Cognee
Open-source graph memory platform that gives AI agents persistent, relationship-aware recall
Cognee is the strongest open-source pick for developers who need persistent, relationship-aware agent memory without managing separate graph and vector databases. The single Postgres backend, MCP compatibility, and free self-hosted tier make adoption fast. But self-hosting demands DevOps chops, and usage-based pricing can creep up at scale—keep an eye on token volume. If you need simple chat history, mem0 or a vector store may be enough; if you need deep graph reasoning, Zep or Supermemory are paid, managed alternatives.
Verified 4d ago · liveness 82/100 · cite: rightaichoice.com/tools/cognee
- Solo developers building coding agents with persistent context
- Data and platform teams integrating memory into customer-facing agents
- Product engineers shipping vertical agents with domain-specific knowledge
- Researchers needing searchable second brains for notes and decisions
- Teams wanting a simple chatbot without memory needs
- Users who prefer fully managed SaaS without self-hosting option
- Projects requiring real-time streaming or very low latency
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Skip Cognee if you need a simple chatbot without memory needs, prefer a fully managed SaaS without self-hosting, require real-time streaming or very low latency (edge is emerging), or are non-technical and cannot work with CLI/API.
Going past 1M tokens on the free tier requires moving to Standard, which costs $2.50 per additional 1M tokens plus $5 per extra workspace per month.
Cognee's free tier (1M tokens, 1 workspace) is great for solo devs experimenting. At $2.50/1M tokens + $5/workspace, it's competitive with Zep and Supermemory for low to moderate volume, but high-volume production may be cheaper with a flat-rate vector store like Qdrant.
In short
Cognee — Open-source graph memory platform that gives AI agents persistent, relationship-aware recall. Best for Solo developers building coding agents with persistent context, Data and platform teams integrating memory into customer-facing agents, Product engineers shipping vertical agents with domain-specific knowledge. Free to start; paid plans from $2.501/mo.
What's new in Cognee
Checked 4 days agoAcross the latest 3 updates: 1 launch and 2 news mentions.
Cognee Joins UC Berkeley Xcelerator's 2026 Agentic AI Cohort
Cognee was selected for Berkeley RDI's Xcelerator 2026 Spring Cohort, a non-dilutive program for agentic AI startups.
Cognee 1.0: The Open-Source Memory Platform for AI Agents
Cognee 1.0 is the first open-source memory platform built around a memory-native API—remember, recall, improve, forget—with full data ownership and deployment flexibility.
Cognee Raises $7.5M Seed to Build Memory for AI Agents
Cognee raised $7.5M seed led by Pebblebed to build the open-source memory layer for AI agents.
What people actually say about Cognee — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
78 mentions across 6 sources (Hacker News, YouTube, Product Hunt, Bluesky, GitHub, Lemmy) · researched Jul 18, 2026.
- +Open-source with no vendor lock-in and full data ownership
- +Graph-based memory architecture beyond simple vector search
- +Single Postgres backend simplifies infrastructure requirements
- +Memory-native API with clear verbs: remember, recall, improve, forget
- +Self-improving feedback loop from real usage data
- −High latency: 30+ second query responses reported by users
- −Requires 2-3 LLM API calls per memory storage operation
- −Setup and integration complexity for non-experts
- −Small local LLMs can't reliably create knowledge graphs
- −626 open GitHub issues hint at ongoing instability
- • LLM API calls for every memory operation can dramatically raise total cost of ownership, especially at scale
- • Self-hosting requires maintaining a Postgres database and, for good results, a large LLM (14B+ parameters), which adds compute costs
Viability Score
How well maintained and how widely used is Cognee? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Graph-based persistent memory for AI agents
- Memory-native API: remember, recall, improve, forget
- Hybrid retrieval with evidence references
- Self-improving feedback loop from real usage
- Temporal cognification for time-aware memory
- Distributed processing for parallel datasets
- MCP server for universal agent compatibility
- Custom ontologies and data models
- Single Postgres backend (no separate graph/vector DB)
- Graph-aware embeddings for enhanced retrieval
- Cognify data from S3, Slack, Notion, Google Drive
- Rust-based edge deployment (cognee-RS)
- Multi-tenant with role-based access control
