overhead, and operational complexity. The cheapest model per token may not be the most cost-effective choice when considering all factors.
+  </Step>
+</Steps>
+
+<Tip>
+Focus on understanding your requirements first, then select models that best match those needs. The best LLM choice is the one that consistently delivers the results you need within your operational constraints.
+</Tip>
+
+### Enterprise-Grade Model Validation
+
+For teams serious about optimizing their LLM selection, the **CrewAI Enterprise platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy.
+
+<Frame>
+  ![Enterprise Testing Interface](/images/enterprise/enterprise-testing.png)
+</Frame>
+
+**Advanced Testing Features:**
+
+- **Multi-Model Comparison**: Test multiple LLMs simultaneously across the same tasks and inputs. Compare performance between GPT-4o, Claude, Llama, Groq, Cerebras, and other leading models in parallel to identify the best fit for your specific use case.
+
+- **Statistical Rigor**: Configure multiple iterations with consistent inputs to measure reliability and performance variance. This helps identify models that not only perform well but do so consistently across runs.
+
+- **Real-World Validation**: Use your actual crew inputs and scenarios rather than synthetic benchmarks. The platform allows you to test with your specific industry context, company information, and real use cases for more accurate evaluation.
+
+- **Comprehensive Analytics**: Access detailed performance metrics, execution times, and cost analysis across all tested models. This enables data-driven decision making rather than relying on general model reputation or theoretical capabilities.
+
+- **Team Collaboration**: Share testing results and model performance data across your team, enabling collaborative decision-making and consistent model selection strategies across projects.
+
+Go to [app.crewai.com](https://app.crewai.com) to get started!
+
+<Info>
+The Enterprise platform transforms model selection from guesswork into a data-driven process, enabling you to validate the principles in this guide with your actual use cases and requirements.
+</Info>
+
+## Key Principles Summary
+
+<CardGroup cols={2}>
+  <Card title="Task-Driven Selection" icon="bullseye">
+    Choose models based on what the task actually requires, not theoretical capabilities or general reputation.
+  </Card>
+  
+  <Card title="Capability Matching" icon="puzzle-piece">
+    Align model strengths with agent roles and responsibilities for optimal performance.
+  </Card>
+  
+  <Card title="Strategic Consistency" icon="link">
+    Maintain coherent model selection strategy across related components and workflows.
+  </Card>
+  
+  <Card title="Practical Testing" icon="flask">
+    Validate choices through real-world usage rather than benchmarks alone.
+  </Card>
+  
+  <Card title="Iterative Improvement" icon="arrow-up">
+    Start simple and optimize based on actual performance and needs.
+  </Card>
+  
+  <Card title="Operational Balance" icon="scale-balanced">
+    Balance performance requirements with cost and complexity constraints.
+  </Card>
+</CardGroup>
+
+<Check>
+Remember: The best LLM choice is the one that consistently delivers the results you need within your operational constraints. Focus on understanding your requirements first, then select models that best match those needs.
+</Check>
+
+## Current Model Landscape (June 2025)
+
+<Warning>
+**Snapshot in Time**: The following model rankings represent current leaderboard standings as of June 2025, compiled from [LMSys Arena](https://arena.lmsys.org/), [Artificial Analysis](https://artificialanalysis.ai/), and other leading benchmarks. LLM performance, availability, and pricing change rapidly. Always conduct your own evaluations with your specific use cases and data.
+</Warning>
+
+### Leading Models by Category
+
+The tables below show a representative sample of current top-performing models across different categories, with guidance on their suitability for CrewAI agents:
+
+<Note>
+These tables/metrics showcase selected leading models in each category and are not exhaustive. Many excellent models exist beyond those listed here. The goal is to illustrate the types of capabilities to look for rather than provide a complete catalog.
+</Note>
+
+<Tabs>
+  <Tab title="Reasoning & Planning">
+    **Best for Manager LLMs and Complex Analysis**
+    
+    | Model | Intelligence Score | Cost ($/M tokens) | Speed | Best Use in CrewAI |
+    |:------|:------------------|:------------------|:------|:------------------|
+    | **o3** | 70 | $17.50 | Fast | Manager LLM for complex multi-agent coordination |
+    | **Gemini 2.5 Pro** | 69 | $3.44 | Fast | Strategic planning agents, research coordination |
+    | **DeepSeek R1** | 68 | $0.96 | Moderate | Cost-effective reasoning for budget-conscious crews |
+    | **Claude 4 Sonnet** | 53 | $6.00 | Fast | Analysis agents requiring nuanced understanding |
+    | **Qwen3 235B (Reasoning)** | 62 | $2.63 | Moderate | Open-source alternative for reasoning tasks |
+
+    These models excel at multi-step reasoning and are ideal for agents that need to develop strategies, coordinate other agents, or analyze complex information.
+  </Tab>
+  
+  <Tab title="Coding & Technical">
+    **Best for Development and Tool-Heavy Workflows**
+    
+    | Model | Coding Performance | Tool Use Score | Cost ($/M tokens) | Best Use in CrewAI |
+    |:------|:------------------|:---------------|:------------------|:------------------|
+    | **Claude 4 Sonnet** | Excellent | 72.7% | $6.00 | Primary coding agent, technical documentation |
+    | **Claude 4 Opus** | Excellent | 72.5% | $30.00 | Complex software architecture, code review |
+    | **DeepSeek V3** | Very Good | High | $0.48 | Cost-effective coding for routine development |
+    | **Qwen2.5 Coder 32B** | Very Good | Medium | $0.15 | Budget-friendly coding agent |
+    | **Llama 3.1 405B** | Good | 81.1% | $3.50 | Function calling LLM for tool-heavy workflows |
+
+    These models are optimized for code generation, debugging, and technical problem-solving, making them ideal for development-focused crews.
+  </Tab>
+  
+  <Tab title="Speed & Efficiency">
+    **Best for High-Throughput and Real-Time Applications**
+    
+    | Model | Speed (tokens/s) | Latency (TTFT) | Cost ($/M tokens) | Best Use in CrewAI |
+    |:------|:-----------------|:---------------|:------------------|:------------------|
+    | **Llama 4 Scout** | 2,600 | 0.33s | $0.27 | High-volume processing agents |
+    | **Gemini 2.5 Flash** | 376 | 0.30s | $0.26 | Real-time response agents |
+    | **DeepSeek R1 Distill** | 383 | Variable | $0.04 | Cost-optimized high-speed processing |
+    | **Llama 3.3 70B** | 2,500 | 0.52s | $0.60 | Balanced speed and capability |
+    | **Nova Micro** | High | 0.30s | $0.04 | Simple, fast task execution |
+
+    These models prioritize speed and efficiency, perfect for agents handling routine operations or requiring quick responses. **Pro tip**: Pairing these models with fast inference providers like Groq can achieve even better performance, especially for open-source models like Llama.
+  </Tab>
+  
+  <Tab title="Balanced Performance">
+    **Best All-Around Models for General Crews**
+    
+    | Model | Overall Score | Versatility | Cost ($/M tokens) | Best Use in CrewAI |
+    |:------|:--------------|:------------|:------------------|:------------------|
+    | **GPT-4.1** | 53 | Excellent | $3.50 | General-purpose crew LLM |
+    | **Claude 3.7 Sonnet** | 48 | Very Good | $6.00 | Balanced reasoning and creativity |
+    | **Gemini 2.0 Flash** | 48 | Good | $0.17 | Cost-effective general use |
+    | **Llama 4 Maverick** | 51 | Good | $0.37 | Open-source general purpose |
+    | **Qwen3 32B** | 44 | Good | $1.23 | Budget-friendly versatility |
+
+    These models offer good performance across multiple dimensions, suitable for crews with diverse task requirements.
+  </Tab>
+</Tabs>
+
+### Selection Framework for Current Models
+
+<AccordionGroup>
+  <Accordion title="High-Performance Crews" icon="rocket">
+    **When performance is the priority**: Use top-tier models like **o3**, **Gemini 2.5 Pro**, or **Claude 4 Sonnet** for manager LLMs and critical agents. These models excel at complex reasoning and coordination but come with higher costs.
+
+    **Strategy**: Implement a multi-model approach where premium models handle strategic thinking while efficient models handle routine operations.
+  </Accordion>
+  
+  <Accordion title="Cost-Conscious Crews" icon="dollar-sign">
+    **When budget is a primary constraint**: Focus on models like **DeepSeek R1**, **Llama 4 Scout**, or **Gemini 2.0 Flash**. These provide strong performance at significantly lower costs.
+
+    **Strategy**: Use cost-effective models for most agents, reserving premium models only for the most critical decision-making roles.
+  </Accordion>
+  
+  <Accordion title="Specialized Workflows" icon="screwdriver-wrench">
+    **For specific domain expertise**: Choose models optimized for your primary use case. **Claude 4** series for coding, **Gemini 2.5 Pro** for research, **Llama 405B** for function calling.
+
+    **Strategy**: Select models based on your crew's primary function, ensuring the core capability aligns with model strengths.
+  </Accordion>
+  
+  <Accordion title="Enterprise & Privacy" icon="shield">
+    **For data-sensitive operations**: Consider open-source models like **Llama 4** series, **DeepSeek V3**, or **Qwen3** that can be deployed locally while maintaining competitive performance.
+
+    **Strategy**: Deploy open-source models on private infrastructure, accepting potential performance trade-offs for data control.
+  </Accordion>
+  </AccordionGroup>
+
+### Key Considerations for Model Selection
+
+- **Performance Trends**: The current landscape shows strong competition between reasoning-focused models (o3, Gemini 2.5 Pro) and balanced models (Claude 4, GPT-4.1). Specialized models like DeepSeek R1 offer excellent cost-performance ratios.
+
+- **Speed vs. Intelligence Trade-offs**: Models like Llama 4 Scout prioritize speed (2,600 tokens/s) while maintaining reasonable intelligence, whereas models like o3 maximize reasoning capability at the cost of speed and price.
+
+- **Open Source Viability**: The gap between open-source and proprietary models continues to narrow, with models like Llama 4 Maverick and DeepSeek V3 offering competitive performance at attractive price points. Fast inference providers particularly shine with open-source models, often delivering better speed-to-cost ratios than proprietary alternatives.
+
+<Info>
+**Testing is Essential**: Leaderboard rankings provide general guidance, but your specific use case, prompting style, and evaluation criteria may produce different results. Always test candidate models with your actual tasks and data before making final decisions.
+</Info>
+
+### Practical Implementation Strategy
+
+<Steps>
+  <Step title="Start with Proven Models">
+    Begin with well-established models like **GPT-4.1**, **Claude 3.7 Sonnet**, or **Gemini 2.0 Flash** that offer good performance across multiple dimensions and have extensive real-world validation.
+  </Step>
+  
+  <Step title="Identify Specialized Needs">
+    Determine if your crew has specific requirements (coding, reasoning, speed) that would benefit from specialized models like **Claude 4 Sonnet** for development or **o3** for complex analysis. For speed-critical applications, consider fast inference providers like **Groq** alongside model selection.
+  </Step>
+  
+  <Step title="Implement Multi-Model Strategy">
+    Use different models for different agents based on their roles. High-capability models for managers and complex tasks, efficient models for routine operations.
+  </Step>
+  
+  <Step title="Monitor and Optimize">
+    Track performance metrics relevant to your use case and be prepared to adjust model selections as new models are released or pricing changes.
+  </Step>
+</Steps> 

docs/learn/overview.mdx

@@ -0,0 +1,158 @@
+---
+title: "Overview"
+description: "Learn how to build, customize, and optimize your CrewAI applications with comprehensive guides and tutorials"
+icon: "face-smile"
+---
+
+## Learn CrewAI
+
+This section provides comprehensive guides and tutorials to help you master CrewAI, from basic concepts to advanced techniques. Whether you're just getting started or looking to optimize your existing implementations, these resources will guide you through every aspect of building powerful AI agent workflows.
+
+## Getting Started Guides
+
+### Core Concepts
+<CardGroup cols={2}>
+  <Card title="Sequential Process" icon="list-ol" href="/learn/sequential-process">
+    Learn how to execute tasks in a sequential order for structured workflows.
+  </Card>
+
+  <Card title="Hierarchical Process" icon="sitemap" href="/learn/hierarchical-process">
+    Implement hierarchical task execution with manager agents overseeing workflows.
+  </Card>
+
+  <Card title="Conditional Tasks" icon="code-branch" href="/learn/conditional-tasks">
+    Create dynamic workflows with conditional task execution based on outcomes.
+  </Card>
+
+  <Card title="Async Kickoff" icon="bolt" href="/learn/kickoff-async">
+    Execute crews asynchronously for improved performance and concurrency.
+  </Card>
+</CardGroup>
+
+### Agent Development
+<CardGroup cols={2}>
+  <Card title="Customizing Agents" icon="user-gear" href="/learn/customizing-agents">
+    Learn how to customize agent behavior, roles, and capabilities.
+  </Card>
+
+  <Card title="Coding Agents" icon="code" href="/learn/coding-agents">
+    Build agents that can write, execute, and debug code automatically.
+  </Card>
+
+  <Card title="Multimodal Agents" icon="images" href="/learn/multimodal-agents">
+    Create agents that can process text, images, and other media types.
+  </Card>
+
+  <Card title="Custom Manager Agent" icon="user-tie" href="/learn/custom-manager-agent">
+    Implement custom manager agents for complex hierarchical workflows.
+  </Card>
+</CardGroup>
+
+## Advanced Features
+
+### Workflow Control
+<CardGroup cols={2}>
+  <Card title="Human in the Loop" icon="user-check" href="/learn/human-in-the-loop">
+    Integrate human oversight and intervention into agent workflows.
+  </Card>
+
+  <Card title="Human Input on Execution" icon="hand-paper" href="/learn/human-input-on-execution">
+    Allow human input during task execution for dynamic decision making.
+  </Card>
+
+  <Card title="Replay Tasks" icon="rotate-left" href="/learn/replay-tasks-from-latest-crew-kickoff">
+    Replay and resume tasks from previous crew executions.
+  </Card>
+
+  <Card title="Kickoff for Each" icon="repeat" href="/learn/kickoff-for-each">
+    Execute crews multiple times with different inputs efficiently.
+  </Card>
+</CardGroup>
+
+### Customization & Integration
+<CardGroup cols={2}>
+  <Card title="Custom LLM" icon="brain" href="/learn/custom-llm">
+    Integrate custom language models and providers with CrewAI.
+  </Card>
+
+  <Card title="LLM Connections" icon="link" href="/learn/llm-connections">
+    Configure and manage connections to various LLM providers.
+  </Card>
+
+  <Card title="Create Custom Tools" icon="wrench" href="/learn/create-custom-tools">
+    Build custom tools to extend agent capabilities.
+  </Card>
+
+  <Card title="Using Annotations" icon="at" href="/learn/using-annotations">
+    Use Python annotations for cleaner, more maintainable code.
+  </Card>
+</CardGroup>
+
+## Specialized Applications
+
+### Content & Media
+<CardGroup cols={2}>
+  <Card title="DALL-E Image Generation" icon="image" href="/learn/dalle-image-generation">
+    Generate images using DALL-E integration with your agents.
+  </Card>
+
+  <Card title="Bring Your Own Agent" icon="user-plus" href="/learn/bring-your-own-agent">
+    Integrate existing agents and models into CrewAI workflows.
+  </Card>
+</CardGroup>
+
+### Tool Management
+<CardGroup cols={2}>
+  <Card title="Force Tool Output as Result" icon="hammer" href="/learn/force-tool-output-as-result">
+    Configure tools to return their output directly as task results.
+  </Card>
+</CardGroup>
+
+## Learning Path Recommendations
+
+### For Beginners
+1. Start with **Sequential Process** to understand basic workflow execution
+2. Learn **Customizing Agents** to create effective agent configurations
+3. Explore **Create Custom Tools** to extend functionality
+4. Try **Human in the Loop** for interactive workflows
+
+### For Intermediate Users
+1. Master **Hierarchical Process** for complex multi-agent systems
+2. Implement **Conditional Tasks** for dynamic workflows
+3. Use **Async Kickoff** for performance optimization
+4. Integrate **Custom LLM** for specialized models
+
+### For Advanced Users
+1. Build **Multimodal Agents** for complex media processing
+2. Create **Custom Manager Agents** for sophisticated orchestration
+3. Implement **Bring Your Own Agent** for hybrid systems
+4. Use **Replay Tasks** for robust error recovery
+
+## Best Practices
+
+### Development
+- **Start Simple**: Begin with basic sequential workflows before adding complexity
+- **Test Incrementally**: Test each component before integrating into larger systems
+- **Use Annotations**: Leverage Python annotations for cleaner, more maintainable code
+- **Custom Tools**: Build reusable tools that can be shared across different agents
+
+### Production
+- **Error Handling**: Implement robust error handling and recovery mechanisms
+- **Performance**: Use async execution and optimize LLM calls for better performance
+- **Monitoring**: Integrate observability tools to track agent performance
+- **Human Oversight**: Include human checkpoints for critical decisions
+
+### Optimization
+- **Resource Management**: Monitor and optimize token usage and API costs
+- **Workflow Design**: Design workflows that minimize unnecessary LLM calls
+- **Tool Efficiency**: Create efficient tools that provide maximum value with minimal overhead
+- **Iterative Improvement**: Use feedback and metrics to continuously improve agent performance
+
+## Getting Help
+
+- **Documentation**: Each guide includes detailed examples and explanations
+- **Community**: Join the [CrewAI Forum](https://community.crewai.com) for discussions and support
+- **Examples**: Check the Examples section for complete working implementations
+- **Support**: Contact [support@crewai.com](mailto:support@crewai.com) for technical assistance
+
+Start with the guides that match your current needs and gradually explore more advanced topics as you become comfortable with the fundamentals. 

docs/mcp/crewai-mcp-integration.mdx

@@ -1,243 +0,0 @@
----
-title: 'MCP Servers as Tools in CrewAI'
-description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
-icon: 'plug'
----
-
-## Overview
-
-The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers. 
-The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents. 
-This gives your crews access to a vast ecosystem of functionalities. For now, we support **Standard Input/Output** (Stdio) and **Server-Sent Events** (SSE) transport mechanisms. 
-
-<Info>
-We will also be integrating **Streamable HTTP** transport in the near future. 
-Streamable HTTP is designed for efficient, bi-directional communication over a single HTTP connection.
-</Info>
-
-## Video Tutorial
-Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI:
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/TpQ45lAZh48"
-  title="CrewAI MCP Integration Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-## Installation
-
-Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
-
-```shell
-uv pip install 'crewai-tools[mcp]'
-```
-
-### Integrating MCP Tools with `MCPServerAdapter`
-
-The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. 
-It supports different transport mechanisms, primarily **Stdio** (for local servers) and **SSE** (Server-Sent Events).You have two main options for managing the connection lifecycle:
-
-### Option 1: Fully Managed Connection (Recommended)
-
-Using a Python context manager (`with` statement) is the recommended approach. It automatically handles starting and stopping the connection to the MCP server.
-
-**For a local Stdio-based MCP server:**
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters
-import os
-
-server_params=StdioServerParameters(
-    command="uxv", # Or your python3 executable i.e. "python3"
-    args=["mock_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-with MCPServerAdapter(server_params) as tools:
-    print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
-
-    # Example: Using the tools from the Stdio MCP server in a CrewAI Agent
-    agent = Agent(
-        role="Web Information Retriever",
-        goal="Scrape content from a specified URL.",
-        backstory="An AI that can fetch and process web page data via an MCP tool.",
-        tools=tools,
-        verbose=True,
-    )
-    task = Task(
-        description="Scrape content from a specified URL.",
-        expected_output="Scraped content from the specified URL.",
-        agent=agent,
-    )
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-    result = crew.kickoff()
-    print(result)
-```
-
-**For a remote SSE-based MCP server:**
-
-```python
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew
-
-server_params = {"url": "http://localhost:8000/sse"}
-
-with MCPServerAdapter(server_params) as tools:
-    print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
-
-    # Example: Using the tools from the SSE MCP server in a CrewAI Agent
-    agent = Agent(
-        role="Web Information Retriever",
-        goal="Scrape content from a specified URL.",
-        backstory="An AI that can fetch and process web page data via an MCP tool.",
-        tools=tools,
-        verbose=True,
-    )
-    task = Task(
-        description="Scrape content from a specified URL.",
-        expected_output="Scraped content from the specified URL.",
-        agent=agent,
-    )
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-    result = crew.kickoff()
-    print(result)
-```
-
-### Option 2: More control over the MCP server connection lifecycle
-
-If you need finer-grained control over the MCP server connection lifecycle, you can instantiate `MCPServerAdapter` directly and manage its `start()` and `stop()` methods.
-
-<Info>
-You **MUST** call `mcp_server_adapter.stop()` to ensure the connection is closed and resources are released. Using a `try...finally` block is highly recommended.
-</Info>
-
-#### Stdio Transport Example (Manual)
-
-```python
-from mcp import StdioServerParameters
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew
-import os
-
-stdio_params = StdioServerParameters(
-    command="uvx", # Or your python3 executable i.e. "python3"
-    args=["--quiet", "your-mcp-server@0.1.3"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
-try:
-    mcp_server_adapter.start() # Manually start the connection
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
-
-    # Use 'tools' with your Agent, Task, Crew setup as in Option 1
-    agent = Agent(
-        role="Medical Researcher", 
-        goal="Find recent studies on a given topic using PubMed.", 
-        backstory="An AI assistant specialized in biomedical literature research.", 
-        tools=tools,
-        verbose=True 
-    )
-    
-    task = Task(
-        description="Search for recent articles on 'crispr gene editing'.",
-        expected_output="A summary of the top 3 recent articles.",
-        agent=agent
-    )
-    
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = crew.kickoff()
-    print(result)
-finally:
-    print("Stopping Stdio MCP server connection (manual)...")
-    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
-```
-
-
-#### SSE Transport Example (Manual)
-
-```python
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew, Process
-from mcp import StdioServerParameters
-
-
-server_params = {"url": "http://localhost:8000/sse"}
-
-try:
-    mcp_server_adapter = MCPServerAdapter(server_params)
-    mcp_server_adapter.start()
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
-
-    agent = Agent(
-        role="Medical Researcher", 
-        goal="Find recent studies on a given topic using PubMed.", 
-        backstory="An AI assistant specialized in biomedical literature research.", 
-        tools=tools,
-        verbose=True 
-    )
-    
-    task = Task(
-        description="Search for recent articles on 'crispr gene editing'.",
-        expected_output="A summary of the top 3 recent articles.",
-        agent=agent
-    )
-    
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = crew.kickoff()
-    print(result)
-finally:
-    print("Stopping SSE MCP server connection (manual)...")
-    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
-```
-
-## Staying Safe with MCP
-<Warning>
-Always ensure that you trust an MCP Server before using it.
-</Warning>
-
-#### Security Warning: DNS Rebinding Attacks
-SSE transports can be vulnerable to DNS rebinding attacks if not properly secured. 
-To prevent this:
-
-1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
-2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
-3. **Implement proper authentication** for all SSE connections
-
-Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
-
-For more details, see the [MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations) documentation.
-
-### Limitations
-*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`. 
-Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
-*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/mcp/multiple-servers.mdx

@@ -0,0 +1,64 @@
+---
+title: Connecting to Multiple MCP Servers
+description: Learn how to use MCPServerAdapter in CrewAI to connect to multiple MCP servers simultaneously and aggregate their tools.
+icon: layer-group
+---
+
+## Overview
+
+`MCPServerAdapter` in `crewai-tools` allows you to connect to multiple MCP servers concurrently. This is useful when your agents need to access tools distributed across different services or environments. The adapter aggregates tools from all specified servers, making them available to your CrewAI agents.
+
+## Configuration
+
+To connect to multiple servers, you provide a list of server parameter dictionaries to `MCPServerAdapter`. Each dictionary in the list should define the parameters for one MCP server.
+
+Supported transport types for each server in the list include `stdio`, `sse`, and `streamable-http`.
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+from mcp import StdioServerParameters # Needed for Stdio example
+
+# Define parameters for multiple MCP servers
+server_params_list = [
+    # Streamable HTTP Server
+    {
+        "url": "http://localhost:8001/mcp", 
+        "transport": "streamable-http"
+    },
+    # SSE Server
+    {
+        "url": "http://localhost:8000/sse",
+        "transport": "sse"
+    },
+    # StdIO Server
+    StdioServerParameters(
+        command="python3",
+        args=["servers/your_stdio_server.py"],
+        env={"UV_PYTHON": "3.12", **os.environ},
+    )
+]
+
+try:
+    with MCPServerAdapter(server_params_list) as aggregated_tools:
+        print(f"Available aggregated tools: {[tool.name for tool in aggregated_tools]}")
+
+        multi_server_agent = Agent(
+            role="Versatile Assistant",
+            goal="Utilize tools from local Stdio, remote SSE, and remote HTTP MCP servers.",
+            backstory="An AI agent capable of leveraging a diverse set of tools from multiple sources.",
+            tools=aggregated_tools, # All tools are available here
+            verbose=True,
+        )
+
+        ... # Your other agent, tasks, and crew code here
+
+except Exception as e:
+    print(f"Error connecting to or using multiple MCP servers (Managed): {e}")
+    print("Ensure all MCP servers are running and accessible with correct configurations.")
+
+```
+
+## Connection Management
+
+When using the context manager (`with` statement), `MCPServerAdapter` handles the lifecycle (start and stop) of all connections to the configured MCP servers. This simplifies resource management and ensures that all connections are properly closed when the context is exited.

