Cognee

What is Cognee?

Cognee is an open-source AI memory engine built for teams that have hit the limits of plain RAG. Instead of treating knowledge as disconnected chunks in a vector database, Cognee turns raw documents, tables, transcripts, and app data into a structured knowledge graph that AI agents can search, reason over, and improve over time. The company was founded in Berlin in 2024 by Vasilije Markovic and a team with backgrounds in big data, knowledge engineering, cognitive science, and clinical psychology. That background shows in the product. Cognee is not framed as a chatbot feature, it is framed as memory infrastructure.

We found that Cognee’s story starts with a simple problem: AI agents forget. Standard retrieval systems can pull similar text, but they struggle when answers depend on relationships across documents, time periods, or systems. Cognee’s answer is its ECL pipeline, Extract, Cognify, Load, which first pulls structure from messy data, then resolves entities and relationships into a canonical graph, then stores that knowledge across graph, vector, and relational layers. In practice, that means an agent can do more than retrieve a paragraph. It can connect entities, follow multi-step relationships, and explain why an answer was produced.

The company has moved quickly. Cognee went from an open-source project to production infrastructure running in more than 70 live environments, with over 12,000 GitHub stars and 80+ contributors. It raised a $7.5 million seed round led by Pebblebed, with backing from investors and angels tied to Google DeepMind, n8n, and Snowplow. The tool is used in places where memory quality matters, including scientific research workflows at Bayer, evidence graph work at the University of Wyoming, and agent systems that need persistent context across sessions.

Key Features

• ECL pipeline: Cognee’s Extract, Cognify, Load workflow is the heart of the product. It takes unstructured data, turns it into typed nodes and edges, resolves entities into a cleaner graph, and stores the results in the right backends for retrieval. This matters because teams do not have to hand-build a knowledge graph from scratch before getting value.

• Hybrid graph plus vector retrieval: Cognee uses vector search to find relevant candidates, then graph traversal to reason through relationships and provenance. In benchmarks, this hybrid setup reached about 93% correctness on complex queries, while standard RAG sat closer to 60%, and on HotpotQA it achieved 0.84 F1 versus 0.12 for base RAG. The practical difference is that answers involving multiple documents or hops are far less brittle.

• Ontology grounding: Teams can bring in RDF or OWL ontologies such as SNOMED CT for healthcare or FIBO for finance. Cognee uses them for canonicalization, fuzzy entity matching, and inherited relationship expansion, so “car manufacturer” and “automobile maker” do not become separate islands in the graph. For regulated domains, that extra structure is often the difference between a demo and something a team can trust.

• Custom Graph Models: Developers can define exactly which entities, properties, and relationships matter in their domain. Instead of asking an LLM to improvise a graph, a team can specify structures like Research, Methodology, Findings, or Customer, Ticket, Product. This keeps outputs more consistent and gives downstream workflows something stable to depend on.

• DataPoints data model: Cognee’s DataPoints let teams define entities and relationships in a way that feels closer to normal Python development, especially for teams already using Pydantic. A DataPoint instance becomes a node, and its fields define edges. That lowers the barrier for developers who want graph behavior without learning a whole new modeling system first.

• Feedback-driven memory optimization: Cognee can capture user reactions to answers, convert them into scores from -5 to +5, and tie those scores back to the graph elements used in the answer. Over time, stronger paths get reinforced and weaker ones are deprioritized, without deleting history. This matters because the memory layer can improve from real usage rather than staying frozen after ingestion.

• Multiple search and retrieval modes: Teams can use GraphCompletion for graph-aware answers, RAGCompletion for simpler retrieval, or direct Cypher queries for precise graph access. That flexibility helps when one team wants explainable agent reasoning and another wants raw query control. It also means Cognee can fit both application builders and data analysts.

• Broad data source support: Cognee supports 30+ data sources and can ingest PDFs, markdown, text, images, audio, CSVs, relational databases, and cloud storage. For enterprises with fragmented information, this matters more than a flashy demo. Memory infrastructure only works if it can meet the data where it already lives.

• Framework and MCP integrations: Cognee integrates with Claude Agent SDK through MCP, plus OpenAI’s Agent SDK, LangGraph, Google ADK, n8n, and dltHub. Through MCP, it exposes tools like add, search, cognify, prune, and status tracking. For teams already building agent systems, this shortens the path from experiment to persistent memory.

• Flexible deployment options: Teams can run Cognee in the cloud, self-host it with Docker, deploy on Modal, or experiment locally with lighter backends like SQLite and LanceDB. It also supports graph backends like Neo4j, Memgraph, Kuzu, and NetworkX, plus vector backends like Qdrant, LanceDB, and Chroma. That flexibility reduces the “rip and replace” problem that often blocks infrastructure adoption.

