{ "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", "dns": "cognee.grade.anchorterminal.com", "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 self-hosted REST server defaults to multi-tenant mode with authentication required (`ENABLE_BACKEND_ACCESS_CONTROL=true`) and takes a Bearer token; single-user mode turns auth off. Since 1.6.0 (18 September 2026) the library builds and searches text memory with local models and no LLM key. 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, delivered as a bring-your-own-cloud engagement (https://www.cognee.ai/pricing). The billing docs give the same rates but say the free workspace includes 10 million tokens. Credit is prepaid from $0.50, with optional auto-recharge, and a request the balance can't cover is rejected with HTTP 402 (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": null, "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": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 47.7, "grade": "D", "agentReady": false, "rank": 1133, "ranked": true, "rankOf": 1486, "categoryRank": 24, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 59, "maintenance": 82, "payments": 35, "reliability": 43, "schema": 85, "security": 39, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": -9, "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). Later breaking changes fold into this term. v1.6.3 (7 October) raises on a `node_set` that isn't a list of strings, v1.6.5 (16 October) turns `CYPHER` and `NATURAL_LANGUAGE` search off, and v1.6.6 (29 October) removes the `fs` session cache and the device-code login behind a bare `serve()`, each in a patch release with the change first posted in its release notes (https://docs.cognee.ai/changelog)", "2026-09-18 to 2026-10-29: released fixes of Cognee's own described flaws with no advisory on OSV, -3. v1.6.0 fixes a cross-tenant leak in which a manager of one tenant could read a user's roles in another, v1.6.3 stops agent connections being listed to other callers, v1.6.5 turns off raw Cypher search because any reader of a dataset could run arbitrary queries, and v1.6.6 confines prompt reads to the prompt directory (GHSA-35v8-jp26-7r4f, which osv.dev doesn't list) (https://docs.cognee.ai/changelog)", "2026-07-07: CVE-2026-58473 (GHSA-49f7-whx5-4256, PYSEC-2026-3816), Critical 9.3, assigned by VulnCheck with no repository advisory. Self-registered users could overwrite the global LLM configuration and send every tenant's prompts and documents to their own endpoint. Fixed (OSV gives 1.5.0), over two months old, -1 (https://osv.dev/vulnerability/GHSA-49f7-whx5-4256)", "2026-09-20 onwards: the privacy notice calls library telemetry \"anonymous\" with \"an anonymous installation identifier\", and the Security and Privacy page calls it anonymous, while default events carry the Cognee user and tenant UUIDs, a persistent machine ID, and, when connected to Cognee Cloud, the tenant ID and the `*.cognee.ai` host. A misleading claim, -2 (https://www.cognee.ai/privacy-notice)" ], "verdict": "Apache-2.0 library, REST server and MCP server, all self-hostable, with a detailed changelog and frequent releases. Cloud has no status page, rate limits or SOC 2, and runs in AWS us-east-1. Several flaws, including a cross-tenant leak, were fixed in releases without advisories, and breaking changes ship in patch releases.", "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", "REST reference pages publish their OpenAPI 3.1 operations, with documented error codes", "Metered Cloud at $1 per million tokens, 1 million free with no card", "More than ten stable releases from 8 August to 29 October 2026, each with dated release notes" ], "weaknesses": [ "No status page, rate limits, SLA figure or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region", "Security fixes, including a cross-tenant leak in 1.6.0, shipped without published advisories", "Breaking changes ship in patch releases (1.6.3, 1.6.5, 1.6.6), and 11 MCP tools went in May without a version bump", "Library telemetry is on by default and carries user and tenant UUIDs, while the privacy notice calls it anonymous", "Billing docs and pricing page disagree on