docs/mcp/overview.mdx

@@ -0,0 +1,164 @@
+---
+title: 'MCP Servers as Tools in CrewAI'
+description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
+icon: plug
+---
+
+## Overview
+
+The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers. 
+The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents. 
+This gives your crews access to a vast ecosystem of functionalities. 
+
+We currently support the following transport mechanisms: 
+
+- **Stdio**: for local servers (communication via standard input/output between processes on the same machine)
+- **Server-Sent Events (SSE)**: for remote servers (unidirectional, real-time data streaming from server to client over HTTP)
+- **Streamable HTTP**: for remote servers (flexible, potentially bi-directional communication over HTTP, often utilizing SSE for server-to-client streams)
+
+## Video Tutorial
+Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI:
+
+<iframe
+  width="100%"
+  height="400"
+  src="https://www.youtube.com/embed/TpQ45lAZh48"
+  title="CrewAI MCP Integration Guide"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+></iframe>
+
+## Installation
+
+Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
+
+```shell
+uv pip install 'crewai-tools[mcp]'
+```
+
+## Key Concepts & Getting Started
+
+The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. It supports different transport mechanisms and simplifies connection management.
+
+Using a Python context manager (`with` statement) is the **recommended approach** for `MCPServerAdapter`. It automatically handles starting and stopping the connection to the MCP server.
+
+```python
+from crewai import Agent
+from crewai_tools import MCPServerAdapter
+from mcp import StdioServerParameters # For Stdio Server
+
+# Example server_params (choose one based on your server type):
+# 1. Stdio Server:
+server_params=StdioServerParameters(
+    command="python3", 
+    args=["servers/your_server.py"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+# 2. SSE Server:
+server_params = {
+    "url": "http://localhost:8000/sse", 
+    "transport": "sse"
+}
+
+# 3. Streamable HTTP Server:
+server_params = {
+    "url": "http://localhost:8001/mcp", 
+    "transport": "streamable-http"
+}
+
+# Example usage (uncomment and adapt once server_params is set):
+with MCPServerAdapter(server_params) as mcp_tools:
+    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
+    
+    my_agent = Agent(
+        role="MCP Tool User",
+        goal="Utilize tools from an MCP server.",
+        backstory="I can connect to MCP servers and use their tools.",
+        tools=mcp_tools, # Pass the loaded tools to your agent
+        reasoning=True,
+        verbose=True
+    )
+    # ... rest of your crew setup ...
+```
+This general pattern shows how to integrate tools. For specific examples tailored to each transport, refer to the detailed guides below.
+
+## Explore MCP Integrations
+
+<CardGroup cols={2}>
+  <Card 
+    title="Stdio Transport" 
+    icon="server" 
+    href="/mcp/stdio"
+    color="#3B82F6"
+  >
+    Connect to local MCP servers via standard input/output. Ideal for scripts and local executables.
+  </Card>
+  <Card 
+    title="SSE Transport" 
+    icon="wifi" 
+    href="/mcp/sse"
+    color="#10B981"
+  >
+    Integrate with remote MCP servers using Server-Sent Events for real-time data streaming.
+  </Card>
+  <Card 
+    title="Streamable HTTP Transport" 
+    icon="globe" 
+    href="/mcp/streamable-http"
+    color="#F59E0B"
+  >
+    Utilize flexible Streamable HTTP for robust communication with remote MCP servers.
+  </Card>
+  <Card 
+    title="Connecting to Multiple Servers" 
+    icon="layer-group" 
+    href="/mcp/multiple-servers"
+    color="#8B5CF6"
+  >
+    Aggregate tools from several MCP servers simultaneously using a single adapter.
+  </Card>
+  <Card 
+    title="Security Considerations" 
+    icon="lock" 
+    href="/mcp/security"
+    color="#EF4444"
+  >
+    Review important security best practices for MCP integration to keep your agents safe.
+  </Card>
+</CardGroup>
+
+Checkout this repository for full demos and examples of MCP integration with CrewAI! 👇
+
+<Card 
+title="GitHub Repository"
+icon="github"
+href="https://github.com/tonykipkemboi/crewai-mcp-demo"
+target="_blank"
+>
+CrewAI MCP Demo
+</Card>
+
+## Staying Safe with MCP
+<Warning>
+Always ensure that you trust an MCP Server before using it.
+</Warning>
+
+#### Security Warning: DNS Rebinding Attacks
+SSE transports can be vulnerable to DNS rebinding attacks if not properly secured. 
+To prevent this:
+
+1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
+2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
+3. **Implement proper authentication** for all SSE connections
+
+Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
+
+For more details, see the [Anthropic's MCP Transport Security docs](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).
+
+### Limitations
+*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`. 
+Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
+*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/mcp/security.mdx

@@ -0,0 +1,166 @@
+---
+title: MCP Security Considerations
+description: Learn about important security best practices when integrating MCP servers with your CrewAI agents.
+icon: lock
+---
+
+## Overview
+
+<Warning>
+The most critical aspect of MCP security is **trust**. You should **only** connect your CrewAI agents to MCP servers that you fully trust.
+</Warning>
+
+When integrating external services like MCP (Model Context Protocol) servers into your CrewAI agents, security is paramount. 
+MCP servers can execute code, access data, or interact with other systems based on the tools they expose. 
+It's crucial to understand the implications and follow best practices to protect your applications and data.
+
+### Risks
+
+- Execute arbitrary code on the machine where the agent is running (especially with `Stdio` transport if the server can control the command executed).
+- Expose sensitive data from your agent or its environment.
+- Manipulate your agent's behavior in unintended ways, including making unauthorized API calls on your behalf.
+- Hijack your agent's reasoning process through sophisticated prompt injection techniques (see below).
+
+### 1. Trusting MCP Servers
+
+<Warning>
+**Only connect to MCP servers that you trust.**
+</Warning>
+
+Before configuring `MCPServerAdapter` to connect to an MCP server, ensure you know:
+- **Who operates the server?** Is it a known, reputable service, or an internal server under your control?
+- **What tools does it expose?** Understand the capabilities of the tools. Could they be misused if an attacker gained control or if the server itself is malicious?
+- **What data does it access or process?** Be aware of any sensitive information that might be sent to or handled by the MCP server.
+
+Avoid connecting to unknown or unverified MCP servers, especially if your agents handle sensitive tasks or data.
+
+### 2. Secure Prompt Injection via Tool Metadata: The "Model Control Protocol" Risk
+
+A significant and subtle risk is the potential for prompt injection through tool metadata. Here's how it works:
+
+1.  When your CrewAI agent connects to an MCP server, it typically requests a list of available tools.
+2.  The MCP server responds with metadata for each tool, including its name, description, and parameter descriptions.
+3.  Your agent's underlying Language Model (LLM) uses this metadata to understand how and when to use the tools. This metadata is often incorporated into the LLM's system prompt or context.
+4.  A malicious MCP server can craft its tool metadata (names, descriptions) to include hidden or overt instructions. These instructions can act as a prompt injection, effectively telling your LLM to behave in a certain way, reveal sensitive information, or perform malicious actions.
+
+**Crucially, this attack can occur simply by connecting to a malicious server and listing its tools, even if your agent never explicitly decides to *use* any of those tools.** The mere exposure to the malicious metadata can be enough to compromise the agent's behavior.
+
+**Mitigation:**
+
+*   **Extreme Caution with Untrusted Servers:** Reiterate: *Do not connect to MCP servers you do not fully trust.* The risk of metadata injection makes this paramount.
+
+### Stdio Transport Security
+
+Stdio (Standard Input/Output) transport is typically used for local MCP servers running on the same machine as your CrewAI application.
+
+- **Process Isolation**: While generally safer as it doesn't involve network exposure by default, ensure the script or command run by `StdioServerParameters` is from a trusted source and has appropriate file system permissions. A malicious Stdio server script could still harm your local system.
+- **Input Sanitization**: If your Stdio server script takes complex inputs derived from agent interactions, ensure the script itself sanitizes these inputs to prevent command injection or other vulnerabilities within the script's logic.
+- **Resource Limits**: Be mindful that a local Stdio server process consumes local resources (CPU, memory). Ensure it's well-behaved and won't exhaust system resources.
+
+### Confused Deputy Attacks
+
+The [Confused Deputy Problem](https://en.wikipedia.org/wiki/Confused_deputy_problem) is a classic security vulnerability that can manifest in MCP integrations, especially when an MCP server acts as a proxy to other third-party services (e.g., Google Calendar, GitHub) that use OAuth 2.0 for authorization.
+
+**Scenario:**
+
+1.  An MCP server (let's call it `MCP-Proxy`) allows your agent to interact with `ThirdPartyAPI`.
+2.  `MCP-Proxy` uses its own single, static `client_id` when talking to `ThirdPartyAPI`'s authorization server.
+3.  You, as the user, legitimately authorize `MCP-Proxy` to access `ThirdPartyAPI` on your behalf. During this, `ThirdPartyAPI`'s auth server might set a cookie in your browser indicating your consent for `MCP-Proxy`'s `client_id`.
+4.  An attacker crafts a malicious link. This link initiates an OAuth flow with `MCP-Proxy`, but is designed to trick `ThirdPartyAPI`'s auth server.
+5.  If you click this link, and `ThirdPartyAPI`'s auth server sees your existing consent cookie for `MCP-Proxy`'s `client_id`, it might *skip* asking for your consent again.
+6.  `MCP-Proxy` might then be tricked into forwarding an authorization code (for `ThirdPartyAPI`) to the attacker, or an MCP authorization code that the attacker can use to impersonate you to `MCP-Proxy`.
+
+**Mitigation (Primarily for MCP Server Developers):**
+
+*   MCP proxy servers using static client IDs for downstream services **must** obtain explicit user consent for *each client application or agent* connecting to them *before* initiating an OAuth flow with the third-party service. This means `MCP-Proxy` itself should show a consent screen.
+
+**CrewAI User Implication:**
+
+*   Be cautious if an MCP server redirects you for multiple OAuth authentications, especially if it seems unexpected or if the permissions requested are overly broad.
+*   Prefer MCP servers that clearly delineate their own identity versus the third-party services they might proxy.
+
+### Remote Transport Security (SSE & Streamable HTTP)
+
+When connecting to remote MCP servers via Server-Sent Events (SSE) or Streamable HTTP, standard web security practices are essential.
+
+### SSE Security Considerations
+
+### a. DNS Rebinding Attacks (Especially for SSE)
+
+<Critical>
+**Protect against DNS Rebinding Attacks.**
+</Critical>
+
+DNS rebinding allows an attacker-controlled website to bypass the same-origin policy and make requests to servers on the user's local network (e.g., `localhost`) or intranet. This is particularly risky if you run an MCP server locally (e.g., for development) and an agent in a browser-like environment (though less common for typical CrewAI backend setups) or if the MCP server is on an internal network.
+
+**Mitigation Strategies for MCP Server Implementers:**
+- **Validate `Origin` and `Host` Headers**: MCP servers (especially SSE ones) should validate the `Origin` and/or `Host` HTTP headers to ensure requests are coming from expected domains/clients.
+- **Bind to `localhost` (127.0.0.1)**: When running MCP servers locally for development, bind them to `127.0.0.1` instead of `0.0.0.0`. This prevents them from being accessible from other machines on the network.
+- **Authentication**: Require authentication for all connections to your MCP server if it's not intended for public anonymous access.
+
+### b. Use HTTPS
+
+- **Encrypt Data in Transit**: Always use HTTPS (HTTP Secure) for the URLs of remote MCP servers. This encrypts the communication between your CrewAI application and the MCP server, protecting against eavesdropping and man-in-the-middle attacks. `MCPServerAdapter` will respect the scheme (`http` or `https`) provided in the URL.
+
+### c. Token Passthrough (Anti-Pattern)
+
+This is primarily a concern for MCP server developers but understanding it helps in choosing secure servers.
+
+"Token passthrough" is when an MCP server accepts an access token from your CrewAI agent (which might be a token for a *different* service, say `ServiceA`) and simply passes it through to another downstream API (`ServiceB`) without proper validation. Specifically, `ServiceB` (or the MCP server itself) should only accept tokens that were explicitly issued *for them* (i.e., the 'audience' claim in the token matches the server/service).
+
+**Risks:**
+
+*   Bypasses security controls (like rate limiting or fine-grained permissions) on the MCP server or the downstream API.
+*   Breaks audit trails and accountability.
+*   Allows misuse of stolen tokens.
+
+**Mitigation (For MCP Server Developers):**
+
+*   MCP servers **MUST NOT** accept tokens that were not explicitly issued for them. They must validate the token's audience claim.
+
+**CrewAI User Implication:**
+
+*   While not directly controllable by the user, this highlights the importance of connecting to well-designed MCP servers that adhere to security best practices.
+
+#### Authentication and Authorization
+
+- **Verify Identity**: If the MCP server provides sensitive tools or access to private data, it MUST implement strong authentication mechanisms to verify the identity of the client (your CrewAI application). This could involve API keys, OAuth tokens, or other standard methods.
+- **Principle of Least Privilege**: Ensure the credentials used by `MCPServerAdapter` (if any) have only the necessary permissions to access the required tools.
+
+### d. Input Validation and Sanitization
+
+- **Input Validation is Critical**: MCP servers **must** rigorously validate all inputs received from agents *before* processing them or passing them to tools. This is a primary defense against many common vulnerabilities:
+    - **Command Injection:** If a tool constructs shell commands, SQL queries, or other interpreted language statements based on input, the server must meticulously sanitize this input to prevent malicious commands from being injected and executed.
+    - **Path Traversal:** If a tool accesses files based on input parameters, the server must validate and sanitize these paths to prevent access to unauthorized files or directories (e.g., by blocking `../` sequences).
+    - **Data Type & Range Checks:** Servers must ensure that input data conforms to the expected data types (e.g., string, number, boolean) and falls within acceptable ranges or adheres to defined formats (e.g., regex for URLs).
+    - **JSON Schema Validation:** All tool parameters should be strictly validated against their defined JSON schema. This helps catch malformed requests early.
+- **Client-Side Awareness**: While server-side validation is paramount, as a CrewAI user, be mindful of the data your agents are constructed to send to MCP tools, especially if interacting with less-trusted or new MCP servers.
+
+### e. Rate Limiting and Resource Management
+
+- **Prevent Abuse**: MCP servers should implement rate limiting to prevent abuse, whether intentional (Denial of Service attacks) or unintentional (e.g., a misconfigured agent making too many requests).
+- **Client-Side Retries**: Implement sensible retry logic in your CrewAI tasks if transient network issues or server rate limits are expected, but avoid aggressive retries that could exacerbate server load.
+
+## 4. Secure MCP Server Implementation Advice (For Developers)
+
+If you are developing an MCP server that CrewAI agents might connect to, consider these best practices in addition to the points above:
+
+- **Follow Secure Coding Practices**: Adhere to standard secure coding principles for your chosen language and framework (e.g., OWASP Top 10).
+- **Principle of Least Privilege**: Ensure the process running the MCP server (especially for `Stdio`) has only the minimum necessary permissions. Tools themselves should also operate with the least privilege required to perform their function.
+- **Dependency Management**: Keep all server-side dependencies, including operating system packages, language runtimes, and third-party libraries, up-to-date to patch known vulnerabilities. Use tools to scan for vulnerable dependencies.
+- **Secure Defaults**: Design your server and its tools to be secure by default. For example, features that could be risky should be off by default or require explicit opt-in with clear warnings.
+- **Access Control for Tools**: Implement robust mechanisms to control which authenticated and authorized agents or users can access specific tools, especially those that are powerful, sensitive, or incur costs.
+- **Secure Error Handling**: Servers should not expose detailed internal error messages, stack traces, or debugging information to the client, as these can reveal internal workings or potential vulnerabilities. Log errors comprehensively on the server-side for diagnostics.
+- **Comprehensive Logging and Monitoring**: Implement detailed logging of security-relevant events (e.g., authentication attempts, tool invocations, errors, authorization changes). Monitor these logs for suspicious activity or abuse patterns.
+- **Adherence to MCP Authorization Spec**: If implementing authentication and authorization, strictly follow the [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) and relevant [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700).
+- **Regular Security Audits**: If your MCP server handles sensitive data, performs critical operations, or is publicly exposed, consider periodic security audits by qualified professionals.
+
+## 5. Further Reading
+
+For more detailed information on MCP security, refer to the official documentation:
+- **[MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
+
+By understanding these security considerations and implementing best practices, you can safely leverage the power of MCP servers in your CrewAI projects. 
+These are by no means exhaustive, but they cover the most common and critical security concerns. 
+The threats will continue to evolve, so it's important to stay informed and adapt your security measures accordingly.
+

docs/mcp/sse.mdx

@@ -0,0 +1,150 @@
+---
+title: SSE Transport
+description: Learn how to connect CrewAI to remote MCP servers using Server-Sent Events (SSE) for real-time communication.
+icon: wifi
+---
+
+## Overview
+
+Server-Sent Events (SSE) provide a standard way for a web server to send updates to a client over a single, long-lived HTTP connection. In the context of MCP, SSE is used for remote servers to stream data (like tool responses) to your CrewAI application in real-time.
+
+## Key Concepts
+
+- **Remote Servers**: SSE is suitable for MCP servers hosted remotely.
+- **Unidirectional Stream**: Typically, SSE is a one-way communication channel from server to client.
+- **`MCPServerAdapter` Configuration**: For SSE, you'll provide the server's URL and specify the transport type.
+
+## Connecting via SSE
+
+You can connect to an SSE-based MCP server using two main approaches for managing the connection lifecycle:
+
+### 1. Fully Managed Connection (Recommended)
+
+Using a Python context manager (`with` statement) is the recommended approach. It automatically handles establishing and closing the connection to the SSE MCP server.
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+
+server_params = {
+    "url": "http://localhost:8000/sse", # Replace with your actual SSE server URL
+    "transport": "sse" 
+}
+
+# Using MCPServerAdapter with a context manager
+try:
+    with MCPServerAdapter(server_params) as tools:
+        print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
+
+        # Example: Using a tool from the SSE MCP server
+        sse_agent = Agent(
+            role="Remote Service User",
+            goal="Utilize a tool provided by a remote SSE MCP server.",
+            backstory="An AI agent that connects to external services via SSE.",
+            tools=tools,
+            reasoning=True,
+            verbose=True,
+        )
+
+        sse_task = Task(
+            description="Fetch real-time stock updates for 'AAPL' using an SSE tool.",
+            expected_output="The latest stock price for AAPL.",
+            agent=sse_agent,
+            markdown=True
+        )
+
+        sse_crew = Crew(
+            agents=[sse_agent],
+            tasks=[sse_task],
+            verbose=True,
+            process=Process.sequential
+        )
+        
+        if tools: # Only kickoff if tools were loaded
+            result = sse_crew.kickoff() # Add inputs={'stock_symbol': 'AAPL'} if tool requires it
+            print("\nCrew Task Result (SSE - Managed):\n", result)
+        else:
+            print("Skipping crew kickoff as tools were not loaded (check server connection).")
+
+except Exception as e:
+    print(f"Error connecting to or using SSE MCP server (Managed): {e}")
+    print("Ensure the SSE MCP server is running and accessible at the specified URL.")
+
+```
+
+<Note>
+Replace `"http://localhost:8000/sse"` with the actual URL of your SSE MCP server.
+</Note>
+
+### 2. Manual Connection Lifecycle
+
+If you need finer-grained control, you can manage the `MCPServerAdapter` connection lifecycle manually.
+
+<Info>
+You **MUST** call `mcp_server_adapter.stop()` to ensure the connection is closed and resources are released. Using a `try...finally` block is highly recommended.
+</Info>
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+
+server_params = {
+    "url": "http://localhost:8000/sse", # Replace with your actual SSE server URL
+    "transport": "sse"
+}
+
+mcp_server_adapter = None 
+try:
+    mcp_server_adapter = MCPServerAdapter(server_params)
+    mcp_server_adapter.start()
+    tools = mcp_server_adapter.tools
+    print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
+
+    manual_sse_agent = Agent(
+        role="Remote Data Analyst",
+        goal="Analyze data fetched from a remote SSE MCP server using manual connection management.",
+        backstory="An AI skilled in handling SSE connections explicitly.",
+        tools=tools,
+        verbose=True
+    )
+    
+    analysis_task = Task(
+        description="Fetch and analyze the latest user activity trends from the SSE server.",
+        expected_output="A summary report of user activity trends.",
+        agent=manual_sse_agent
+    )
+    
+    analysis_crew = Crew(
+        agents=[manual_sse_agent],
+        tasks=[analysis_task],
+        verbose=True,
+        process=Process.sequential
+    )
+    
+    result = analysis_crew.kickoff()
+    print("\nCrew Task Result (SSE - Manual):\n", result)
+
+except Exception as e:
+    print(f"An error occurred during manual SSE MCP integration: {e}")
+    print("Ensure the SSE MCP server is running and accessible.")
+finally:
+    if mcp_server_adapter and mcp_server_adapter.is_connected:
+        print("Stopping SSE MCP server connection (manual)...")
+        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
+    elif mcp_server_adapter:
+        print("SSE MCP server adapter was not connected. No stop needed or start failed.")
+
+```
+
+## Security Considerations for SSE
+
+<Warning>
+**DNS Rebinding Attacks**: SSE transports can be vulnerable to DNS rebinding attacks if the MCP server is not properly secured. This could allow malicious websites to interact with local or intranet-based MCP servers.
+</Warning>
+
+To mitigate this risk:
+- MCP server implementations should **validate `Origin` headers** on incoming SSE connections.
+- When running local SSE MCP servers for development, **bind only to `localhost` (`127.0.0.1`)** rather than all network interfaces (`0.0.0.0`).
+- Implement **proper authentication** for all SSE connections if they expose sensitive tools or data.
+
+For a comprehensive overview of security best practices, please refer to our [Security Considerations](./security.mdx) page and the official [MCP Transport Security documentation](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).

docs/mcp/stdio.mdx

@@ -0,0 +1,134 @@
+---
+title: Stdio Transport
+description: Learn how to connect CrewAI to local MCP servers using the Stdio (Standard Input/Output) transport mechanism.
+icon: server
+---
+
+## Overview
+
+The Stdio (Standard Input/Output) transport is designed for connecting `MCPServerAdapter` to local MCP servers that communicate over their standard input and output streams. This is typically used when the MCP server is a script or executable running on the same machine as your CrewAI application.
+
+## Key Concepts
+
+- **Local Execution**: Stdio transport manages a locally running process for the MCP server.
+- **`StdioServerParameters`**: This class from the `mcp` library is used to configure the command, arguments, and environment variables for launching the Stdio server.
+
+## Connecting via Stdio
+
+You can connect to an Stdio-based MCP server using two main approaches for managing the connection lifecycle:
+
+### 1. Fully Managed Connection (Recommended)
+
+Using a Python context manager (`with` statement) is the recommended approach. It automatically handles starting the MCP server process and stopping it when the context is exited.
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+from mcp import StdioServerParameters
+import os
+
+# Create a StdioServerParameters object
+server_params=StdioServerParameters(
+    command="python3", 
+    args=["servers/your_stdio_server.py"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+with MCPServerAdapter(server_params) as tools:
+    print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
+
+    # Example: Using the tools from the Stdio MCP server in a CrewAI Agent
+    research_agent = Agent(
+        role="Local Data Processor",
+        goal="Process data using a local Stdio-based tool.",
+        backstory="An AI that leverages local scripts via MCP for specialized tasks.",
+        tools=tools,
+        reasoning=True,
+        verbose=True,
+    )
+    
+    processing_task = Task(
+        description="Process the input data file 'data.txt' and summarize its contents.",
+        expected_output="A summary of the processed data.",
+        agent=research_agent,
+        markdown=True
+    )
+    
+    data_crew = Crew(
+        agents=[research_agent],
+        tasks=[processing_task],
+        verbose=True,
+        process=Process.sequential 
+    )
+   
+    result = data_crew.kickoff()
+    print("\nCrew Task Result (Stdio - Managed):\n", result)
+
+```
+
+### 2. Manual Connection Lifecycle
+
+If you need finer-grained control over when the Stdio MCP server process is started and stopped, you can manage the `MCPServerAdapter` lifecycle manually.
+
+<Info>
+You **MUST** call `mcp_server_adapter.stop()` to ensure the server process is terminated and resources are released. Using a `try...finally` block is highly recommended.
+</Info>
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+from mcp import StdioServerParameters
+import os
+
+# Create a StdioServerParameters object
+stdio_params=StdioServerParameters(
+    command="python3", 
+    args=["servers/your_stdio_server.py"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
+try:
+    mcp_server_adapter.start()  # Manually start the connection and server process
+    tools = mcp_server_adapter.tools
+    print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
+
+    # Example: Using the tools with your Agent, Task, Crew setup
+    manual_agent = Agent(
+        role="Local Task Executor",
+        goal="Execute a specific local task using a manually managed Stdio tool.",
+        backstory="An AI proficient in controlling local processes via MCP.",
+        tools=tools,
+        verbose=True
+    )
+    
+    manual_task = Task(
+        description="Execute the 'perform_analysis' command via the Stdio tool.",
+        expected_output="Results of the analysis.",
+        agent=manual_agent
+    )
+    
+    manual_crew = Crew(
+        agents=[manual_agent],
+        tasks=[manual_task],
+        verbose=True,
+        process=Process.sequential
+    )
+        
+       
+    result = manual_crew.kickoff() # Actual inputs depend on your tool
+    print("\nCrew Task Result (Stdio - Manual):\n", result)
+            
+except Exception as e:
+    print(f"An error occurred during manual Stdio MCP integration: {e}")
+finally:
+    if mcp_server_adapter and mcp_server_adapter.is_connected: # Check if connected before stopping
+        print("Stopping Stdio MCP server connection (manual)...")
+        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
+    elif mcp_server_adapter: # If adapter exists but not connected (e.g. start failed)
+        print("Stdio MCP server adapter was not connected. No stop needed or start failed.")
+
+```
+
+Remember to replace placeholder paths and commands with your actual Stdio server details. The `env` parameter in `StdioServerParameters` can 
+be used to set environment variables for the server process, which can be useful for configuring its behavior or providing necessary paths (like `PYTHONPATH`).

