AI Memory & Search🔴Developer

Cognee

Name: Cognee
Brand: Cognee
Availability: InStock

Open-source framework that builds knowledge graphs from your data so AI systems can analyze and reason over connected information rather than isolated text chunks.

Starting atFree

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In Plain English

Builds a knowledge graph from your data that AI can reason over — your AI understands relationships between concepts, not just keywords.

Overview

Cognee is an AI memory and search framework that builds knowledge graphs from unstructured data so LLM applications can reason over connected information instead of isolated chunks, with pricing starting free via the open-source library and a managed cloud tier available. It targets AI engineers and RAG developers building production systems that need structured, multi-hop reasoning beyond simple vector retrieval.

Founded in 2023 and open-sourced on GitHub, Cognee has grown to over 4,000 stars and is used by teams building agent memory, enterprise knowledge bases, and domain-specific RAG pipelines. The framework positions itself as the cognitive layer between raw data and LLM applications — processing documents, conversations, web pages, and API responses through a configurable pipeline of chunking, entity extraction, relationship identification, and graph construction. The output is a dual representation: a knowledge graph stored in Neo4j (or alternative graph backends) alongside vector embeddings in stores like Qdrant, LanceDB, or pgvector, giving you both relational traversal and semantic similarity from a single ingestion pass.

Cognee's pipeline-based architecture is its key differentiator. Processing steps are composable Python tasks — you can swap chunking strategies, plug in custom entity extractors, define domain-specific ontologies, and choose from 30+ supported LLM providers via LiteLLM integration. This modularity gives teams control over how knowledge is structured but means more configuration than turn-key solutions like Mem0 or hosted RAG APIs. The library ships with the cognee.add() and cognee.cognify() functions that get a basic graph running in under 10 lines of code, while advanced users can define custom DataPoint schemas and Pydantic models for structured extraction.

Based on our analysis of 870+ AI tools, Cognee sits in a niche between flat-vector RAG frameworks (LlamaIndex, LangChain) and conversational memory layers (Mem0, Zep). Compared to the other AI memory tools in our directory, Cognee uniquely emphasizes graph structure as a first-class citizen for retrieval — making it the strongest open-source option when your application's value depends on understanding relationships between entities rather than just finding similar text. The trade-off is operational complexity: you're running a graph database and tuning extraction quality, which is overkill for simple chatbot memory but essential for legal, medical, or compliance-heavy domains where multi-hop reasoning matters.

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Using with OpenClaw

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Integrate Cognee with OpenClaw through available APIs or create custom skills for specific workflows and automation tasks.

Use Case Example:

Extend OpenClaw's capabilities by connecting to Cognee for specialized functionality and data processing.

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Vibe Coding Friendly?

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Difficulty:intermediate

Requires Neo4j setup and Python pipeline configuration. Suitable for developers comfortable with graph databases.

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Editorial Review

Cognee brings knowledge graph construction to AI memory, using graph databases (Neo4j) to store relationships between entities rather than just vector similarity. This graph-based approach excels at complex reasoning over interconnected information. The pipeline for extracting and structuring knowledge from documents is well-designed. Still an early-stage project with a small community, limited documentation, and fewer production deployments. Best for teams that need relationship-aware memory and are comfortable with emerging tools.

Key Features

Cognify Pipeline for Graph Construction+

The core cognee.cognify() function processes raw text through chunking, entity extraction, relationship identification, and graph storage in a single call. Each stage is a composable Python task that can be swapped or extended, letting you customize behavior without rewriting the pipeline. This makes the simplest case (ingest a PDF and query it) trivially easy while keeping advanced customization within reach.

Dual Vector + Graph Storage+

Cognee stores every ingested entity in both a graph database (Neo4j, Kuzu, or NetworkX) and a vector store (Qdrant, LanceDB, pgvector, Weaviate, or Milvus). Retrieval can combine graph traversal for relational queries with vector similarity for semantic search, giving you flexibility to answer different question types from the same knowledge base. This dual representation is the key technical differentiator versus pure-vector RAG frameworks.

Custom Ontologies via Pydantic Models+

You can define domain-specific schemas as Pydantic DataPoint subclasses — for example, a Patient class with fields for diagnoses, medications, and providers. The pipeline uses these schemas to guide structured extraction from documents, producing typed entities rather than generic strings. This is critical for regulated domains where extracted data feeds downstream systems requiring strict typing and validation.

Multi-Provider LLM Support via LiteLLM+

Cognee integrates with 30+ LLM providers through LiteLLM, including OpenAI, Anthropic, Google, Azure, AWS Bedrock, Groq, Ollama, and self-hosted models. You can mix providers across the pipeline — for example, using a cheaper model for chunking and a stronger model for entity extraction. This flexibility avoids vendor lock-in and lets teams optimize cost vs quality per pipeline stage.

Cognee Cloud Managed Platform+

The hosted cloud tier provides a dashboard for graph exploration, pipeline monitoring, and data source management without requiring teams to operate Neo4j or vector infrastructure themselves. It includes visualization of the knowledge graph, ingestion job tracking, and team collaboration features. This bridges the gap for teams that want Cognee's capabilities without the DevOps burden of self-hosting the full stack.

Pricing Plans

Open Source

✓Full MIT-licensed framework on GitHub
✓Self-hosted on your own infrastructure
✓All graph and vector backend integrations
✓Custom ontologies and pipeline tasks
✓Community support via Discord and GitHub issues

Cloud

Contact for pricing

✓Managed Cognee infrastructure
✓Hosted graph and vector storage
✓Web dashboard for graph exploration
✓Pipeline monitoring and observability
✓Email and priority support

Enterprise

Custom

✓Dedicated deployment options
✓SSO and advanced access controls
✓SLA-backed uptime guarantees
✓Custom ontology consulting
✓Dedicated solutions engineering

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with Cognee

1Install Cognee via pip install cognee and set up a Neo4j database instance locally or in the cloud
2Configure your LLM provider credentials (OpenAI, Anthropic) in the Cognee environment settings
3Upload your first document set using cognee.add() and run cognee.cognify() to build the knowledge graph
4Query your knowledge graph using cognee.query() with natural language or traverse relationships with graph queries

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Best Use Cases

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Production RAG applications requiring multi-hop reasoning across thousands of interconnected documents, where vector similarity alone returns irrelevant chunks

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Enterprise knowledge management systems unifying PDFs, wikis, Slack exports, and API data into a single queryable graph

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Legal document analysis where case citations, regulatory cross-references, and party relationships must be preserved and traversed

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Medical and life-sciences knowledge systems connecting symptoms, treatments, drug interactions, and research papers with structured entity types

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Financial compliance applications tracking ownership chains, transaction relationships, and regulatory exposure across entities

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AI agent memory systems where long-running agents need structured recall of past tasks, learned facts, and entity relationships beyond flat conversation history

Integration Ecosystem

8 integrations

Cognee works with these platforms and services:

🧠 LLM Providers

OpenAIAnthropic

📊 Vector Databases

QdrantWeaviatepgvector

🗄️ Databases

PostgreSQL

⚡ Code Execution

Docker

🔗 Other

GitHub

View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Cognee doesn't handle well:

⚠Knowledge graph quality is highly dependent on input data quality and domain-specific extraction configuration — defaults work for general text but specialized domains need custom ontologies
⚠Graph database dependency (Neo4j or alternative) adds infrastructure complexity and operational overhead compared to vector-only approaches
⚠Entity extraction accuracy varies by domain and LLM choice — extraction costs can grow significantly for large corpora since every chunk requires LLM calls
⚠Incremental updates and graph consistency management require careful engineering for dynamic data sources, with no automatic cleanup of stale nodes
⚠Project is still pre-1.0 with breaking changes between minor versions — teams should pin versions and budget for periodic migration work

Pros & Cons

✓ Pros

✓Dual knowledge representation (graph + vectors) enables both relational traversal and semantic similarity from a single ingestion pipeline
✓Open-source MIT-licensed core with 4,000+ GitHub stars eliminates vendor lock-in and allows full self-hosting
✓Supports 30+ LLM providers via LiteLLM, plus multiple graph backends (Neo4j, Kuzu, NetworkX) and vector stores (Qdrant, LanceDB, pgvector, Weaviate)
✓Pipeline-based architecture with composable Python tasks gives engineers fine-grained control over chunking, extraction, and graph construction
✓Custom Pydantic ontologies allow domain-specific schemas — legal, medical, or financial entities can be extracted with structured types rather than generic NER
✓Get a working knowledge graph in under 10 lines of code with cognee.add() and cognee.cognify(), then progressively customize as needs grow

