104 lines
No EOL
3.9 KiB
Markdown
104 lines
No EOL
3.9 KiB
Markdown
# Vertical AI Agents
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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.
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Cognee provides the memory layer that makes agentic AI actually work.
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## The Problem: Agents That Forget
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Consider an AI agent designed to automate legal contract review. Without persistent memory, every document is a blank slate:
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* The agent doesn't remember that your company uses specific non-standard clauses
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* It can't recall that the counterparty had issues with similar terms last quarter
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* 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
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Agentic AI systems need three capabilities that standard RAG cannot provide:
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### 1. Domain Understanding
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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
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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
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As the agent operates, it should learn and improve. Patterns from successful task completions should inform future actions.
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Our memory layer provides:
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**Structured Context for Reasoning**
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Rather than raw text chunks, agents receive graph-structured knowledge that captures relationships, hierarchies, and domain logic.
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**Continuous Learning**
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Through `memify()`, feedback mechanism and many more advanced features, agents consolidate experiences into persistent memory, improving task execution over time.
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**Advanced Retrieval**
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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
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Define tools that give your agent persistent memory:
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```python theme={null}
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import cognee
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from cognee.api.v1.search import SearchType
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# Tool 1: Remember information
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async def remember(text: str):
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"""Store information in long-term memory."""
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await cognee.add(text)
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await cognee.cognify()
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return "Saved to memory"
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# Tool 2: Recall information
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async def recall(query: str) -> str:
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"""Search memory for relevant context."""
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results = await cognee.search(
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query_text=query,
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search_type=SearchType.GRAPH_COMPLETION
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)
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return results
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```
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Wire them into your agent:
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```python theme={null}
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tools = [remember, recall]
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agent = Agent(
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model="gpt-4o",
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system_prompt="You are a contract analyst. Use remember() to store important details and recall() to retrieve past context.",
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tools=tools
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)
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```
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Now the agent has memory:
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```python theme={null}
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# Session 1: Learn client preferences
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agent.run("Remember: Acme Corp requires 30-day payment terms and California arbitration.")
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# Session 2: Use memory for analysis
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agent.run("Review this contract for Acme Corp: 60-day terms, New York jurisdiction.")
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# Agent calls recall() → flags mismatches with stored preferences
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```
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## Integration with Agentic Frameworks
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Cognee integrates with the frameworks you're already using:
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* LangGraph, CrewAI, LlamaIndex, Agent Development Kit, etc.
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* **Custom implementations**: Direct SDK integration with any agent framework
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## Next Steps
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Learn more about [Core Concepts](/core-concepts/overview) or review [Integrations](/integrations) for available options. If we don't have your favorite agent framework yet, let us know by [opening an issue on GitHub](https://github.com/topoteretes/cognee/issues).
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---
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> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt |