cognee-integration-strands 0.1.1

A powerful integration between Strands and Cognee providing intelligent knowledge management and retrieval capabilities for AI agents

Cognee-Integration-Strands

A powerful integration between Cognee and Strands that provides intelligent knowledge management and retrieval capabilities for AI agents.

Note: This package requires Python 3.10+.

Overview

cognee-integration-strands combines Cognee's advanced knowledge storage and retrieval system with Strands' agent framework. This integration allows you to build AI agents that can efficiently store, search, and retrieve information from a persistent knowledge base using Cognee's graph-based storage.

Features

• Smart Knowledge Storage: Add and persist information using Cognee's advanced indexing.
• Semantic Search: Retrieve relevant information using natural language queries.
• Session Management: Support for user-specific data isolation using node_set.
• Strands Integration: Seamless integration with Strands' agent framework.
• Background Async Support: Automatically handles Cognee's async operations in a background thread, making it easy to use with Strands tools.

Installation

pip install cognee-integration-strands

Quick Start

import os
import asyncio
from cognee_integration_strands import get_sessionized_cognee_tools
from cognee_integration_strands.tools import run_cognee_task
from strands import Agent
from strands.models.openai import OpenAIModel
import cognee

# Configure your OpenAI model
model = OpenAIModel(
    client_args={"api_key": os.getenv("LLM_API_KEY")},
    model_id="gpt-4o",
)

# Get sessionized tools for a specific user
add_tool, search_tool = get_sessionized_cognee_tools("user-123")

# Create an agent with the tools
agent = Agent(
    model=model,
    tools=[add_tool, search_tool]
)

# Use the agent to store information
agent("Remember that we have signed a contract with Meditech Solutions for £1.2M.")

# Use the agent to retrieve information
response = agent("What is the value of the Meditech Solutions contract?")
print(response)

Available Tools

get_sessionized_cognee_tools(session_id: Optional[str] = None)

Returns cognee tools with optional user-specific sessionization.

Parameters:

• session_id (optional): User identifier for data isolation. If not provided, a random session ID is auto-generated.

Returns: (add_tool, search_tool) - A list of tools for storing and searching data.

Usage:

# With sessionization (recommended for multi-user apps)
add_tool, search_tool = get_sessionized_cognee_tools("user-123")

# Without explicit session (auto-generates session ID)
add_tool, search_tool = get_sessionized_cognee_tools()

Individual Tools

• add_tool: Stores information in the knowledge base. It handles the asynchronous addition and cognition process in the background.
• search_tool: Searches and retrieves previously stored information from the knowledge base.

Session Management

cognee-integration-strands supports user-specific sessions to isolate data between different users or contexts. This is achieved by passing a session_id to get_sessionized_cognee_tools, which ensures that all data stored via add_tool is associated with that specific user ID (via Cognee's node_set feature).

# User 1 Session
user1_tools = get_sessionized_cognee_tools("user-123")
agent1 = Agent(model=model, tools=user1_tools)

# User 2 Session
user2_tools = get_sessionized_cognee_tools("user-456")
agent2 = Agent(model=model, tools=user2_tools)

Configuration

Copy the .env.template file to .env and fill out the required API keys:

cp .env.template .env

Then edit the .env file and set your API keys:

LLM_API_KEY=your-openai-api-key-here
# Add other configuration as needed

Examples

Check out the examples/ directory for more comprehensive usage examples:

• examples/example.py: Demonstrates setting up Cognee, ingesting data, and using an agent to query that data.
• examples/session_example.py: Demonstrates advanced session handling with visualization.

Requirements

• Python 3.10+
• Cognee
• Strands Agents
• OpenAI API key (for the example model)