{ "data": { "a": { "slug": "cognee", "name": "Cognee", "vendor": "Cognee", "vendorUrl": "https://www.cognee.ai", "kind": "platform", "category": "agent-memory", "summary": "Open-source memory engine that turns documents, conversations and synced sources into a knowledge graph plus a vector index and answers queries over both.", "url": "https://www.anchorterminal.com/tools/cognee", "markdownUrl": "https://www.anchorterminal.com/tools/cognee.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cognee.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cognee.json", "repo": "https://github.com/topoteretes/cognee", "license": "Apache-2.0", "transports": [ "http", "stdio", "sse" ], "remoteUrl": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1", "packages": [ { "registry": "pypi", "name": "cognee" }, { "registry": "oci", "name": "cognee/cognee-mcp" } ], "auth": "mixed", "authNotes": "Cognee Cloud takes an `X-Api-Key` header on a per-tenant host, and keys can be rotated. A local Docker server runs without auth unless you turn it on, then takes a Bearer token. Since 1.6.0 (18 September 2026) the library builds and searches text memory with local models and no LLM key, and LLM-dependent stages skip when none is set. Set `LLM_API_KEY` for the full pipeline with a hosted model. Cognee MCP and Cognee Cloud are separate systems with different auth.", "pricing": "freemium", "pricingNotes": "Cognee Cloud Free is $0 with 1 million tokens included, 1 workspace, unlimited users and API calls, and no card. Standard is $1 per million tokens processed plus $5 a month for each extra workspace, and adds Slack, Notion, Linear and Google Drive sources. Enterprise is quoted, with bring-your-own-cloud (https://www.cognee.ai/pricing). The billing docs still show older plans (Hobby with 10 million tokens, Growth at $5 a tenant, Enterprise at $2,916 a month), which disagree with the pricing page. Credit is prepaid from $0.50, with optional auto-recharge (https://docs.cognee.ai/cognee-cloud/functionality/account-and-billing). The open-source library is free to run yourself.", "priceSummary": "$5 / mo", "where": "both", "x402": { "level": "no", "endpoints": [] }, "toolCount": 7, "popularity": { "githubStars": 31200, "npmWeekly": null, "pypiWeekly": 20830, "asOf": "2026-09-30" }, "docsUrl": "https://docs.cognee.ai", "llmsTxt": "https://docs.cognee.ai/llms.txt", "openapi": "https://docs.cognee.ai/cognee_openapi_spec.json", "capabilities": [ "memory.store", "memory.search", "memory.graph", "memory.delete" ], "tags": [ "open-source", "self-hosted", "hosted", "freemium", "free-tier", "no-card", "mcp", "llms-txt", "python", "eu" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 54.6, "grade": "C", "agentReady": false, "rank": 610, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 59, "maintenance": 82, "payments": 35, "reliability": 50, "schema": 85, "security": 39, "transparency": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -3, "negativeNotes": [ "2026-05-01: a commit titled as a fix removed 11 MCP tools, including cognify, search, delete and prune, with cognee-mcp at 0.5.4 before and after, and the README the day before listed them as \"still available\" with no deprecation note. The replacements remember, recall and forget had shipped on 10 April and the tools reference now lists what went, so we deduct at the low end (https://github.com/topoteretes/cognee/commit/b52fcc335f6bfc090d1c892afa9c4e81909336fe)" ], "verdict": "Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.", "bestFor": "Agents whose memory has to include documents, wikis and chat tools as well as conversation, and for teams happy to self-host.", "strengths": [ "Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0", "7-tool MCP server with search_tools and call_tool for reaching the rest on demand", "Public OpenAPI 3.1 file with 46 paths and error models", "Metered Cloud at $1 per million tokens, 1 million free with no card", "Eight stable PyPI releases from 15 August to 29 September 2026, with CI passing on main" ], "weaknesses": [ "No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region", "Cloud calls hung rather than failing when a tenant ran out of credit (August 2026), and the issue is still open", "Billing docs and pricing page disagree on plans and the free allowance", "Library telemetry is on by default and sends a persistent machine ID and an ID derived from the LLM key", "11 MCP tools were removed in May 2026 without a version bump or notice" ], "agentNotes": [ "Use remember, recall and forget. The older cognify, search and delete MCP