Cognee
Open-source memory platform giving AI agents graph-based, relationship-aware recall with citations.
If your agents keep re-solving tasks they already solved, Cognee attacks the right problem — relationship-aware memory with citations, not a pile of embeddings. The open-source package runs locally for free forever, and the Standard tier is honest about where costs land: $1.00 per 1M tokens processed plus $5 per additional workspace per month, not per seat. Watch the token meter if you ingest a lot. Compared with lighter chat-memory tools such as mem0 or Zep, Cognee earns its extra setup only when facts connect and rules matter — coding agents with project context, research agents that must trace provenance, vertical agents that cannot invent policy.
Verified 6d ago · liveness 82/100 · cite: rightaichoice.com/tools/cognee
- Solo developers building coding agents that keep project context across sessions
- Data and platform teams unifying GitHub, Slack, and Linear into one recallable company brain
- Product engineers shipping vertical agents that must follow domain rules and cite sources
- Enterprises that need memory deployed in their own cloud with SLAs
- Teams building a plain chatbot with no memory requirement
- Latency-critical streaming apps where a graph traversal adds too much time
- Non-technical users who won't work with a CLI, API, or self-hosted setup
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Cognee if you only need basic chat history for a single assistant — a plain vector store will be cheaper and simpler than running graph-based memory with ontologies and citations.
Workspaces past the first one cost $5 each per month on Standard, so a team that spins up a workspace per project pays per project.
Cognee fits solo developers and small teams on the free open-source package or Standard at $1.00 per 1M tokens with workspaces at $5 each per month, and mid-market or enterprise buyers who need in-cloud deployment, conflict resolution, and SLAs via a scoped BYOC engagement. Against lighter chat-memory tools like mem0 it costs more to run but replaces a separate graph and vector database; against building on a raw vector store it is the pricier option unless relationships and citations matter.
In short
Cognee — Open-source memory platform giving AI agents graph-based, relationship-aware recall with citations. Best for Solo developers building coding agents that keep project context across sessions, Data and platform teams unifying GitHub, Slack, and Linear into one recallable company brain, Product engineers shipping vertical agents that must follow domain rules and cite sources. Free to start; paid plans from $1/mo.
What's new in Cognee
Checked 6 days agoAcross the latest 5 updates: 1 feature update, 2 launches 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, alongside Narada AI, RELAI, and Headroom.
cognee 1.0: The Open-Source Memory Platform for AI Agents
Cognee 1.0 shipped as the first open-source memory platform built around a memory-native API — remember, recall, improve, forget — with full data ownership and deployment flexibility from managed cloud to edge.
Cognee Raises $7.5M Seed to Build Memory for AI Agents
Cognee raised a $7.5M seed led by Pebblebed to build the open-source memory layer for AI agents, backed by founders of OpenAI and Facebook AI Research.
Coming to the Edge: Introducing cognee-RS for Private, On-Device AI Memory
The cognee-RS Rust engine brought private, on-device AI memory to phones, wearables, and IoT for better latency, accuracy, and privacy.
Unlock Your LLM's Time Awareness: Introducing Temporal Cognification
Temporal cognification added time-awareness so LLMs can understand when events happened, how information evolved, and what context was relevant at any given moment.
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.