docs/mcp/streamable-http.mdx

@@ -0,0 +1,135 @@
+---
+title: Streamable HTTP Transport
+description: Learn how to connect CrewAI to remote MCP servers using the flexible Streamable HTTP transport.
+icon: globe
+---
+
+## Overview
+
+Streamable HTTP transport provides a flexible way to connect to remote MCP servers. It's often built upon HTTP and can support various communication patterns, including request-response and streaming, sometimes utilizing Server-Sent Events (SSE) for server-to-client streams within a broader HTTP interaction.
+
+## Key Concepts
+
+- **Remote Servers**: Designed for MCP servers hosted remotely.
+- **Flexibility**: Can support more complex interaction patterns than plain SSE, potentially including bi-directional communication if the server implements it.
+- **`MCPServerAdapter` Configuration**: You'll need to provide the server's base URL for MCP communication and specify `"streamable-http"` as the transport type.
+
+## Connecting via Streamable HTTP
+
+You have two primary methods for managing the connection lifecycle with a Streamable HTTP MCP server:
+
+### 1. Fully Managed Connection (Recommended)
+
+The recommended approach is to use a Python context manager (`with` statement), which handles the connection's setup and teardown automatically.
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+
+server_params = {
+    "url": "http://localhost:8001/mcp", # Replace with your actual Streamable HTTP server URL
+    "transport": "streamable-http"
+}
+
+try:
+    with MCPServerAdapter(server_params) as tools:
+        print(f"Available tools from Streamable HTTP MCP server: {[tool.name for tool in tools]}")
+
+        http_agent = Agent(
+            role="HTTP Service Integrator",
+            goal="Utilize tools from a remote MCP server via Streamable HTTP.",
+            backstory="An AI agent adept at interacting with complex web services.",
+            tools=tools,
+            verbose=True,
+        )
+
+        http_task = Task(
+            description="Perform a complex data query using a tool from the Streamable HTTP server.",
+            expected_output="The result of the complex data query.",
+            agent=http_agent,
+        )
+
+        http_crew = Crew(
+            agents=[http_agent],
+            tasks=[http_task],
+            verbose=True,
+            process=Process.sequential
+        )
+        
+        result = http_crew.kickoff() 
+        print("\nCrew Task Result (Streamable HTTP - Managed):\n", result)
+
+except Exception as e:
+    print(f"Error connecting to or using Streamable HTTP MCP server (Managed): {e}")
+    print("Ensure the Streamable HTTP MCP server is running and accessible at the specified URL.")
+
+```
+**Note:** Replace `"http://localhost:8001/mcp"` with the actual URL of your Streamable HTTP MCP server.
+
+### 2. Manual Connection Lifecycle
+
+For scenarios requiring more explicit control, you can manage the `MCPServerAdapter` connection manually.
+
+<Info>
+It is **critical** to call `mcp_server_adapter.stop()` when you are done to close the connection and free up resources. A `try...finally` block is the safest way to ensure this.
+</Info>
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import MCPServerAdapter
+
+server_params = {
+    "url": "http://localhost:8001/mcp", # Replace with your actual Streamable HTTP server URL
+    "transport": "streamable-http"
+}
+
+mcp_server_adapter = None 
+try:
+    mcp_server_adapter = MCPServerAdapter(server_params)
+    mcp_server_adapter.start()
+    tools = mcp_server_adapter.tools
+    print(f"Available tools (manual Streamable HTTP): {[tool.name for tool in tools]}")
+
+    manual_http_agent = Agent(
+        role="Advanced Web Service User",
+        goal="Interact with an MCP server using manually managed Streamable HTTP connections.",
+        backstory="An AI specialist in fine-tuning HTTP-based service integrations.",
+        tools=tools,
+        verbose=True
+    )
+    
+    data_processing_task = Task(
+        description="Submit data for processing and retrieve results via Streamable HTTP.",
+        expected_output="Processed data or confirmation.",
+        agent=manual_http_agent
+    )
+    
+    data_crew = Crew(
+        agents=[manual_http_agent],
+        tasks=[data_processing_task],
+        verbose=True,
+        process=Process.sequential
+    )
+    
+    result = data_crew.kickoff()
+    print("\nCrew Task Result (Streamable HTTP - Manual):\n", result)
+
+except Exception as e:
+    print(f"An error occurred during manual Streamable HTTP MCP integration: {e}")
+    print("Ensure the Streamable HTTP MCP server is running and accessible.")
+finally:
+    if mcp_server_adapter and mcp_server_adapter.is_connected:
+        print("Stopping Streamable HTTP MCP server connection (manual)...")
+        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
+    elif mcp_server_adapter:
+        print("Streamable HTTP MCP server adapter was not connected. No stop needed or start failed.")
+```
+
+## Security Considerations
+
+When using Streamable HTTP transport, general web security best practices are paramount:
+- **Use HTTPS**: Always prefer HTTPS (HTTP Secure) for your MCP server URLs to encrypt data in transit.
+- **Authentication**: Implement robust authentication mechanisms if your MCP server exposes sensitive tools or data.
+- **Input Validation**: Ensure your MCP server validates all incoming requests and parameters.
+
+For a comprehensive guide on securing your MCP integrations, please refer to our [Security Considerations](./security.mdx) page and the official [MCP Transport Security documentation](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).

docs/observability/overview.mdx

@@ -0,0 +1,118 @@
+---
+title: "Overview"
+description: "Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools"
+icon: "face-smile"
+---
+
+## Observability for CrewAI
+
+Observability is crucial for understanding how your CrewAI agents perform, identifying bottlenecks, and ensuring reliable operation in production environments. This section covers various tools and platforms that provide monitoring, evaluation, and optimization capabilities for your agent workflows.
+
+## Why Observability Matters
+
+- **Performance Monitoring**: Track agent execution times, token usage, and resource consumption
+- **Quality Assurance**: Evaluate output quality and consistency across different scenarios
+- **Debugging**: Identify and resolve issues in agent behavior and task execution
+- **Cost Management**: Monitor LLM API usage and associated costs
+- **Continuous Improvement**: Gather insights to optimize agent performance over time
+
+## Available Observability Tools
+
+### Monitoring & Tracing Platforms
+
+<CardGroup cols={2}>
+  <Card title="AgentOps" icon="paperclip" href="/observability/agentops">
+    Session replays, metrics, and monitoring for agent development and production.
+  </Card>
+
+  <Card title="OpenLIT" icon="magnifying-glass-chart" href="/observability/openlit">
+    OpenTelemetry-native monitoring with cost tracking and performance analytics.
+  </Card>
+
+  <Card title="MLflow" icon="bars-staggered" href="/observability/mlflow">
+    Machine learning lifecycle management with tracing and evaluation capabilities.
+  </Card>
+
+  <Card title="Langfuse" icon="link" href="/observability/langfuse">
+    LLM engineering platform with detailed tracing and analytics.
+  </Card>
+
+  <Card title="Langtrace" icon="chart-line" href="/observability/langtrace">
+    Open-source observability for LLMs and agent frameworks.
+  </Card>
+
+  <Card title="Arize Phoenix" icon="meteor" href="/observability/arize-phoenix">
+    AI observability platform for monitoring and troubleshooting.
+  </Card>
+
+  <Card title="Portkey" icon="key" href="/observability/portkey">
+    AI gateway with comprehensive monitoring and reliability features.
+  </Card>
+
+  <Card title="Opik" icon="meteor" href="/observability/opik">
+    Debug, evaluate, and monitor LLM applications with comprehensive tracing.
+  </Card>
+
+  <Card title="Weave" icon="network-wired" href="/observability/weave">
+    Weights & Biases platform for tracking and evaluating AI applications.
+  </Card>
+</CardGroup>
+
+### Evaluation & Quality Assurance
+
+<CardGroup cols={2}>
+  <Card title="Patronus AI" icon="shield-check" href="/observability/patronus-evaluation">
+    Comprehensive evaluation platform for LLM outputs and agent behaviors.
+  </Card>
+</CardGroup>
+
+## Key Observability Metrics
+
+### Performance Metrics
+- **Execution Time**: How long agents take to complete tasks
+- **Token Usage**: Input/output tokens consumed by LLM calls
+- **API Latency**: Response times from external services
+- **Success Rate**: Percentage of successfully completed tasks
+
+### Quality Metrics
+- **Output Accuracy**: Correctness of agent responses
+- **Consistency**: Reliability across similar inputs
+- **Relevance**: How well outputs match expected results
+- **Safety**: Compliance with content policies and guidelines
+
+### Cost Metrics
+- **API Costs**: Expenses from LLM provider usage
+- **Resource Utilization**: Compute and memory consumption
+- **Cost per Task**: Economic efficiency of agent operations
+- **Budget Tracking**: Monitoring against spending limits
+
+## Getting Started
+
+1. **Choose Your Tools**: Select observability platforms that match your needs
+2. **Instrument Your Code**: Add monitoring to your CrewAI applications
+3. **Set Up Dashboards**: Configure visualizations for key metrics
+4. **Define Alerts**: Create notifications for important events
+5. **Establish Baselines**: Measure initial performance for comparison
+6. **Iterate and Improve**: Use insights to optimize your agents
+
+## Best Practices
+
+### Development Phase
+- Use detailed tracing to understand agent behavior
+- Implement evaluation metrics early in development
+- Monitor resource usage during testing
+- Set up automated quality checks
+
+### Production Phase
+- Implement comprehensive monitoring and alerting
+- Track performance trends over time
+- Monitor for anomalies and degradation
+- Maintain cost visibility and control
+
+### Continuous Improvement
+- Regular performance reviews and optimization
+- A/B testing of different agent configurations
+- Feedback loops for quality improvement
+- Documentation of lessons learned
+
+Choose the observability tools that best fit your use case, infrastructure, and monitoring requirements to ensure your CrewAI agents perform reliably and efficiently. 

docs/observability/patronus-evaluation.mdx

@@ -1,16 +1,26 @@
 ---
-title: Patronus Evaluation Tools
-description: The Patronus evaluation tools enable CrewAI agents to evaluate and score model inputs and outputs using the Patronus AI platform.
-icon: check
+title: Patronus AI Evaluation
+description: Monitor and evaluate CrewAI agent performance using Patronus AI's comprehensive evaluation platform for LLM outputs and agent behaviors.
+icon: shield-check
 ---
 
-# `Patronus Evaluation Tools`
+# Patronus AI Evaluation
 
-## Description
+## Overview
 
-The [Patronus evaluation tools](https://patronus.ai) are designed to enable CrewAI agents to evaluate and score model inputs and outputs using the Patronus AI platform. These tools provide different levels of control over the evaluation process, from allowing agents to select the most appropriate evaluator and criteria to using predefined criteria or custom local evaluators.
+[Patronus AI](https://patronus.ai) provides comprehensive evaluation and monitoring capabilities for CrewAI agents, enabling you to assess model outputs, agent behaviors, and overall system performance. This integration allows you to implement continuous evaluation workflows that help maintain quality and reliability in production environments.
 
-There are three main Patronus evaluation tools:
+## Key Features
+
+- **Automated Evaluation**: Real-time assessment of agent outputs and behaviors
+- **Custom Criteria**: Define specific evaluation criteria tailored to your use cases  
+- **Performance Monitoring**: Track agent performance metrics over time
+- **Quality Assurance**: Ensure consistent output quality across different scenarios
+- **Safety & Compliance**: Monitor for potential issues and policy violations
+
+## Evaluation Tools
+
+Patronus provides three main evaluation tools for different use cases:
 
 1. **PatronusEvalTool**: Allows agents to select the most appropriate evaluator and criteria for the evaluation task.
 2. **PatronusPredefinedCriteriaEvalTool**: Uses predefined evaluator and criteria specified by the user.

docs/observability/portkey.mdx

@@ -0,0 +1,824 @@
+---
+title: Portkey Integration
+description: How to use Portkey with CrewAI
+icon: key
+---
+
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
+
+
+
+## Introduction
+
+Portkey enhances CrewAI with production-readiness features, turning your experimental agent crews into robust systems by providing:
+
+- **Complete observability** of every agent step, tool use, and interaction
+- **Built-in reliability** with fallbacks, retries, and load balancing
+- **Cost tracking and optimization** to manage your AI spend
+- **Access to 200+ LLMs** through a single integration
+- **Guardrails** to keep agent behavior safe and compliant
+- **Version-controlled prompts** for consistent agent performance
+
+
+### Installation & Setup
+
+<Steps>
+<Step title="Install the required packages">
+```bash
+pip install -U crewai portkey-ai
+```
+</Step>
+
+<Step title="Generate API Key" icon="lock">
+Create a Portkey API key with optional budget/rate limits from the [Portkey dashboard](https://app.portkey.ai/). You can also attach configurations for reliability, caching, and more to this key. More on this later.
+</Step>
+
+<Step title="Configure CrewAI with Portkey">
+The integration is simple - you just need to update the LLM configuration in your CrewAI setup:
+
+```python
+from crewai import LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Create an LLM instance with Portkey integration
+gpt_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",  # We are using a Virtual key, so this is a placeholder
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_LLM_VIRTUAL_KEY",
+        trace_id="unique-trace-id",               # Optional, for request tracing
+    )
+)
+
+#Use them in your Crew Agents like this:
+
+	@agent
+	def lead_market_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['lead_market_analyst'],
+			verbose=True,
+			memory=False,
+			llm=gpt_llm
+		)
+
+```
+
+<Info>
+**What are Virtual Keys?** Virtual keys in Portkey securely store your LLM provider API keys (OpenAI, Anthropic, etc.) in an encrypted vault. They allow for easier key rotation and budget management. [Learn more about virtual keys here](https://portkey.ai/docs/product/ai-gateway/virtual-keys).
+</Info>
+</Step>
+</Steps>
+
+## Production Features
+
+### 1. Enhanced Observability
+
+Portkey provides comprehensive observability for your CrewAI agents, helping you understand exactly what's happening during each execution.
+
+<Tabs>
+  <Tab title="Traces">
+<Frame>
+    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Product%2011.1.webp"/>
+</Frame>
+
+Traces provide a hierarchical view of your crew's execution, showing the sequence of LLM calls, tool invocations, and state transitions.
+
+```python
+# Add trace_id to enable hierarchical tracing in Portkey
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        trace_id="unique-session-id"  # Add unique trace ID
+    )
+)
+```
+  </Tab>
+
+  <Tab title="Logs">
+<Frame>
+    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs%20Metadata.png"/>
+</Frame>
+
+Portkey logs every interaction with LLMs, including:
+
+- Complete request and response payloads
+- Latency and token usage metrics
+- Cost calculations
+- Tool calls and function executions
+
+All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific crew runs.
+  </Tab>
+
+  <Tab title="Metrics & Dashboards">
+<Frame>
+    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Dashboard.png"/>
+</Frame>
+
+Portkey provides built-in dashboards that help you:
+
+- Track cost and token usage across all crew runs
+- Analyze performance metrics like latency and success rates
+- Identify bottlenecks in your agent workflows
+- Compare different crew configurations and LLMs
+
+You can filter and segment all metrics by custom metadata to analyze specific crew types, user groups, or use cases.
+  </Tab>
+
+  <Tab title="Metadata Filtering">
+<Frame>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Metadata%20Filters%20from%20CrewAI.png" alt="Analytics with metadata filters" />
+</Frame>
+
+Add custom metadata to your CrewAI LLM configuration to enable powerful filtering and segmentation:
+
+```python
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        metadata={
+            "crew_type": "research_crew",
+            "environment": "production",
+            "_user": "user_123",   # Special _user field for user analytics
+            "request_source": "mobile_app"
+        }
+    )
+)
+```
+
+This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific crew runs, users, or environments.
+  </Tab>
+</Tabs>
+
+### 2. Reliability - Keep Your Crews Running Smoothly
+
+When running crews in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey's reliability features ensure your agents keep running smoothly even when problems occur.
+
+It's simple to enable fallback in your CrewAI setup by using a Portkey Config:
+
+```python
+from crewai import LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Create LLM with fallback configuration
+portkey_llm = LLM(
+    model="gpt-4o",
+    max_tokens=1000,
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        config={
+            "strategy": {
+                "mode": "fallback"
+            },
+            "targets": [
+                {
+                    "provider": "openai",
+                    "api_key": "YOUR_OPENAI_API_KEY",
+                    "override_params": {"model": "gpt-4o"}
+                },
+                {
+                    "provider": "anthropic",
+                    "api_key": "YOUR_ANTHROPIC_API_KEY",
+                    "override_params": {"model": "claude-3-opus-20240229"}
+                }
+            ]
+        }
+    )
+)
+
+# Use this LLM configuration with your agents
+```
+
+This configuration will automatically try Claude if the GPT-4o request fails, ensuring your crew can continue operating.
+
+<CardGroup cols="2">
+  <Card title="Automatic Retries" icon="rotate" href="https://portkey.ai/docs/product/ai-gateway/automatic-retries">
+    Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.
+  </Card>
+  <Card title="Request Timeouts" icon="clock" href="https://portkey.ai/docs/product/ai-gateway/request-timeouts">
+    Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.
+  </Card>
+  <Card title="Conditional Routing" icon="route" href="https://portkey.ai/docs/product/ai-gateway/conditional-routing">
+    Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.
+  </Card>
+  <Card title="Fallbacks" icon="shield" href="https://portkey.ai/docs/product/ai-gateway/fallbacks">
+    Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.
+  </Card>
+  <Card title="Load Balancing" icon="scale-balanced" href="https://portkey.ai/docs/product/ai-gateway/load-balancing">
+    Spread requests across multiple API keys or providers. Great for high-volume crew operations and staying within rate limits.
+  </Card>
+</CardGroup>
+
+### 3. Prompting in CrewAI
+
+Portkey's Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your CrewAI agents. Instead of hardcoding prompts or instructions, use Portkey's prompt rendering API to dynamically fetch and apply your versioned prompts.
+
+<Frame caption="Manage prompts in Portkey's Prompt Library">
+![Prompt Playground Interface](https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs.webp)
+</Frame>
+
+<Tabs>
+  <Tab title="Prompt Playground">
+Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It's where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:
+
+1. Iteratively develop prompts before using them in your agents
+2. Test prompts with different variables and models
+3. Compare outputs between different prompt versions
+4. Collaborate with team members on prompt development
+
+This visual environment makes it easier to craft effective prompts for each step in your CrewAI agents' workflow.
+  </Tab>
+
+  <Tab title="Using Prompt Templates">
+The Prompt Render API retrieves your prompt templates with all parameters configured:
+
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL, Portkey
+
+# Initialize Portkey admin client
+portkey_admin = Portkey(api_key="YOUR_PORTKEY_API_KEY")
+
+# Retrieve prompt using the render API
+prompt_data = portkey_client.prompts.render(
+    prompt_id="YOUR_PROMPT_ID",
+    variables={
+        "agent_role": "Senior Research Scientist",
+    }
+)
+
+backstory_agent_prompt=prompt_data.data.messages[0]["content"]
+
+
+# Set up LLM with Portkey integration
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY"
+    )
+)
+
+# Create agent using the rendered prompt
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory=backstory_agent,  # Use the rendered prompt
+    verbose=True,
+    llm=portkey_llm
+)
+```
+  </Tab>
+
+  <Tab title="Prompt Versioning">
+You can:
+- Create multiple versions of the same prompt
+- Compare performance between versions
+- Roll back to previous versions if needed
+- Specify which version to use in your code:
+
+```python
+# Use a specific prompt version
+prompt_data = portkey_admin.prompts.render(
+    prompt_id="YOUR_PROMPT_ID@version_number",
+    variables={
+        "agent_role": "Senior Research Scientist",
+        "agent_goal": "Discover groundbreaking insights"
+    }
+)
+```
+  </Tab>
+
+  <Tab title="Mustache Templating for variables">
+Portkey prompts use Mustache-style templating for easy variable substitution:
+
+```
+You are a {{agent_role}} with expertise in {{domain}}.
+
+Your mission is to {{agent_goal}} by leveraging your knowledge
+and experience in the field.
+
+Always maintain a {{tone}} tone and focus on providing {{focus_area}}.
+```
+
+When rendering, simply pass the variables:
+
+```python
+prompt_data = portkey_admin.prompts.render(
+    prompt_id="YOUR_PROMPT_ID",
+    variables={
+        "agent_role": "Senior Research Scientist",
+        "domain": "artificial intelligence",
+        "agent_goal": "discover groundbreaking insights",
+        "tone": "professional",
+        "focus_area": "practical applications"
+    }
+)
+```
+  </Tab>
+</Tabs>
+
+<Card title="Prompt Engineering Studio" icon="wand-magic-sparkles" href="https://portkey.ai/docs/product/prompt-library">
+  Learn more about Portkey's prompt management features
+</Card>
+
+### 4. Guardrails for Safe Crews
+
+Guardrails ensure your CrewAI agents operate safely and respond appropriately in all situations.
+
+**Why Use Guardrails?**
+
+CrewAI agents can experience various failure modes:
+- Generating harmful or inappropriate content
+- Leaking sensitive information like PII
+- Hallucinating incorrect information
+- Generating outputs in incorrect formats
+
+Portkey's guardrails add protections for both inputs and outputs.
+
+**Implementing Guardrails**
+
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Create LLM with guardrails
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        config={
+            "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
+            "output_guardrails": ["guardrails-id-zzz"]
+        }
+    )
+)
+
+# Create agent with guardrailed LLM
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=portkey_llm
+)
+```
+
+Portkey's guardrails can:
+- Detect and redact PII in both inputs and outputs
+- Filter harmful or inappropriate content
+- Validate response formats against schemas
+- Check for hallucinations against ground truth
+- Apply custom business logic and rules
+
+<Card title="Learn More About Guardrails" icon="shield-check" href="https://portkey.ai/docs/product/guardrails">
+  Explore Portkey's guardrail features to enhance agent safety
+</Card>
+
+### 5. User Tracking with Metadata
+
+Track individual users through your CrewAI agents using Portkey's metadata system.
+
+**What is Metadata in Portkey?**
+
+Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special `_user` field is specifically designed for user tracking.
+
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Configure LLM with user tracking
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        metadata={
+            "_user": "user_123",  # Special _user field for user analytics
+            "user_tier": "premium",
+            "user_company": "Acme Corp",
+            "session_id": "abc-123"
+        }
+    )
+)
+
+# Create agent with tracked LLM
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=portkey_llm
+)
+```
+
+**Filter Analytics by User**
+
+With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:
+
+<Frame caption="Filter analytics by user">
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Metadata%20Filters%20from%20CrewAI.png"/>
+</Frame>
+
+This enables:
+- Per-user cost tracking and budgeting
+- Personalized user analytics
+- Team or organization-level metrics
+- Environment-specific monitoring (staging vs. production)
+
+<Card title="Learn More About Metadata" icon="tags" href="https://portkey.ai/docs/product/observability/metadata">
+  Explore how to use custom metadata to enhance your analytics
+</Card>
+
+### 6. Caching for Efficient Crews
+
+Implement caching to make your CrewAI agents more efficient and cost-effective:
+
+<Tabs>
+  <Tab title="Simple Caching">
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Configure LLM with simple caching
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        config={
+            "cache": {
+                "mode": "simple"
+            }
+        }
+    )
+)
+
+# Create agent with cached LLM
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=portkey_llm
+)
+```
+
+Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.
+  </Tab>
+
+  <Tab title="Semantic Caching">
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Configure LLM with semantic caching
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
+        config={
+            "cache": {
+                "mode": "semantic"
+            }
+        }
+    )
+)
+
+# Create agent with semantically cached LLM
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=portkey_llm
+)
+```
+
+Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.
+  </Tab>
+</Tabs>
+
+### 7. Model Interoperability
+
+CrewAI supports multiple LLM providers, and Portkey extends this capability by providing access to over 200 LLMs through a unified interface. You can easily switch between different models without changing your core agent logic:
+
+```python
+from crewai import Agent, LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+# Set up LLMs with different providers
+openai_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_OPENAI_VIRTUAL_KEY"
+    )
+)
+
+anthropic_llm = LLM(
+    model="claude-3-5-sonnet-latest",
+    max_tokens=1000,
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY"
+    )
+)
+
+# Choose which LLM to use for each agent based on your needs
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=openai_llm  # Use anthropic_llm for Anthropic
+)
+```
+
+Portkey provides access to LLMs from providers including:
+
+- OpenAI (GPT-4o, GPT-4 Turbo, etc.)
+- Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
+- Mistral AI (Mistral Large, Mistral Medium, etc.)
+- Google Vertex AI (Gemini 1.5 Pro, etc.)
+- Cohere (Command, Command-R, etc.)
+- AWS Bedrock (Claude, Titan, etc.)
+- Local/Private Models
+
+<Card title="Supported Providers" icon="server" href="https://portkey.ai/docs/integrations/llms">
+  See the full list of LLM providers supported by Portkey
+</Card>
+
+## Set Up Enterprise Governance for CrewAI
+
+**Why Enterprise Governance?**
+If you are using CrewAI inside your organization, you need to consider several governance aspects:
+- **Cost Management**: Controlling and tracking AI spending across teams
+- **Access Control**: Managing which teams can use specific models
+- **Usage Analytics**: Understanding how AI is being used across the organization
+- **Security & Compliance**: Maintaining enterprise security standards
+- **Reliability**: Ensuring consistent service across all users
+
+Portkey adds a comprehensive governance layer to address these enterprise needs. Let's implement these controls step by step.
+
+<Steps>
+<Step title="Create Virtual Key">
+Virtual Keys are Portkey's secure way to manage your LLM provider API keys. They provide essential controls like:
+- Budget limits for API usage
+- Rate limiting capabilities
+- Secure API key storage
+
+To create a virtual key:
+Go to [Virtual Keys](https://app.portkey.ai/virtual-keys) in the Portkey App. Save and copy the virtual key ID
+
+<Frame>
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Virtual%20Key%20from%20Portkey%20Docs.png" width="500"/>
+</Frame>
+
+<Note>
+Save your virtual key ID - you'll need it for the next step.
+</Note>
+</Step>
+
+<Step title="Create Default Config">
+Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries.
+
+To create your config:
+1. Go to [Configs](https://app.portkey.ai/configs) in Portkey dashboard
+2. Create new config with:
+    ```json
+    {
+        "virtual_key": "YOUR_VIRTUAL_KEY_FROM_STEP1",
+       	"override_params": {
+          "model": "gpt-4o" // Your preferred model name
+        }
+    }
+    ```
+3. Save and note the Config name for the next step
+
+<Frame>
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs%20Config.png" width="500"/>
+
+</Frame>
+</Step>
+
+<Step title="Configure Portkey API Key">
+Now create a Portkey API key and attach the config you created in Step 2:
+
+1. Go to [API Keys](https://app.portkey.ai/api-keys) in Portkey and Create new API key
+2. Select your config from `Step 2`
+3. Generate and save your API key
+
+<Frame>
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20API%20Key.png" width="500"/>
+
+</Frame>
+</Step>
+
+<Step title="Connect to CrewAI">
+After setting up your Portkey API key with the attached config, connect it to your CrewAI agents:
+
+```python
+from crewai import Agent, LLM
+from portkey_ai import PORTKEY_GATEWAY_URL
+
+# Configure LLM with your API key
+portkey_llm = LLM(
+    model="gpt-4o",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="YOUR_PORTKEY_API_KEY"
+)
+
+# Create agent with Portkey-enabled LLM
+researcher = Agent(
+    role="Senior Research Scientist",
+    goal="Discover groundbreaking insights about the assigned topic",
+    backstory="You are an expert researcher with deep domain knowledge.",
+    verbose=True,
+    llm=portkey_llm
+)
+```
+</Step>
+</Steps>
+
+<AccordionGroup>
+  <Accordion title="Step 1: Implement Budget Controls & Rate Limits">
+### Step 1: Implement Budget Controls & Rate Limits
+
+Virtual Keys enable granular control over LLM access at the team/department level. This helps you:
+- Set up [budget limits](https://portkey.ai/docs/product/ai-gateway/virtual-keys/budget-limits)
+- Prevent unexpected usage spikes using Rate limits
+- Track departmental spending
+
+#### Setting Up Department-Specific Controls:
+1. Navigate to [Virtual Keys](https://app.portkey.ai/virtual-keys) in Portkey dashboard
+2. Create new Virtual Key for each department with budget limits and rate limits
+3. Configure department-specific limits
+
+<Frame>
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Virtual%20Key%20from%20Portkey%20Docs.png" width="500"/>
+</Frame>
+  </Accordion>
+
+  <Accordion title="Step 2: Define Model Access Rules">
+### Step 2: Define Model Access Rules
+
+As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:
+
+#### Access Control Features:
+- **Model Restrictions**: Limit access to specific models
+- **Data Protection**: Implement guardrails for sensitive data
+- **Reliability Controls**: Add fallbacks and retry logic
+
+#### Example Configuration:
+Here's a basic configuration to route requests to OpenAI, specifically using GPT-4o:
+
+```json
+{
+	"strategy": {
+		"mode": "single"
+	},
+	"targets": [
+		{
+			"virtual_key": "YOUR_OPENAI_VIRTUAL_KEY",
+			"override_params": {
+				"model": "gpt-4o"
+			}
+		}
+	]
+}
+```
+
+  Create your config on the [Configs page](https://app.portkey.ai/configs) in your Portkey dashboard.
+
+    <Note>
+    Configs can be updated anytime to adjust controls without affecting running applications.
+    </Note>
+  </Accordion>
+
+  <Accordion title="Step 3: Implement Access Controls">
+    ### Step 3: Implement Access Controls
+
+    Create User-specific API keys that automatically:
+    - Track usage per user/team with the help of virtual keys
+    - Apply appropriate configs to route requests
+    - Collect relevant metadata to filter logs
+    - Enforce access permissions
+
+    Create API keys through:
+    - [Portkey App](https://app.portkey.ai/)
+    - [API Key Management API](/api-reference/admin-api/control-plane/api-keys/create-api-key)
+
+    Example using Python SDK:
+    ```python
+    from portkey_ai import Portkey
+
+    portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")
+
+    api_key = portkey.api_keys.create(
+        name="engineering-team",
+        type="organisation",
+        workspace_id="YOUR_WORKSPACE_ID",
+        defaults={
+            "config_id": "your-config-id",
+            "metadata": {
+                "environment": "production",
+                "department": "engineering"
+            }
+        },
+        scopes=["logs.view", "configs.read"]
+    )
+    ```
+
+    For detailed key management instructions, see our [API Keys documentation](/api-reference/admin-api/control-plane/api-keys/create-api-key).
+  </Accordion>
+
+  <Accordion title="Step 4: Deploy & Monitor">
+    ### Step 4: Deploy & Monitor
+    After distributing API keys to your team members, your enterprise-ready CrewAI setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls.
+
+    Monitor usage in Portkey dashboard:
+    - Cost tracking by department
+    - Model usage patterns
+    - Request volumes
+    - Error rates
+  </Accordion>
+
+</AccordionGroup>
+
+<Note>
+### Enterprise Features Now Available
+**Your CrewAI integration now has:**
+- Departmental budget controls
+- Model access governance
+- Usage tracking & attribution
+- Security guardrails
+- Reliability features
+</Note>
+
+## Frequently Asked Questions
+
+<AccordionGroup>
+  <Accordion title="How does Portkey enhance CrewAI?">
+    Portkey adds production-readiness to CrewAI through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 200+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.
+  </Accordion>
+
+  <Accordion title="Can I use Portkey with existing CrewAI applications?">
+    Yes! Portkey integrates seamlessly with existing CrewAI applications. You just need to update your LLM configuration code with the Portkey-enabled version. The rest of your agent and crew code remains unchanged.
+  </Accordion>
+
+  <Accordion title="Does Portkey work with all CrewAI features?">
+    Portkey supports all CrewAI features, including agents, tools, human-in-the-loop workflows, and all task process types (sequential, hierarchical, etc.). It adds observability and reliability without limiting any of the framework's functionality.
+  </Accordion>
+
+  <Accordion title="Can I track usage across multiple agents in a crew?">
+    Yes, Portkey allows you to use a consistent `trace_id` across multiple agents in a crew to track the entire workflow. This is especially useful for complex crews where you want to understand the full execution path across multiple agents.
+  </Accordion>
+
+  <Accordion title="How do I filter logs and traces for specific crew runs?">
+    Portkey allows you to add custom metadata to your LLM configuration, which you can then use for filtering. Add fields like `crew_name`, `crew_type`, or `session_id` to easily find and analyze specific crew executions.
+  </Accordion>
+
+  <Accordion title="Can I use my own API keys with Portkey?">
+    Yes! Portkey uses your own API keys for the various LLM providers. It securely stores them as virtual keys, allowing you to easily manage and rotate keys without changing your code.
+  </Accordion>
+
+</AccordionGroup>
+
+## Resources
+
+<CardGroup cols="3">
+  <Card title="CrewAI Docs" icon="book" href="https://docs.crewai.com/">
+    <p>Official CrewAI documentation</p>
+  </Card>
+  <Card title="Book a Demo" icon="calendar" href="https://calendly.com/portkey-ai">
+    <p>Get personalized guidance on implementing this integration</p>
+  </Card>
+</CardGroup>