- Cloud runs on gpt-oss-120b
- Open-source self-hosting, free forever
About Cognee
Cognee is an open-source memory platform for AI agents, built around a memory-native API—remember, recall, improve, forget—that turns captured context into graph-based long-term memory. It's designed for developers, from solo coders to enterprise teams, who want agents that remember facts, domain rules, and decisions across sessions, instead of forgetting everything on each run. With a single Postgres backend, it eliminates the need for separate graph and vector databases, simplifying infrastructure while preserving relationships between facts for richer context. You can start locally with the free self-hosted version, then scale to Cognee Cloud when your agents are ready. Cognee works with any agent via MCP and offers first-party integrations for Claude Code, Cursor, LangGraph, and OpenClaw. Data adapters connect sources like Slack, Notion, Google Drive, and S3 into one recallable memory layer. Hybrid retrieval keeps citations attached to answers, and the improve command lets agents self-correct from real usage feedback. Recent milestones include Cognee 1.0 (June 2026), a Rust-based edge version (cognee-RS) for on-device memory, a $7.5M seed raise, and selection for UC Berkeley Xcelerator's 2026 Agentic AI cohort. Compared to simpler chat-memory tools like mem0, Cognee's graph architecture preserves relationships, making it suitable for complex agent workflows—but for basic chat history, a lightweight vector store may suffice.
Behind the Verdict
Cognee stands out in the agent-memory space because it's open source with a single Postgres backend, which simplifies deployment compared to solutions that require both graph and vector databases. The memory-native API (remember, recall, improve, forget) is clean and maps directly to how agents need to store and retrieve information. The MCP server ensures compatibility with a wide range of agents, and first-party integrations with Claude Code, Cursor, LangGraph, and OpenClaw mean you can get started quickly without custom glue. Strengths: Graph-based memory preserves relationships between facts, enabling more accurate and context-rich recall than simple vector stores. The improve command allows agents to self-correct based on real usage feedback, which can boost accuracy over time. Data adapters for Slack, Notion, Google Drive, and S3 make it easy to bring in existing sources. The open-source self-hosting option gives you full data ownership and the ability to run on your own infrastructure, which is important for privacy-sensitive use cases. Recent additions like temporal cognification, distributed processing, and graph-aware embeddings add significant depth. Weaknesses: The free tier includes only 1M tokens and 1 workspace, and the Standard plan costs $2.50 per 1M tokens plus $5 per additional workspace per month, which can add up for high-volume use. Self-hosting requires DevOps skills; you need to manage Postgres and the application. The Rust edge version (cognee-RS) is announced but may still be emerging, so real-time or low-latency on-device use cases are not fully mature yet. The cloud runs on gpt-oss-120b, which may not match the quality of frontier models. Where it fits: Solo developers building coding agents with persistent context, data/platform teams integrating memory into customer-facing agents, product engineers shipping vertical agents with domain-specific knowledge, researchers needing searchable second brains, and enterprises requiring self-hosted memory with governance. Where it doesn't: Teams wanting a simple chatbot without memory needs, users who prefer fully managed SaaS without self-hosting, projects requiring real-time streaming or very low latency, and non-technical users who cannot work with CLI/API.
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Real-world workflow fit
Concrete scenarios for the personas Cognee actually fits — and what changes day-one when you adopt it.
Install cognee, connect to Claude Code, remember project context and past fixes.
Outcome: Coding agent recalls prior decisions and fixes across sessions, reducing rework.
Connect Slack, Notion, and Google Drive as data sources, set up RBAC for multi-tenant access.
Outcome: Team agents retrieve cited answers from unified memory, with permissions enforced.
Define custom ontology for domain, cognify documents, deploy agent with MCP.
Outcome: Agent follows domain rules and improves from user feedback, launched in ~1 week.
Use Cases
- Give your coding agent persistent project context across sessions using Claude Code or Cursor.
- Build a searchable second brain by uploading notes, decisions, and personal documents.