Average across the 6 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Memory-native API: remember, recall, improve, forget
- Graph-based persistent memory with relationship links between facts
- Hybrid retrieval with citations attached to every answer
- Self-improvement loop that updates recall weights from user feedback
- Temporal cognification for time-aware memory
- Custom ontologies that generate domain rules for agents
- Single Postgres backend — no separate graph or vector database
- MCP server so compatible agents read and write Cognee memory
- First-party integrations for Claude Code, Cursor, LangGraph, OpenClaw, Hermes
- Data connectors for GitHub, Slack, Linear, Google Drive, Notion, S3
- Code indexing for your repositories
- cognee-RS Rust engine for edge and on-device memory
- Distributed processing across parallel datasets
- Permissions control and multi-tenant role-based access
- Notebook and Graph Explorer UI for local and cloud memory
About Cognee
Cognee is an open-source memory platform that gives AI agents durable, relationship-aware recall so they stop forgetting between sessions. It runs on a memory-native API — remember, recall, improve, forget — that captures context from your connected tools and data, links facts into a knowledge graph, and returns answers with citations attached. Rather than storing a pile of embeddings, Cognee builds a graph with explicit relationships and lets you define custom ontologies that generate the domain rules your agents should follow. Hybrid retrieval keeps citations on every answer, and the improve command lets agents self-correct from real usage feedback so recall weights shift over time. A single Postgres backend means no separate graph and vector database to run, and a Rust engine (cognee-RS) supports edge and on-device deployment. The project reached 31.4k GitHub stars and shipped cognee 1.0 on 2026-06-26; Cognee Cloud runs on gpt-oss-120b. Adoption starts free with the open-source package, while Cognee Cloud prices per token processed plus a fixed per-workspace charge. It fits solo developers keeping project context in Claude Code or Cursor, data teams unifying GitHub, Slack, and Linear into one recallable company brain, and product engineers shipping vertical agents that must follow rules and cite sources. If all you need is basic chat history, a vector store will be cheaper and simpler.
Behind the Verdict
Cognee's pitch is that most agents do not have a retrieval problem, they have a memory problem: the answer exists somewhere in a doc, a ticket, and a Claude session, and nobody connects them. The product addresses this with a memory-native API — remember, recall, improve, forget — sitting on a knowledge graph where facts carry explicit relationships. That structure is the differentiator. A vector store returns whatever is semantically close; Cognee returns linked facts with citations attached, which is what makes the improve command meaningful, since feedback can shift recall weights rather than just re-rank a static index. The engineering choices matter for buyers running real workloads. A single Postgres backend means you are not operating a separate graph database and vector database side by side. Distributed processing across parallel datasets dropped large-batch runs from 8+ hours to roughly 45 minutes per the November 2025 release notes. Temporal cognification adds time-awareness so agents understand when events happened and what context was relevant then. Graph-aware embeddings, advanced node/edge weights, multi-tenant role-based access and dataset sharing, and the cognee-RS Rust engine for on-device memory round out a feature set that is broader than most memory startups ship. The honest weaknesses. Free covers 1 workspace and 1M tokens; every additional workspace is $5 per month and every token beyond the included allowance is billed at $1.00 per 1M tokens, so a team ingesting many repositories and Slack histories will see the meter move. Enterprise capabilities that vertical-agent builders actually want — bi-temporal memory and conflict resolution, provenance on every answer, personalization per user and agent — are gated behind a BYOC engagement with a fixed scope rather than self-serve. And graph traversal adds latency that a pure vector lookup does not, which matters if you are building a streaming UI. Where it fits: coding agents that should keep project context across sessions in Claude Code, Cursor, or any MCP client; a company brain that unifies GitHub, Slack, and Linear behind one recallable surface; vertical agents that must follow domain rules and cite sources, which is exactly the ontology use case. Where it does not: plain chatbots with no memory requirement, non-technical users who will not touch a CLI or self-hosted setup, and latency-critical streaming apps. Knowunity ran a POC on 40,000 students in two days, the University of Wyoming launched its first memory system within 30 days, and SlideSpeak added shared-context memory to slide creation — all reasonable signals that the graph approach pays off once relationships matter more than raw similarity.
Researching Cognee? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Cognee actually fits — and what changes day-one when you adopt it.
Install the open-source package with pip install cognee, connect it to Claude Code or Cursor through the MCP server, and let the agent remember project decisions with cognee.remember() and pull them back with cognee.recall() on the next session.
Outcome: Your agent keeps project context across sessions instead of re-solving tasks it already solved, with citations attached to the recalled facts and no new infrastructure to run.
Connect GitHub, Slack, and Linear to Cognee, add sources once, and expose them through one recallable memory layer that every agent in the company can search.
Outcome: Knowledge stops living in a doc, a ticket, and someone's session — colleagues and agents query one company brain instead of redoing work already done.
