6 Best Cognee Alternatives for Agent Memory and Graph RAG in 2026

The best Cognee alternative for teams whose agents write memory as they work, and need conflicting facts resolved and every answer traced to its source, is Caura. It ships contradiction detection, supersession and an audit trail in its Apache 2.0 open-source release. As of 30 September 2026, Cognee’s pricing page, bi-temporal memory, conflict resolution and provenance are listed only on the Enterprise BYOC plan.

If what you need is a lighter graph over documents, LightRAG is the closest replacement for Cognee’s core job and appears in none of the ranking roundups. Graphiti, Hindsight, Mem0 and Supermemory cover narrower reasons for leaving, below.

Cognee alternatives covered:

• Caura: governed shared memory with contradiction handling and provenance in the open-source engine.
• LightRAG: an MIT graph RAG framework for documents, with Postgres as an all-in-one store.
• Graphiti: a temporal knowledge graph with custom entity types.
• Hindsight: a single-container memory engine with multi-strategy retrieval.
• Mem0: a simple memory API for per-user personalization.
• Supermemory: managed memory plus document retrieval with built-in connectors.

Why do teams choose Cognee in the first place?

Cognee turns what an organization has already written into a graph agents can reason over. Documents, code, tickets and conversations go in through remember. Cognee extracts entities and relationships, stores them across a graph, a vector index and a relational store, and answers through recall, with improve to enrich the graph and forget to remove data. Text ingestion and retrieval work even without an LLM configured.

It runs locally with embedded defaults, which makes a first graph possible on a laptop. It has integrations for Claude Code, Codex, MCP and OpenClaw, and a managed Standard plan adds Slack, Notion, Linear and Google Drive connectors. The topoteretes/cognee repository has 31.2k GitHub stars and 10,979 commits as of 30 September 2026.

For grounding agents in a corpus, that is a strong design. The reasons people look for Cognee alternatives usually start when agents begin writing memory themselves, or when a graph pipeline has to run in production.

Why are Cognee users looking for alternatives?

Five problems show up in Cognee’s pricing, releases and issue tracker.

1. Conflict resolution and provenance are Enterprise features

The capabilities that make shared memory trustworthy are listed only on the Enterprise plan.

Cognee’s pricing page lists “bi-temporal memory and conflict resolution”, “provenance on every answer” and “personalization per user and agent” under Enterprise, which is delivered as a fixed-scope BYOC engagement running a proprietary Cognee runtime. The Free and Standard plans list graph memory and connectors. For a documentation assistant that reads a stable corpus, that may not matter. For agents that learn a price on Monday and a new price on Thursday, conflict resolution is the difference between one answer and two.

2. Every token you process is an LLM cost

Building a graph means asking a model to extract entities and relations from everything you ingest. Cognee’s README notes that the default setup uses OpenAI for both the LLM and embeddings, and that processing makes provider calls. The managed Standard plan bills $1.00 per million tokens processed, plus $5 per additional workspace. Cognee has treated this as a known cost: a 2026 hackathon issue targeted optimizing LLM API call usage in cognify through concurrency, batching, and prompt caching. Budget ingestion by corpus size, then again for every re-ingest.

3. The deployment footprint is heavy

Size, release pace, and gating all land on the team that runs Cognee.

In issue #3691, the cognee-mcp 1.2.2 Docker image measured 27.8 GB. About 9 GB came from a duplicated layer, and about 6.6 GB was a CUDA and torch stack that an API-only deployment never uses. Image size slows every deploy, every autoscale event, and every CI run.

4. The API moves fast

Cognee’s 2026 release stream continued through v1.6.1 on 24 September 2026, and its 1.x line renamed the core operations to remember, recall, improve, and forget. Fast releases mean fast fixes, and they also mean pinning versions, reading changelogs, and retesting pipelines on a schedule your team has to own.

5. It is built for documents more than for conversations

Cognee’s own published benchmarks measure multi-hop document question answering on HotPotQA, TwoWikiMultiHop and MuSiQue. It does not publish a LongMemEval or LoCoMo score, the two benchmarks that test whether an assistant remembers a user across long conversations. Several comparisons also note that per-user preference tracking is where Cognee is thinnest, which matches the Enterprise placement of “personalization per user and agent”.

What do Cognee alternatives roundups leave out?

We read the four pages currently ranking for “Cognee alternatives” and counted the tools named and the problems covered.

Mem0, Zep, and Letta appear in all four. LightRAG appears in none.