tools are gone", "Always include /api/v1 in REST paths", "Check cognify_status before querying data you added with background=true", "Never pass everything=true to forget unless you mean to wipe all of the user's memory", "Set a client timeout on Cloud calls and treat HTTP 402 as an empty balance, since an empty balance has also shown up as hangs" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.6 } ], "editorialScores": { "ergonomics": 59, "maintenance": 82, "payments": 35, "reliability": 50, "schema": 85, "security": 39, "transparency": 69 }, "provenanceScore": 63 }, "connect": { "install": "pip install cognee", "http": "# COGNEE_URL is your tenant host, e.g. https://\u003ctenant\u003e.aws.cognee.ai\ncurl -X POST \"$COGNEE_URL/api/v1/search\" -H \"X-Api-Key: $COGNEE_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"query\":\"What does the user prefer?\"}'", "claudeCode": "docker run -d -e TRANSPORT_MODE=http -e LLM_API_KEY=$LLM_API_KEY -p 8000:8000 cognee/cognee-mcp:main\nclaude mcp add --transport http cognee http://localhost:8000/mcp", "config": { "mcpServers": { "cognee": { "args": [ "run", "-i", "--rm", "-e", "LLM_API_KEY", "cognee/cognee-mcp:main" ], "command": "docker", "env": { "LLM_API_KEY": "${LLM_API_KEY}" } } } } }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/cognee" }, "area": "agent-runtime", "unitPrices": [ { "item": "Standard token processing", "unit": "1m-tokens", "usd": 1, "note": "1 million tokens free" }, { "item": "Extra workspace", "unit": "month", "usd": 5 } ], "provenance": { "legalEntity": "Topoteretes UG (haftungsbeschränkt)", "domain": "cognee.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.cognee.ai/gtc-eu", "privacy": "https://www.cognee.ai/privacy-notice", "statusPage": "", "changelog": "https://github.com/topoteretes/cognee/releases", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The general terms, updated 27 March 2026, name Topoteretes UG (haftungsbeschränkt), Amtsgericht Charlottenburg HRB 252065 B, Paul-Lincke-Ufer 39-40, 10999 Berlin, under German law.", "www.cognee.ai/.well-known/security.txt returns 404, and we found no status page.", "The privacy notice, current version 20 September 2026, says Cognee Cloud is operated by Cognee Inc. and hosted in AWS us-east-1, while the general terms name Topoteretes UG in Berlin. SECURITY.md in the repository sends reports to security@cognee.ai." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cognee.json", "live": { "slug": "cognee", "probe": { "target": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1", "method": "get", "lastAt": "2026-10-09T11:46:25.657377009Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 259, "samples30d": 2109, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 272, "ok": 0 }, { "date": "2026-10-05", "probes": 272, "ok": 0 }, { "date": "2026-10-06", "probes": 272, "ok": 0 }, { "date": "2026-10-07", "probes": 272, "ok": 0 }, { "date": "2026-10-08", "probes": 268, "ok": 0 }, { "date": "2026-10-09", "probes": 125, "ok": 0 } ] }, "versions": [ { "registry": "github", "name": "topoteretes/cognee", "version": "v1.6.3", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:07.639395162Z" }, { "registry": "pypi", "name": "cognee", "version": "1.6.3", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:07.451622547Z" } ], "githubStars": 31698, "pypiWeekly": 25805, "securityTxt": { "url": "https://cognee.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:05.482170079Z" }, "llmsTxt": { "url": "https://docs.cognee.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:13.96500195Z" }, "domain": { "domain": "cognee.ai", "registered": "2023-12-21", "source": "https://rdap.identitydigital.services/rdap/domain/cognee.ai", "checkedAt": "2026-10-04T13:09:11.691283649Z" }, "pages": [ { "url": "https://www.cognee.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:27:07.745215006Z", "changedAt": "2026-10-08T18:27:07.745215006Z", "fingerprint": "9cd625b1b9b1" }, { "url": "https://www.cognee.ai/privacy-notice", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:27:09.740259646Z", "changedAt": "2026-10-08T18:27:09.740259646Z", "fingerprint": "b62c91a1088d" }, { "url": "https://www.cognee.ai/gtc-eu", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:27:05.646146507Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9bd64eccc606" } ], "updatedAt": "2026-10-09T11:46:25.657377009Z" } }, "answer": "Cognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.", "b": { "slug": "langmem", "name": "LangMem", "vendor": "LangChain", "vendorUrl": "https://www.langchain.com", "kind": "sdk", "category": "agent-memory", "summary": "LangMem is LangChain's open-source Python library for long-term agent memory. It extracts facts from conversations with an LLM, stores and searches them in a LangGraph store the owner runs, and includes two memory tools, message summarisation and prompt optimisation.", "url": "https://www.anchorterminal.com/tools/langmem", "markdownUrl": "https://www.anchorterminal.com/tools/langmem.