the free token allowance" ], "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", "Treat HTTP 402 as an empty Cloud balance and set a client timeout, since a run that exhausts credit part-way reports Failed in dataset status instead", "Read the \"Upgrading\" list in each release's notes before moving to a new patch version" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.7 } ], "editorialScores": { "ergonomics": 59, "maintenance": 82, "payments": 35, "reliability": 43, "schema": 85, "security": 39, "transparency": 78 }, "provenanceScore": 65, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "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": "2026-11-04", "notes": [ "The EU general terms (effective 27 March 2026) name Topoteretes UG (haftungsbeschränkt), Amtsgericht Charlottenburg HRB 252065 B, under German law. US general terms of the same date name Cognee Inc. (https://www.cognee.ai/gtc-us).", "No security.txt or status page found; SECURITY.md in the repository sends reports to security@cognee.ai or GitHub's private reporting, with a 2-business-day acknowledgement.", "The privacy notice, current version 20 September 2026, says Cognee Cloud is operated by Cognee Inc. and hosted in AWS us-east-1, and names Topoteretes UG as its EU operating subsidiary." ], "score": 65 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cognee.json", "live": { "slug": "cognee", "probe": { "target": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1", "method": "get", "lastAt": "2026-11-07T18:20:18.115079252Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 239, "samples30d": 7910, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 0 }, { "date": "2026-10-08", "probes": 268, "ok": 0 }, { "date": "2026-10-09", "probes": 250, "ok": 0 }, { "date": "2026-10-10", "probes": 247, "ok": 0 }, { "date": "2026-10-11", "probes": 247, "ok": 0 }, { "date": "2026-10-12", "probes": 247, "ok": 0 }, { "date": "2026-10-13", "probes": 248, "ok": 0 }, { "date": "2026-10-14", "probes": 249, "ok": 0 }, { "date": "2026-10-15", "probes": 253, "ok": 0 }, { "date": "2026-10-16", "probes": 252, "ok": 0 }, { "date": "2026-10-17", "probes": 253, "ok": 0 }, { "date": "2026-10-18", "probes": 256, "ok": 0 }, { "date": "2026-10-19", "probes": 255, "ok": 0 }, { "date": "2026-10-20", "probes": 252, "ok": 0 }, { "date": "2026-10-21", "probes": 253, "ok": 0 }, { "date": "2026-10-22", "probes": 253, "ok": 0 }, { "date": "2026-10-23", "probes": 248, "ok": 0 }, { "date": "2026-10-24", "probes": 251, "ok": 0 }, { "date": "2026-10-25", "probes": 250, "ok": 0 }, { "date": "2026-10-26", "probes": 252, "ok": 0 }, { "date": "2026-10-27", "probes": 249, "ok": 0 }, { "date": "2026-10-28", "probes": 251, "ok": 0 }, { "date": "2026-10-29", "probes": 249, "ok": 0 }, { "date": "2026-10-30", "probes": 248, "ok": 0 }, { "date": "2026-10-31", "probes": 251, "ok": 0 }, { "date": "2026-11-01", "probes": 249, "ok": 0 }, { "date": "2026-11-02", "probes": 250, "ok": 0 }, { "date": "2026-11-03", "probes": 248, "ok": 0 }, { "date": "2026-11-04", "probes": 246, "ok": 0 }, { "date": "2026-11-05", "probes": 185, "ok": 0 }, { "date": "2026-11-06", "probes": 241, "ok": 0 }, { "date": "2026-11-07", "probes": 187, "ok": 0 } ] }, "versions": [ { "registry": "github", "name": "topoteretes/cognee", "version": "v1.6.7", "released": "2026-11-06", "seenAt": "2026-11-06T23:16:24.652822343Z" }, { "registry": "pypi", "name": "cognee", "version": "1.6.7", "released": "2026-11-06", "seenAt": "2026-11-06T23:16:24.544693781Z" } ], "githubStars": 33114, "pypiWeekly": 38033, "securityTxt": { "url": "https://cognee.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:38.055376681Z" }, "llmsTxt": { "url": "https://docs.cognee.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:05.867692681Z" }, "domain": { "domain": "cognee.ai", "registered": "2023-12-21", "source": "https://rdap.identitydigital.services/rdap/domain/cognee.ai", "checkedAt": "2026-11-01T14:17:40.492155131Z" }, "pages": [ { "url": "https://docs.cognee.ai/changelog", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:05:38.327099277Z", "changedAt": "2026-11-07T14:05:38.327099277Z", "fingerprint": "ce1ff92f1e6e" }, { "url": "https://www.cognee.