Use Cases

Cognee shows up most clearly in systems where memory is not optional. In customer support, teams can build agents that do not treat every ticket as a fresh start. A support agent backed by Cognee can connect current issues to prior tickets, product versions, customer history, and resolution patterns. The important shift is from retrieval of similar past tickets to reasoning over a structured record of customers, products, and recurring problems.

In research environments, Cognee has been used to build evidence graphs that connect papers, methods, findings, and domain concepts. The University of Wyoming is one named example from the research, and Bayer is cited for scientific research workflows. Those are good fits because research knowledge is spread across documents and often depends on relationships that do not live in a single paragraph. Cognee’s ontology support is especially relevant here, since scientific and medical teams often already work with formal vocabularies.

We also found strong signals around enterprise data unification. Cognee is built for organizations where CRM systems, support platforms, contracts, and operational data all describe the same entities in different ways. Instead of forcing a giant warehouse project first, teams can use Cognee as a semantic layer across 30+ sources. That lets an agent answer questions like customer health, risk exposure, or project status by stitching together relationships across systems.

Another notable direction is edge AI. Cognee-RS, the Rust SDK, is aimed at bringing semantic memory to phones, wearables, smart home hubs, and other resource-constrained devices. The stated goal is sub-100ms recall with full offline operation in some scenarios. That opens up privacy-sensitive use cases in healthcare and finance, where sending every interaction to the cloud is either too slow or not acceptable.

Strengths and Weaknesses

Strengths:

• Cognee is unusually strong on multi-hop reasoning. In the research we reviewed, it scored 0.84 F1 on HotpotQA-style tasks, compared with 0.54 for LightRAG, 0.41 for Graphiti, 0.31 for Mem0, and 0.12 for base RAG. If your agent needs to connect facts across documents instead of retrieving a single relevant chunk, this is where Cognee stands out.

• It treats graphs as the core memory structure, not a decorative add-on. A lot of competing tools start with vectors and bolt on graph features later. Cognee’s architecture goes the other direction, with vectors helping narrow the search space and graphs carrying the reasoning path. That design choice explains why it performs better on relational questions and why answers can be more traceable.

• Ontology support is a real differentiator for domain-heavy teams. Healthcare and finance examples come up repeatedly in the research because formal vocabularies already exist there. Teams that need consistency, inherited relationships, and alignment with enterprise standards will likely get more value from Cognee than from simpler memory stores.

• The open-source core matters. Cognee has over 12,000 GitHub stars, 80+ contributors, and has graduated from GitHub’s Secure Open Source Program. For infrastructure that may sit under sensitive enterprise agents, that transparency is not a minor detail.

• Deployment flexibility is stronger than many younger tools. Teams can start with SQLite and LanceDB locally, move to Neo4j and PostgreSQL in production, or use the managed cloud. That lowers the risk of trying it.

Weaknesses:

• Cognee asks more from the team than a lightweight vector memory product. If you just want to save snippets from conversations and retrieve them later, tools like Mem0 are simpler to understand and get running. Cognee pays off when relationships matter, but that also means more architecture decisions, more modeling, and more moving parts.

• It works best when the domain model is thought through. The research is pretty clear that curated ontologies and well-defined Custom Graph Models improve results. Teams without clear schemas or domain expertise can still start without ontologies, but they may not see the full benefit right away.

• Feedback-based optimization is promising, but it needs usage volume. In a small deployment with only a handful of users, the signal will be weak and the self-improving loop will move slowly. This is less of a problem for enterprise rollouts and more of a reality check for early pilots.

• Complex graph reasoning can be slower than plain vector lookup. Cognee uses vectors to speed up candidate retrieval, but if the question requires multi-hop traversal and explanation, latency will be higher than a simple nearest-neighbor search in Pinecone or Chroma. That is the tradeoff for better reasoning.

• The pricing can surprise teams processing large datasets with premium models. Cognee’s own calculator gives an example of about $429 one-time processing cost for 100MB of data with GPT-4 Mini and text-embedding-3-small, plus about $10 per month for storage on Modal. That is not outrageous for enterprise memory infrastructure, but it is far from “throw it in and forget it” pricing.

Pricing

• Free: $0 Built for developers exploring the platform with basic capabilities and community support. This is the natural place to test the workflow and understand whether your data benefits from graph-based memory.

• Developer: $35/month Aimed at individual developers using Cognee more seriously, with dedicated resources and priority support. For solo builders, this looks like the entry point where experimentation turns into actual product work.

• Cloud / Team: Custom Team pricing is available through Cognee’s website. This tier is for collaborative use and managed infrastructure, and actual spend will depend on data volume, model choices, and retrieval patterns.