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/langmem.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/langmem.json", "repo": "https://github.com/langchain-ai/langmem", "license": "MIT", "transports": [], "packages": [ { "registry": "pypi", "name": "langmem" } ], "auth": "none", "authNotes": "The library has no account or key of its own. It runs in the owner's process and needs a key for the chosen LLM provider (the README uses `ANTHROPIC_API_KEY`) and, for semantic search, an embedding provider. Access to memories is whatever the owner's LangGraph store and namespace settings allow.", "pricing": "free", "pricingNotes": "Free under the MIT licence, with nothing to buy and no signup. The owner pays for the LLM calls that extract memories, the embedding calls behind search, and the database behind the store (https://github.com/langchain-ai/langmem).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 1698, "npmWeekly": null, "pypiWeekly": 214379, "asOf": "2026-10-08" }, "docsUrl": "https://langchain-ai.github.io/langmem/", "capabilities": [ "memory.store", "memory.search", "memory.user", "memory.delete" ], "tags": [ "sdk", "open-source", "self-hosted", "local", "python", "free", "langgraph", "pre-1.0" ], "lastRelease": "2025-10-27", "graded": true, "anchor": { "graded": true, "score": 47.9, "grade": "D", "agentReady": false, "rank": 734, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 66, "maintenance": 15, "payments": 60, "reliability": 32, "schema": 55, "security": 45, "transparency": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply.", "bestFor": "Teams already on LangGraph that want memory tools and background extraction over a store they run.", "strengths": [ "MIT licence, installed with `pip install -U langmem`, with no account, key or fee of its own", "Two agent tools, `manage_memory` and `search_memory`, with typed inputs, an action enum and `limit`, `offset` and `filter` on search", "`actions_permitted` limits the manage tool to any subset of create, update and delete, and `create_memory_store_manager` leaves deletes off by default", "Namespace templates such as `(\"memories\", \"{langgraph_user_id}\")` keep each user's memories apart at run time", "Works with any LangGraph `BaseStore`, including `InMemoryStore` for tests and `AsyncPostgresStore` for durable storage" ], "weaknesses": [ "The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that", "No changelog, GitHub releases or tags, and versions are still 0.0.x", "The repository's two workflows deploy docs and publish to PyPI. Neither runs the tests", "54 open issues, 23 with no comment, and 19 open pull requests, the oldest from June 2025", "No guidance on memory poisoning or prompt injection found, and two open issues asking for it (May 2026) have no reply" ], "agentNotes": [ "Pass a store with an embedding index (`index={\"dims\": 1536, \"embed\": \"openai:text-embedding-3-small\"}`), or `search_memory` cannot rank by meaning", "Use a database-backed store such as `AsyncPostgresStore` for anything that must survive a restart. `InMemoryStore` loses everything", "Put a per-user placeholder in the namespace and set it in `config[\"configurable\"]` on every call, or users share one memory space", "Send the memory `id` with `update` and `delete`, and never with `create`. A retried `create` writes a duplicate under a new UUID", "Run on Python 3.11 or later. `src/langmem/knowledge/extraction.py` uses `typing.NotRequired`, which Python 3.10 lacks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.9 } ], "editorialScores": { "ergonomics": 66, "maintenance": 15, "payments": 60, "reliability": 32, "schema": 55, "security": 45, "transparency": 63 }, "provenanceScore": 55 }, "connect": { "install": "pip install -U langmem" }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/langmem" }, "sameCompany": [ "langgraph", "langsmith" ], "area": "agent-runtime", "provenance": { "legalEntity": "LangChain (the licence file gives no legal form)", "domain": "langchain.com", "domainRegistered": "2019-12-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "A library the owner runs, with no hosted endpoint. The code is on github.com under the langchain-ai organisation and the docs are on langchain-ai.github.io/langmem.", "The LICENSE file reads Copyright (c) 2025 LangChain. PyPI metadata names no author.", "No terms or privacy document governs the library, so both fields are left out. The MIT licence is the only agreement.", "No changelog file, GitHub releases or tags exist in the repository. PyPI's release history is the only record of versions.", "www.langchain.com/.well-known/security.txt returns 404. The organisation's SECURITY.md points to two Intigriti disclosure programmes.", "RDAP for langchain.com gives a registration date of 2019-12-03." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/langmem.json" }, "facts": [ { "a": "Model platform", "b": "SDK + MCP", "name": "Kind" }, { "a": "Cognee", "b": "LangChain", "name": "Vendor" }, { "a": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, stdio, SSE (legacy)", "b": "", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Freemium", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "MIT", "name": "Licence" }, { "a": "7", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-29", "b": "2025-10-27", "name": "Last release" }, { "a": "2026-03-27", "b": "no document linked", "name": "Terms last updated" }, { "a": "no date given", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "31k stars, 21k PyPI/wk", "b": "1.7k stars, 214k PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Cognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.", "question": "Which is better for AI agents, Cognee or LangMem?" }, { "answer": "Yes. Cognee is open source (Apache-2.0). LangMem is open source (MIT).", "question": "Are Cognee and LangMem open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 50 against 32", "Schema \u0026 documentation, 85 against 55", "Maintenance \u0026 community, 82 against 15", "Transparency \u0026 trust, 66 against 59" ], "also": [ "A hosted endpoint, with nothing to install", "Runs on your own machine", "Free to start without a card" ], "goodFor": "Agents whose memory has to include documents, wikis and chat tools as well as conversation, and for teams happy to self-host.", "slug": "cognee", "watchFor": "No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region" }, { "aheadOn": [ "Agent ergonomics, 66 against 59", "Security \u0026 auth, 45 against 39", "Payments \u0026 pricing, 60 against 35" ], "also": [ "No key needed to call it", "No incidents deducted, where Cognee loses 3 points for them" ], "goodFor": "Teams already on LangGraph that want memory tools and background extraction over a store they run.", "slug": "langmem", "watchFor": "The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that" } ], "job": { "capability": "memory.store", "name": "Memory store" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.json", "title": "Amazon Bedrock AgentCore Memory vs Cognee", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee" }, { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.json", "title": "Amazon Bedrock AgentCore Memory vs LangMem", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-graphiti.json", "title": "Cognee vs Graphiti", "url": "https://www.anchorterminal.com/compare/cognee-vs-graphiti" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-hindsight.json", "title": "Cognee vs Hindsight", "url": "https://www.anchorterminal.com/compare/cognee-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-honcho.json", "title": "Cognee vs Honcho", "url": "https://www.anchorterminal.com/compare/cognee-vs-honcho" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-localghost.json", "title": "Cognee vs LocalGhost", "url": "https://www.anchorterminal.com/compare/cognee-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-mem0.json", "title": "Cognee vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/cognee-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-supermemory.json", "title": "Cognee vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/cognee-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-zep.json", "title": "Cognee vs Zep", "url": "https://www.anchorterminal.com/compare/cognee-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-langmem.json", "title": "Graphiti vs LangMem", "url": "https://www.anchorterminal.com/compare/graphiti-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-langmem.json", "title": "Hindsight vs LangMem", "url": "https://www.anchorterminal.com/compare/hindsight-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/honcho-vs-langmem.json", "title": "Honcho vs