- Create sales deal intelligence by connecting CRM data and letting agents query deals, ICPs, and accounts.
- Deploy customer-facing support agents that recall past interactions and domain knowledge.
- Enable research agents to process large document sets and retrieve cited facts.
- Run agent memory on-device for privacy-sensitive IoT, wearables, or offline robots.
Models Under the Hood
as of 2026-08-19
Limitations
- Free tier includes 1M tokens and 1 workspace; Standard plan charges $2.50 per 1M tokens plus $5 per additional workspace per month, which can add up for high-volume usage.
- The cloud runs on gpt-oss-120b, but open-source self-hosting is available for free.
- The Rust edge version (cognee-RS) is announced but may still be emerging.
as of 2026-08-19
Verification history
We have re-verified Cognee 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Cognee tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developer or hobbyist building a first coding agent with memory, wanting to test cognee with up to 1M tokens and unlimited users at no cost.
What this tier adds
Free entry point: includes 1 workspace, 1M tokens, unlimited users, unlimited API calls, and agentic integrations (Claude Code, Codex, MCP).
Standard
$2.50/1M tokens
Ideal for
Growing team or startup that needs more tokens and unlimited workspaces, plus data source integrations like Slack, Notion, and Google Drive, and in-app support.
What this tier adds
Adds unlimited workspaces ($5 each/month), more data source integrations, and in-app support over Free, at $2.50 per 1M tokens.
Enterprise
Contact us
Ideal for
Enterprise that requires dedicated support, SLAs, and the ability to bring your own cloud, with governance and compliance needs for production agent deployments.
What this tier adds
Adds dedicated Slack channel, dedicated support engineer, BYO cloud, and support SLA over Standard.
Where the pricing makes sense
The company stage and team size where Cognee's pricing actually pencils out — and where peers do it cheaper.
Cognee's free tier (1M tokens, 1 workspace) is great for solo devs experimenting. At $2.50/1M tokens + $5/workspace, it's competitive with Zep and Supermemory for low to moderate volume, but high-volume production may be cheaper with a flat-rate vector store like Qdrant.
Setup time & first value
How long it actually takes to get something useful out of Cognee — broken out by persona, not the marketing-page minute.
Solo devs: ~5 minutes to install and connect to Claude Code. Data/platform teams: ~1 day to connect data sources and configure permissions. Product engineers: ~1 week to ship a production-ready vertical agent.
Switching to or from Cognee
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From mem0: Re-import your conversation history or documents via cognee's adapters, then use cognee's graph memory for richer recall.
- →From Graphiti: Cognee provides a similar graph-based memory with a simpler Postgres backend, so you can migrate by re-cognifying your data sources.
- ↗To Zep: Export your memories via cognee's API and import into Zep's managed service if you prefer a fully managed solution.
- ↗To Supermemory: Similar to Zep, you can export your graph data and re-index in Supermemory's platform.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Cognee
Common stack mates teams adopt alongside Cognee, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Cognee vs Presto Voice
Presto Voice is the clear choice for QSR drive-thru automation—its specialized voice AI delivers measurable revenue lift and accuracy, with proven deployments at major chains. Cognee is ideal for developers needing persistent memory for AI agents, offering open-source flexibility and state-of-the-art retrieval. Choose based on your domain: restaurant operations vs. agent development.
Cognee vs Temporal Ai
If your priority is reliable agent execution that survives crashes and retries, Temporal AI is the obvious choice with its mature durable workflow engine and broad SDK support. If you instead need persistent graph memory so your agent remembers context across sessions (e.g. coding assistants), Cognee's new memory-native API and self-improving feedback loop are compelling. For many real-world AI agents, the best answer may be using both together: Temporal for orchestration reliability, Cognee for persistent recall.
Cognee vs Spider Cloud
If you need to fetch fresh web data for RAG or LLM agents, Spider Cloud is the clear choice with its cost-effective, high-success scraping API. If you need AI agents to remember past interactions and knowledge persistently across sessions, Cognee's graph memory platform is unmatched. Choose based on whether your pain point is data ingestion or memory retention.
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