Letta
A memory-first platform for agents that persist across sessions
4.0
Letta gives agents editable persistent memory, but memory quality, deletion, model data paths, and long-running costs need controlled testing.
FreemiumCodeProductivity
Cognee Review 2026: AI Memory, Knowledge Graphs, and Pricing
Turn documents, code, tables, and conversations into graph-based AI memory
Recently Updated
Who should use this?
Agents needing persistent relationship-aware context and Teams comparing graph and vector retrieval.
Who should avoid it?
Simple document search served by basic RAG, Mixed-permission corpora without access enforcement
What problem does it solve?
Cognee is an open-source AI memory engine that combines ingestion, knowledge graphs, embeddings, relational storage, session context, retrieval, and improvement operations.
Would I recommend it?
Cognee earns a controlled pilot for teams whose retrieval problem genuinely depends on relationships and persistent context. Begin with a small, permission-homogeneous corpus and ground truth. Expand only when its supported-answer rate beats a simpler baseline enough to justify graph governance and operational complexity.
Advisor score
8.2/10
Premium review framework
Cognee is an open-source AI memory engine that combines ingestion, knowledge graphs, embeddings, relational storage, session context, retrieval, and improvement operations.
Direct verdict
Cognee earns a controlled pilot for teams whose retrieval problem genuinely depends on relationships and persistent context. Begin with a small, permission-homogeneous corpus and ground truth. Expand only when its supported-answer rate beats a simpler baseline enough to justify graph governance and operational complexity.
What to verify
Ingest a 10,000-document corpus with duplicates, contradictions, changing policies, aliases, access groups, and deletion requests. Create 300 answerable and unanswerable questions before configuration. Measure answer precision, citation support, relationship accuracy, stale-memory rate, permission leakage, deletion completeness, ingest latency, query latency, and cost per supported answer against a vector-only baseline.
Personal Recommendation
Cognee earns a controlled pilot for teams whose retrieval problem genuinely depends on relationships and persistent context. Begin with a small, permission-homogeneous corpus and ground truth. Expand only when its supported-answer rate beats a simpler baseline enough to justify graph governance and operational complexity.
Try the recommendation
See whether Cognee belongs in your stack
Graph, vector, and relational memory
Overall Score
8.2/10
Research Based
- Last reviewed
- Sep 12, 2026
- Last updated
- Sep 12, 2026
Editorial Review Framework
How Cognee scores
Recently Updated
Who should use this?
Agents needing persistent relationship-aware context, Teams comparing graph and vector retrieval, Organizations wanting open-source or BYOC deployment.
Who should avoid it?
Simple document search served by basic RAG, Mixed-permission corpora without access enforcement
What problem does it solve?
Cognee is an open-source AI memory engine that combines ingestion, knowledge graphs, embeddings, relational storage, session context, retrieval, and improvement operations.
Would I recommend it?
Cognee earns a controlled pilot for teams whose retrieval problem genuinely depends on relationships and persistent context. Begin with a small, permission-homogeneous corpus and ground truth. Expand only when its supported-answer rate beats a simpler baseline enough to justify graph governance and operational complexity.
Overall Score
8.2
Ease of Use
7.8
AI Quality
8.0
Features
8.6
Speed
8.0
Integrations
8.2
Value for Money
8.0
Customer Support
7.6
Learning Curve
7.4
Recommended For
- Agents needing persistent relationship-aware context
- Teams comparing graph and vector retrieval
- Organizations wanting open-source or BYOC deployment
Not Recommended For
- Simple document search served by basic RAG
- Mixed-permission corpora without access enforcement
- High-stakes memory without ground-truth evaluation
Recommended Because…
Graph, vector, and relational memory
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Reusable trial worksheet
Test Cognee before you commit
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
0/7 checks complete
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Agents needing persistent relationship-aware context; Teams comparing graph and vector retrieval; Organizations wanting open-source or BYOC deployment
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Cognee Cloud lists Free at $0 with one workspace and 1 million included tokens, then Standard at $1 per 1 million processed tokens plus $5 per additional workspace. Enterprise is a custom BYOC engagement. The open-source engine can run locally, but databases, storage, embeddings, models, hardware, operations, and staff remain real costs. Reviewed September…
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.3/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: Codex, Claude Code, MCP, Slack, Notion, Google Drive
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Extracted relationships can be wrong; Complete cost exceeds token processing; Deletion and permissions span multiple stores
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Product interface evidence
Evaluation
Research-based
Price posture
From $0/month
Reviewed
2026-09-12
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Pricing
Freemium
Cognee Cloud lists Free at $0 with one workspace and 1 million included tokens, then Standard at $1 per 1 million processed tokens plus $5 per additional workspace. Enterprise is a custom BYOC engagement. The open-source engine can run locally, but databases, storage, embeddings, models, hardware, operations, and staff remain real costs. Reviewed September 12, 2026.
Free plan: Yes. Cloud includes one workspace and 1 million tokens; the open-source engine can be self-hosted.
Editorial freshness
Checked this month
Pricing and material product claims were checked September 12, 2026.
Pros & Cons
Pros
- Graph, vector, and relational memory
- Open-source and managed deployment paths
- Transparent entry-level token pricing
Cons
- Extracted relationships can be wrong
- Complete cost exceeds token processing
- Deletion and permissions span multiple stores
Best For
Agents needing persistent relationship-aware contextTeams comparing graph and vector retrievalOrganizations wanting open-source or BYOC deployment
Community evidence
How verified users put Cognee to work
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Key Features
- Knowledge graph memory
- Session memory
- Permanent memory
- Document and code ingestion
- Provenance
- MCP integrations
Integrations
- Codex
- Claude Code
- MCP
- Slack
- Notion
- Google Drive
FAQs
What is Cognee?
Cognee is an AI memory engine that turns documents and other data into graph, vector, and relational context for search and agents.
How much does Cognee cost?
Cloud has a free 1-million-token allowance; Standard lists $1 per million processed tokens plus $5 per added workspace, while Enterprise is custom.
Can Cognee be self-hosted?
Yes. Cognee is open source and supports local or buyer-controlled deployment, with infrastructure and operations then owned by the buyer.
Is a knowledge graph more accurate than vector search?
Not automatically. It can represent relationships and provenance, but extracted facts and edges still require corpus-specific evaluation against a simpler baseline.
Keep Deciding
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