docs/tools/ai-ml/overview.mdx

@@ -37,9 +37,7 @@ These tools integrate with AI and machine learning services to enhance your agen
     Execute Python code and perform data analysis.
   </Card>
 
-  <Card title="Patronus Tools" icon="shield" href="/tools/ai-ml/patronustools">
-    AI safety and content moderation capabilities.
-  </Card>
+
 </CardGroup>
 
 ## **Common Use Cases**

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "crewai"
-version = "0.121.0"
+version = "0.126.0"
 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"
+requires-python = ">=3.10,<3.14"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
@@ -22,6 +22,8 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
     "chromadb>=0.5.23",
+    "tokenizers>=0.20.3",
+    "onnxruntime==1.22.0",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
@@ -45,12 +47,11 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.45.0"]
+tools = ["crewai-tools~=0.46.0"]
 embeddings = [
-    "tiktoken~=0.7.0"
+    "tiktoken~=0.8.0"
 ]
 agentops = ["agentops>=0.3.0"]
-fastembed = ["fastembed>=0.4.1"]
 pdfplumber = [
     "pdfplumber>=0.11.4",
 ]
@@ -100,6 +101,27 @@ exclude = ["cli/templates"]
 [tool.bandit]
 exclude_dirs = ["src/crewai/cli/templates"]
 
+# PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
+[[tool.uv.index]]
+name = "pytorch-nightly"
+url = "https://download.pytorch.org/whl/nightly/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-nightly", marker = "python_version >= '3.13'" },
+  { index = "pytorch", marker = "python_version < '3.13'" },
+]
+torchvision = [
+  { index = "pytorch-nightly", marker = "python_version >= '3.13'" },
+  { index = "pytorch", marker = "python_version < '3.13'" },
+]
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

score.json

@@ -0,0 +1,3 @@
+{
+  "score": 4
+}

src/crewai/__init__.py

@@ -18,7 +18,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.121.0"
+__version__ = "0.126.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -2,7 +2,7 @@ import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
 
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import Field, InstanceOf, PrivateAttr, field_validator, model_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -71,6 +71,7 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
+    _last_reasoning_output: Optional[Any] = PrivateAttr(default=None)
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -135,6 +136,21 @@ class Agent(BaseAgent):
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
+    reasoning_interval: Optional[int] = Field(
+        default=None,
+        description="Interval of steps after which the agent should reason again during execution. If None, reasoning only happens before execution.",
+    )
+
+    @field_validator('reasoning_interval')
+    @classmethod
+    def validate_reasoning_interval(cls, v):
+        if v is not None and v < 1:
+            raise ValueError("reasoning_interval must be >= 1")
+        return v
+    adaptive_reasoning: bool = Field(
+        default=False,
+        description="Whether the agent should adaptively decide when to reason during execution based on context.",
+    )
     embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
@@ -166,6 +182,9 @@ class Agent(BaseAgent):
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
 
+        if getattr(self, "adaptive_reasoning", False) and not getattr(self, "reasoning", False):
+            self.reasoning = True
+
         self.llm = create_llm(self.llm)
         if self.function_calling_llm and not isinstance(
             self.function_calling_llm, BaseLLM
@@ -200,6 +219,7 @@ class Agent(BaseAgent):
                         collection_name=self.role,
                         storage=self.knowledge_storage or None,
                     )
+                    self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
 
@@ -243,21 +263,28 @@ class Agent(BaseAgent):
         """
         if self.reasoning:
             try:
-                from crewai.utilities.reasoning_handler import AgentReasoning, AgentReasoningOutput
-                
+                from crewai.utilities.reasoning_handler import (
+                    AgentReasoning,
+                    AgentReasoningOutput,
+                )
+
                 reasoning_handler = AgentReasoning(task=task, agent=self)
-                reasoning_output: AgentReasoningOutput = reasoning_handler.handle_agent_reasoning()
-                
+                reasoning_output: AgentReasoningOutput = (
+                    reasoning_handler.handle_agent_reasoning()
+                )
+
                 # Add the reasoning plan to the task description
                 task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
             except Exception as e:
-                if hasattr(self, '_logger'):
-                    self._logger.log("error", f"Error during reasoning process: {str(e)}")
+                if hasattr(self, "_logger"):
+                    self._logger.log(
+                        "error", f"Error during reasoning process: {str(e)}"
+                    )
                 else:
                     print(f"Error during reasoning process: {str(e)}")
-            
+
         self._inject_date_to_task(task)
-            
+
         if self.tools_handler:
             self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
@@ -369,6 +396,44 @@ class Agent(BaseAgent):
         else:
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
+        if self.reasoning:
+            try:
+                from crewai.utilities.reasoning_handler import (
+                    AgentReasoning,
+                    AgentReasoningOutput,
+                )
+
+                reasoning_handler = AgentReasoning(
+                    task=task,
+                    agent=self,
+                    extra_context=context or "",
+                )
+
+                reasoning_output: AgentReasoningOutput = reasoning_handler.handle_agent_reasoning()
+
+                # Store the reasoning output for the executor to use
+                self._last_reasoning_output = reasoning_output
+
+                plan_text = reasoning_output.plan.plan
+
+                internal_plan_msg = (
+                    "### INTERNAL PLAN (do NOT reveal or repeat)\n" + plan_text
+                )
+
+                task_prompt = (
+                    task_prompt
+                    + "\n\n"
+                    + internal_plan_msg
+                )
+
+            except Exception as e:
+                if hasattr(self, "_logger"):
+                    self._logger.log(
+                        "error", f"Error during reasoning process: {str(e)}"
+                    )
+                else:
+                    print(f"Error during reasoning process: {str(e)}")
+
         try:
             crewai_event_bus.emit(
                 self,
@@ -444,6 +509,10 @@ class Agent(BaseAgent):
             self,
             event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
         )
+
+        # Clean up reasoning output after task completion
+        self._last_reasoning_output = None
+
         return result
 
     def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> str:
@@ -622,22 +691,33 @@ class Agent(BaseAgent):
         """Inject the current date into the task description if inject_date is enabled."""
         if self.inject_date:
             from datetime import datetime
+
             try:
-                valid_format_codes = ['%Y', '%m', '%d', '%H', '%M', '%S', '%B', '%b', '%A', '%a']
+                valid_format_codes = [
+                    "%Y",
+                    "%m",
+                    "%d",
+                    "%H",
+                    "%M",
+                    "%S",
+                    "%B",
+                    "%b",
+                    "%A",
+                    "%a",
+                ]
                 is_valid = any(code in self.date_format for code in valid_format_codes)
-                
+
                 if not is_valid:
                     raise ValueError(f"Invalid date format: {self.date_format}")
-                
+
                 current_date: str = datetime.now().strftime(self.date_format)
                 task.description += f"\n\nCurrent Date: {current_date}"
             except Exception as e:
-                if hasattr(self, '_logger'):
+                if hasattr(self, "_logger"):
                     self._logger.log("warning", f"Failed to inject date: {str(e)}")
                 else:
                     print(f"Warning: Failed to inject date: {str(e)}")
 
-
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
         if not shutil.which("docker"):

src/crewai/agents/agent_state.py

@@ -0,0 +1,386 @@
+"""Agent state management for long-running tasks with focus on progress tracking."""
+
+from typing import Any, Dict, List, Optional, Union, Set
+from pydantic import BaseModel, Field
+from datetime import datetime
+import json
+
+
+class CriterionProgress(BaseModel):
+    """Progress tracking for a single acceptance criterion."""
+    criterion: str = Field(description="The acceptance criterion")
+    status: str = Field(default="not_started", description="Status: not_started, in_progress, completed")
+    progress_notes: str = Field(default="", description="Specific progress made towards this criterion")
+    completion_percentage: int = Field(default=0, description="Estimated completion percentage (0-100)")
+    remaining_work: str = Field(default="", description="What still needs to be done for this criterion")
+
+    # Enhanced tracking
+    processed_items: Set[str] = Field(default_factory=set, description="IDs or identifiers of processed items")
+    total_items_expected: Optional[int] = Field(default=None, description="Total number of items expected (if known)")
+    items_to_process: List[str] = Field(default_factory=list, description="Queue of specific items to process next")
+    last_updated: datetime = Field(default_factory=datetime.now)
+
+
+class ProgressLog(BaseModel):
+    """Single log entry for progress tracking."""
+    timestamp: datetime = Field(default_factory=datetime.now)
+    action: str = Field(description="What action was taken")
+    result: str = Field(description="Result or outcome of the action")
+    items_processed: List[str] = Field(default_factory=list, description="Items processed in this action")
+    criterion: Optional[str] = Field(default=None, description="Related acceptance criterion")
+
+
+class AgentState(BaseModel):
+    """Enhanced state management with deterministic progress tracking.
+
+    This state helps agents maintain focus during long executions by tracking
+    specific progress against each acceptance criterion with detailed logging.
+    """
+
+    # Core planning elements
+    plan: List[str] = Field(
+        default_factory=list,
+        description="The current plan steps"
+    )
+
+    acceptance_criteria: List[str] = Field(
+        default_factory=list,
+        description="Concrete criteria that must be met for task completion"
+    )
+
+    # Progress tracking
+    criteria_progress: Dict[str, CriterionProgress] = Field(
+        default_factory=dict,
+        description="Detailed progress for each acceptance criterion"
+    )
+
+    # Data storage
+    scratchpad: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Storage for intermediate results and data"
+    )
+
+    # Simple tracking
+    current_focus: str = Field(
+        default="",
+        description="What the agent should be focusing on right now"
+    )
+
+    next_steps: List[str] = Field(
+        default_factory=list,
+        description="Immediate next steps to take"
+    )
+
+    overall_progress: int = Field(
+        default=0,
+        description="Overall task completion percentage (0-100)"
+    )
+
+    # Enhanced tracking
+    progress_logs: List[ProgressLog] = Field(
+        default_factory=list,
+        description="Detailed log of all progress made"
+    )
+
+    work_queue: List[Dict[str, Any]] = Field(
+        default_factory=list,
+        description="Queue of specific work items to process"
+    )
+
+    # Metadata tracking
+    metadata: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional metadata for tracking (e.g., total count expectations)"
+    )
+
+    def initialize_criteria_progress(self) -> None:
+        """Initialize progress tracking for all acceptance criteria."""
+        for criterion in self.acceptance_criteria:
+            if criterion not in self.criteria_progress:
+                self.criteria_progress[criterion] = CriterionProgress(criterion=criterion)
+
+    def update_criterion_progress(
+        self,
+        criterion: str,
+        status: str,
+        progress_notes: str,
+        completion_percentage: int,
+        remaining_work: str,
+        processed_items: Optional[List[str]] = None,
+        items_to_process: Optional[List[str]] = None,
+        total_items_expected: Optional[int] = None
+    ) -> None:
+        """Update progress for a specific criterion with enhanced tracking."""
+        if criterion in self.criteria_progress:
+            progress = self.criteria_progress[criterion]
+            progress.status = status
+            progress.progress_notes = progress_notes
+            progress.completion_percentage = max(0, min(100, completion_percentage))
+            progress.remaining_work = remaining_work
+            progress.last_updated = datetime.now()
+
+            # Update processed items
+            if processed_items:
+                progress.processed_items.update(processed_items)
+
+            # Update items to process queue
+            if items_to_process is not None:
+                progress.items_to_process = items_to_process
+
+            # Update total expected if provided
+            if total_items_expected is not None:
+                progress.total_items_expected = total_items_expected
+
+            # Recalculate completion percentage based on actual items if possible
+            if progress.total_items_expected and progress.total_items_expected > 0:
+                actual_percentage = int((len(progress.processed_items) / progress.total_items_expected) * 100)
+                progress.completion_percentage = actual_percentage
+
+            # Update overall progress
+            self._recalculate_overall_progress()
+
+    def _recalculate_overall_progress(self) -> None:
+        """Recalculate overall progress based on all criteria."""
+        if not self.criteria_progress:
+            self.overall_progress = 0
+            return
+
+        total_progress = sum(p.completion_percentage for p in self.criteria_progress.values())
+        self.overall_progress = int(total_progress / len(self.criteria_progress))
+
+    def add_to_scratchpad(self, key: str, value: Any) -> None:
+        """Add or update a value in the scratchpad."""
+        self.scratchpad[key] = value
+
+        # Analyze the data for item tracking
+        self._analyze_scratchpad_for_items(key, value)
+
+    def _analyze_scratchpad_for_items(self, key: str, value: Any) -> None:
+        """Analyze scratchpad data to extract trackable items."""
+        # If it's a list, try to extract IDs
+        if isinstance(value, list) and value:
+            item_ids = []
+            for item in value:
+                if isinstance(item, dict):
+                    # Look for common ID fields
+                    for id_field in ['id', 'ID', 'uid', 'uuid', 'message_id', 'email_id']:
+                        if id_field in item:
+                            item_ids.append(str(item[id_field]))
+                            break
+
+            if item_ids:
+                # Store metadata about this list
+                self.metadata[f"{key}_ids"] = item_ids
+                self.metadata[f"{key}_count"] = len(value)
+
+    def log_progress(self, action: str, result: str, items_processed: Optional[List[str]] = None, criterion: Optional[str] = None) -> None:
+        """Add a progress log entry."""
+        log_entry = ProgressLog(
+            action=action,
+            result=result,
+            items_processed=items_processed or [],
+            criterion=criterion
+        )
+        self.progress_logs.append(log_entry)
+
+    def add_to_work_queue(self, work_item: Dict[str, Any]) -> None:
+        """Add an item to the work queue."""
+        self.work_queue.append(work_item)
+
+    def get_next_work_item(self) -> Optional[Dict[str, Any]]:
+        """Get and remove the next item from the work queue."""
+        if self.work_queue:
+            return self.work_queue.pop(0)
+        return None
+
+    def set_focus_and_next_steps(self, focus: str, next_steps: List[str]) -> None:
+        """Update current focus and next steps."""
+        self.current_focus = focus
+        self.next_steps = next_steps
+
+    def get_progress_context(self) -> str:
+        """Generate a focused progress update for the agent."""
+        context = f"📊 PROGRESS UPDATE (Overall: {self.overall_progress}%)\n"
+        context += "="*50 + "\n\n"
+
+        # Current focus
+        if self.current_focus:
+            context += f"🎯 CURRENT FOCUS: {self.current_focus}\n\n"
+
+        # Progress on each criterion with detailed tracking
+        if self.criteria_progress:
+            context += "📋 ACCEPTANCE CRITERIA PROGRESS:\n"
+            for criterion, progress in self.criteria_progress.items():
+                status_emoji = "✅" if progress.status == "completed" else "🔄" if progress.status == "in_progress" else "⏸️"
+                context += f"\n{status_emoji} {criterion}\n"
+
+                # Show detailed progress
+                if progress.total_items_expected:
+                    context += f"   Progress: {len(progress.processed_items)}/{progress.total_items_expected} items ({progress.completion_percentage}%)\n"
+                else:
+                    context += f"   Progress: {progress.completion_percentage}%"
+                    if progress.processed_items:
+                        context += f" - {len(progress.processed_items)} items processed"
+                    context += "\n"
+
+                if progress.progress_notes:
+                    context += f"   Notes: {progress.progress_notes}\n"
+
+                # Show next items to process
+                if progress.items_to_process and progress.status != "completed":
+                    next_items = progress.items_to_process[:3]  # Show next 3
+                    context += f"   Next items: {', '.join(next_items)}"
+                    if len(progress.items_to_process) > 3:
+                        context += f" (and {len(progress.items_to_process) - 3} more)"
+                    context += "\n"
+
+                if progress.remaining_work and progress.status != "completed":
+                    context += f"   Still needed: {progress.remaining_work}\n"
+
+        # Work queue status
+        if self.work_queue:
+            context += f"\n📝 WORK QUEUE: {len(self.work_queue)} items pending\n"
+            next_work = self.work_queue[0]
+            context += f"   Next: {next_work.get('description', 'Process next item')}\n"
+
+        # Next steps
+        if self.next_steps:
+            context += f"\n📍 IMMEDIATE NEXT STEPS:\n"
+            for i, step in enumerate(self.next_steps, 1):
+                context += f"{i}. {step}\n"
+
+        # Available data
+        if self.scratchpad:
+            context += f"\n💾 AVAILABLE DATA IN SCRATCHPAD:\n"
+            for key, value in self.scratchpad.items():
+                if isinstance(value, list):
+                    context += f"  • '{key}' - {len(value)} items"
+                    if f"{key}_ids" in self.metadata:
+                        context += f" (IDs tracked)"
+                    context += "\n"
+                elif isinstance(value, dict):
+                    context += f"  • '{key}' - dictionary data\n"
+                else:
+                    context += f"  • '{key}'\n"
+
+        # Recent progress logs
+        if self.progress_logs:
+            context += f"\n📜 RECENT ACTIVITY:\n"
+            for log in self.progress_logs[-3:]:  # Show last 3 logs
+                context += f"  • {log.timestamp.strftime('%H:%M:%S')} - {log.action}"
+                if log.items_processed:
+                    context += f" ({len(log.items_processed)} items)"
+                context += "\n"
+
+        context += "\n" + "="*50
+        return context
+
+    def analyze_scratchpad_for_criterion_progress(self, criterion: str) -> Dict[str, Any]:
+        """Analyze scratchpad data to determine specific progress on a criterion."""
+        analysis = {
+            "relevant_data": [],
+            "item_count": 0,
+            "processed_ids": set(),
+            "data_completeness": 0,
+            "specific_gaps": []
+        }
+
+        criterion_lower = criterion.lower()
+
+        # Look for data that relates to this criterion
+        for key, value in self.scratchpad.items():
+            key_lower = key.lower()
+
+            # Check if this data is relevant to the criterion
+            is_relevant = False
+            for keyword in criterion_lower.split():
+                if len(keyword) > 3 and keyword in key_lower:  # Skip short words
+                    is_relevant = True
+                    break
+
+            if is_relevant:
+                analysis["relevant_data"].append(key)
+
+                # Count items and extract IDs
+                if isinstance(value, list):
+                    analysis["item_count"] += len(value)
+
+                    # Try to extract IDs from metadata
+                    if f"{key}_ids" in self.metadata:
+                        analysis["processed_ids"].update(self.metadata[f"{key}_ids"])
+
+                elif isinstance(value, dict):
+                    analysis["item_count"] += 1
+
+        # Calculate completeness based on what we know
+        if analysis["item_count"] > 0:
+            # Check if criterion mentions specific numbers
+            import re
+            number_match = re.search(r'\b(\d+)\b', criterion)
+            if number_match:
+                expected_count = int(number_match.group(1))
+                analysis["data_completeness"] = min(100, int((analysis["item_count"] / expected_count) * 100))
+                if analysis["item_count"] < expected_count:
+                    analysis["specific_gaps"].append(f"Need {expected_count - analysis['item_count']} more items")
+            else:
+                # For criteria without specific numbers, use heuristics
+                if "all" in criterion_lower or "every" in criterion_lower:
+                    # For "all" criteria, we need to be more careful
+                    analysis["data_completeness"] = 50 if analysis["item_count"] > 0 else 0
+                    analysis["specific_gaps"].append("Verify all items are included")
+                else:
+                    analysis["data_completeness"] = min(100, analysis["item_count"] * 20)  # Rough estimate
+
+        return analysis
+
+    def generate_specific_next_steps(self, criterion: str) -> List[str]:
+        """Generate specific, actionable next steps for a criterion."""
+        analysis = self.analyze_scratchpad_for_criterion_progress(criterion)
+        progress = self.criteria_progress.get(criterion)
+        next_steps = []
+
+        if not progress:
+            return ["Initialize progress tracking for this criterion"]
+
+        # If we have a queue of items to process
+        if progress.items_to_process:
+            next_item = progress.items_to_process[0]
+            next_steps.append(f"Query/process item: {next_item}")
+            if len(progress.items_to_process) > 1:
+                next_steps.append(f"Then process {len(progress.items_to_process) - 1} remaining items")
+
+        # If we have processed some items but not all
+        elif progress.processed_items and progress.total_items_expected:
+            remaining = progress.total_items_expected - len(progress.processed_items)
+            if remaining > 0:
+                next_steps.append(f"Process {remaining} more items to reach target of {progress.total_items_expected}")
+
+        # If we have data but haven't accessed it
+        elif analysis["relevant_data"] and not progress.processed_items:
+            for data_key in analysis["relevant_data"][:2]:  # First 2 relevant keys
+                next_steps.append(f"Access and process data from '{data_key}'")
+
+        # Generic steps based on criterion keywords
+        else:
+            criterion_lower = criterion.lower()
+            if "email" in criterion_lower:
+                next_steps.append("Use email search/fetch tool to gather emails")
+            elif "analyze" in criterion_lower or "summary" in criterion_lower:
+                next_steps.append("Access stored data and create analysis/summary")
+            else:
+                next_steps.append(f"Use appropriate tools to gather data for: {criterion}")
+
+        return next_steps
+
+    def reset(self) -> None:
+        """Reset state for a new task."""
+        self.plan = []
+        self.acceptance_criteria = []
+        self.criteria_progress = {}
+        self.scratchpad = {}
+        self.current_focus = ""
+        self.next_steps = []
+        self.overall_progress = 0
+        self.progress_logs = []
+        self.work_queue = []
+        self.metadata = {}

