Cognee-Integration-Claude

A powerful integration between Cognee and Claude Agent SDK that provides intelligent memory management and retrieval capabilities for AI agents.

Overview

cognee-integration-claude combines Cognee's advanced memory layer with Anthropic's Claude Agent SDK. This integration allows you to build AI agents that can efficiently store, search, and retrieve information from a persistent knowledge base.

Features

• Smart Knowledge Storage: Add and persist information using Cognee's advanced indexing
• Semantic Search: Retrieve relevant information using natural language queries
• Two memory tiers: a permanent knowledge graph plus a fast session cache you persist with improve()
• Claude Agent SDK Integration: Seamless integration with Claude's agent framework
• Async Support: Built with async/await for high-performance applications
• Cross-Session Persistence: Memory survives between agent instances

Upgrading from 0.1.x ⚠️

cognee-integration-claude 0.2.0 moves the integration to cognee v1.0 and replaces the old tool API. It's a breaking change with no compatibility shim — update your imports:

0.1.x	0.2.0
from cognee_integration_claude import add_tool, search_tool	from cognee_integration_claude import cognee_tools
tools=[add_tool, search_tool]	tools=cognee_tools()
get_sessionized_cognee_tools("user-1")	cognee_tools(session_id="user-1")
allowed_tools=["mcp__tools__add_tool", "mcp__tools__search_tool"]	allowed_tools=["mcp__tools__remember", "mcp__tools__recall"]
cognee>=0.3.4,<0.5.4	cognee>=1.0.0,<=1.1.2

In 0.1.x a session_id tagged data to isolate it per user. In 0.2.0 it routes writes to cognee's session cache; run cognee.improve(session_ids=[session_id]) to persist a session into the permanent graph (see Session Management).

Installation

pip install cognee-integration-claude

Or using uv:

uv add cognee-integration-claude

Quick Start

import asyncio
import os
from dotenv import load_dotenv
import cognee
from claude_agent_sdk import (
    create_sdk_mcp_server,
    ClaudeAgentOptions,
    ClaudeSDKClient,
    AssistantMessage,
    TextBlock,
)
from cognee_integration_claude import cognee_tools

load_dotenv()

async def main():
    # Clean up memory to start fresh (Optional)
    await cognee.forget(everything=True)
    
    # Create an MCP server with Cognee tools
    server = create_sdk_mcp_server(
        name="cognee-tools",
        version="1.0.0",
        tools=cognee_tools()
    )
    
    # Configure the agent
    options = ClaudeAgentOptions(
        mcp_servers={"tools": server},
        allowed_tools=["mcp__tools__remember", "mcp__tools__recall"],
    )
    
    # Use the agent to store information
    async with ClaudeSDKClient(options=options) as client:
        await client.query(
            "Remember that our company signed a contract with HealthBridge Systems "
            "in the healthcare industry, starting Feb 2023, ending Jan 2026, worth £2.4M"
        )
        
        async for msg in client.receive_response():
            if isinstance(msg, AssistantMessage):
                for block in msg.content:
                    if isinstance(block, TextBlock):
                        print(f"Claude: {block.text}")
    
    # Query the stored information (new agent instance)
    async with ClaudeSDKClient(options=options) as client:
        await client.query("What contracts do we have in the healthcare industry?")
        
        async for msg in client.receive_response():
            if isinstance(msg, AssistantMessage):
                for block in msg.content:
                    if isinstance(block, TextBlock):
                        print(f"Claude: {block.text}")

if __name__ == "__main__":
    asyncio.run(main())

Available Tools

Basic Tools

from cognee_integration_claude import cognee_tools

# cognee_tools() -> [remember_tool, recall_tool]
#   remember (mcp__<server>__remember): store information   (cognee.remember)
#   recall   (mcp__<server>__recall):   retrieve information (cognee.recall)

Session Tools

Pass a session_id to route writes to cognee's session cache instead of the permanent graph. Cached entries are persisted into the graph when you call cognee.improve(session_ids=[session_id]) — see Session Management.

