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

Name: Cognee
Availability: InStock
Author: Vinkius

cognee.ai

Built by Vinkius GDPR ToolsFree

Build knowledge graphs from unstructured data — ingest text, extract entities and relationships, and search with graph-aware AI reasoning.

Vinkius AI Gateway supports streamable HTTP and SSE.

Works with every AI agent you already use

…and any MCP-compatible client

Cognee MCP Server: see your AI Agent in action

AI Agent→Vinkius→Cognee

You

Vinkius AI Gateway

GDPR·High Security·Kill Switch·Ultra-Low Latency·Plug and Play

Built-in capabilities (4)

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

What this connector unlocks

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

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

How it works

1. Subscribe to this server
2. Enter your Cognee API key
3. Ingest data → Cognify → Search with graph-aware reasoning

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

Frequently asked questions

Give your AI agents the power of Cognee

Access Cognee and 2,000+ MCP servers — ready for your agents to use, right now. No glue code. No custom integrations. Just plug Vinkius AI Gateway and let your agents work.

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