LangMem", "url": "https://www.anchorterminal.com/compare/honcho-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-localghost.json", "title": "LangMem vs LocalGhost", "url": "https://www.anchorterminal.com/compare/langmem-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-mem0.json", "title": "LangMem vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/langmem-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-supermemory.json", "title": "LangMem vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/langmem-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-zep.json", "title": "LangMem vs Zep", "url": "https://www.anchorterminal.com/compare/langmem-vs-zep" } ], "scores": [ { "by": 18, "cognee": 50, "edge": "cognee", "key": "reliability", "langmem": 32, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 30, "cognee": 85, "edge": "cognee", "key": "schema", "langmem": 55, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "cognee": 59, "edge": "langmem", "key": "ergonomics", "langmem": 66, "name": "Agent ergonomics", "weight": 13 }, { "by": 6, "cognee": 39, "edge": "langmem", "key": "security", "langmem": 45, "name": "Security \u0026 auth", "weight": 14 }, { "by": 25, "cognee": 35, "edge": "langmem", "key": "payments", "langmem": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 67, "cognee": 82, "edge": "cognee", "key": "maintenance", "langmem": 15, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 7, "cognee": 66, "edge": "cognee", "key": "transparency", "langmem": 59, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Cognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing. Both do memory store.", "verdicts": { "cognee": "Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.", "langmem": "Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cognee-vs-langmem", "json": "https://www.anchorterminal.com/compare/cognee-vs-langmem.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cognee-vs-langmem.md", "slim": "https://www.anchorterminal.com/compare/cognee-vs-langmem.min.md" }, "markdown": "Cognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing. Both do memory store.\n\n- Cognee: grade C, 54.6/100, rank #610 of 842. Markdown https://www.anchorterminal.com/tools/cognee.md · JSON https://www.anchorterminal.com/api/v1/tools/cognee.json\n- LangMem: grade D, 47.9/100, rank #734 of 842. Markdown https://www.anchorterminal.com/tools/langmem.md · JSON https://www.anchorterminal.com/api/v1/tools/langmem.json\n\n## Which one, for what\n\n### Cognee (C)\n\nGood for: Agents whose memory has to include documents, wikis and chat tools as well as conversation, and for teams happy to self-host.\n\nAhead on:\n- Reliability, 50 against 32\n- Schema \u0026 documentation, 85 against 55\n- Maintenance \u0026 community, 82 against 15\n- Transparency \u0026 trust, 66 against 59\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n- Runs on your own machine\n- Free to start without a card\n\nWatch for: No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region\n\n### LangMem (D)\n\nGood for: Teams already on LangGraph that want memory tools and background extraction over a store they run.\n\nAhead on:\n- Agent ergonomics, 66 against 59\n- Security \u0026 auth, 45 against 39\n- Payments \u0026 pricing, 60 against 35\n\nAlso in its favour:\n- No key needed to call it\n- No incidents deducted, where Cognee loses 3 points for them\n\nWatch for: The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that\n\n\n## Score by category\n\n| Category | Weight | Cognee | LangMem | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 32 | Cognee +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 55 | Cognee +30 |\n| Agent ergonomics | 13% (16.2 this run) | 59 | 66 | LangMem +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 39 | 45 | LangMem +6 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 60 | LangMem +25 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 15 | Cognee +67 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 66 | 59 | Cognee +7 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **54.6 · C** | **47.9 · D** | |\n\n## Facts side by side\n\n| Fact | Cognee | LangMem |\n| --- | --- | --- |\n| Kind | Model platform | SDK + MCP |\n| Vendor | Cognee | LangChain |\n| Hosted endpoint | `https://\u003ctenant\u003e.aws.cognee.ai/api/v1` | no (local only) |\n| Transports | HTTP, stdio, SSE (legacy) | |\n| Auth | OAuth or key | None |\n| Pricing | Freemium | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT |\n| Tools exposed | 7 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-29 | 2025-10-27 |\n| Terms last updated | 2026-03-27 | no document linked |\n| Privacy policy last updated | no date given | no document linked |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | not found in the text | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 31k stars, 21k PyPI/wk | 1.7k stars, 214k PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## Verdicts\n\n**Cognee.** Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.\n\n**LangMem.** Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply.\n\n## Before you call either\n\n### Cognee\n\n1. Use remember, recall and forget. The older cognify, search and delete MCP tools are gone\n2. Always include /api/v1 in REST paths\n3. Check cognify_status before querying data you added with background=true\n4. Never pass everything=true to forget unless you mean to wipe all of the user's memory\n5. Set a client timeout on Cloud calls and treat HTTP 402 as an empty balance, since an empty balance has also shown up as hangs\n\n### LangMem\n\n1. Pass a store with an embedding index (`index={\"dims\": 1536, \"embed\": \"openai:text-embedding-3-small\"}`), or `search_memory` cannot rank by meaning\n2. Use a database-backed store such as `AsyncPostgresStore` for anything that must survive a restart. `InMemoryStore` loses everything\n3. Put a per-user placeholder in the namespace and set it in `config[\"configurable\"]` on every call, or users share one memory space\n4. Send the memory `id` with `update` and `delete`, and never with `create`. A retried `create` writes a duplicate under a new UUID\n5. Run on Python 3.11 or later. `src/langmem/knowledge/extraction.py` uses `typing.NotRequired`, which Python 3.10 lacks\n\n## Questions\n\n### Which is better for AI agents, Cognee or LangMem?\n\nCognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.\n\n### Are Cognee and LangMem open source?\n\nYes. Cognee is open source (Apache-2.0). LangMem is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cognee-vs-langmem.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cognee-vs-langmem.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cognee\", \"b\": \"langmem\"}`. From a terminal: `anchor compare cognee langmem`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cognee.json and https://www.anchorterminal.com/api/v1/tools/langmem.json\n\n## Other comparisons with Cognee or LangMem\n\n- [Amazon Bedrock AgentCore Memory vs Cognee](https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.md)\n- [Amazon Bedrock AgentCore Memory vs LangMem](https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.md)\n- [Cognee vs Graphiti](https://www.anchorterminal.com/compare/cognee-vs-graphiti.md)\n- [Cognee vs Hindsight](https://www.anchorterminal.com/compare/cognee-vs-hindsight.md)\n- [Cognee vs Honcho](https://www.anchorterminal.com/compare/cognee-vs-honcho.md)\n- [Cognee vs LocalGhost](https://www.anchorterminal.com/compare/cognee-vs-localghost.md)\n- [Cognee vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/cognee-vs-mem0.md)\n- [Cognee vs Supermemory API + MCP](https://www.anchorterminal.com/compare/cognee-vs-supermemory.md)\n- [Cognee vs Zep](https://www.anchorterminal.com/compare/cognee-vs-zep.md)\n- [Graphiti vs LangMem](https://www.anchorterminal.com/compare/graphiti-vs-langmem.md)\n- [Hindsight vs LangMem](https://www.anchorterminal.com/compare/hindsight-vs-langmem.md)\n- [Honcho vs LangMem](https://www.anchorterminal.com/compare/honcho-vs-langmem.md)\n- [LangMem vs LocalGhost](https://www.anchorterminal.com/compare/langmem-vs-localghost.md)\n- [LangMem vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/langmem-vs-mem0.md)\n- [LangMem vs Supermemory API + MCP](https://www.anchorterminal.com/compare/langmem-vs-supermemory.md)\n- [LangMem vs Zep](https://www.anchorterminal.com/compare/langmem-vs-zep.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Cognee vs LangMem", "url": "" } ], "description": "Cognee scores 54.6 (C) on agent readiness against LangMem's 47.9 (D), and leads in 4 of 7 scored categories. LangMem leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing. Both do memory store. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Cognee C 54.6", "LangMem D 47.9", "scores" ], "h1": "Cognee vs LangMem", "image": "https://www.anchorterminal.com/assets/og/compare-cognee-vs-langmem.png", "path": "/compare/cognee-vs-langmem", "published": "2026-10-01", "section": "tools", "title": "Cognee vs LangMem for AI agents, C 54.6 vs D 47.9 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/cognee-vs-langmem" }, "tokens": { "markdown": 2050, "slim": 730 }, "version": 1 }