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:26:59.613514784Z", "changedAt": "2026-11-05T13:10:57.169704735Z", "fingerprint": "78b4a35f57a1" }, { "url": "https://www.cognee.ai/privacy-notice", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:27:01.748287383Z", "changedAt": "2026-11-05T13:10:59.22394133Z", "fingerprint": "b018b51db53f" }, { "url": "https://www.cognee.ai/gtc-eu", "kind": "terms", "status": 304, "checkedAt": "2026-11-07T14:26:57.492553915Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9bd64eccc606" } ], "github": { "repo": "topoteretes/cognee", "checkedAt": "2026-11-02T09:32:30.198422991Z", "stars": 32996, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-02T07:53:50Z", "openIssues": 781, "openPRs": 294, "issuesOpened90d": 884, "issuesClosed90d": 263, "prsMerged90d": 796, "ci": { "branch": "main", "runs": 50, "success": 43, "failure": 6, "other": 1, "latest": "failure", "latestAt": "2026-10-29T22:20:34Z" }, "advisories": [ { "ghsa": "GHSA-8pr4-p4c8-8gr9", "cve": "CVE-2026-31231", "severity": "critical", "published": "2026-09-15T15:55:05Z" } ], "replies": { "sampled": 12, "unanswered": 12 } }, "updatedAt": "2026-11-07T18:20:18.115079252Z" } }, "answer": "MemMachine scores 58 (C) on agent readiness against Cognee's 47.7 (D), and leads in 5 of 7 scored categories. Cognee leads on schema \u0026 documentation and transparency \u0026 trust.", "b": { "slug": "memmachine", "name": "MemMachine", "vendor": "MemVerge, Inc.", "vendorUrl": "https://memmachine.ai", "kind": "http-api", "category": "agent-memory", "summary": "MemMachine is an open-source long-term memory layer for AI agents, incubated by MemVerge, with episodic, profile and working memory behind a self-hosted REST server, Python and TypeScript clients and an MCP server, plus a hosted platform.", "url": "https://www.anchorterminal.com/tools/memmachine", "markdownUrl": "https://www.anchorterminal.com/tools/memmachine.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/memmachine.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/memmachine.json", "dns": "memmachine.grade.anchorterminal.com", "repo": "https://github.com/MemMachine/MemMachine", "license": "Apache-2.0", "transports": [ "http", "stdio", "streamable-http" ], "packages": [ { "registry": "pypi", "name": "memmachine-server" }, { "registry": "pypi", "name": "memmachine-client" }, { "registry": "npm", "name": "@memmachine/client" } ], "auth": "none", "authNotes": "The self-hosted server takes no credential; the owner supplies LLM and embedding keys (OpenAI, Bedrock or Ollama) in its config. The hosted MemMachine Platform at api.memmachine.ai uses a bearer API key created and revoked in the console after a browser signup.", "pricing": "freemium", "pricingNotes": "Free under Apache-2.0 to self-host, with model costs paid to the chosen provider. The hosted platform has a free account per the README, but no prices were found, and the terms bill hosted services by order, monthly or annually.", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the repository, the OpenAPI documents or the docs (checked 2026-11-06).", "endpoints": [] }, "toolCount": 3, "popularity": { "githubStars": 3085, "npmWeekly": 28, "pypiWeekly": 581, "asOf": "2026-11-06" }, "docsUrl": "https://docs.memmachine.ai", "llmsTxt": "https://docs.memmachine.ai/llms.txt", "openapi": "https://github.com/MemMachine/MemMachine/blob/main/docs/openapi.json", "capabilities": [ "memory.store", "memory.search", "memory.user", "memory.graph", "memory.delete" ], "tags": [ "open-source", "self-hosted", "hosted", "apache-2.0", "python", "typescript", "docker", "mcp", "openapi", "llms-txt", "no-auth", "pre-1.0" ], "lastRelease": "2026-10-08", "graded": true, "anchor": { "graded": true, "score": 58, "grade": "C", "agentReady": false, "rank": 767, "ranked": true, "rankOf": 1486, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 83, "payments": 40, "reliability": 51, "schema": 79, "security": 41, "transparency": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "MemMachine is an Apache-2.0 memory server with episodic, profile and working memory, a described OpenAPI of 54 