src/crewai/cli/cli.py

@@ -16,6 +16,7 @@ from .deploy.main import DeployCommand
 from .evaluate_crew import evaluate_crew
 from .install_crew import install_crew
 from .kickoff_flow import kickoff_flow
+from .organization.main import OrganizationCommand
 from .plot_flow import plot_flow
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
@@ -353,5 +354,33 @@ def chat():
     run_chat()
 
 
+@crewai.group(invoke_without_command=True)
+def org():
+    """Organization management commands."""
+    pass
+
+
+@org.command()
+def list():
+    """List available organizations."""
+    org_command = OrganizationCommand()
+    org_command.list()
+
+
+@org.command()
+@click.argument("id")
+def switch(id):
+    """Switch to a specific organization."""
+    org_command = OrganizationCommand()
+    org_command.switch(id)
+
+
+@org.command()
+def current():
+    """Show current organization when 'crewai org' is called without subcommands."""
+    org_command = OrganizationCommand()
+    org_command.current()
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/config.py

@@ -14,6 +14,12 @@ class Settings(BaseModel):
     tool_repository_password: Optional[str] = Field(
         None, description="Password for interacting with the Tool Repository"
     )
+    org_name: Optional[str] = Field(
+        None, description="Name of the currently active organization"
+    )
+    org_uuid: Optional[str] = Field(
+        None, description="UUID of the currently active organization"
+    )
     config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):

src/crewai/cli/organization/__init__.py

@@ -0,0 +1 @@
+

src/crewai/cli/organization/main.py

@@ -0,0 +1,63 @@
+from rich.console import Console
+from rich.table import Table
+
+from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.config import Settings
+
+console = Console()
+
+class OrganizationCommand(BaseCommand, PlusAPIMixin):
+    def __init__(self):
+        BaseCommand.__init__(self)
+        PlusAPIMixin.__init__(self, telemetry=self._telemetry)
+
+    def list(self):
+        try:
+            response = self.plus_api_client.get_organizations()
+            response.raise_for_status()
+            orgs = response.json()
+            
+            if not orgs:
+                console.print("You don't belong to any organizations yet.", style="yellow")
+                return
+
+            table = Table(title="Your Organizations")
+            table.add_column("Name", style="cyan")
+            table.add_column("ID", style="green")
+            for org in orgs:
+                table.add_row(org["name"], org["uuid"])
+            
+            console.print(table)
+        except Exception as e:
+            console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red")
+            raise SystemExit(1)
+
+    def switch(self, org_id):
+        try:
+            response = self.plus_api_client.get_organizations()
+            response.raise_for_status()
+            orgs = response.json()
+            
+            org = next((o for o in orgs if o["uuid"] == org_id), None)
+            if not org:
+                console.print(f"Organization with id '{org_id}' not found.", style="bold red")
+                return
+            
+            settings = Settings()
+            settings.org_name = org["name"]
+            settings.org_uuid = org["uuid"]
+            settings.dump()
+            
+            console.print(f"Successfully switched to {org['name']} ({org['uuid']})", style="bold green")
+        except Exception as e:
+            console.print(f"Failed to switch organization: {str(e)}", style="bold red")
+            raise SystemExit(1)
+
+    def current(self):
+        settings = Settings()
+        if settings.org_uuid:
+            console.print(f"Currently logged in to organization {settings.org_name} ({settings.org_uuid})", style="bold green")
+        else:
+            console.print("You're not currently logged in to any organization.", style="yellow")
+            console.print("Use 'crewai org list' to see available organizations.", style="yellow")
+            console.print("Use 'crewai org switch <id>' to switch to an organization.", style="yellow")

src/crewai/cli/plus_api.py

@@ -1,9 +1,10 @@
 from os import getenv
-from typing import Optional
+from typing import List, Optional
 from urllib.parse import urljoin
 
 import requests
 
+from crewai.cli.config import Settings
 from crewai.cli.version import get_crewai_version
 
 
@@ -13,6 +14,7 @@ class PlusAPI:
     """
 
     TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
+    ORGANIZATIONS_RESOURCE = "/crewai_plus/api/v1/me/organizations"
     CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
     AGENTS_RESOURCE = "/crewai_plus/api/v1/agents"
 
@@ -24,6 +26,9 @@ class PlusAPI:
             "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
             "X-Crewai-Version": get_crewai_version(),
         }
+        settings = Settings()
+        if settings.org_uuid:
+            self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
         self.base_url = getenv("CREWAI_BASE_URL", "https://app.crewai.com")
 
     def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
@@ -48,6 +53,7 @@ class PlusAPI:
         version: str,
         description: Optional[str],
         encoded_file: str,
+        available_exports: Optional[List[str]] = None,
     ):
         params = {
             "handle": handle,
@@ -55,6 +61,7 @@ class PlusAPI:
             "version": version,
             "file": encoded_file,
             "description": description,
+            "available_exports": available_exports,
         }
         return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params)
 
@@ -101,3 +108,7 @@ class PlusAPI:
 
     def create_crew(self, payload) -> requests.Response:
         return self._make_request("POST", self.CREWS_RESOURCE, json=payload)
+    
+    def get_organizations(self) -> requests.Response:
+        return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
+    

src/crewai/cli/templates/crew/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.14 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.121.0,<1.0.0"
+    "crewai[tools]>=0.126.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.14 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.121.0,<1.0.0",
+    "crewai[tools]>=0.126.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/README.md

@@ -5,7 +5,7 @@ custom tools to power up your crews.
 
 ## Installing
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project
+Ensure you have Python >=3.10 <3.14 installed on your system. This project
 uses [UV](https://docs.astral.sh/uv/) for dependency management and package
 handling, offering a seamless setup and execution experience.
 

src/crewai/cli/templates/tool/pyproject.toml

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.121.0"
+    "crewai[tools]>=0.126.0"
 ]
 
 [tool.crewai]

src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py

@@ -0,0 +1,3 @@
+from .tool import {{class_name}}
+
+__all__ = ["{{class_name}}"]

src/crewai/cli/tools/main.py

@@ -3,6 +3,7 @@ import os
 import subprocess
 import tempfile
 from pathlib import Path
+from typing import Any
 
 import click
 from rich.console import Console
@@ -11,6 +12,7 @@ from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
 from crewai.cli.config import Settings
 from crewai.cli.utils import (
+    extract_available_exports,
     get_project_description,
     get_project_name,
     get_project_version,
@@ -82,6 +84,14 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         project_description = get_project_description(require=False)
         encoded_tarball = None
 
+        console.print("[bold blue]Discovering tools from your project...[/bold blue]")
+        available_exports = extract_available_exports()
+
+        if available_exports:
+            console.print(
+                f"[green]Found these tools to publish: {', '.join([e['name'] for e in available_exports])}[/green]"
+            )
+
         with tempfile.TemporaryDirectory() as temp_build_dir:
             subprocess.run(
                 ["uv", "build", "--sdist", "--out-dir", temp_build_dir],
@@ -105,12 +115,14 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
             encoded_tarball = base64.b64encode(tarball_contents).decode("utf-8")
 
+        console.print("[bold blue]Publishing tool to repository...[/bold blue]")
         publish_response = self.plus_api_client.publish_tool(
             handle=project_name,
             is_public=is_public,
             version=project_version,
             description=project_description,
             encoded_file=f"data:application/x-gzip;base64,{encoded_tarball}",
+            available_exports=available_exports,
         )
 
         self._validate_response(publish_response)
@@ -161,13 +173,20 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings.tool_repository_password = login_response_json["credential"][
             "password"
         ]
+        settings.org_uuid = login_response_json["current_organization"][
+            "uuid"
+        ]
+        settings.org_name = login_response_json["current_organization"][
+            "name"
+        ]
         settings.dump()
 
         console.print(
             "Successfully authenticated to the tool repository.", style="bold green"
         )
 
-    def _add_package(self, tool_details):
+    def _add_package(self, tool_details: dict[str, Any]):
+        is_from_pypi = tool_details.get("source", None) == "pypi"
         tool_handle = tool_details["handle"]
         repository_handle = tool_details["repository"]["handle"]
         repository_url = tool_details["repository"]["url"]
@@ -176,10 +195,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         add_package_command = [
             "uv",
             "add",
-            "--index",
-            index,
-            tool_handle,
         ]
+
+        if is_from_pypi:
+            add_package_command.append(tool_handle)
+        else:
+            add_package_command.extend(["--index", index, tool_handle])
+
         add_package_result = subprocess.run(
             add_package_command,
             capture_output=False,

src/crewai/cli/utils.py

@@ -1,8 +1,10 @@
+import importlib.util
 import os
 import shutil
 import sys
 from functools import reduce
-from inspect import isfunction, ismethod
+from inspect import getmro, isclass, isfunction, ismethod
+from pathlib import Path
 from typing import Any, Dict, List, get_type_hints
 
 import click
@@ -339,3 +341,112 @@ def fetch_crews(module_attr) -> list[Crew]:
             if crew_instance := get_crew_instance(attr):
                 crew_instances.append(crew_instance)
     return crew_instances
+
+
+def is_valid_tool(obj):
+    from crewai.tools.base_tool import Tool
+
+    if isclass(obj):
+        try:
+            return any(base.__name__ == "BaseTool" for base in getmro(obj))
+        except (TypeError, AttributeError):
+            return False
+
+    return isinstance(obj, Tool)
+
+
+def extract_available_exports(dir_path: str = "src"):
+    """
+    Extract available tool classes from the project's __init__.py files.
+    Only includes classes that inherit from BaseTool or functions decorated with @tool.
+
+    Returns:
+        list: A list of valid tool class names or ["BaseTool"] if none found
+    """
+    try:
+        init_files = Path(dir_path).glob("**/__init__.py")
+        available_exports = []
+
+        for init_file in init_files:
+            tools = _load_tools_from_init(init_file)
+            available_exports.extend(tools)
+
+        if not available_exports:
+            _print_no_tools_warning()
+            raise SystemExit(1)
+
+        return available_exports
+
+    except Exception as e:
+        console.print(f"[red]Error: Could not extract tool classes: {str(e)}[/red]")
+        console.print(
+            "Please ensure your project contains valid tools (classes inheriting from BaseTool or functions with @tool decorator)."
+        )
+        raise SystemExit(1)
+
+
+def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]:
+    """
+    Load and validate tools from a given __init__.py file.
+    """
+    spec = importlib.util.spec_from_file_location("temp_module", init_file)
+
+    if not spec or not spec.loader:
+        return []
+
+    module = importlib.util.module_from_spec(spec)
+    sys.modules["temp_module"] = module
+
+    try:
+        spec.loader.exec_module(module)
+
+        if not hasattr(module, "__all__"):
+            console.print(
+                f"[bold yellow]Warning: No __all__ defined in {init_file}[/bold yellow]"
+            )
+            raise SystemExit(1)
+
+        return [
+            {
+                "name": name,
+            }
+            for name in module.__all__
+            if hasattr(module, name) and is_valid_tool(getattr(module, name))
+        ]
+
+    except Exception as e:
+        console.print(f"[red]Warning: Could not load {init_file}: {str(e)}[/red]")
+        raise SystemExit(1)
+
+    finally:
+        sys.modules.pop("temp_module", None)
+
+
+def _print_no_tools_warning():
+    """
+    Display warning and usage instructions if no tools were found.
+    """
+    console.print(
+        "\n[bold yellow]Warning: No valid tools were exposed in your __init__.py file![/bold yellow]"
+    )
+    console.print(
+        "Your __init__.py file must contain all classes that inherit from [bold]BaseTool[/bold] "
+        "or functions decorated with [bold]@tool[/bold]."
+    )
+    console.print(
+        "\nExample:\n[dim]# In your __init__.py file[/dim]\n"
+        "[green]__all__ = ['YourTool', 'your_tool_function'][/green]\n\n"
+        "[dim]# In your tool.py file[/dim]\n"
+        "[green]from crewai.tools import BaseTool, tool\n\n"
+        "# Tool class example\n"
+        "class YourTool(BaseTool):\n"
+        '    name = "your_tool"\n'
+        '    description = "Your tool description"\n'
+        "    # ... rest of implementation\n\n"
+        "# Decorated function example\n"
+        "@tool\n"
+        "def your_tool_function(text: str) -> str:\n"
+        '    """Your tool description"""\n'
+        "    # ... implementation\n"
+        "    return result\n"
+    )

src/crewai/crew.py

@@ -314,7 +314,7 @@ class Crew(FlowTrackable, BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Initialize private memory attributes."""
         self._external_memory = (
-            # External memory doesn’t support a default value since it was designed to be managed entirely externally
+            # External memory doesn't support a default value since it was designed to be managed entirely externally
             self.external_memory.set_crew(self) if self.external_memory else None
         )
 
@@ -1081,6 +1081,23 @@ class Crew(FlowTrackable, BaseModel):
             token_usage=token_usage,
         )
 
+    def _finish_execution(self, final_string_output: str) -> None:
+        if self.max_rpm:
+            self._rpm_controller.stop_rpm_counter()
+
+    def calculate_usage_metrics(self) -> UsageMetrics:
+        """Calculates and returns the usage metrics."""
+        total_usage_metrics = UsageMetrics()
+        for agent in self.agents:
+            if hasattr(agent, "_token_process"):
+                token_sum = agent._token_process.get_summary()
+                total_usage_metrics.add_usage_metrics(token_sum)
+        if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
+            token_sum = self.manager_agent._token_process.get_summary()
+            total_usage_metrics.add_usage_metrics(token_sum)
+        self.usage_metrics = total_usage_metrics
+        return total_usage_metrics
+
     def _process_async_tasks(
         self,
         futures: List[Tuple[Task, Future[TaskOutput], int]],
@@ -1284,23 +1301,6 @@ class Crew(FlowTrackable, BaseModel):
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
-    def _finish_execution(self, final_string_output: str) -> None:
-        if self.max_rpm:
-            self._rpm_controller.stop_rpm_counter()
-
-    def calculate_usage_metrics(self) -> UsageMetrics:
-        """Calculates and returns the usage metrics."""
-        total_usage_metrics = UsageMetrics()
-        for agent in self.agents:
-            if hasattr(agent, "_token_process"):
-                token_sum = agent._token_process.get_summary()
-                total_usage_metrics.add_usage_metrics(token_sum)
-        if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
-            token_sum = self.manager_agent._token_process.get_summary()
-            total_usage_metrics.add_usage_metrics(token_sum)
-        self.usage_metrics = total_usage_metrics
-        return total_usage_metrics
-
     def test(
         self,
         n_iterations: int,

src/crewai/flow/visualization_utils.py

@@ -17,7 +17,7 @@ Example
 
 import ast
 import inspect
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Tuple, Union
 
 from .utils import (
     build_ancestor_dict,
@@ -140,7 +140,7 @@ def compute_positions(
     flow: Any,
     node_levels: Dict[str, int],
     y_spacing: float = 150,
-    x_spacing: float = 150
+    x_spacing: float = 300
 ) -> Dict[str, Tuple[float, float]]:
     """
     Compute the (x, y) positions for each node in the flow graph.
@@ -154,7 +154,7 @@ def compute_positions(
     y_spacing : float, optional
         Vertical spacing between levels, by default 150.
     x_spacing : float, optional
-        Horizontal spacing between nodes, by default 150.
+        Horizontal spacing between nodes, by default 300.
 
     Returns
     -------

src/crewai/knowledge/embedder/fastembed.py

@@ -1,93 +0,0 @@
-from pathlib import Path
-from typing import List, Optional, Union
-
-import numpy as np
-
-from .base_embedder import BaseEmbedder
-
-try:
-    from fastembed_gpu import TextEmbedding  # type: ignore
-
-    FASTEMBED_AVAILABLE = True
-except ImportError:
-    try:
-        from fastembed import TextEmbedding
-
-        FASTEMBED_AVAILABLE = True
-    except ImportError:
-        FASTEMBED_AVAILABLE = False
-
-
-class FastEmbed(BaseEmbedder):
-    """
-    A wrapper class for text embedding models using FastEmbed
-    """
-
-    def __init__(
-        self,
-        model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: Optional[Union[str, Path]] = None,
-    ):
-        """
-        Initialize the embedding model
-
-        Args:
-            model_name: Name of the model to use
-            cache_dir: Directory to cache the model
-            gpu: Whether to use GPU acceleration
-        """
-        if not FASTEMBED_AVAILABLE:
-            raise ImportError(
-                "FastEmbed is not installed. Please install it with: "
-                "uv pip install fastembed or uv pip install fastembed-gpu for GPU support"
-            )
-
-        self.model = TextEmbedding(
-            model_name=model_name,
-            cache_dir=str(cache_dir) if cache_dir else None,
-        )
-
-    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of text chunks
-
-        Args:
-            chunks: List of text chunks to embed
-
-        Returns:
-            List of embeddings
-        """
-        embeddings = list(self.model.embed(chunks))
-        return embeddings
-
-    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
-        """
-        Generate embeddings for a list of texts
-
-        Args:
-            texts: List of texts to embed
-
-        Returns:
-            List of embeddings
-        """
-        embeddings = list(self.model.embed(texts))
-        return embeddings
-
-    def embed_text(self, text: str) -> np.ndarray:
-        """
-        Generate embedding for a single text
-
-        Args:
-            text: Text to embed
-
-        Returns:
-            Embedding array
-        """
-        return self.embed_texts([text])[0]
-
-    @property
-    def dimension(self) -> int:
-        """Get the dimension of the embeddings"""
-        # Generate a test embedding to get dimensions
-        test_embed = self.embed_text("test")
-        return len(test_embed)

src/crewai/llm.py

@@ -5,7 +5,7 @@ import sys
 import threading
 import warnings
 from collections import defaultdict
-from contextlib import contextmanager, redirect_stderr, redirect_stdout
+from contextlib import contextmanager
 from typing import (
     Any,
     DefaultDict,
@@ -18,7 +18,7 @@ from typing import (
     Union,
     cast,
 )
-
+from datetime import datetime
 from dotenv import load_dotenv
 from litellm.types.utils import ChatCompletionDeltaToolCall
 from pydantic import BaseModel, Field
@@ -30,6 +30,11 @@ from crewai.utilities.events.llm_events import (
     LLMCallType,
     LLMStreamChunkEvent,
 )
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageStartedEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageErrorEvent,
+)
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
@@ -74,7 +79,7 @@ class FilteredStream(io.TextIOBase):
                 "give feedback / get help" in lower_s
                 or "litellm.info:" in lower_s
                 or "litellm" in lower_s
-                or "Consider using a smaller input or implementing a text splitting strategy" in lower_s
+                or "consider using a smaller input or implementing a text splitting strategy" in lower_s
             ):
                 return 0
 
@@ -833,7 +838,26 @@ class LLM(BaseLLM):
                 fn = available_functions[function_name]
 
                 # --- 3.2) Execute function
+                assert hasattr(crewai_event_bus, "emit")
+                started_at = datetime.now()
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageStartedEvent(
+                        tool_name=function_name,
+                        tool_args=function_args,
+                    ),
+                )
                 result = fn(**function_args)
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageFinishedEvent(
+                        output=result,
+                        tool_name=function_name,
+                        tool_args=function_args,
+                        started_at=started_at,
+                        finished_at=datetime.now(),
+                    ),
+                )
 