Across 6,527 words, the roundups mostly recommend conversation-memory layers. That is a sensible answer for teams leaving Cognee because of weak personalization. It is the wrong answer for teams that chose Cognee to build a graph from documents, because Mem0 and Letta do not do that job. LightRAG, a graph RAG framework with 39.9k GitHub stars as of 30 September 2026 and the most direct peer for Cognee’s document pipeline, is never mentioned. None of the four examines which Cognee features sit on which plan.

Where is Cognee actually strong, and what fully replaces it there?

Cognee does two jobs. Which one you use decides your shortlist.

Building a graph from documents and code

This is Cognee’s core. LightRAG is the closest complete replacement: it extracts entities and relations from documents, supports incremental updates, and stores everything in PostgreSQL, Neo4j, MongoDB or OpenSearch under an MIT license. Graphiti also builds graphs from business data and adds custom entity types, at the cost of operating a graph database.

Memory that agents write as they work

When agents record decisions, outcomes, and changed facts, you need contradiction handling and provenance by default. Caura includes both in its open-source engine: conflicting facts are detected, and the stale one is superseded, and every write carries the agent that wrote it. As-Of Recall answers questions about what was true on a past date, which covers the bi-temporal job on Cognee’s Enterprise list.

Connectors into company tools

If Slack, Notion, and Drive connectors were the draw, Supermemory’s managed plans include Gmail, S3, and web crawling, and Cognee’s own Standard plan remains a reasonable place to stay.

How do the best Cognee alternatives compare?

The table scores each of the six Cognee alternatives on the reasons teams leave.

1. Caura: memory that agents write, with conflicts and provenance handled

Repository: github.com/caura-ai/caura

Caura is the Cognee alternative for teams whose knowledge comes from agents working, more than from documents sitting in a drive.

An agent writes plain text with caura_write. One LLM pass classifies it, extracts entities into a knowledge graph, scans for PII, checks it against existing memories for contradictions and stamps its scope. Recall blends vector search, keyword search and graph hops.

How Caura answers each reason teams leave Cognee

Conflict resolution in the open-source engine: Contradiction detection and supersession ship in the open-source release, with no Enterprise tier required. The caura-long-run-fleet reference shows it over 14 simulated days: a competitor price holds at $299, changes to $349 on day 9, and eight stale memories are superseded so the next brief recalls one number.

Provenance on every memory: Each memory records which agent wrote it and when the fact took effect, and every write, update and delete is audit-logged. Agents carry trust levels that decide what they may read, write or delete.

One enrichment pass per write: Caura calls the LLM once per memory, and enrichment is included on every plan, including Free. The memory crystallizer merges small memories about the same entity into denser ones in the background.

A standard stack: The engine runs on PostgreSQL with pgvector and Redis through docker compose up, and a local embedder profile runs air-gapped.

Knowledge that outlives one agent: In Caura’s read-only analysis of an eToro deployment snapshot dated 25 August 2026, Caura’s Interviewer which writes memories from agent transcripts on a schedule, recovered 48% of decisions and 66% of preferences in that snapshot.

Caura benchmark results and production proof

Caura scores 92.2% on LongMemEval, 461 of 500 questions under the GPT-4o reference judge, with 97.4% on knowledge-update questions and a 22.4k-token median context. In a separate warm, single-tenant benchmark, search ran at 23 ms p50. The evaluation code is public in caura-longmemeval.

eToro’s Company Brain runs 300+ agents on Caura with 26,500+ memories and 1,372 shared skills.

As of 30 September 2026, Pro is $49 a month for unlimited agents on the pricing page.

Where Caura is weaker: it does not ship Slack, Notion or Drive connectors, and it is not tuned for bulk document ingestion. Its community is smaller than Cognee’s.

Best for: agents that learn as they work and share what they learn, where a wrong or stale fact has a cost.

2. LightRAG: graph RAG over documents, lighter than Cognee

Repository: github.com/HKUDS/LightRAG

LightRAG is the Cognee alternative that keeps Cognee’s core job and drops most of the platform around it. It extracts an entity-relation graph from documents, retrieves with both graph and vector signals, and updates incrementally when documents change. PostgreSQL can serve as its all-in-one store, and it also supports Neo4j, MongoDB and OpenSearch. It is a RAG framework, so it has no user profiles, sessions or agent memory writes.

Best for: teams that used Cognee to answer from documents and want a simpler, MIT-licensed graph pipeline.

3. Graphiti: a temporal graph with custom entity types

Repository: github.com/getzep/graphiti

Graphiti, the engine behind Zep, is the open-source way to get what Cognee lists as Enterprise: bi-temporal facts. Every edge carries when it became true and when it stopped, and you can define your own entity and edge types. It needs Neo4j, FalkorDB or Neptune, and makes several LLM calls per episode.

Best for: teams with graph database experience who need facts tracked over time.