operations, Python and TypeScript clients and a three-tool MCP server. It is version 0.4.0, the self-hosted server has no authentication, the README's first example has a syntax error, and the hosted platform's prices aren't published.", "bestFor": "Self-hosted memory for agent frameworks that want episodic and profile memory with many integrations.", "strengths": [ "Apache-2.0 server and clients on PyPI, npm and Docker, self-hostable with Ollama or AWS Bedrock", "OpenAPI documents for the server (54 operations) and the hosted platform (12), every operation described", "MCP server over stdio or HTTP with three tools, add_memory, search_memory and delete_memory", "SECURITY.md with private reporting and a two-business-day acknowledgement, plus security-audit and SBOM workflows", "Hosted platform terms commit to 99.5% availability" ], "weaknesses": [ "Version 0.4.0 of 6 October 2026, pre-1.0, with no changelog file in the repository", "The self-hosted server has no authentication and no security scheme in its OpenAPI document", "The README's quick start imports with `from memmachine_client import import MemMachineClient`, which is invalid Python", "292 open issues and pull requests, including recent reports that a provider 429 becomes a 500 and that a 0.4.0 config no longer starts", "No prices for the hosted platform, and the Enterprise edition is marked coming soon" ], "agentNotes": [ "Import with `from memmachine_client import MemMachineClient`; the README line has a doubled `import`", "Keep the server on a private network or put an authenticating proxy in front, since it accepts any caller", "Scope every call with org, project, group, agent, user and session ids so memories don't mix", "Retry search after a short wait on 500s, which a recent issue ties to upstream provider rate limits", "Treat recalled episodes as untrusted text from past conversations" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58 } ], "editorialScores": { "ergonomics": 65, "maintenance": 83, "payments": 40, "reliability": 51, "schema": 79, "security": 41, "transparency": 55 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install memmachine-server memmachine-client", "http": "curl http://localhost:8080/api/v2/health" }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/memmachine" }, "area": "agent-runtime", "provenance": { "legalEntity": "MemVerge, Inc.", "domain": "memmachine.ai", "domainRegistered": "2025-08-15", "endpointOnVendorDomain": null, "terms": "https://memmachine.ai/terms-of-service/", "privacy": "https://memmachine.ai/privacy-policy/", "statusPage": "", "changelog": "https://github.com/MemMachine/MemMachine/releases", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The terms name MemVerge, Inc., under California law, with no last-updated date shown. They forbid web scraping or harvesting data from the services, recorded here as a fact. The open-source software is Apache-2.0 and outside the terms.", "memmachine.ai/robots.txt allows all crawlers. docs.memmachine.ai carries Content-Signal ai-input=yes.", "RDAP gives memmachine.ai a registration date of 15 August 2025.", "/.well-known/security.txt returns 404. No status page found." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/memmachine.json", "live": { "slug": "memmachine", "versions": [ { "registry": "github", "name": "MemMachine/MemMachine", "version": "v0.4.0", "released": "2026-10-08", "seenAt": "2026-11-06T23:43:57.45502049Z" }, { "registry": "npm", "name": "@memmachine/client", "version": "0.3.9", "seenAt": "2026-11-06T23:43:56.945798416Z" }, { "registry": "pypi", "name": "memmachine-client", "version": "0.4.0", "released": "2026-10-08", "seenAt": "2026-11-06T23:43:55.050118334Z" }, { "registry": "pypi", "name": "memmachine-server", "version": "0.4.0", "released": "2026-10-08", "seenAt": "2026-11-06T23:43:54.864236924Z" } ], "githubStars": 3085, "npmWeekly": 28, "pypiWeekly": 54, "securityTxt": { "url": "https://memmachine.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:09.818703565Z" }, "llmsTxt": { "url": "https://docs.memmachine.