                 # --- 3.3) Emit success event
                 self._handle_emit_call_events(result, LLMCallType.TOOL_CALL)
@@ -849,6 +873,14 @@ class LLM(BaseLLM):
                     self,
                     event=LLMCallFailedEvent(error=f"Tool execution error: {str(e)}"),
                 )
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageErrorEvent(
+                        tool_name=function_name,
+                        tool_args=function_args,
+                        error=f"Tool execution error: {str(e)}"
+                    ),
+                )
         return None
 
     def call(

src/crewai/task.py

@@ -527,10 +527,10 @@ class Task(BaseModel):
 
     def prompt(self) -> str:
         """Generates the task prompt with optional markdown formatting.
-        
+
         When the markdown attribute is True, instructions for formatting the
         response in Markdown syntax will be added to the prompt.
-        
+
         Returns:
             str: The formatted prompt string containing the task description,
                  expected output, and optional markdown formatting instructions.
@@ -541,7 +541,7 @@ class Task(BaseModel):
             expected_output=self.expected_output
         )
         tasks_slices = [self.description, output]
-        
+
         if self.markdown:
             markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.
 Follow these guidelines:
@@ -550,7 +550,8 @@ Follow these guidelines:
 - Use * for italic text
 - Use - or * for bullet points
 - Use `code` for inline code
-- Use ```language for code blocks"""
+- Use ```language for code blocks
+- Don't start your answer with a code block"""
             tasks_slices.append(markdown_instruction)
         return "\n".join(tasks_slices)
 

src/crewai/tools/__init__.py

@@ -1 +1,7 @@
-from .base_tool import BaseTool, tool
+from .base_tool import BaseTool, tool, EnvVar
+
+__all__ = [
+    "BaseTool",
+    "tool",
+    "EnvVar",
+]

src/crewai/tools/agent_tools/__init__.py

@@ -1 +1,6 @@
 """Agent tools for crewAI."""
+
+from .agent_tools import AgentTools
+from .scratchpad_tool import ScratchpadTool
+
+__all__ = ["AgentTools", "ScratchpadTool"]

src/crewai/tools/agent_tools/scratchpad_tool.py

@@ -0,0 +1,174 @@
+"""Tool for accessing data stored in the agent's scratchpad during reasoning."""
+
+from typing import Any, Dict, Optional, Type, Union, Callable
+from pydantic import BaseModel, Field
+from crewai.tools import BaseTool
+
+
+class ScratchpadToolSchema(BaseModel):
+    """Input schema for ScratchpadTool."""
+    key: str = Field(
+        ...,
+        description=(
+            "The key name to retrieve data from the scratchpad. "
+            "Must be one of the available keys shown in the tool description. "
+            "Example: if 'email_data' is listed as available, use {\"key\": \"email_data\"}"
+        )
+    )
+
+
+class ScratchpadTool(BaseTool):
+    """Tool that allows agents to access data stored in their scratchpad during task execution.
+
+    This tool's description is dynamically updated to show all available keys,
+    making it easy for agents to know what data they can retrieve.
+    """
+
+    name: str = "Access Scratchpad Memory"
+    description: str = "Access data stored in your scratchpad memory during task execution."
+    args_schema: Type[BaseModel] = ScratchpadToolSchema
+    scratchpad_data: Dict[str, Any] = Field(default_factory=dict)
+
+    # Allow repeated usage of this tool - scratchpad access should not be limited
+    cache_function: Callable = lambda _args, _result: False  # Don't cache scratchpad access
+    allow_repeated_usage: bool = True  # Allow accessing the same key multiple times
+
+    def __init__(self, scratchpad_data: Optional[Dict[str, Any]] = None, **kwargs):
+        """Initialize the scratchpad tool with optional initial data.
+
+        Args:
+            scratchpad_data: Initial scratchpad data (usually from agent state)
+        """
+        super().__init__(**kwargs)
+        if scratchpad_data:
+            self.scratchpad_data = scratchpad_data
+        self._update_description()
+
+    def _run(
+        self,
+        key: str,
+        **kwargs: Any,
+    ) -> Union[str, Dict[str, Any], Any]:
+        """Retrieve data from the scratchpad using the specified key.
+
+        Args:
+            key: The key to look up in the scratchpad
+
+        Returns:
+            The value associated with the key, or an error message if not found
+        """
+        print(f"[DEBUG] ScratchpadTool._run called with key: '{key}'")
+        print(f"[DEBUG] Current scratchpad keys: {list(self.scratchpad_data.keys())}")
+        print(f"[DEBUG] Scratchpad data size: {len(self.scratchpad_data)}")
+
+        if not self.scratchpad_data:
+            return (
+                "❌ SCRATCHPAD IS EMPTY\n\n"
+                "The scratchpad does not contain any data yet.\n"
+                "Data will be automatically stored here as you use other tools.\n"
+                "Try executing other tools first to gather information.\n\n"
+                "💡 TIP: Tools like search, read, or fetch operations will automatically store their results in the scratchpad."
+            )
+
+        if key not in self.scratchpad_data:
+            available_keys = list(self.scratchpad_data.keys())
+            keys_formatted = "\n".join(f"  - '{k}'" for k in available_keys)
+
+            # Create more helpful examples based on actual keys
+            example_key = available_keys[0] if available_keys else 'example_key'
+
+            # Check if the user tried a similar key (case-insensitive or partial match)
+            similar_keys = [k for k in available_keys if key.lower() in k.lower() or k.lower() in key.lower()]
+            similarity_hint = ""
+            if similar_keys:
+                similarity_hint = f"\n\n🔍 Did you mean one of these?\n" + "\n".join(f"  - '{k}'" for k in similar_keys)
+
+            return (
+                f"❌ KEY NOT FOUND: '{key}'\n"
+                f"{'='*50}\n\n"
+                f"The key '{key}' does not exist in the scratchpad.\n\n"
+                f"📦 AVAILABLE KEYS IN SCRATCHPAD:\n{keys_formatted}\n"
+                f"{similarity_hint}\n\n"
+                f"✅ CORRECT USAGE EXAMPLE:\n"
+                f"Action: Access Scratchpad Memory\n"
+                f"Action Input: {{\"key\": \"{example_key}\"}}\n\n"
+                f"⚠️ IMPORTANT:\n"
+                f"- Keys are case-sensitive and must match EXACTLY\n"
+                f"- Use the exact key name from the list above\n"
+                f"- Do NOT modify or guess key names\n\n"
+                f"{'='*50}"
+            )
+
+        value = self.scratchpad_data[key]
+
+        # Format the output nicely based on the type
+        if isinstance(value, dict):
+            import json
+            formatted_output = f"✅ Successfully retrieved data for key '{key}':\n\n"
+            formatted_output += json.dumps(value, indent=2)
+            return formatted_output
+        elif isinstance(value, list):
+            import json
+            formatted_output = f"✅ Successfully retrieved data for key '{key}':\n\n"
+            formatted_output += json.dumps(value, indent=2)
+            return formatted_output
+        else:
+            return f"✅ Successfully retrieved data for key '{key}':\n\n{str(value)}"
+
+    def update_scratchpad(self, new_data: Dict[str, Any]) -> None:
+        """Update the scratchpad data and refresh the tool description.
+
+        Args:
+            new_data: The new complete scratchpad data
+        """
+        self.scratchpad_data = new_data
+        self._update_description()
+
+    def _update_description(self) -> None:
+        """Update the tool description to include all available keys."""
+        base_description = (
+            "Access data stored in your scratchpad memory during task execution.\n\n"
+            "HOW TO USE THIS TOOL:\n"
+            "Provide a JSON object with a 'key' field containing the exact name of the data you want to retrieve.\n"
+            "Example: {\"key\": \"email_data\"}"
+        )
+
+        if not self.scratchpad_data:
+            self.description = (
+                f"{base_description}\n\n"
+                "📝 STATUS: Scratchpad is currently empty.\n"
+                "Data will be automatically stored here as you use other tools."
+            )
+            return
+
+        # Build a description of available keys with a preview of their contents
+        key_descriptions = []
+        example_key = None
+
+        for key, value in self.scratchpad_data.items():
+            if not example_key:
+                example_key = key
+
+            # Create a brief description of what's stored
+            if isinstance(value, dict):
+                preview = f"dict with {len(value)} items"
+                if 'data' in value and isinstance(value['data'], list):
+                    preview = f"list of {len(value['data'])} items"
+            elif isinstance(value, list):
+                preview = f"list of {len(value)} items"
+            elif isinstance(value, str):
+                preview = f"string ({len(value)} chars)"
+            else:
+                preview = type(value).__name__
+
+            key_descriptions.append(f"  📌 '{key}': {preview}")
+
+        available_keys_text = "\n".join(key_descriptions)
+
+        self.description = (
+            f"{base_description}\n\n"
+            f"📦 AVAILABLE DATA IN SCRATCHPAD:\n{available_keys_text}\n\n"
+            f"💡 EXAMPLE USAGE:\n"
+            f"To retrieve the '{example_key}' data, use:\n"
+            f"Action Input: {{\"key\": \"{example_key}\"}}"
+        )

src/crewai/tools/base_tool.py

@@ -1,7 +1,7 @@
 import asyncio
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import Any, Callable, Type, get_args, get_origin
+from typing import Any, Callable, Type, get_args, get_origin, Optional, List
 
 from pydantic import (
     BaseModel,
@@ -14,6 +14,11 @@ from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
+class EnvVar(BaseModel):
+    name: str
+    description: str
+    required: bool = True
+    default: Optional[str] = None
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
@@ -25,6 +30,8 @@ class BaseTool(BaseModel, ABC):
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
+    env_vars: List[EnvVar] = []
+    """List of environment variables used by the tool."""
     args_schema: Type[PydanticBaseModel] = Field(
         default_factory=_ArgsSchemaPlaceholder, validate_default=True
     )
@@ -39,6 +46,8 @@ class BaseTool(BaseModel, ABC):
     """Maximum number of times this tool can be used. None means unlimited usage."""
     current_usage_count: int = 0
     """Current number of times this tool has been used."""
+    allow_repeated_usage: bool = False
+    """Flag to allow this tool to be used repeatedly with the same arguments."""
 
     @field_validator("args_schema", mode="before")
     @classmethod
@@ -57,7 +66,7 @@ class BaseTool(BaseModel, ABC):
                 },
             },
         )
-        
+
     @field_validator("max_usage_count", mode="before")
     @classmethod
     def validate_max_usage_count(cls, v: int | None) -> int | None:
@@ -81,11 +90,11 @@ class BaseTool(BaseModel, ABC):
         # If _run is async, we safely run it
         if asyncio.iscoroutine(result):
             result = asyncio.run(result)
-            
+
         self.current_usage_count += 1
-        
+
         return result
-        
+
     def reset_usage_count(self) -> None:
         """Reset the current usage count to zero."""
         self.current_usage_count = 0
@@ -109,6 +118,8 @@ class BaseTool(BaseModel, ABC):
             result_as_answer=self.result_as_answer,
             max_usage_count=self.max_usage_count,
             current_usage_count=self.current_usage_count,
+            allow_repeated_usage=self.allow_repeated_usage,
+            cache_function=self.cache_function,
         )
 
     @classmethod
@@ -272,7 +283,7 @@ def to_langchain(
 def tool(*args, result_as_answer: bool = False, max_usage_count: int | None = None) -> Callable:
     """
     Decorator to create a tool from a function.
-    
+
     Args:
         *args: Positional arguments, either the function to decorate or the tool name.
         result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.

src/crewai/tools/structured_tool.py

@@ -25,6 +25,8 @@ class CrewStructuredTool:
         result_as_answer: bool = False,
         max_usage_count: int | None = None,
         current_usage_count: int = 0,
+        allow_repeated_usage: bool = False,
+        cache_function: Optional[Callable] = None,
     ) -> None:
         """Initialize the structured tool.
 
@@ -36,6 +38,8 @@ class CrewStructuredTool:
             result_as_answer: Whether to return the output directly
             max_usage_count: Maximum number of times this tool can be used. None means unlimited usage.
             current_usage_count: Current number of times this tool has been used.
+            allow_repeated_usage: Whether to allow this tool to be used repeatedly with the same arguments.
+            cache_function: Function that will be used to determine if the tool should be cached.
         """
         self.name = name
         self.description = description
@@ -45,6 +49,8 @@ class CrewStructuredTool:
         self.result_as_answer = result_as_answer
         self.max_usage_count = max_usage_count
         self.current_usage_count = current_usage_count
+        self.allow_repeated_usage = allow_repeated_usage
+        self.cache_function = cache_function if cache_function is not None else lambda _args=None, _result=None: True
 
         # Validate the function signature matches the schema
         self._validate_function_signature()
@@ -197,6 +203,42 @@ class CrewStructuredTool:
             validated_args = self.args_schema.model_validate(raw_args)
             return validated_args.model_dump()
         except Exception as e:
+            # Check if this is a "Field required" error and try to fix it
+            error_str = str(e)
+            if "Field required" in error_str:
+                # Try to parse missing fields from the error
+                import re
+                from pydantic import ValidationError
+
+                if isinstance(e, ValidationError):
+                    # Extract missing fields from validation error
+                    missing_fields = []
+                    for error_detail in e.errors():
+                        if error_detail.get('type') == 'missing':
+                            field_path = error_detail.get('loc', ())
+                            if field_path:
+                                missing_fields.append(field_path[0])
+
+                    if missing_fields:
+                        # Create a copy of raw_args and add missing fields with None
+                        fixed_args = dict(raw_args) if isinstance(raw_args, dict) else {}
+                        for field in missing_fields:
+                            if field not in fixed_args:
+                                fixed_args[field] = None
+
+                        # Try validation again with fixed args
+                        try:
+                            self._logger.log("debug", f"Auto-fixing missing fields: {missing_fields}")
+                            validated_args = self.args_schema.model_validate(fixed_args)
+                            return validated_args.model_dump()
+                        except Exception as retry_e:
+                            # If it still fails, raise the original error with additional context
+                            raise ValueError(
+                                f"Arguments validation failed: {e}\n"
+                                f"Attempted to auto-fix missing fields {missing_fields} but still failed: {retry_e}"
+                            )
+
+            # For other validation errors, raise as before
             raise ValueError(f"Arguments validation failed: {e}")
 
     async def ainvoke(

src/crewai/tools/tool_usage.py

@@ -149,7 +149,13 @@ class ToolUsage:
         tool: CrewStructuredTool,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:
-        if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
+        # Check if tool allows repeated usage before blocking
+        allows_repeated = False
+        if hasattr(tool, 'allow_repeated_usage'):
+            allows_repeated = tool.allow_repeated_usage
+        elif hasattr(tool, '_tool') and hasattr(tool._tool, 'allow_repeated_usage'):
+            allows_repeated = tool._tool.allow_repeated_usage
+        if not allows_repeated and self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
@@ -180,7 +186,7 @@ class ToolUsage:
                 event_data.update(self.agent.fingerprint)
 
             crewai_event_bus.emit(self,ToolUsageStartedEvent(**event_data))
-            
+
         started_at = time.time()
         from_cache = False
         result = None  # type: ignore
@@ -250,9 +256,54 @@ class ToolUsage:
                 self._run_attempts += 1
                 if self._run_attempts > self._max_parsing_attempts:
                     self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                    error_message = self._i18n.errors("tool_usage_exception").format(
-                        error=e, tool=tool.name, tool_inputs=tool.description
-                    )
+
+                    # Check if this is a validation error with missing fields
+                    error_str = str(e)
+                    if "Arguments validation failed" in error_str and "Field required" in error_str:
+                        # Extract the field name that's missing
+                        import re
+                        field_match = re.search(r'(\w+)\s*Field required', error_str)
+                        if field_match:
+                            missing_field = field_match.group(1)
+
+                            # Create a more helpful error message
+                            error_message = (
+                                f"Tool validation error: The '{missing_field}' parameter is required but was not provided.\n\n"
+                                f"SOLUTION: Include ALL parameters in your tool call, even optional ones (use null for optional parameters):\n"
+                                f'{{"tool_name": "{tool.name}", "arguments": {{'
+                            )
+
+                            # Get all expected parameters from the tool schema
+                            if hasattr(tool, 'args_schema'):
+                                schema_props = tool.args_schema.model_json_schema().get('properties', {})
+                                param_examples = []
+                                for param_name, param_info in schema_props.items():
+                                    if param_name == missing_field:
+                                        param_examples.append(f'"{param_name}": "YOUR_VALUE_HERE"')
+                                    else:
+                                        # Check if it's optional by looking at required fields
+                                        is_required = param_name in tool.args_schema.model_json_schema().get('required', [])
+                                        if not is_required:
+                                            param_examples.append(f'"{param_name}": null')
+                                        else:
+                                            param_examples.append(f'"{param_name}": "value"')
+
+                                error_message += ', '.join(param_examples)
+                                error_message += '}}\n\n'
+
+                            error_message += f"Original error: {e}\n"
+                            error_message += f"Tool description: {tool.description}"
+                        else:
+                            # Use the original error message
+                            error_message = self._i18n.errors("tool_usage_exception").format(
+                                error=e, tool=tool.name, tool_inputs=tool.description
+                            )
+                    else:
+                        # Use the original error message for non-validation errors
+                        error_message = self._i18n.errors("tool_usage_exception").format(
+                            error=e, tool=tool.name, tool_inputs=tool.description
+                        )
+
                     error = ToolUsageErrorException(
                         f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                     ).message
@@ -281,49 +332,54 @@ class ToolUsage:
                 self.tools_handler.on_tool_use(
                     calling=calling, output=result, should_cache=should_cache
                 )
-        self._telemetry.tool_usage(
-            llm=self.function_calling_llm,
-            tool_name=tool.name,
-            attempts=self._run_attempts,
-        )
-        result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-        data = {
-            "result": result,
-            "tool_name": tool.name,
-            "tool_args": calling.arguments,
-        }
+            self._telemetry.tool_usage(
+                llm=self.function_calling_llm,
+                tool_name=tool.name,
+                attempts=self._run_attempts,
+            )
+            result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+            data = {
+                "result": result,
+                "tool_name": tool.name,
+                "tool_args": calling.arguments,
+            }
 
-        self.on_tool_use_finished(
-            tool=tool,
-            tool_calling=calling,
-            from_cache=from_cache,
-            started_at=started_at,
-            result=result,
-        )
+            self.on_tool_use_finished(
+                tool=tool,
+                tool_calling=calling,
+                from_cache=from_cache,
+                started_at=started_at,
+                result=result,
+            )
 
-        if (
-            hasattr(available_tool, "result_as_answer")
-            and available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
-        ):
-            result_as_answer = available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
-            data["result_as_answer"] = result_as_answer  # type: ignore
+            if (
+                hasattr(available_tool, "result_as_answer")
+                and available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+            ):
+                result_as_answer = available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
+                data["result_as_answer"] = result_as_answer  # type: ignore
 
-        if self.agent and hasattr(self.agent, "tools_results"):
-            self.agent.tools_results.append(data)
+            if self.agent and hasattr(self.agent, "tools_results"):
+                self.agent.tools_results.append(data)
 
-        if available_tool and hasattr(available_tool, 'current_usage_count'):
-            available_tool.current_usage_count += 1
-            if hasattr(available_tool, 'max_usage_count') and available_tool.max_usage_count is not None:
-                self._printer.print(
-                    content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                    color="blue"
-                )
+            if available_tool and hasattr(available_tool, 'current_usage_count'):
+                available_tool.current_usage_count += 1
+                if hasattr(available_tool, 'max_usage_count') and available_tool.max_usage_count is not None:
+                    self._printer.print(
+                        content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
+                        color="blue"
+                    )
 
-        return result
+            return result
 
     def _format_result(self, result: Any) -> str:
         if self.task:
             self.task.used_tools += 1
+
+        # Handle None results explicitly
+        if result is None:
+            result = "No result returned from tool"
+
         if self._should_remember_format():
             result = self._remember_format(result=result)
         return str(result)
@@ -346,24 +402,34 @@ class ToolUsage:
         if not self.tools_handler:
             return False
         if last_tool_usage := self.tools_handler.last_used_tool:
-            return (calling.tool_name == last_tool_usage.tool_name) and (
+            # Add debug logging
+            print(f"[DEBUG] _check_tool_repeated_usage:")
+            print(f"  Current tool: {calling.tool_name}")
+            print(f"  Current args: {calling.arguments}")
+            print(f"  Last tool: {last_tool_usage.tool_name}")
+            print(f"  Last args: {last_tool_usage.arguments}")
+
+            is_repeated = (calling.tool_name == last_tool_usage.tool_name) and (
                 calling.arguments == last_tool_usage.arguments
             )
+            print(f"  Is repeated: {is_repeated}")
+
+            return is_repeated
         return False
-        
+
     def _check_usage_limit(self, tool: Any, tool_name: str) -> str | None:
         """Check if tool has reached its usage limit.
-        
+
         Args:
             tool: The tool to check
             tool_name: The name of the tool (used for error message)
-            
+
         Returns:
             Error message if limit reached, None otherwise
         """
         if (
-            hasattr(tool, 'max_usage_count') 
-            and tool.max_usage_count is not None 
+            hasattr(tool, 'max_usage_count')
+            and tool.max_usage_count is not None
             and tool.current_usage_count >= tool.max_usage_count
         ):
             return f"Tool '{tool_name}' has reached its usage limit of {tool.max_usage_count} times and cannot be used anymore."

src/crewai/translations/en.json

@@ -25,7 +25,7 @@
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
-    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
+    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the complete final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
     "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
     "knowledge_search_query": "The original query is: {task_prompt}.",
@@ -41,7 +41,8 @@
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
     "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
     "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}",
-    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
+    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error.",
+    "criteria_validation_error": "### Your answer did not meet all acceptance criteria\n\n### Unmet criteria:\n{unmet_criteria}\n\n### Previous result:\n{task_output}\n\n\nPlease revise your answer to ensure ALL acceptance criteria are met. Use the 'Access Scratchpad Memory' tool if you need to retrieve any previously collected information."
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don't reference things but instead explain them.",
@@ -55,7 +56,12 @@
   "reasoning": {
     "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
     "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
-    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
-    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
+    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nIMPORTANT: Structure your plan as follows:\n\nSTEPS:\n1. [First concrete action step]\n2. [Second concrete action step]\n3. [Continue with numbered steps...]\n\nACCEPTANCE CRITERIA:\n- [First criterion that must be met]\n- [Second criterion that must be met]\n- [Continue with criteria...]\n\nRemember: your ultimate objective is to produce the most COMPLETE Final Answer that fully meets the **Expected output** criteria.\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
+    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nIMPORTANT: Structure your refined plan as follows:\n\nSTEPS:\n1. [First concrete action step]\n2. [Second concrete action step]\n3. [Continue with numbered steps...]\n\nACCEPTANCE CRITERIA:\n- [First criterion that must be met]\n- [Second criterion that must be met]\n- [Continue with criteria...]\n\nMake sure your refined strategy directly guides you toward producing the most COMPLETE Final Answer that fully satisfies the **Expected output**.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\"",
+    "adaptive_reasoning_decision": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are currently executing a task and need to decide whether to pause and reassess your plan based on the current context.",
+    "mid_execution_reasoning": "You are currently executing a task and need to reassess your plan based on progress so far.\n\nTASK DESCRIPTION:\n{description}\n\nEXPECTED OUTPUT:\n{expected_output}\n\nCURRENT PROGRESS:\nSteps completed: {current_steps}\nTools used: {tools_used}\nProgress summary: {current_progress}\n\nRECENT CONVERSATION:\n{recent_messages}\n\nYour reassessment MUST focus on steering the remaining work toward a FINAL ANSWER that is as complete as possible and perfectly matches the **Expected output**.\n\nBased on the current progress and context, please reassess your plan for completing this task.\nConsider what has been accomplished, what challenges you've encountered, and what steps remain.\nAdjust your strategy if needed or confirm your current approach is still optimal.\n\nIMPORTANT: Structure your updated plan as follows:\n\nREMAINING STEPS:\n1. [First remaining action step]\n2. [Second remaining action step]\n3. [Continue with numbered steps...]\n\nUPDATED ACCEPTANCE CRITERIA (if changed):\n- [First criterion that must be met]\n- [Second criterion that must be met]\n- [Continue with criteria...]\n\nProvide a detailed updated plan for completing the task.\nEnd with \"READY: I am ready to continue executing the task.\" if you're confident in your plan.",
+    "mid_execution_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are reassessing your plan during task execution based on the progress made so far.",
+    "mid_execution_reasoning_update": "I've reassessed my approach based on progress so far. Updated plan:\n\n{plan}",
+    "adaptive_reasoning_context": "\n\nTASK DESCRIPTION:\n{description}\n\nEXPECTED OUTPUT:\n{expected_output}\n\nCURRENT EXECUTION CONTEXT:\n- Steps completed: {current_steps}\n- Tools used: {tools_used}\n- Progress summary: {current_progress}\n\nConsider whether the current approach is optimal or if a strategic pause to reassess would be beneficial. You should reason when:\n- You might be approaching the task inefficiently\n- The context suggests a different strategy might be better\n- You're uncertain about the next steps\n- The progress suggests you need to reconsider your approach\n- Tool usage patterns indicate issues (e.g., repeated failures, same tool used many times, rapid switching)\n- Multiple tools have returned errors or empty results\n- You're using the same tool repeatedly without making progress\n\nPay special attention to the TOOL USAGE STATISTICS section if present, as it reveals patterns that might not be obvious from the tool list alone.\n\nDecide whether reasoning/re-planning is needed at this point."
   }
 }

src/crewai/utilities/agent_utils.py

@@ -215,9 +215,6 @@ def handle_agent_action_core(
     if show_logs:
         show_logs(formatted_answer)
 
-    if messages is not None:
-        messages.append({"role": "assistant", "content": tool_result.result})
-
     return formatted_answer
 
 
@@ -296,6 +293,8 @@ def handle_context_length(
     llm: Any,
     callbacks: List[Any],
     i18n: Any,
+    task_description: Optional[str] = None,
+    expected_output: Optional[str] = None,
 ) -> None:
     """Handle context length exceeded by either summarizing or raising an error.
 
@@ -306,13 +305,22 @@ def handle_context_length(
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
         i18n: I18N instance for messages
+        task_description: Optional original task description
+        expected_output: Optional expected output
     """
     if respect_context_window:
         printer.print(
             content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
             color="yellow",
         )
-        summarize_messages(messages, llm, callbacks, i18n)
+        summarize_messages(
+            messages,
+            llm,
+            callbacks,
+            i18n,
+            task_description=task_description,
+            expected_output=expected_output,
+        )
     else:
         printer.print(
             content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
@@ -328,6 +336,8 @@ def summarize_messages(
     llm: Any,
     callbacks: List[Any],
     i18n: Any,
+    task_description: Optional[str] = None,
+    expected_output: Optional[str] = None,
 ) -> None:
     """Summarize messages to fit within context window.
 