✗ Cons

✗Requires running a graph database (Neo4j or alternative) which adds infrastructure overhead vs vector-only stacks
✗Knowledge extraction quality depends heavily on input data and prompt tuning — specialized domains often need custom ontologies
✗Documentation and example coverage still catching up to the rapidly evolving codebase, with breaking changes between minor versions
✗Steeper learning curve for teams unfamiliar with graph query patterns or Cypher
✗Incremental updates and graph consistency for frequently changing source data require careful engineering — deletions in source documents don't automatically prune graph nodes

Frequently Asked Questions

How does Cognee compare to building a RAG system with just a vector database?+

Vector-only RAG retrieves text chunks by semantic similarity, which works well for direct lookup questions but struggles with multi-hop reasoning. Cognee adds structured relationships between entities, enabling queries like 'find all regulations affecting suppliers of company X' that require traversing connections. Based on our analysis of 870+ AI tools, this graph+vector hybrid approach is becoming the standard for enterprise RAG where questions span multiple documents. If your queries can be answered by finding similar text, a plain vector DB is simpler and cheaper; if they require understanding how entities connect, Cognee's overhead pays off.

Do I need Neo4j expertise to use Cognee?+

For basic use, no — Cognee abstracts graph construction behind high-level functions like cognee.cognify() and cognee.search(), so you can ingest data and query it without writing any Cypher. The framework also supports lighter alternatives like Kuzu (embedded) and NetworkX (in-memory) if you want to avoid running Neo4j entirely. For advanced custom queries, ontology design, or performance tuning at scale, graph database knowledge becomes valuable. Most teams start with the defaults and only learn Cypher when they hit specific retrieval requirements that the high-level API doesn't cover.

How does Cognee handle knowledge updates when source documents change?+

Cognee supports incremental ingestion where new or updated documents are reprocessed and added to the graph, with deduplication on entity IDs to merge mentions of the same concept across documents. However, true update semantics are imperfect: if information is removed from a source document, the corresponding graph nodes don't automatically disappear — you need to explicitly delete and re-ingest, or implement custom cleanup logic. For frequently changing data sources, teams typically version their datasets and rebuild graphs periodically rather than relying on continuous incremental updates.

Is Cognee suitable for production applications?+

The open-source library is used in production by multiple teams, particularly for agent memory systems and domain-specific RAG pipelines. The managed cloud platform adds a dashboard, hosted infrastructure, and monitoring for teams that don't want to operate Neo4j themselves. For mission-critical applications, you should benchmark extraction quality against your specific document types, define custom ontologies for your domain, and implement evaluation pipelines — Cognee is mature enough for production but young enough that you should plan for some integration work and occasional API changes between releases.

How does Cognee compare to Mem0 and other agent memory tools?+

Mem0 focuses on conversational memory for chatbots — remembering user preferences, facts, and past interactions across sessions with a simple key-value-like API. Cognee is broader and more structural: it builds full knowledge graphs from documents, conversations, and structured data, optimized for retrieval over large bodies of connected information rather than per-user chat memory. Compared to the other AI memory tools in our directory, choose Mem0 for lightweight chatbot personalization and Cognee when you need structured knowledge representation, multi-hop queries, or domain-specific ontologies. Many teams use both — Mem0 for user state, Cognee for the underlying knowledge base.

🔒 Security & Compliance

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SOC2

Unknown

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GDPR

Unknown

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HIPAA

Unknown

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SSO

Unknown

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Self-Hosted

Yes

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On-Prem

Yes

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RBAC

Unknown

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Audit Log

Unknown

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API Key Auth

Yes

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Open Source

Yes

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Encryption at Rest

Unknown

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Encryption in Transit

Yes

Data Retention: configurable

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What's New in 2026

Recent releases have expanded backend support to include Kuzu as an embedded graph database, added more vector store integrations (LanceDB, Milvus), and improved ontology-driven extraction with custom Pydantic DataPoint schemas. The managed Cognee Cloud platform has continued to mature with dashboard improvements for graph exploration and pipeline monitoring.

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