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:59.84146138Z" }, "pages": [ { "url": "https://memmachine.ai/privacy-policy/", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:14:11.012100581Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "19edc6281451" }, { "url": "https://memmachine.ai/terms-of-service/", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:14:13.19202427Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "079364130152" } ], "updatedAt": "2026-11-07T16:00:09.818703565Z" } }, "edges": [ { "by": 15, "category": "transparency", "leads": "cognee", "name": "Transparency \u0026 trust", "reasons": { "a": "The library, REST server and MCP server are Apache-2.0, and Cloud is closed (25 of 30). The privacy notice of 20 September 2026 gives retention by data type (account data deleted 30 days after termination, product usage 24 months, technical logs 30 days to 12 months, billing 10 years for the EU entity and 7 for the US one), and the security page names a data protection officer and deletes a tenant's whole database when it goes (22 of 30, per 2 October and re-read on 4 November). Deprecations now come as dated changelog notices. v1.6.5 (16 October) deprecates `cognee.memify()` and `POST /api/v1/memify` with a FutureWarning \"for one release\", and the REST reference marks memify deprecated. No written policy with a notice period, so 15 of 20 (6 on 1 October). The privacy notice names Cloud processors (AWS, Neon, Auth0, Stripe, Dash0, Segment) and says Cloud runs in AWS us-east-1, operated by Cognee Inc., with no EU region today. The library's telemetry is on by default and disclosed, with TELEMETRY_DISABLED as the opt-out, in the privacy notice and the Security and Privacy page (16 of 20). The \"anonymous\" wording is charged once as a negative event, not here. 25 + 22 + 15 + 16 = 78.", "b": "Apache-2.0 (30). The terms say hosted customer data is destroyed on termination, with no period, and use usage data only in aggregate; the privacy policy wasn't read (12 of 30). No deprecation policy or notices found (3 of 20). No telemetry calls found in the server or client source, and the docs don't address it (10 of 20). Total 55." }, "scores": { "a": 72, "b": 57 } }, { "by": 8, "category": "reliability", "leads": "memmachine", "name": "Reliability", "reasons": { "a": "We departed from the checklist here. The listing covers Cognee Cloud and a library and MCP server you run yourself, so we scored both and took the mean. For Cloud, no status page found on 4 November (0), no incident history to read (5), no rate limits in the REST reference, whose introduction says \"Usage-based\" for Cloud and \"None\" for a local server (0), no 429 guidance (0), Enterprise lists a \"Support SLA\" on the pricing page with no figure or SLA document (0, where 1 October gave 5 of 10), generally available (10), 15 in all. For the local package, cognee on PyPI with Python 3.10 to 3.14 stated in SECURITY.md and pyproject.toml (20). 64 GitHub Actions workflows, and test_suites.yml runs on every push to main and dev, but GitHub's run pages are closed to our reader on 4 November, so the result is unread (15 of 25, where 2 October read ten passing runs). Open regressions not counted, per the 2 October check (10 of 25). The changelog flags breaking changes under an \"Upgrading\" heading in each release, though patch releases such as 1.6.3, 1.6.5 and 1.6.6 carry them and 11 MCP tools went in May without a version bump (10 of 15). Version 1.6.6 (15). 70 in all. Mean 42.5, rounded half up to 43.", "b": "Local-software lines, as a server the owner runs. `memmachine-server` and `memmachine-client` on PyPI with Python versions stated, a Docker image and a compose script (20). pytest, integration and installation-test workflows exist, but GitHub's API answered with a rate limit, so run results were unread (15 of 25, unchecked). 