4. Hindsight: one-container memory with multi-strategy retrieval

Repository: github.com/vectorize-io/hindsight

Hindsight answers Cognee’s footprint problem: one Docker command with embedded Postgres. It combines semantic, keyword, graph and temporal retrieval per query, and its reflect operation builds observations and mental models from stored facts. Its README states its LongMemEval results were independently reproduced by Virginia Tech’s Sanghani Center.

Best for: teams that want graph-aware agent memory without operating a pipeline.

5. Mem0: per-user personalization

Repository: github.com/mem0ai/mem0

Mem0 covers the job Cognee does least well: remembering a user’s preferences across sessions. Two calls, add() and search(), and the largest community in the category. It does not build graphs from documents, and its v3 algorithm keeps both versions of a changed fact for your code to resolve.

Best for: assistants whose memory is about people, not documents.

6. Supermemory: managed memory and document retrieval

Repository: github.com/supermemoryai/supermemory

Supermemory puts a memory graph per user next to document retrieval and connectors for Gmail, S3, and web crawling, all behind one managed API. Its SDKs and plugins are open source, while the self-hosted engine is a compiled binary.

Best for: small teams that want documents and memory managed for them.

How do you choose the right Cognee alternative?

Start from the reason you are leaving Cognee, then answer four questions before you rebuild a pipeline.

Where does your knowledge come from?

If it lives in documents, stay with a graph RAG tool: LightRAG or Graphiti. If agents create it while they work, choose a memory layer that handles conflicts: Caura. If it is about individual users, Mem0.

Do facts change after you store them?

Store a fact, store its replacement, and ask for the current value. Caura and Graphiti resolve it. Document graphs usually return both unless you re-ingest.

What does ingestion cost at your corpus size?

Estimate tokens in your corpus, multiply by the extraction passes each tool makes, and add every re-ingest. Caura’s analysis of the token tax in multi-agent systems shows how quickly repeated work dominates that bill.

Who needs to trust the answer?

If a compliance team asks where an answer came from, you need provenance per fact. Caura records it on every write; confirm what your Cognee plan includes before you rely on it.

Which Cognee alternative should you pick?

Pick Caura if your agents write memory as they work, if conflicting facts must resolve to one answer, or if you need provenance and audit without an Enterprise engagement. It is the only option here that ships contradiction handling, per-write provenance and per-agent trust levels in the open-source engine, and it runs in production at 300+ agents.

Pick LightRAG if you used Cognee to answer from documents and want a lighter graph pipeline. Pick Graphiti for temporal facts you can model yourself, Hindsight for one-container agent memory, Mem0 for user personalization, and Supermemory for managed memory with connectors.

The quickest test is to point one agent at Caura’s free tier through its MCP server, write a price, write a changed price, and recall it. Then ask your current Cognee setup the same question. The two answers tell you whether conflict resolution is a feature you can wait for.

Frequently Asked Questions

Is Cognee free and open source?

Yes. Cognee is Apache 2.0 and can run locally for free. Its managed Free plan includes 1 million tokens and one workspace; Standard costs $1.00 per million tokens processed plus $5 per extra workspace, and Enterprise is a BYOC engagement.

Does Cognee handle conflicting facts?

Cognee’s pricing page lists bi-temporal memory and conflict resolution on its Enterprise plan. On other plans, test how your deployment handles a fact and its replacement before relying on it. Caura includes contradiction detection and supersession in its open-source engine.

What is the best open-source Cognee alternative?

For memory that agents write and share, Caura under Apache 2.0. For graphs built from documents, LightRAG under MIT. For temporal facts, Graphiti under Apache 2.0. For single-container agent memory, Hindsight under MIT.

Is LightRAG a good replacement for Cognee?

For document question answering, yes. LightRAG builds an entity-relation graph from documents, updates incrementally, and can use PostgreSQL as its only store. It does not provide user memory, sessions, or agent-written memory, so it replaces Cognee’s document pipeline and nothing else.

Why is the Cognee Docker image so large?

An issue on the cognee-mcp 1.2.2 image measured 27.8 GB, attributing about 9 GB to a duplicated layer from a recursive ownership change and about 6.6 GB to a CUDA and torch stack that API-only deployments do not use. Check the current image size before deploying to constrained environments.

Does Cognee publish LongMemEval results?

Cognee publishes benchmark results on multi-hop document QA datasets such as HotPotQA, TwoWikiMultiHop and MuSiQue, and a head-to-head against Mem0, Graphiti and LightRAG on HotPotQA. It does not publish a LongMemEval score, which is the standard test for long-term conversational memory.

Related reading: What Is Agent Fleet Memory? · Reflective Memory: The Interviewer · Why a Vector Database Is Not Enough