Deploy Cognee BYOC in your own cloud behind a customer-facing agent, define the ontology for your domain, and run cognee.search() so the agent retrieves cited facts and follows your rules.
Outcome: The agent stops inventing policy, answers with provenance attached, and improves from real usage feedback through the improve command.
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.
- Connect Slack, GitHub, and Linear so every agent can search what your company already knows.
Models Under the Hood
as of 2026-10-10
Limitations
- Free tier includes 1M tokens and 1 workspace with unlimited users and API calls; Standard bills $1.00 per 1M tokens processed plus $5 per additional workspace per month.
- Enterprise capabilities such as bi-temporal memory and conflict resolution, provenance on every answer, and per-user/agent personalization are delivered through a fixed-scope BYOC engagement deployed in your own VPC rather than self-serve.
- Cognee is open source, so the full memory engine can be run locally or on your own stack for free.
as of 2026-10-04
Verification history
We have re-verified Cognee 9 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
Showing the 6 most recent of 9 verification passes.
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 agent hacker running a first agent with memory locally — no card required, free forever.
What this tier adds
Starting tier: 1 workspace and 1M tokens included, unlimited users and API calls, plus Claude Code, Codex, and MCP integrations.
Standard
$1.00/1M tokens + $5 per additional workspace/mo
Ideal for
Small teams running memory in production who need data-source connectors and code indexing but not enterprise governance.
What this tier adds
Adds unlimited workspaces at $5 each per month, Slack, Notion, Linear, and Google Drive connectors, code indexing, and in-app support — billed at $1.00 per 1M tokens processed.
Enterprise
Custom
Ideal for
Enterprises and vertical-agent teams that need memory deployed in their own cloud with SLAs and per-user personalization.
What this tier adds
Adds bi-temporal memory and conflict resolution, provenance on every answer, personalization per user and agent, a dedicated Slack channel, a dedicated support engineer, and a support SLA with BYO cloud.
Startup
Custom (heavily discounted, pre–Series B)
Ideal for
Pre–Series B startups that want the same BYOC deployment at a fraction of the price over a 12-month engagement.
What this tier adds
Heavily discounted 12-month BYOC deployment with a proprietary Cognee runtime, ontology starter, Postgres adapter, domain pack with auto-generated schema, and initial validation.
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 fits solo developers and small teams on the free open-source package or Standard at $1.00 per 1M tokens with workspaces at $5 each per month, and mid-market or enterprise buyers who need in-cloud deployment, conflict resolution, and SLAs via a scoped BYOC engagement. Against lighter chat-memory tools like mem0 it costs more to run but replaces a separate graph and vector database; against building on a raw vector store it is the pricier option unless relationships and citations matter.
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 developers reach first value in about 5 minutes with pip install cognee connected to Claude Code, Cursor, or any MCP client. Teams connecting GitHub, Slack, or Linear into one memory layer should plan on roughly a day to add sources and verify retrieval. Product teams shipping customer-facing vertical agents into their own cloud should budget about a week for BYOC deployment with ontology
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: Cognee publishes a dedicated migration guide covering the move to graph-based memory with citations.
- →From Zep: Cognee publishes a dedicated migration guide for teams moving off Zep's memory layer.
- →From Graphiti: Cognee publishes a migration guide, and community reports describe multi-month production use after switching from Graphiti.
- →From Letta: Cognee publishes a migration guide for teams replacing Letta's agent memory with a memory-native API.
- ↗To a plain vector store: export your graph-derived memories and rebuild retrieval without relationship links or citations, accepting lower recall precision for simpler infrastructure.
- ↗To OpenClaw or Hermes: keep the same MCP-based agent clients but point them at a different memory backend if you no longer need ontologies.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Cognee”, and we withheld 6: 6 could not be judged, because “Cognee” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Cognee.
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 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.
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.
Alternatives to Cognee
View all
Popular in Agent Memory & Runtimes
Frequently Asked Questions
Used Cognee? Help shape our editorial sentiment research.