292 open issues and pull requests; the newest bug reports, including a 429 from a provider surfacing as a 500 and a 0.4.0 configuration that no longer starts, have zero to two replies (10 of 25). No changelog file, release notes only on GitHub, which weren't read, and pre-1.0 tags (6 of 15). Version 0.4.0 (0). Total 51." }, "scores": { "a": 43, "b": 51 } }, { "by": 6, "category": "schema", "leads": "cognee", "name": "Schema \u0026 documentation", "reasons": { "a": "Each REST reference page publishes its OpenAPI 3.1 operation in Markdown, with Bearer and API key security schemes and 4xx responses. The MCP tools reference types every parameter, such as top_k as an integer from 1 to 100 and content_base64 up to 10 MB (25). llms.txt plus separate llms-mcp.md and llms-api.md indexes (10). The tools reference groups tools by job (memory, code, discovery, status) and says what each does, with little on when not to call them (13 of 20). Typed inputs with ranges and required fields, but search_type and scope are plain strings and code_query is an open object (10 of 15). Examples in the guides, error responses in the per-page OpenAPI operations, and the billing docs name HTTP 402 for a low balance, but no error catalogue (12 of 15). Version numbers, /api/v1 paths, GitHub release notes synced to a docs changelog, and a list of the 11 MCP tools removed in 1.x (15). Re-read on 4 November, the reference lists 144 pages. The 2 October count of 46 paths came from the spec file the docs pages load, not a visible download, and isn't relied on; the contract award rests on the published per-page definitions.", "b": "OpenAPI documents for the server (54 operations) and the hosted platform (12) (25). llms.txt on docs.memmachine.ai with Markdown pages (10). Every operation has a description, and the docs explain memory types and scoping (15 of 20). Typed request models generated from the server, with search options for limits and filters (12 of 15). Examples for each client and the MCP tools, but only 422 is documented on the server and the README's first example doesn't run (8 of 15). `/api/v2` paths and tagged releases, no changelog (9 of 15). Total 79." }, "scores": { "a": 85, "b": 79 } }, { "by": 6, "category": "ergonomics", "leads": "memmachine", "name": "Agent ergonomics", "reasons": { "a": "The tools reference lists 7 MCP tools, remember, recall, forget, code_search, search_tools, call_tool and cognify_status, with search_tools and call_tool reaching further tools on demand and COGNEE_MCP_TOOL_MODE=minimal cutting the list to the memory tools (25). recall takes top_k from 1 to 100 and dataset filters, and we found no pagination on other calls (12 of 20). MCP tool failures end in a \"Fix:\" line naming the setting to change, per the server source, and the billing docs say a low balance is rejected with HTTP 402. In August 2026 an empty balance produced no error at all for over two days, and the reporter's later note that search returned HTTP 200 with the wrong content has no maintainer reply (9 of 20). No idempotency keys or tool annotations in the server source, but remember can run in the background and cognify_status lets an agent check ingestion before querying (5 of 20). Python only, and the MCP server starts with one docker run (8 of 15). On 4 November the tools reference and cognee-mcp 0.5.7 source still register the same seven tools, with no tool annotations.", "b": "Searches take limit and filter options for episodic and semantic results (20 of 25). Memories can be listed and filtered by scope (15 of 20). Validation errors are typed; other failures are less clear, as the 429-to-500 issue shows (10 of 20). No idempotency keys or safe-retry guidance found (5 of 20). Python and TypeScript clients and a five-line quick start, once the import is fixed (15). Total 65." }, "scores": { "a": 59, "b": 65 } }, { "by": 5, "category": "payments", "leads": "memmachine", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Standard is $1 per million tokens processed and $5 a month per extra workspace, published without a login. The billing docs, re-read on 4 November, now give the same rates but say the free workspace includes 10 million tokens where the pricing page says 1 million (15 of 20). Free includes a workspace, \"no card required\" (20). A person signs up for a Cloud tenant in the browser (0). Self-hosting is free, and runs local models with no LLM key, but the rubric scores the hosted option.", "b": "Self-hosted rule, with the price lines read for the hosted platform. No x402, MPP or L402 (0 of 40). No hosted