• Enterprise / Self-hosted: Custom For organizations that need to run Cognee in their own environment, with full control over databases, security, and deployment. This is the likely route for regulated industries or companies with strict data governance rules.

The main pricing gotcha is that Cognee is not just a subscription decision, it is also an LLM and embeddings cost decision. The company’s cost calculator shows how quickly ingestion costs can climb depending on graph extraction model, embedding model, and dataset size. Compared with a plain vector database, the upfront processing bill can be higher. Compared with building a custom knowledge graph system in-house, it can still be dramatically cheaper.

Alternatives

Mem0 Mem0 is the simpler answer to AI memory. It focuses on lightweight, vector-based memory storage and is easier to adopt if your main goal is to remember user preferences, snippets from conversations, or short-term context. We would point visitors toward Mem0 when they want speed and simplicity, and toward Cognee when they need structured relationships, ontology support, or multi-hop reasoning.

Graphiti by Zep Graphiti sits closer to Cognee philosophically because it also mixes graph and vector ideas. The difference in the research we reviewed is emphasis. Graphiti appears to lean more on vectors, with graph features supporting the experience, while Cognee makes graph reasoning central. On benchmarked multi-hop tasks, Cognee came out ahead, so teams building serious reasoning-heavy agents may prefer it, while teams already in the Zep ecosystem may still find Graphiti a more natural fit.

LightRAG LightRAG is the middle-ground option for teams that want something beyond basic RAG without stepping all the way into a heavier memory infrastructure layer. It is lighter and easier to conceptualize, but the benchmark gap is notable, with Cognee at 0.84 F1 versus LightRAG at 0.54 on the cited multi-hop evaluation. We would consider LightRAG for smaller teams that want incremental improvement, and Cognee for teams that know relational reasoning is core to the product.

Traditional vector databases like Pinecone, Weaviate, or Milvus These tools are excellent at semantic similarity search, and for many search applications that is enough. But they do not model relationships the way Cognee does. If your use case is document retrieval, FAQ search, or semantic lookup, a vector database may be the cleaner and cheaper option. If your use case involves entities, evolving facts, and cross-document reasoning, Cognee is solving a different problem.

RAG frameworks like LangChain or LlamaIndex LangChain and LlamaIndex are not direct substitutes for Cognee, but many teams compare them because they sit in the same build stack. Those frameworks help orchestrate retrieval and generation workflows, while Cognee focuses on the memory layer underneath. In practice, some teams will use both, with LangChain or LlamaIndex handling agent orchestration and Cognee handling persistent structured memory.

FAQ

What is Cognee used for?

Cognee is used to give AI agents persistent, structured memory. Teams use it for support agents, research knowledge systems, enterprise data unification, and other cases where relationships between facts matter.

How is Cognee different from RAG?

Standard RAG retrieves similar chunks of text. Cognee builds a knowledge graph, combines graph and vector retrieval, and can reason across relationships instead of only returning semantically similar passages.

Is Cognee open source?

Yes. Cognee has an open-source core, an active GitHub community, and premium cloud and enterprise offerings around it.

Who built Cognee?

Cognee was founded in Berlin in 2024 by Vasilije Markovic and a team with backgrounds in big data, cognitive science, and knowledge engineering.

How do I get started?

The simplest path is to install the Python package with pip install cognee, add some sample data, run cognify(), and then test retrieval with search(). If you want managed infrastructure, you can also start through Cognee Cloud.

How long to set up?

A basic local test can be done in minutes. A production setup takes longer because you need to choose LLM providers, embeddings, and storage backends like PostgreSQL, Neo4j, or Qdrant.

Does Cognee require Neo4j?

No. Cognee supports multiple graph backends including Neo4j, Memgraph, Kuzu, and NetworkX, depending on your scale and deployment preferences.

Can Cognee work with my existing vector database?

Often, yes. It supports multiple vector backends including Qdrant, LanceDB, and Chroma, and the product is designed to fit into existing infrastructure rather than forcing a full replacement.

What kinds of data can it ingest?

Cognee supports PDFs, text, markdown, images, audio, CSVs, relational databases, cloud storage, and 30+ data sources. It is meant for messy enterprise reality, not just clean demo datasets.

Does Cognee support ontologies?

Yes. It can use RDF and OWL ontologies for entity resolution and graph enrichment. This is especially useful in domains like healthcare, finance, and research.

Is Cognee good for small projects?

It can be, but it is most compelling when your project actually needs structured memory and relationship reasoning. For simpler memory tasks, a lighter vector-based tool may be easier to manage.

How much does Cognee actually cost?

The subscription starts free, with a $35/month developer plan, but real cost depends heavily on data volume and model choices. In Cognee’s own example, processing 100MB with GPT-4 Mini and text-embedding-3-small was about $429 one time plus about $10/month for storage.