@@ -336,6 +346,8 @@ def summarize_messages(
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
         i18n: I18N instance for messages
+        task_description: Optional original task description
+        expected_output: Optional expected output
     """
     messages_string = " ".join([message["content"] for message in messages])
     messages_groups = []
@@ -368,12 +380,19 @@ def summarize_messages(
 
     merged_summary = " ".join(content["content"] for content in summarized_contents)
 
+    # Build the summary message and optionally inject the task reminder.
+    summary_message = i18n.slice("summary").format(merged_summary=merged_summary)
+
+    if task_description or expected_output:
+        summary_message += "\n\n"  # blank line before the reminder
+        if task_description:
+            summary_message += f"Original task: {task_description}\n"
+        if expected_output:
+            summary_message += f"Expected output: {expected_output}"
+
+    # Replace the conversation with the new summary message.
     messages.clear()
-    messages.append(
-        format_message_for_llm(
-            i18n.slice("summary").format(merged_summary=merged_summary)
-        )
-    )
+    messages.append(format_message_for_llm(summary_message))
 
 
 def show_agent_logs(
@@ -464,7 +483,7 @@ def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
                 attributes[key] = []
                 for tool in value:
                     try:
-                        module = importlib.import_module("crewai_tools")
+                        module = importlib.import_module(tool["module"])
                         tool_class = getattr(module, tool["name"])
                         attributes[key].append(tool_class())
                     except Exception as e:

src/crewai/utilities/events/event_listener.py

@@ -2,7 +2,7 @@ from io import StringIO
 from typing import Any, Dict
 
 from pydantic import Field, PrivateAttr
-
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.telemetry.telemetry import Telemetry
 from crewai.utilities import Logger
@@ -61,6 +61,8 @@ from .reasoning_events import (
     AgentReasoningStartedEvent,
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
+    AgentMidExecutionReasoningStartedEvent,
+    AgentMidExecutionReasoningCompletedEvent,
 )
 
 
@@ -108,6 +110,7 @@ class EventListener(BaseEventListener):
                 event.crew_name or "Crew",
                 source.id,
                 "completed",
+                final_result=final_string_output,
             )
 
         @crewai_event_bus.on(CrewKickoffFailedEvent)
@@ -283,27 +286,43 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(ToolUsageStartedEvent)
         def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.formatter.handle_tool_usage_started(
-                self.formatter.current_agent_branch,
-                event.tool_name,
+            if isinstance(source, LLM):
+                self.formatter.handle_llm_tool_usage_started(
+                    event.tool_name,
+                )
+            else:
+                self.formatter.handle_tool_usage_started(
+                    self.formatter.current_agent_branch,
+                    event.tool_name,
                 self.formatter.current_crew_tree,
             )
 
         @crewai_event_bus.on(ToolUsageFinishedEvent)
         def on_tool_usage_finished(source, event: ToolUsageFinishedEvent):
-            self.formatter.handle_tool_usage_finished(
-                self.formatter.current_tool_branch,
-                event.tool_name,
-                self.formatter.current_crew_tree,
-            )
+            if isinstance(source, LLM):
+                self.formatter.handle_llm_tool_usage_finished(
+                    event.tool_name,
+                )
+            else:
+                self.formatter.handle_tool_usage_finished(
+                    self.formatter.current_tool_branch,
+                    event.tool_name,
+                    self.formatter.current_crew_tree,
+                )
 
         @crewai_event_bus.on(ToolUsageErrorEvent)
         def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            self.formatter.handle_tool_usage_error(
-                self.formatter.current_tool_branch,
-                event.tool_name,
-                event.error,
-                self.formatter.current_crew_tree,
+            if isinstance(source, LLM):
+                self.formatter.handle_llm_tool_usage_error(
+                    event.tool_name,
+                    event.error,
+                )
+            else:
+                self.formatter.handle_tool_usage_error(
+                    self.formatter.current_tool_branch,
+                    event.tool_name,
+                    event.error,
+                    self.formatter.current_crew_tree,
             )
 
         # ----------- LLM EVENTS -----------
@@ -421,8 +440,6 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
-        # ----------- REASONING EVENTS -----------
-
         @crewai_event_bus.on(AgentReasoningStartedEvent)
         def on_agent_reasoning_started(source, event: AgentReasoningStartedEvent):
             self.formatter.handle_reasoning_started(
@@ -446,5 +463,37 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
+        @crewai_event_bus.on(AgentMidExecutionReasoningStartedEvent)
+        def on_mid_execution_reasoning_started(source, event: AgentMidExecutionReasoningStartedEvent):
+            self.formatter.handle_reasoning_started(
+                self.formatter.current_agent_branch,
+                event.attempt if hasattr(event, "attempt") else 1,
+                self.formatter.current_crew_tree,
+                current_step=event.current_step,
+                reasoning_trigger=event.reasoning_trigger,
+            )
+
+        @crewai_event_bus.on(AgentMidExecutionReasoningCompletedEvent)
+        def on_mid_execution_reasoning_completed(source, event: AgentMidExecutionReasoningCompletedEvent):
+            self.formatter.handle_reasoning_completed(
+                event.updated_plan,
+                True,
+                self.formatter.current_crew_tree,
+                duration_seconds=event.duration_seconds,
+                current_step=event.current_step,
+                reasoning_trigger=event.reasoning_trigger,
+            )
+
+        from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
+
+        @crewai_event_bus.on(AgentAdaptiveReasoningDecisionEvent)
+        def on_adaptive_reasoning_decision(source, event: AgentAdaptiveReasoningDecisionEvent):
+            self.formatter.handle_adaptive_reasoning_decision(
+                self.formatter.current_agent_branch,
+                event.should_reason,
+                event.reasoning,
+                self.formatter.current_crew_tree,
+            )
+
 
 event_listener = EventListener()

src/crewai/utilities/events/reasoning_events.py

@@ -19,6 +19,7 @@ class AgentReasoningCompletedEvent(BaseEvent):
     plan: str
     ready: bool
     attempt: int = 1
+    duration_seconds: float = 0.0  # Time taken for reasoning in seconds
 
 
 class AgentReasoningFailedEvent(BaseEvent):
@@ -28,4 +29,37 @@ class AgentReasoningFailedEvent(BaseEvent):
     agent_role: str
     task_id: str
     error: str
-    attempt: int = 1
+    attempt: int = 1
+
+
+class AgentMidExecutionReasoningStartedEvent(BaseEvent):
+    """Event emitted when an agent starts mid-execution reasoning."""
+
+    type: str = "agent_mid_execution_reasoning_started"
+    agent_role: str
+    task_id: str
+    current_step: int
+    reasoning_trigger: str  # "interval" or "adaptive"
+
+
+class AgentMidExecutionReasoningCompletedEvent(BaseEvent):
+    """Event emitted when an agent completes mid-execution reasoning."""
+
+    type: str = "agent_mid_execution_reasoning_completed"
+    agent_role: str
+    task_id: str
+    current_step: int
+    updated_plan: str
+    reasoning_trigger: str
+    duration_seconds: float = 0.0  # Time taken for reasoning in seconds
+
+
+class AgentAdaptiveReasoningDecisionEvent(BaseEvent):
+    """Event emitted after the agent decides whether to trigger adaptive reasoning."""
+
+    type: str = "agent_adaptive_reasoning_decision"
+    agent_role: str
+    task_id: str
+    should_reason: bool  # Whether the agent decided to reason
+    reasoning: str  # Brief explanation / rationale from the LLM
+    reasoning_trigger: str = "adaptive"  # Always adaptive for this event

src/crewai/utilities/events/tool_usage_events.py

@@ -7,11 +7,11 @@ from .base_events import BaseEvent
 class ToolUsageEvent(BaseEvent):
     """Base event for tool usage tracking"""
 
-    agent_key: str
-    agent_role: str
+    agent_key: Optional[str] = None
+    agent_role: Optional[str] = None
     tool_name: str
     tool_args: Dict[str, Any] | str
-    tool_class: str
+    tool_class: Optional[str] = None
     run_attempts: int | None = None
     delegations: int | None = None
     agent: Optional[Any] = None

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

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+import threading
 
 from rich.console import Console
 from rich.panel import Panel
@@ -17,6 +18,15 @@ class ConsoleFormatter:
     current_lite_agent_branch: Optional[Tree] = None
     tool_usage_counts: Dict[str, int] = {}
     current_reasoning_branch: Optional[Tree] = None  # Track reasoning status
+    current_adaptive_decision_branch: Optional[Tree] = None  # Track last adaptive decision branch
+    # Spinner support ---------------------------------------------------
+    _spinner_frames = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
+    _spinner_index: int = 0
+    _spinner_branches: Dict[Tree, tuple[str, str, str]] = {}  # branch -> (icon, name, style)
+    _spinner_thread: Optional[threading.Thread] = None
+    _stop_spinner_event: Optional[threading.Event] = None
+    _spinner_running: bool = False
+    current_llm_tool_tree: Optional[Tree] = None
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
@@ -48,6 +58,8 @@ class ConsoleFormatter:
 
         for label, value in fields.items():
             content.append(f"{label}: ", style="white")
+            if label == "Result":
+                content.append("\n")
             content.append(
                 f"{value}\n", style=fields.get(f"{label}_style", status_style)
             )
@@ -137,6 +149,7 @@ class ConsoleFormatter:
         crew_name: str,
         source_id: str,
         status: str = "completed",
+        final_result: Optional[str] = None,
     ) -> None:
         """Handle crew tree updates with consistent formatting."""
         if not self.verbose or tree is None:
@@ -162,15 +175,26 @@ class ConsoleFormatter:
             style,
         )
 
+        # Prepare additional fields for the completion panel
+        additional_fields: Dict[str, Any] = {"ID": source_id}
+
+        # Include the final result if provided and the status is completed
+        if status == "completed" and final_result is not None:
+            additional_fields["Result"] = final_result
+
         content = self.create_status_content(
             content_title,
             crew_name or "Crew",
             style,
-            ID=source_id,
+            **additional_fields,
         )
 
         self.print_panel(content, title, style)
 
+        # Clear all spinners when crew completes or fails
+        if status in {"completed", "failed"}:
+            self._clear_all_spinners()
+
     def create_crew_tree(self, crew_name: str, source_id: str) -> Optional[Tree]:
         """Create and initialize a new crew tree with initial status."""
         if not self.verbose:
@@ -218,6 +242,15 @@ class ConsoleFormatter:
         # Set the current_task_branch attribute directly
         self.current_task_branch = task_branch
 
+        # When a new task starts, clear pointers to previous agent, reasoning,
+        # and tool branches so that any upcoming Reasoning / Tool logs attach
+        # to the correct task.
+        if self.current_tool_branch:
+            self._unregister_spinner_branch(self.current_tool_branch)
+        self.current_agent_branch = None
+        # Keep current_reasoning_branch; reasoning may still be in progress
+        self.current_tool_branch = None
+
         return task_branch
 
     def update_task_status(
@@ -265,6 +298,17 @@ class ConsoleFormatter:
         )
         self.print_panel(content, panel_title, style)
 
+        # Clear task-scoped pointers after the task is finished so subsequent
+        # events don't mistakenly attach to the old task branch.
+        if status in {"completed", "failed"}:
+            self.current_task_branch = None
+            self.current_agent_branch = None
+            self.current_tool_branch = None
+            # Ensure spinner is stopped if reasoning branch exists
+            if self.current_reasoning_branch is not None:
+                self._unregister_spinner_branch(self.current_reasoning_branch)
+                self.current_reasoning_branch = None
+
     def create_agent_branch(
         self, task_branch: Optional[Tree], agent_role: str, crew_tree: Optional[Tree]
     ) -> Optional[Tree]:
@@ -426,6 +470,51 @@ class ConsoleFormatter:
         self.print()
         return method_branch
 
+    def get_llm_tree(self, tool_name: str):
+        text = Text()
+        text.append(f"🔧 Using {tool_name} from LLM available_function", style="yellow")
+
+        tree = self.current_flow_tree or self.current_crew_tree
+
+        if tree:
+            tree.add(text)
+
+        return tree or Tree(text)
+
+    def handle_llm_tool_usage_started(
+        self,
+        tool_name: str,
+    ):
+        tree = self.get_llm_tree(tool_name)
+        self.add_tree_node(tree, "🔄 Tool Usage Started", "green")
+        self.print(tree)
+        self.print()
+        return tree
+
+    def handle_llm_tool_usage_finished(
+        self,
+        tool_name: str,
+    ):
+        tree = self.get_llm_tree(tool_name)
+        self.add_tree_node(tree, "✅ Tool Usage Completed", "green")
+        self.print(tree)
+        self.print()
+
+    def handle_llm_tool_usage_error(
+        self,
+        tool_name: str,
+        error: str,
+    ):
+        tree = self.get_llm_tree(tool_name)
+        self.add_tree_node(tree, "❌ Tool Usage Failed", "red")
+        self.print(tree)
+        self.print()
+
+        error_content = self.create_status_content(
+            "Tool Usage Failed", tool_name, "red", Error=error
+        )
+        self.print_panel(error_content, "Tool Error", "red")
+
     def handle_tool_usage_started(
         self,
         agent_branch: Optional[Tree],
@@ -456,19 +545,20 @@ class ConsoleFormatter:
         # Update tool usage count
         self.tool_usage_counts[tool_name] = self.tool_usage_counts.get(tool_name, 0) + 1
 
-        # Find or create tool node
-        tool_branch = self.current_tool_branch
-        if tool_branch is None:
-            tool_branch = branch_to_use.add("")
-            self.current_tool_branch = tool_branch
+        # Always create a new branch for each tool invocation so that previous
+        # tool usages remain visible in the tree.
+        tool_branch = branch_to_use.add("")
+        self.current_tool_branch = tool_branch
 
         # Update label with current count
+        spinner_char = self._next_spinner()
         self.update_tree_label(
             tool_branch,
-            "🔧",
+            f"🔧 {spinner_char}",
             f"Using {tool_name} ({self.tool_usage_counts[tool_name]})",
             "yellow",
         )
+        self._register_spinner_branch(tool_branch, "🔧", f"Using {tool_name} ({self.tool_usage_counts[tool_name]})", "yellow")
 
         # Print updated tree immediately
         self.print(tree_to_use)
@@ -498,9 +588,7 @@ class ConsoleFormatter:
             f"Used {tool_name} ({self.tool_usage_counts[tool_name]})",
             "green",
         )
-
-        # Clear the current tool branch as we're done with it
-        self.current_tool_branch = None
+        self._unregister_spinner_branch(tool_branch)
 
         # Only print if we have a valid tree and the tool node is still in it
         if isinstance(tree_to_use, Tree) and tool_branch in tree_to_use.children:
@@ -528,6 +616,7 @@ class ConsoleFormatter:
                 f"{tool_name} ({self.tool_usage_counts[tool_name]})",
                 "red",
             )
+            self._unregister_spinner_branch(tool_branch)
             if tree_to_use:
                 self.print(tree_to_use)
                 self.print()
@@ -567,7 +656,9 @@ class ConsoleFormatter:
         # Only add thinking status if we don't have a current tool branch
         if self.current_tool_branch is None:
             tool_branch = branch_to_use.add("")
-            self.update_tree_label(tool_branch, "🧠", "Thinking...", "blue")
+            spinner_char = self._next_spinner()
+            self.update_tree_label(tool_branch, f"🧠 {spinner_char}", "Thinking...", "blue")
+            self._register_spinner_branch(tool_branch, "🧠", "Thinking...", "blue")
             self.current_tool_branch = tool_branch
             self.print(tree_to_use)
             self.print()
@@ -601,6 +692,8 @@ class ConsoleFormatter:
             for parent in parents:
                 if isinstance(parent, Tree) and tool_branch in parent.children:
                     parent.children.remove(tool_branch)
+                    # Stop spinner for the thinking branch before removing
+                    self._unregister_spinner_branch(tool_branch)
                     removed = True
                     break
 
@@ -625,6 +718,7 @@ class ConsoleFormatter:
         # Update tool branch if it exists
         if tool_branch:
             tool_branch.label = Text("❌ LLM Failed", style="red bold")
+            self._unregister_spinner_branch(tool_branch)
             if tree_to_use:
                 self.print(tree_to_use)
                 self.print()
@@ -1060,17 +1154,23 @@ class ConsoleFormatter:
         agent_branch: Optional[Tree],
         attempt: int,
         crew_tree: Optional[Tree],
+        current_step: Optional[int] = None,
+        reasoning_trigger: Optional[str] = None,
     ) -> Optional[Tree]:
         """Handle agent reasoning started (or refinement) event."""
         if not self.verbose:
             return None
 
-        # Prefer LiteAgent > Agent > Task branch as the parent for reasoning
-        branch_to_use = (
-            self.current_lite_agent_branch
-            or agent_branch
-            or self.current_task_branch
-        )
+        # Prefer to nest under the latest adaptive decision branch when this is a
+        # mid-execution reasoning cycle so the tree indents nicely.
+        if current_step is not None and self.current_adaptive_decision_branch is not None:
+            branch_to_use = self.current_adaptive_decision_branch
+        else:
+            branch_to_use = (
+                self.current_lite_agent_branch
+                or agent_branch
+                or self.current_task_branch
+            )
 
         # We always want to render the full crew tree when possible so the
         # Live view updates coherently. Fallbacks: crew tree → branch itself.
@@ -1086,11 +1186,21 @@ class ConsoleFormatter:
             reasoning_branch = branch_to_use.add("")
             self.current_reasoning_branch = reasoning_branch
 
-        # Build label text depending on attempt
-        status_text = (
-            f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
-        )
-        self.update_tree_label(reasoning_branch, "🧠", status_text, "blue")
+        # Build label text depending on attempt and whether it's mid-execution
+        if current_step is not None:
+            status_text = "Mid-Execution Reasoning"
+        else:
+            status_text = (
+                f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
+            )
+
+        # ⠋ is the first frame of a braille spinner – visually hints progress even
+        # without true animation.
+        spinner_char = self._next_spinner()
+        self.update_tree_label(reasoning_branch, f"🧠 {spinner_char}", status_text, "yellow")
+
+        # Register branch for continuous spinner
+        self._register_spinner_branch(reasoning_branch, "🧠", status_text, "yellow")
 
         self.print(tree_to_use)
         self.print()
@@ -1102,6 +1212,9 @@ class ConsoleFormatter:
         plan: str,
         ready: bool,
         crew_tree: Optional[Tree],
+        duration_seconds: float = 0.0,
+        current_step: Optional[int] = None,
+        reasoning_trigger: Optional[str] = None,
     ) -> None:
         """Handle agent reasoning completed event."""
         if not self.verbose:
@@ -1115,10 +1228,31 @@ class ConsoleFormatter:
             or crew_tree
         )
 
-        style = "green" if ready else "yellow"
-        status_text = "Reasoning Completed" if ready else "Reasoning Completed (Not Ready)"
+        # Completed reasoning should always display in green.
+        style = "green"
+        # Build duration part separately for cleaner formatting
+        duration_part = f"{duration_seconds:.2f}s" if duration_seconds > 0 else ""
 
-        if reasoning_branch is not None:
+        if current_step is not None:
+            # Build label manually to style duration differently and omit trigger info.
+            if reasoning_branch is not None:
+                label = Text()
+                label.append("✅ ", style=f"{style} bold")
+                label.append("Mid-Execution Reasoning Completed", style=style)
+                if duration_part:
+                    label.append(f" ({duration_part})", style="cyan")
+                reasoning_branch.label = label
+
+            status_text = None  # Already set label manually
+        else:
+            status_text = (
+                f"Reasoning Completed ({duration_part})" if duration_part else "Reasoning Completed"
+            ) if ready else (
+                f"Reasoning Completed (Not Ready • {duration_part})" if duration_part else "Reasoning Completed (Not Ready)"
+            )
+
+        # If we didn't build a custom label (non-mid-execution case), use helper
+        if status_text and reasoning_branch is not None:
             self.update_tree_label(reasoning_branch, "✅", status_text, style)
 
         if tree_to_use is not None:
@@ -1126,9 +1260,17 @@ class ConsoleFormatter:
 
         # Show plan in a panel (trim very long plans)
         if plan:
+            # Derive duration text for panel title
+            duration_text = f" ({duration_part})" if duration_part else ""
+
+            if current_step is not None:
+                title = f"🧠 Mid-Execution Reasoning Plan{duration_text}"
+            else:
+                title = f"🧠 Reasoning Plan{duration_text}"
+
             plan_panel = Panel(
                 Text(plan, style="white"),
-                title="🧠 Reasoning Plan",
+                title=title,
                 border_style=style,
                 padding=(1, 2),
             )
@@ -1136,9 +1278,17 @@ class ConsoleFormatter:
 
         self.print()
 
+        # Unregister spinner before clearing
+        if reasoning_branch is not None:
+            self._unregister_spinner_branch(reasoning_branch)
+
         # Clear stored branch after completion
         self.current_reasoning_branch = None
 
+        # After reasoning finished, we also clear the adaptive decision branch to
+        # avoid nesting unrelated future nodes.
+        self.current_adaptive_decision_branch = None
+
     def handle_reasoning_failed(
         self,
         error: str,
@@ -1158,6 +1308,7 @@ class ConsoleFormatter:
 
         if reasoning_branch is not None:
             self.update_tree_label(reasoning_branch, "❌", "Reasoning Failed", "red")
+            self._unregister_spinner_branch(reasoning_branch)
 
         if tree_to_use is not None:
             self.print(tree_to_use)
@@ -1173,3 +1324,115 @@ class ConsoleFormatter:
 
         # Clear stored branch after failure
         self.current_reasoning_branch = None
+
+    # ----------- ADAPTIVE REASONING DECISION EVENTS -----------
+
+    def handle_adaptive_reasoning_decision(
+        self,
+        agent_branch: Optional[Tree],
+        should_reason: bool,
+        reasoning: str,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        """Render the decision on whether to trigger adaptive reasoning."""
+        if not self.verbose:
+            return
+
+        # Prefer LiteAgent > Agent > Task as parent
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+
+        if branch_to_use is None or tree_to_use is None:
+            return
+
+        decision_branch = branch_to_use.add("")
+
+        decision_text = "YES" if should_reason else "NO"
+        style = "green" if should_reason else "yellow"
+
+        self.update_tree_label(
+            decision_branch,
+            "🤔",
+            f"Adaptive Reasoning Decision: {decision_text}",
+            style,
+        )
+
+        # Print tree first (live update)
+        self.print(tree_to_use)
+
+        # Also show explanation if available
+        if reasoning:
+            truncated_reasoning = reasoning[:500] + "..." if len(reasoning) > 500 else reasoning
+            panel = Panel(
+                Text(truncated_reasoning, style="white"),
+                title="🤔 Adaptive Reasoning Rationale",
+                border_style=style,
+                padding=(1, 2),
+            )
+            self.print(panel)
+
+        self.print()
+
+        # Store the decision branch so that subsequent mid-execution reasoning nodes
+        # can be rendered as children of this decision (for better indentation).
+        self.current_adaptive_decision_branch = decision_branch
+
+    # ------------------------------------------------------------------
+    # Spinner helpers
+    # ------------------------------------------------------------------
+
+    def _next_spinner(self) -> str:
+        """Return next spinner frame."""
+        frame = self._spinner_frames[self._spinner_index]
+        self._spinner_index = (self._spinner_index + 1) % len(self._spinner_frames)
+        return frame
+
+    def _register_spinner_branch(self, branch: Tree, icon: str, name: str, style: str):
+        """Start animating spinner for given branch."""
+        self._spinner_branches[branch] = (icon, name, style)
+        if not self._spinner_running:
+            self._start_spinner_thread()
+
+    def _unregister_spinner_branch(self, branch: Optional[Tree]):
+        if branch is None:
+            return
+        self._spinner_branches.pop(branch, None)
+        if not self._spinner_branches:
+            self._stop_spinner_thread()
+
+    def _start_spinner_thread(self):
+        if self._spinner_running:
+            return
+        self._stop_spinner_event = threading.Event()
+        self._spinner_thread = threading.Thread(target=self._spinner_loop, daemon=True)
+        self._spinner_thread.start()
+        self._spinner_running = True
+
+    def _stop_spinner_thread(self):
+        if self._stop_spinner_event:
+            self._stop_spinner_event.set()
+        self._spinner_running = False
+
+    def _clear_all_spinners(self):
+        """Clear all active spinners. Used as a safety mechanism."""
+        self._spinner_branches.clear()
+        self._stop_spinner_thread()
+
+    def _spinner_loop(self):
+        import time
+        while self._stop_spinner_event and not self._stop_spinner_event.is_set():
+            if self._live and self._spinner_branches:
+                for branch, (icon, name, style) in list(self._spinner_branches.items()):
+                    spinner_char = self._next_spinner()
+                    self.update_tree_label(branch, f"{icon} {spinner_char}", name, style)
+                # Refresh live view
+                try:
+                    self._live.update(self._live.renderable, refresh=True)
+                except Exception:
+                    pass
+            time.sleep(0.15)

src/crewai/utilities/reasoning_handler.py

@@ -1,6 +1,6 @@
 import logging
 import json
-from typing import Tuple, cast
+from typing import Tuple, cast, List, Optional, Dict, Any
 
 from pydantic import BaseModel, Field
 
@@ -16,10 +16,17 @@ from crewai.utilities.events.reasoning_events import (
 )
 
 
+class StructuredPlan(BaseModel):
+    """Structured representation of a task plan."""
+    steps: List[str] = Field(description="List of steps to complete the task")
+    acceptance_criteria: List[str] = Field(description="Criteria that must be met before task is considered complete")
+
+
 class ReasoningPlan(BaseModel):
     """Model representing a reasoning plan for a task."""
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
+    structured_plan: Optional[StructuredPlan] = Field(default=None, description="Structured version of the plan")
 
 
 class AgentReasoningOutput(BaseModel):
@@ -31,6 +38,8 @@ class ReasoningFunction(BaseModel):
     """Model for function calling with reasoning."""
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
+    steps: Optional[List[str]] = Field(default=None, description="List of steps to complete the task")
+    acceptance_criteria: Optional[List[str]] = Field(default=None, description="Criteria that must be met before task is complete")
 
 
 class AgentReasoning:
@@ -38,7 +47,7 @@ class AgentReasoning:
     Handles the agent reasoning process, enabling an agent to reflect and create a plan
     before executing a task.
     """
-    def __init__(self, task: Task, agent: Agent):
+    def __init__(self, task: Task, agent: Agent, extra_context: str | None = None):
         if not task or not agent:
             raise ValueError("Both task and agent must be provided.")
         self.task = task
@@ -46,6 +55,7 @@ class AgentReasoning:
         self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
         self.i18n = I18N()
+        self.extra_context = extra_context or ""
 
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
         """
@@ -55,6 +65,9 @@ class AgentReasoning:
         Returns:
             AgentReasoningOutput: The output of the agent reasoning process.
         """
+        import time
+        start_time = time.time()
+
         # Emit a reasoning started event (attempt 1)
         try:
             crewai_event_bus.emit(
@@ -72,6 +85,8 @@ class AgentReasoning:
         try:
             output = self.__handle_agent_reasoning()
 
+            duration_seconds = time.time() - start_time
+
             # Emit reasoning completed event
             try:
                 crewai_event_bus.emit(
@@ -82,6 +97,7 @@ class AgentReasoning:
                         plan=output.plan.plan,
                         ready=output.plan.ready,
                         attempt=1,
+                        duration_seconds=duration_seconds,
                     ),
                 )
             except Exception:
@@ -112,25 +128,25 @@ class AgentReasoning:
         Returns:
             AgentReasoningOutput: The output of the agent reasoning process.
         """
-        plan, ready = self.__create_initial_plan()
+        plan, ready, structured_plan = self.__create_initial_plan()
 
-        plan, ready = self.__refine_plan_if_needed(plan, ready)
+        plan, ready, structured_plan = self.__refine_plan_if_needed(plan, ready, structured_plan)
 
-        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready, structured_plan=structured_plan)
         return AgentReasoningOutput(plan=reasoning_plan)
 
-    def __create_initial_plan(self) -> Tuple[str, bool]:
+    def __create_initial_plan(self) -> Tuple[str, bool, Optional[StructuredPlan]]:
         """
         Creates the initial reasoning plan for the task.
 
