Google ADK

Give your Google ADK agents cognee’s memory. Store structured knowledge, retrieve via natural language, and maintain context across sessions - all through two async tools.

Why Use This Integration

Advanced Memory Features: Structured knowledge graphs with embeddings for multi-hop reasoning
Cross-Session Persistence: Memory survives agent restarts and conversation threads
Semantic Recall: Natural language queries with graph traversal
Session Isolation: Multi-tenant support with per-user data boundaries
Native ADK Tools: Works as LongRunningFunctionTool—async-first, production-ready

Installation

pip install cognee-integration-google-adk

Quick Start

Before using the integration, configure your environment variables:

export GOOGLE_API_KEY="your-google-api-key-here"  # for Google ADK
export LLM_API_KEY="your-openai-api-key-here"     # for cognee

Add memory tools to your Google ADK agent:

import asyncio
from google.adk.agents import Agent
from google.adk.runners import InMemoryRunner
from cognee_integration_google_adk import add_tool, search_tool

async def main():
    # Create agent with memory
    agent = Agent(
        model="gemini-2.5-flash",
        name="assistant",
        description="An assistant with persistent memory",
        instruction="You are a helpful assistant with access to a knowledge base.",
        tools=[add_tool, search_tool],
    )

    runner = InMemoryRunner(agent=agent)

    # Store information
    await runner.run_debug(
        "Remember: Acme Corp, healthcare, $1.2M contract"
    )

    # Retrieve information
    events = await runner.run_debug(
        "What healthcare contracts do we have?"
    )

    for event in events:
        if event.is_final_response() and event.content:
            for part in event.content.parts:
                if part.text:
                    print(part.text)

asyncio.run(main())

Cross-Session Persistence

Memory persists across different agent instances:

from cognee_integration_google_adk import get_sessionized_cognee_tools

# Session 1: Store information
add_tool, search_tool = get_sessionized_cognee_tools("user-123")

agent_1 = Agent(
    model="gemini-2.5-flash",
    name="assistant",
    description="Assistant with memory",
    instruction="You are a helpful assistant.",
    tools=[add_tool, search_tool],
)
runner_1 = InMemoryRunner(agent=agent_1)
await runner_1.run_debug("I'm working on authentication")

# Session 2: Different instance, same memory
add_tool, search_tool = get_sessionized_cognee_tools("user-123")

agent_2 = Agent(
    model="gemini-2.5-flash",
    name="assistant",
    description="Assistant with memory",
    instruction="You are a helpful assistant.",
    tools=[add_tool, search_tool],
)
runner_2 = InMemoryRunner(agent=agent_2)
events = await runner_2.run_debug("What was I working on?")
# Returns: "authentication"

Custom Session Management

Control session isolation with custom session IDs:

# User-specific memory
add_tool, search_tool = get_sessionized_cognee_tools(session_id="user_123")

# Org-specific memory
add_tool, search_tool = get_sessionized_cognee_tools(session_id="org_acme")

# Generate unique session automatically
add_tool, search_tool = get_sessionized_cognee_tools()  # Uses UUID-based session ID

Sessionized tools scope data to their session ID. Use non-sessionized add_tool and search_tool to access data across all sessions.

How It Works

Add Tool: Stores data in cognee’s knowledge graph with embeddings
Search Tool: Retrieves relevant information via cognee’s recall pipeline
Auto-Processing: cognee extracts entities, relationships, and context automatically
Session Scoping: Sessionized tools filter data by session ID; non-sessionized tools access all data

Use Cases

Knowledge Accumulation

Build domain knowledge incrementally over multiple sessions:

# Pre-load documents into cognee
import cognee

for doc_path in document_paths:
    with open(doc_path, 'r') as f:
        content = f.read()
        await cognee.remember(content)

# Query across all documents
events = await runner.run_debug(
    "Find information about contract terms"
)

Context-Aware Assistance

Maintain user context across work sessions:

# Monday
await runner.run_debug("Debugging payment flow")

# Wednesday
events = await runner.run_debug("What was I debugging?")

Multi-Tenant Applications

Isolate data per user/organization while sharing global knowledge:

# Per-user isolation
add_tool, search_tool = get_sessionized_cognee_tools(session_id=user_id)

agent = Agent(
    model="gemini-2.5-flash",
    name="assistant",
    description="User assistant",
    instruction="You are a helpful assistant.",
    tools=[add_tool, search_tool]
)

Multi-Agent Workflows

Share knowledge between specialized agents:

# Data collector agent
data_agent = Agent(
    model="gemini-2.5-flash",
    name="data_collector",
    description="Collects and stores information",
    instruction="You collect and store important information.",
    tools=[add_tool]
)

# Research agent
research_agent = Agent(
    model="gemini-2.5-flash",
    name="researcher",
    description="Searches stored information",
    instruction="You search and analyze the knowledge base.",
    tools=[search_tool]
)

GitHub Repository

View source code and examples

Examples

Runnable example scripts

Overview

Cloud LLM Providers

Observability

Evaluation

Data Ingestion

Agent Frameworks

Coding Agents

No-Code & Low-Code

Why Use This Integration

Installation

Quick Start

Cross-Session Persistence

Custom Session Management

How It Works

Use Cases

GitHub Repository

Examples

​Why Use This Integration

​Installation

​Quick Start

​Cross-Session Persistence

​Custom Session Management

​How It Works

​Use Cases

GitHub Repository

Examples

Why Use This Integration

Installation

Quick Start

Cross-Session Persistence

Custom Session Management

How It Works

Use Cases