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 OpenAI Agents SDK 4 tools — connect in under 2 minutes

Name: Cognee
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 4 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Cognee through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Cognee Assistant",
            instructions=(
                "You help users interact with Cognee. "
                "You have access to 4 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Cognee"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

The OpenAI Agents SDK auto-discovers all 4 tools from Cognee through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Cognee, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Cognee MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 4 tools from Cognee

Why Use OpenAI Agents SDK with the Cognee MCP Server

OpenAI Agents SDK provides unique advantages when paired with Cognee through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Cognee + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Cognee MCP Server delivers measurable value.

Automated workflows: build agents that query Cognee, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Cognee, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Cognee tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Cognee to resolve tickets, look up records, and update statuses without human intervention

Cognee MCP Tools for OpenAI Agents SDK (4)

These 4 tools become available when you connect Cognee to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

Common issues when connecting Cognee to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Cognee + OpenAI Agents SDK FAQ

Common questions about integrating Cognee MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.