from cognee_integration_claude import cognee_tools

# Writes go to the session cache (until improve())
tools = cognee_tools(session_id="mission-briefing")

# No session -> writes go straight to the permanent graph
tools = cognee_tools()

Session Management

A session_id selects cognee's session cache tier instead of the permanent graph:

• No session_id → remember writes straight to the permanent knowledge graph.
• With session_id → remember writes to that session's cache (cheap, no graph extraction); recall is session-aware.
• cognee.improve(session_ids=[session_id]) → promotes a session's cached entries into the permanent graph.

So an agent can capture context cheaply during a session, then persist the useful parts later. Pass remember_kwargs={"self_improvement": False} to keep cached writes out of the graph until you call improve() (otherwise cognee bridges them in the background).

import cognee
from cognee_integration_claude import cognee_tools

SESSION_ID = "mission-briefing"

# Session agent: writes land in the cache, not the graph (until improve()).
session_tools = cognee_tools(
    session_id=SESSION_ID,
    remember_kwargs={"self_improvement": False},
)
# ... drive an agent with session_tools to remember/recall during the session ...

# Persist everything captured in the session into the permanent graph:
await cognee.improve(session_ids=[SESSION_ID])

For a full runnable walkthrough — a permanent agent that can't see a session's cached data until improve() bridges it — see examples/session_memory.py.

Tool Reference

cognee_tools(session_id=None, *, remember_kwargs=None, recall_kwargs=None)

Builds the remember and recall MCP tools. Pass the result to create_sdk_mcp_server; the agent calls them as mcp__<server>__remember and mcp__<server>__recall. With session_id, writes go to cognee's session cache (persist later with cognee.improve(session_ids=[session_id])); without it, writes go straight to the permanent graph. remember_kwargs / recall_kwargs bind extra cognee params per call (e.g. remember_kwargs={"self_improvement": False}).

Returns: [remember_tool, recall_tool]

from cognee_integration_claude import cognee_tools

server = create_sdk_mcp_server(
    name="memory-tools",
    version="1.0.0",
    tools=cognee_tools(),                  # or cognee_tools(session_id="user-123")
)

options = ClaudeAgentOptions(
    mcp_servers={"tools": server},
    allowed_tools=["mcp__tools__remember", "mcp__tools__recall"],
)

async with ClaudeSDKClient(options=options) as client:
    await client.query("Store this: Our Q4 revenue was $2.5M with 15% growth")
    async for msg in client.receive_response():
        pass

remember(data, **kwargs)

Thin passthrough to cognee.remember, for pre-loading data directly (outside an agent). The integration imposes no defaults of its own — pass any keyword argument cognee.remember accepts (dataset_name, session_id, self_improvement, run_in_background, custom_prompt, node_set, importance_weight, …), and anything you omit falls back to cognee's own defaults. Returns cognee's RememberResult.

from cognee_integration_claude import remember

await remember("Einstein was born in Ulm.")                       # cognee defaults

recall(query_text, **kwargs)

Thin passthrough to cognee.recall. Again no integration defaults — pass any keyword argument cognee.recall accepts (query_type, datasets, top_k, session_id, node_name, scope, user, …). With no query_type, cognee auto-routes the search strategy. Returns cognee's native RecallResponse list; use render_results(...) to flatten it to plain strings.

import cognee
from cognee_integration_claude import recall, render_results

results = await recall("healthcare contracts", query_type=cognee.SearchType.GRAPH_COMPLETION, top_k=20)
texts = render_results(results)

render_results(results)

Flattens cognee's native RecallResponse list (what recall returns) into a list of plain strings, picking the right text field per result source.

from cognee_integration_claude import recall, render_results

texts = render_results(await recall("healthcare contracts"))

Configuration

Environment Variables

Create a .env file in your project root:

# OpenAI API key (used by Cognee for LLM operations)
LLM_API_KEY=your-openai-api-key-here

You can also use other LLM providers with Cognee. Check the Cognee documentation for more details.

