How is Cognee different from standard RAG?

Standard RAG splits documents into chunks and finds similar text using vector search — but it loses the relationships between facts. Cognee builds a knowledge graph that preserves entity relationships, temporal connections, and hierarchical structures. When you search, Cognee uses graph traversal combined with vector similarity and LLM reasoning, resulting in more accurate, context-aware answers that understand HOW facts relate to each other.

What search types are available?

Cognee supports four retrieval strategies: GRAPH_COMPLETION (default — combines vector search + graph traversal + LLM reasoning for context-aware answers), SUMMARIES (fast hierarchical overview search), INSIGHTS (structured entity relationships), and CHUNKS (pure vector similarity for raw text passages). Each strategy optimizes for different use cases.

Is Cognee open-source?

Yes! Cognee is fully open-source under the Apache 2.0 license. You can self-host the entire platform including the knowledge graph engine, vector database, and API server. A managed cloud version with API keys is also available for teams that prefer not to manage infrastructure.

Cognee MCP Server for AutoGen 4 tools — connect in under 2 minutes

Name: Cognee
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 4 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Cognee as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

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Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="cognee_agent",
            tools=tools,
            system_message=(
                "You help users with Cognee. "
                "4 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Cognee MCP Server

Connect your AI agent to Cognee — the open-source knowledge graph platform that transforms unstructured data into structured, searchable knowledge.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Cognee tools. Connect 4 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

What you can do

Add Data — Ingest raw text, documents, or structured data into named datasets. Cognee processes and stores the data for subsequent graph construction
Cognify — Transform ingested data into a structured knowledge graph by automatically extracting entities, relationships, and semantic connections
Search Knowledge — Query the knowledge graph using four retrieval strategies: graph-aware completion (LLM + graph traversal), summaries, structured insights, or raw vector similarity
Get Insights — Retrieve structured entity relationships showing how concepts connect across your knowledge base

Why Cognee over standard RAG?

Relationship-aware — understands HOW facts connect, not just what they say
Graph + Vector hybrid — combines graph traversal with semantic search for superior recall
Temporal awareness — tracks when facts were added and reason over time-based connections

The Cognee MCP Server exposes 4 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Cognee to AutoGen via MCP

Follow these steps to integrate the Cognee MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 4 tools from Cognee automatically

Why Use AutoGen with the Cognee MCP Server

AutoGen provides unique advantages when paired with Cognee through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Cognee tools to solve complex tasks

Role-based architecture lets you assign Cognee tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Cognee tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Cognee tool responses in an isolated environment

Cognee + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Cognee MCP Server delivers measurable value.

Collaborative analysis: one agent queries Cognee while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Cognee, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Cognee data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Cognee responses in a sandboxed execution environment

Cognee MCP Tools for AutoGen (4)

These 4 tools become available when you connect Cognee to AutoGen via MCP:

cognee_add_data

After ingestion, use the cognify tool to process the data into a structured knowledge graph with entities and relationships. Ingest text or documents into the Cognee knowledge base. This is the first step before building a knowledge graph

cognee_cognify

This step extracts entities, identifies relationships, generates embeddings, and creates the graph structure needed for intelligent search. Process ingested data into a structured knowledge graph. Extracts entities, relationships, and builds a searchable graph structure

cognee_get_insights

Useful for understanding relationships between topics, discovering hidden connections, and building comprehensive knowledge views. Retrieve structured entity relationships and insights from the knowledge graph

cognee_search

Search the knowledge graph using natural language. Returns context-aware answers using graph traversal and semantic search

Example Prompts for Cognee in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with Cognee immediately.

"Add this research data to my knowledge base: 'Transformer models were introduced by Vaswani et al. in 2017 in the paper Attention Is All You Need. They use self-attention mechanisms and have become the foundation for models like GPT, BERT, and T5.'"

"Process my data into a knowledge graph."

"What is the relationship between Transformers and GPT?"

Troubleshooting Cognee MCP Server with AutoGen

Common issues when connecting Cognee to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Cognee + AutoGen FAQ

Common questions about integrating Cognee MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Cognee tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.