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The future of AI is autonomous agents that execute complex, multi-step tasks in specialized domains. But agents without memory are agents without context. They can’t learn from past interactions, can’t understand organizational nuances, and can’t improve over time. Cognee provides the memory layer that makes agentic AI actually work.

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The Problem: Agents That Forget

Consider an AI agent designed to automate legal contract review. Without persistent memory, every document is a blank slate:
  • The agent doesn’t remember that your company uses specific non-standard clauses
  • It can’t recall that the counterparty had issues with similar terms last quarter
  • It has no context about your organization’s risk tolerance or negotiation patterns

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Why Memory Matters for Agents and What Cognee Brings

Agentic AI systems need three capabilities that standard RAG cannot provide:

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1. Domain Understanding

The agent must understand how your enterprise works instead of only generic industry knowledge, in terms of your specific organizational structure, terminology, and processes.

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2. Personalization

Each user, client, or session can have tailored context. The agent adapts its responses based on individual preferences, history, and past interactions stored in memory.

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3. Dynamically Evolving Memory

As the agent operates, it should learn and improve. Patterns from successful task completions should inform future actions. Our memory layer provides: Structured Context for Reasoning Rather than raw text chunks, agents receive graph-structured knowledge that captures relationships, hierarchies, and domain logic. Continuous Learning Through memify(), feedback mechanism and many more advanced features, agents consolidate experiences into persistent memory, improving task execution over time. Advanced Retrieval Multiple search types—graph completion, semantic chunks, summaries—let agents retrieve exactly the context they need for each decision.

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Example: Contract Review Agent with Memory

Define tools that give your agent persistent memory: 
import cognee
from cognee.api.v1.search import SearchType

# Tool 1: Remember information
async def remember(text: str):
    """Store information in long-term memory."""
    await cognee.add(text)
    await cognee.cognify()
    return "Saved to memory"

# Tool 2: Recall information  
async def recall(query: str) -> str:
    """Search memory for relevant context."""
    results = await cognee.search(
        query_text=query,
        search_type=SearchType.GRAPH_COMPLETION
    )
    return results
Wire them into your agent: 
tools = [remember, recall]

agent = Agent(
    model="gpt-4o",
    system_prompt="You are a contract analyst. Use remember() to store important details and recall() to retrieve past context.",
    tools=tools
)
Now the agent has memory: 
# Session 1: Learn client preferences
agent.run("Remember: Acme Corp requires 30-day payment terms and California arbitration.")

# Session 2: Use memory for analysis
agent.run("Review this contract for Acme Corp: 60-day terms, New York jurisdiction.")
# Agent calls recall() → flags mismatches with stored preferences

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Integration with Agentic Frameworks

Cognee integrates with the frameworks you’re already using:
  • LangGraph, CrewAI, LlamaIndex, Agent Development Kit, etc.
  • Custom implementations: Direct SDK integration with any agent framework

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Next Steps

Learn more about Core Concepts or review Integrations for available options. If we don’t have your favorite agent framework yet, let us know by opening an issue on GitHub.