The Claude Agent SDK uses a bundled Claude Code CLI that handles authentication automatically.

1. If you're using Cursor: You're likely already authenticated through your Cursor/Claude session. The bundled CLI will use your existing credentials.

2. If you're running standalone: The first time you run the SDK, it will guide you through authentication via OAuth or API key through the bundled CLI.

3. For CI/CD or automated environments: You may need to authenticate the CLI separately. See the Claude Agent SDK documentation for details.

Cognee Configuration (Optional)

You can customize Cognee's data and system directories:

from cognee.api.v1.config import config
import os

config.data_root_directory(
    os.path.join(os.path.dirname(__file__), ".cognee/data_storage")
)

config.system_root_directory(
    os.path.join(os.path.dirname(__file__), ".cognee/system")
)

Examples

Check out the examples/ directory for simple examples:

• examples/example.py: Basic usage with add and search tools

And the interactive guide:

• cognee_integration_claude/guide.ipynb: Step-by-step Jupyter notebook tutorial

Advanced Usage

Pre-loading Data

You can pre-load data into Cognee before creating agents:

import asyncio
import cognee
from claude_agent_sdk import (
    create_sdk_mcp_server,
    ClaudeAgentOptions,
    ClaudeSDKClient,
    AssistantMessage,
    TextBlock,
)
from cognee_integration_claude import cognee_tools

async def main():
    # Pre-load data directly into Cognee. cognee.remember extracts entities and
    # relationships and persists them — no separate cognify() step needed.
    await cognee.remember("Important company information here...")
    await cognee.remember("More data to remember...")
    
    # Now create an agent that can search this data
    server = create_sdk_mcp_server(
        name="cognee-tools",
        version="1.0.0",
        tools=cognee_tools()
    )
    
    # Allow only recall if you want a read-only agent
    options = ClaudeAgentOptions(
        mcp_servers={"tools": server},
        allowed_tools=["mcp__tools__recall"],
    )
    
    async with ClaudeSDKClient(options=options) as client:
        await client.query("What information do we have?")
        
        async for msg in client.receive_response():
            if isinstance(msg, AssistantMessage):
                for block in msg.content:
                    if isinstance(block, TextBlock):
                        print(block.text)

if __name__ == "__main__":
    asyncio.run(main())

Data Management

import asyncio
import cognee

async def reset_knowledge_base():
    """Clear all data and reset the knowledge base"""
    await cognee.forget(everything=True)

async def visualize_knowledge_graph():
    """Render the knowledge graph.

    Plain cognee.visualize_graph() can't see per-dataset graphs when cognee's
    access control is enabled (the default), so name the datasets explicitly.
    """
    from cognee.api.v1.visualize import visualize_multi_user_graph
    from cognee.modules.users.methods import get_default_user

    user = await get_default_user()
    pairs = [(user, ds) for ds in await cognee.datasets.list_datasets(user=user)]
    await visualize_multi_user_graph(pairs, destination_file_path="graph.html")

Disabling Default Cursor Tools

When using Claude Agent SDK in environments like Cursor, you may want to disable default tools:

options = ClaudeAgentOptions(
    mcp_servers={"tools": server},
    allowed_tools=["mcp__tools__remember", "mcp__tools__recall"],
    disallowed_tools=[
        "Task", "Bash", "Glob", "Grep", "ExitPlanMode",
        "Read", "Edit", "Write", "NotebookEdit", "WebFetch",
        "TodoWrite", "WebSearch", "BashOutput", "KillShell", "SlashCommand",
    ],
)

Requirements

• Python 3.13+
• OpenAI API key (or other LLM provider supported by Cognee)

Related Projects

• cognee - The core Cognee memory layer
• claude-agent-sdk - Claude Agent SDK

License

MIT License

Contributing

Contributions are welcome! Please feel free fork to submit a PR.