         Returns:
-            Tuple[str, bool]: The initial plan and whether the agent is ready to execute the task.
+            Tuple[str, bool, Optional[StructuredPlan]]: The initial plan, whether the agent is ready, and structured plan.
         """
         reasoning_prompt = self.__create_reasoning_prompt()
 
         if self.llm.supports_function_calling():
-            plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
-            return plan, ready
+            plan, ready, structured_plan = self.__call_with_function(reasoning_prompt, "initial_plan")
+            return plan, ready, structured_plan
         else:
             system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
                 role=self.agent.role,
@@ -145,18 +161,21 @@ class AgentReasoning:
                 ]
             )
 
-            return self.__parse_reasoning_response(str(response))
+            plan, ready = self.__parse_reasoning_response(str(response))
+            structured_plan = self.__extract_structured_plan(plan)
+            return plan, ready, structured_plan
 
-    def __refine_plan_if_needed(self, plan: str, ready: bool) -> Tuple[str, bool]:
+    def __refine_plan_if_needed(self, plan: str, ready: bool, structured_plan: Optional[StructuredPlan]) -> Tuple[str, bool, Optional[StructuredPlan]]:
         """
         Refines the reasoning plan if the agent is not ready to execute the task.
 
         Args:
             plan: The current reasoning plan.
             ready: Whether the agent is ready to execute the task.
+            structured_plan: The current structured plan.
 
         Returns:
-            Tuple[str, bool]: The refined plan and whether the agent is ready to execute the task.
+            Tuple[str, bool, Optional[StructuredPlan]]: The refined plan, ready status, and structured plan.
         """
         attempt = 1
         max_attempts = self.agent.max_reasoning_attempts
@@ -178,7 +197,7 @@ class AgentReasoning:
             refine_prompt = self.__create_refine_prompt(plan)
 
             if self.llm.supports_function_calling():
-                plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
+                plan, ready, structured_plan = self.__call_with_function(refine_prompt, "refine_plan")
             else:
                 system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
                     role=self.agent.role,
@@ -193,6 +212,7 @@ class AgentReasoning:
                     ]
                 )
                 plan, ready = self.__parse_reasoning_response(str(response))
+                structured_plan = self.__extract_structured_plan(plan)
 
             attempt += 1
 
@@ -202,9 +222,9 @@ class AgentReasoning:
                 )
                 break
 
-        return plan, ready
+        return plan, ready, structured_plan
 
-    def __call_with_function(self, prompt: str, prompt_type: str) -> Tuple[str, bool]:
+    def __call_with_function(self, prompt: str, prompt_type: str) -> Tuple[str, bool, Optional[StructuredPlan]]:
         """
         Calls the LLM with function calling to get a reasoning plan.
 
@@ -213,7 +233,7 @@ class AgentReasoning:
             prompt_type: The type of prompt (initial_plan or refine_plan).
 
         Returns:
-            Tuple[str, bool]: A tuple containing the plan and whether the agent is ready.
+            Tuple[str, bool, Optional[StructuredPlan]]: A tuple containing the plan, ready status, and structured plan.
         """
         self.logger.debug(f"Using function calling for {prompt_type} reasoning")
 
@@ -232,6 +252,16 @@ class AgentReasoning:
                         "ready": {
                             "type": "boolean",
                             "description": "Whether the agent is ready to execute the task."
+                        },
+                        "steps": {
+                            "type": "array",
+                            "items": {"type": "string"},
+                            "description": "List of steps to complete the task"
+                        },
+                        "acceptance_criteria": {
+                            "type": "array",
+                            "items": {"type": "string"},
+                            "description": "Criteria that must be met before task is considered complete"
                         }
                     },
                     "required": ["plan", "ready"]
@@ -247,9 +277,14 @@ class AgentReasoning:
             )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(plan: str, ready: bool):  # noqa: N802
+            def _create_reasoning_plan(plan: str, ready: bool, steps: Optional[List[str]] = None, acceptance_criteria: Optional[List[str]] = None):  # noqa: N802
                 """Return the reasoning plan result in JSON string form."""
-                return json.dumps({"plan": plan, "ready": ready})
+                return json.dumps({
+                    "plan": plan,
+                    "ready": ready,
+                    "steps": steps,
+                    "acceptance_criteria": acceptance_criteria
+                })
 
             response = self.llm.call(
                 [
@@ -265,12 +300,19 @@ class AgentReasoning:
             try:
                 result = json.loads(response)
                 if "plan" in result and "ready" in result:
-                    return result["plan"], result["ready"]
+                    structured_plan = None
+                    if result.get("steps") or result.get("acceptance_criteria"):
+                        structured_plan = StructuredPlan(
+                            steps=result.get("steps", []),
+                            acceptance_criteria=result.get("acceptance_criteria", [])
+                        )
+                    return result["plan"], result["ready"], structured_plan
             except (json.JSONDecodeError, KeyError):
                 pass
 
             response_str = str(response)
-            return response_str, "READY: I am ready to execute the task." in response_str
+            structured_plan = self.__extract_structured_plan(response_str)
+            return response_str, "READY: I am ready to execute the task." in response_str, structured_plan
 
         except Exception as e:
             self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
@@ -290,10 +332,11 @@ class AgentReasoning:
                 )
 
                 fallback_str = str(fallback_response)
-                return fallback_str, "READY: I am ready to execute the task." in fallback_str
+                structured_plan = self.__extract_structured_plan(fallback_str)
+                return fallback_str, "READY: I am ready to execute the task." in fallback_str, structured_plan
             except Exception as inner_e:
                 self.logger.error(f"Error during fallback text parsing: {str(inner_e)}")
-                return "Failed to generate a plan due to an error.", True  # Default to ready to avoid getting stuck
+                return "Failed to generate a plan due to an error.", True, None  # Default to ready to avoid getting stuck
 
     def __get_agent_backstory(self) -> str:
         """
@@ -317,7 +360,7 @@ class AgentReasoning:
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
-            description=self.task.description,
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
             expected_output=self.task.expected_output,
             tools=available_tools
         )
@@ -368,7 +411,7 @@ class AgentReasoning:
         plan = response
         ready = False
 
-        if "READY: I am ready to execute the task." in response:
+        if "READY: I am ready to execute the task." in response or "READY: I am ready to continue executing the task." in response:
             ready = True
 
         return plan, ready
@@ -385,3 +428,403 @@ class AgentReasoning:
             "The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
         )
         return self.handle_agent_reasoning()
+
+    def _emit_reasoning_event(self, event_class, **kwargs):
+        """Centralized method for emitting reasoning events."""
+        try:
+            reasoning_trigger = "interval"
+            if hasattr(self.agent, 'adaptive_reasoning') and self.agent.adaptive_reasoning:
+                reasoning_trigger = "adaptive"
+
+            crewai_event_bus.emit(
+                self.agent,
+                event_class(
+                    agent_role=self.agent.role,
+                    task_id=str(self.task.id),
+                    reasoning_trigger=reasoning_trigger,
+                    **kwargs
+                ),
+            )
+        except Exception:
+            # Ignore event bus errors to avoid breaking execution
+            pass
+
+    def handle_mid_execution_reasoning(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> AgentReasoningOutput:
+        """
+        Handle reasoning during task execution with context about current progress.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            AgentReasoningOutput: Updated reasoning plan based on current context
+        """
+        import time
+        start_time = time.time()
+
+        from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningStartedEvent
+
+        self._emit_reasoning_event(
+            AgentMidExecutionReasoningStartedEvent,
+            current_step=current_steps
+        )
+
+        try:
+            output = self.__handle_mid_execution_reasoning(
+                current_steps, tools_used, current_progress, iteration_messages
+            )
+
+            duration_seconds = time.time() - start_time
+
+            # Emit completed event
+            from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningCompletedEvent
+
+            self._emit_reasoning_event(
+                AgentMidExecutionReasoningCompletedEvent,
+                current_step=current_steps,
+                updated_plan=output.plan.plan,
+                duration_seconds=duration_seconds
+            )
+
+            return output
+        except Exception as e:
+            # Emit failed event
+            from crewai.utilities.events.reasoning_events import AgentReasoningFailedEvent
+
+            self._emit_reasoning_event(
+                AgentReasoningFailedEvent,
+                error=str(e),
+                attempt=1
+            )
+
+            raise
+
+    def __handle_mid_execution_reasoning(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> AgentReasoningOutput:
+        """
+        Private method that handles the mid-execution reasoning process.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            AgentReasoningOutput: The output of the mid-execution reasoning process.
+        """
+        mid_execution_prompt = self.__create_mid_execution_prompt(
+            current_steps, tools_used, current_progress, iteration_messages
+        )
+
+        if self.llm.supports_function_calling():
+            plan, ready, structured_plan = self.__call_with_function(mid_execution_prompt, "mid_execution_plan")
+        else:
+            # Use the same prompt for system context
+            system_prompt = self.i18n.retrieve("reasoning", "mid_execution_plan").format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory()
+            )
+
+            response = self.llm.call(
+                [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": mid_execution_prompt}
+                ]
+            )
+
+            plan, ready = self.__parse_reasoning_response(str(response))
+            structured_plan = self.__extract_structured_plan(plan)
+
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready, structured_plan=structured_plan)
+        return AgentReasoningOutput(plan=reasoning_plan)
+
+    def __create_mid_execution_prompt(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> str:
+        """
+        Creates a prompt for the agent to reason during task execution.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            str: The mid-execution reasoning prompt.
+        """
+        tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
+
+        recent_messages = ""
+        if iteration_messages:
+            recent_msgs = iteration_messages[-6:] if len(iteration_messages) > 6 else iteration_messages
+            for msg in recent_msgs:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if content:
+                    recent_messages += f"{role.upper()}: {content[:200]}...\n\n"
+
+        return self.i18n.retrieve("reasoning", "mid_execution_reasoning").format(
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
+            expected_output=self.task.expected_output,
+            current_steps=current_steps,
+            tools_used=tools_used_str,
+            current_progress=current_progress,
+            recent_messages=recent_messages
+        )
+
+    def should_adaptive_reason_llm(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        tool_usage_stats: Optional[Dict[str, Any]] = None
+    ) -> bool:
+        """
+        Use LLM function calling to determine if adaptive reasoning should be triggered.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            tool_usage_stats: Optional statistics about tool usage patterns
+
+        Returns:
+            bool: True if reasoning should be triggered, False otherwise.
+        """
+        try:
+            decision_prompt = self.__create_adaptive_reasoning_decision_prompt(
+                current_steps, tools_used, current_progress, tool_usage_stats
+            )
+
+            if self.llm.supports_function_calling():
+                should_reason, reasoning_expl = self.__call_adaptive_reasoning_function(decision_prompt)
+            else:
+                should_reason, reasoning_expl = self.__call_adaptive_reasoning_text(decision_prompt)
+
+            # Emit an event so the UI/console can display the decision
+            try:
+                from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
+
+                self._emit_reasoning_event(
+                    AgentAdaptiveReasoningDecisionEvent,
+                    should_reason=should_reason,
+                    reasoning=reasoning_expl,
+                )
+            except Exception:
+                # Ignore event bus errors to avoid breaking execution
+                pass
+
+            return should_reason
+        except Exception as e:
+            self.logger.warning(f"Error during adaptive reasoning decision: {str(e)}. Defaulting to no reasoning.")
+            return False
+
+    def __call_adaptive_reasoning_function(self, prompt: str) -> tuple[bool, str]:
+        """Call LLM with function calling for adaptive reasoning decision."""
+        function_schema = {
+            "type": "function",
+            "function": {
+                "name": "decide_reasoning_need",
+                "description": "Decide whether reasoning is needed based on current task execution context",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "should_reason": {
+                            "type": "boolean",
+                            "description": "Whether reasoning/re-planning is needed at this point in task execution."
+                        },
+                        "reasoning": {
+                            "type": "string",
+                            "description": "Brief explanation of why reasoning is or isn't needed."
+                        },
+                        "detected_issues": {
+                            "type": "array",
+                            "items": {"type": "string"},
+                            "description": "List of specific issues detected (e.g., 'repeated tool failures', 'no progress', 'inefficient approach')"
+                        }
+                    },
+                    "required": ["should_reason", "reasoning"]
+                }
+            }
+        }
+
+        def _decide_reasoning_need(should_reason: bool, reasoning: str, detected_issues: Optional[List[str]] = None):
+            """Return the reasoning decision result in JSON string form."""
+            result = {
+                "should_reason": should_reason,
+                "reasoning": reasoning
+            }
+            if detected_issues:
+                result["detected_issues"] = detected_issues
+                # Append detected issues to reasoning explanation
+                issues_str = ", ".join(detected_issues)
+                result["reasoning"] = f"{reasoning} Detected issues: {issues_str}"
+            return json.dumps(result)
+
+        system_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        response = self.llm.call(
+            [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt}
+            ],
+            tools=[function_schema],
+            available_functions={"decide_reasoning_need": _decide_reasoning_need},
+        )
+
+        try:
+            result = json.loads(response)
+            reasoning_text = result.get("reasoning", "No explanation provided")
+            if result.get("detected_issues"):
+                # Include detected issues in the reasoning text for logging
+                self.logger.debug(f"Adaptive reasoning detected issues: {result['detected_issues']}")
+            return result.get("should_reason", False), reasoning_text
+        except (json.JSONDecodeError, KeyError):
+            return False, "No explanation provided"
+
+    def __call_adaptive_reasoning_text(self, prompt: str) -> tuple[bool, str]:
+        """Fallback text-based adaptive reasoning decision."""
+        system_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        response = self.llm.call([
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": prompt + "\n\nRespond with 'YES' if reasoning is needed, 'NO' if not."}
+        ])
+
+        return "YES" in str(response).upper(), "No explanation provided"
+
+    def __create_adaptive_reasoning_decision_prompt(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        tool_usage_stats: Optional[Dict[str, Any]] = None
+    ) -> str:
+        """Create prompt for adaptive reasoning decision."""
+        tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
+
+        # Add tool usage statistics to the prompt
+        tool_stats_str = ""
+        if tool_usage_stats:
+            tool_stats_str = f"\n\nTOOL USAGE STATISTICS:\n"
+            tool_stats_str += f"- Total tool invocations: {tool_usage_stats.get('total_tool_uses', 0)}\n"
+            tool_stats_str += f"- Unique tools used: {tool_usage_stats.get('unique_tools', 0)}\n"
+
+            if tool_usage_stats.get('tools_by_frequency'):
+                tool_stats_str += "- Tool frequency:\n"
+                for tool, count in tool_usage_stats['tools_by_frequency'].items():
+                    tool_stats_str += f"  • {tool}: {count} times\n"
+
+            if tool_usage_stats.get('recent_patterns'):
+                tool_stats_str += f"- Recent patterns: {tool_usage_stats['recent_patterns']}\n"
+
+        # Use the prompt from i18n and format it with the current context
+        base_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        context_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_context").format(
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
+            expected_output=self.task.expected_output,
+            current_steps=current_steps,
+            tools_used=tools_used_str,
+            current_progress=current_progress + tool_stats_str
+        )
+
+        prompt = base_prompt + context_prompt
+
+        return prompt
+
+    def __extract_structured_plan(self, plan: str) -> Optional[StructuredPlan]:
+        """
+        Extracts a structured plan from the given plan text.
+
+        Args:
+            plan: The plan text.
+
+        Returns:
+            Optional[StructuredPlan]: The extracted structured plan or None if no plan was found.
+        """
+        if not plan:
+            return None
+
+        import re
+
+        steps = []
+        acceptance_criteria = []
+
+        # Look for numbered steps (1., 2., etc.)
+        step_pattern = r'^\s*(?:\d+\.|\-|\*)\s*(.+)$'
+
+        # Look for acceptance criteria section
+        in_acceptance_section = False
+
+        lines = plan.split('\n')
+        for line in lines:
+            line = line.strip()
+
+            # Check if we're entering acceptance criteria section
+            if any(marker in line.lower() for marker in ['acceptance criteria', 'success criteria', 'completion criteria']):
+                in_acceptance_section = True
+                continue
+
+            # Skip empty lines
+            if not line:
+                continue
+
+            # Extract steps or criteria
+            match = re.match(step_pattern, line, re.MULTILINE)
+            if match:
+                content = match.group(1).strip()
+                if in_acceptance_section:
+                    acceptance_criteria.append(content)
+                else:
+                    steps.append(content)
+            elif line and not line.endswith(':'):  # Non-empty line that's not a header
+                if in_acceptance_section:
+                    acceptance_criteria.append(line)
+                else:
+                    # Check if it looks like a step (starts with action verb)
+                    action_verbs = ['create', 'implement', 'design', 'build', 'test', 'verify', 'check', 'ensure', 'analyze', 'review']
+                    if any(line.lower().startswith(verb) for verb in action_verbs):
+                        steps.append(line)
+
+        # If we found steps or criteria, return structured plan
+        if steps or acceptance_criteria:
+            return StructuredPlan(
+                steps=steps,
+                acceptance_criteria=acceptance_criteria
+            )
+
+        return None

tests/adaptive_reasoning_test.py

@@ -0,0 +1,89 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+
+
+def _create_executor(agent):  # noqa: D401,E501
+    """Utility to build a minimal CrewAgentExecutor with the given agent.
+
+    A real LLM call is not required for these unit-tests, so we stub it with
+    MagicMock to avoid any network interaction.
+    """
+    return CrewAgentExecutor(
+        llm=MagicMock(),
+        task=MagicMock(),
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock(),
+    )
+
+
+def test_agent_adaptive_reasoning_default():
+    """Agent.adaptive_reasoning should be False by default."""
+    agent = Agent(role="Test", goal="Goal", backstory="Backstory")
+    assert agent.adaptive_reasoning is False
+
+
+@pytest.mark.parametrize("adaptive_decision,expected", [(True, True), (False, False)])
+def test_should_trigger_reasoning_with_adaptive_reasoning(adaptive_decision, expected):
+    """Verify _should_trigger_reasoning defers to _should_adaptive_reason when
+    adaptive_reasoning is enabled and reasoning_interval is None."""
+    # Use a lightweight mock instead of a full Agent instance to isolate the logic
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = None
+    agent.adaptive_reasoning = True
+
+    executor = _create_executor(agent)
+
+    # Ensure the helper returns the desired decision
+    with patch.object(executor, "_should_adaptive_reason", return_value=adaptive_decision) as mock_adaptive:
+        assert executor._should_trigger_reasoning() is expected
+        mock_adaptive.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_adaptive_reasoning_full_execution():
+    """End-to-end test that triggers adaptive reasoning in a real execution flow.
+
+    The task description intentionally contains the word "error" to activate the
+    simple error-based heuristic inside `_should_adaptive_reason`, guaranteeing
+    that the agent reasons mid-execution without relying on patched internals.
+    """
+    agent = Agent(
+        role="Math Analyst",
+        goal="Solve arithmetic problems flawlessly",
+        backstory="You excel at basic calculations and always double-check your steps.",
+        llm="gpt-4o-mini",
+        reasoning=True,
+        adaptive_reasoning=True,
+        verbose=False,
+    )
+
+    task = Task(
+        description="There was an unexpected error earlier. Now, please calculate 3 + 5 and return only the number.",
+        expected_output="The result of the calculation (a single number).",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+
+    result = crew.kickoff()
+
+    # Validate the answer is correct and numeric
+    assert result.raw.strip() == "8"
+
+    # Confirm that an adaptive reasoning message (Updated plan) was injected
+    assert any(
+        "updated plan" in msg.get("content", "").lower()
+        for msg in agent.agent_executor.messages
+    )

tests/agent_test.py

@@ -309,7 +309,9 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (patch.object(CacheHandler, "read") as read,):
+    with (
+        patch.object(CacheHandler, "read") as read,
+    ):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1628,13 +1630,13 @@ def test_agent_execute_task_with_ollama():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources():
-    # Create a knowledge source with some content
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
     with patch("crewai.knowledge") as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
         mock_knowledge_instance.search.return_value = [{"content": content}]
+        MockKnowledge.add_sources.return_value = [string_source]
 
         agent = Agent(
             role="Information Agent",
@@ -1644,7 +1646,6 @@ def test_agent_with_knowledge_sources():
             knowledge_sources=[string_source],
         )
 
-        # Create a task that requires the agent to use the knowledge
         task = Task(
             description="What is Brandon's favorite color?",
             expected_output="Brandon's favorite color.",
@@ -1652,10 +1653,11 @@ def test_agent_with_knowledge_sources():
         )
 
         crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        # Assert that the agent provides the correct information
-        assert "red" in result.raw.lower()
+        with patch.object(Knowledge, "add_sources") as mock_add_sources:
+            result = crew.kickoff()
+            assert mock_add_sources.called, "add_sources() should have been called"
+            mock_add_sources.assert_called_once()
+            assert "red" in result.raw.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -2036,7 +2038,7 @@ def mock_get_auth_token():
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
-    from crewai_tools import SerperDevTool
+    from crewai_tools import SerperDevTool, XMLSearchTool
 
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200
@@ -2044,7 +2046,10 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": [{"name": "SerperDevTool"}],
+        "tools": [
+            {"module": "crewai_tools", "name": "SerperDevTool"},
+            {"module": "crewai_tools", "name": "XMLSearchTool"},
+        ],
     }
     mock_get_agent.return_value = mock_get_response
     agent = Agent(from_repository="test_agent")
@@ -2052,8 +2057,9 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
     assert agent.role == "test role"
     assert agent.goal == "test goal"
     assert agent.backstory == "test backstory"
-    assert len(agent.tools) == 1
+    assert len(agent.tools) == 2
     assert isinstance(agent.tools[0], SerperDevTool)
+    assert isinstance(agent.tools[1], XMLSearchTool)
 
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
@@ -2066,7 +2072,7 @@ def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": [{"name": "SerperDevTool"}],
+        "tools": [{"name": "SerperDevTool", "module": "crewai_tools"}],
     }
     mock_get_agent.return_value = mock_get_response
     agent = Agent(from_repository="test_agent", role="Custom Role")
@@ -2086,7 +2092,7 @@ def test_agent_from_repository_with_invalid_tools(mock_get_agent, mock_get_auth_
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": [{"name": "DoesNotExist"}],
+        "tools": [{"name": "DoesNotExist", "module": "crewai_tools",}],
     }
     mock_get_agent.return_value = mock_get_response
     with pytest.raises(