> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# CrewAI
> Add persistent memory to CrewAI agents with Cognee.
Give your [CrewAI](https://www.crewai.com/) agents a shared, persistent knowledge base powered by cognee. Agents store information with `add_tool` and retrieve it via natural language with `search_tool` — memory that survives across crews and runs.
## Why Use This Integration
* **Shared crew memory**: Multiple agents read and write the same knowledge graph
* **Semantic recall**: Retrieve information using natural language queries
* **Session isolation**: Multi-tenant support with per-user/session data separation
* **Drop-in tools**: Add `add_tool` and `search_tool` to any CrewAI agent
## Installation
```bash theme={null}
pip install cognee-integration-crewai
```
Requires Python 3.10+. Pins `cognee>=0.4.0`, `crewai>=1.5.0`, and `crewai-tools>=1.5.0`.
## Quick Start
Set your LLM key, then attach the tools to a CrewAI agent:
```bash theme={null}
export LLM_API_KEY="your-openai-api-key-here" # or OPENAI_API_KEY
```
```python theme={null}
import asyncio
from dotenv import load_dotenv
import cognee
from crewai import Agent
from cognee_integration_crewai import add_tool, search_tool
load_dotenv()
async def main():
# Optional: start from a clean knowledge base
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
agent = Agent(
role="Research Analyst",
goal="Find and analyze information using the knowledge base",
backstory="You are an expert analyst with access to a comprehensive knowledge base.",
tools=[add_tool, search_tool],
verbose=True,
)
# Store information
response = agent.kickoff(
"Remember that our company signed a contract with HealthBridge Systems "
"in the healthcare industry, starting Feb 2023, ending Jan 2026, worth £2.4M"
)
print(response.raw)
# Query the stored information
response = agent.kickoff("What contracts do we have in the healthcare industry?")
print(response.raw)
if __name__ == "__main__":
asyncio.run(main())
```
## Tools
| Tool | Signature | Description |
| ------------- | ----------------------------------------------------------- | ------------------------------------------------------------ |
| `add_tool` | `add_tool(data: str, node_set: Optional[List[str]] = None)` | Store information in the knowledge base for later retrieval |
| `search_tool` | `search_tool(query_text: str)` | Search and retrieve stored information with natural language |
`add_tool` queues writes and runs `cognee.cognify()` after each batch, so data is indexed before searches. Async operations have a default 120-second timeout.
## Session Management
Use `get_sessionized_cognee_tools(session_id)` to isolate data per user or session:
```python theme={null}
from cognee_integration_crewai import get_sessionized_cognee_tools
user1_add, user1_search = get_sessionized_cognee_tools("user-123")
user2_add, user2_search = get_sessionized_cognee_tools("user-456")
agent1 = Agent(
role="Assistant",
goal="Help user 1",
backstory="You are a helpful assistant.",
tools=[user1_add, user1_search],
)
```
If you omit `session_id`, a UUID-based session ID is generated automatically. Session isolation is implemented by injecting the session ID into the `node_set` of `add_tool`.
## How It Works
1. **Add Tool**: Stores data in cognee's knowledge graph with embeddings, then cognifies it
2. **Search Tool**: Retrieves relevant information via `cognee.search()`
3. **Auto-Processing**: cognee extracts entities, relationships, and context automatically
4. **Background loop**: cognee's async API runs on a dedicated background event loop with safe concurrent writes
***
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