prices found on the site or docs (0). Free to self-host with no card (20). An agent can install it from PyPI with no account (20). Total 40." }, "scores": { "a": 35, "b": 40 } } ], "facts": [ { "a": "Platform", "b": "HTTP API", "name": "Kind" }, { "a": "Cognee", "b": "MemVerge, Inc.", "name": "Vendor" }, { "a": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, stdio, SSE (legacy)", "b": "HTTP, stdio, Streamable HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "7", "b": "3", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-10-08", "name": "Last release" }, { "a": "2026-03-27", "b": "no date given", "name": "Terms last updated" }, { "a": "no date given", "b": "2026-05-20", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "31k stars", "b": "3.1k stars, 28 npm/wk, 581 PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "MemMachine scores 58 (C) on agent readiness against Cognee's 47.7 (D), and leads in 5 of 7 scored categories. Cognee leads on schema \u0026 documentation and transparency \u0026 trust.", "question": "Which is better for AI agents, Cognee or MemMachine?" }, { "answer": "Cognee has a hosted endpoint at https://\u003ctenant\u003e.aws.cognee.ai/api/v1. MemMachine runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call Cognee and MemMachine without installing anything?" }, { "answer": "Yes. Cognee is open source (Apache-2.0). MemMachine is open source (Apache-2.0).", "question": "Are Cognee and MemMachine open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 85 against 79", "Transparency \u0026 trust, 72 against 57" ], "also": [ "A hosted endpoint, with nothing to install", "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 figure or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region" }, { "aheadOn": [ "Reliability, 51 against 43", "Agent ergonomics, 65 against 59", "Payments \u0026 pricing, 40 against 35" ], "also": [ "No key needed to call it", "No incidents deducted, where Cognee loses 9 points for them" ], "goodFor": "Self-hosted memory for agent frameworks that want episodic and profile memory with many integrations.", "slug": "memmachine", "watchFor": "Version 0.4.0 of 6 October 2026, pre-1.0, with no changelog file in the repository" } ], "history": { "cognee": [ { "causes": [ "product", "correction", "event" ], "date": "2026-11-04", "delta": -6.9, "from": 54.6, "fromGrade": "C", "to": 47.7, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0, "from": 54.6, "fromGrade": "C", "to": 54.6, "toGrade": "C" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": 0, "from": 54.6, "fromGrade": "C", "to": 54.6, "toGrade": "C" } ], "memmachine": null }, "job": { "capability": "memory.store", "name": "Agent memory" }, "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-memmachine.json", "title": "Amazon Bedrock AgentCore Memory vs MemMachine", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/basic-memory-vs-cognee.json", "title": "Basic Memory vs Cognee", "url": "https://www.anchorterminal.com/compare/basic-memory-vs-cognee" }, { "json": "https://www.anchorterminal.com/compare/basic-memory-vs-memmachine.json", "title": "Basic Memory vs MemMachine", "url": "https://www.anchorterminal.com/compare/basic-memory-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/claude-mem-vs-cognee.json", "title": "claude-mem vs Cognee", "url": "https://www.anchorterminal.com/compare/claude-mem-vs-cognee" }, { "json": "https://www.anchorterminal.com/compare/claude-mem-vs-memmachine.json", "title": "claude-mem vs MemMachine", "url": "https://www.anchorterminal.com/compare/claude-mem-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-everos.json", "title": "Cognee vs EverOS", "url": "https://www.anchorterminal.com/compare/cognee-vs-everos" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-google-memory-bank.json", "title": "Cognee vs Agent Platform Memory Bank", "url": "https://www.anchorterminal.com/compare/cognee-vs-google-memory-bank" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-graphicle.json", "title": "Cognee vs Graphicle", "url": "https://www.anchorterminal.com/compare/cognee-vs-graphicle" }, { "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-hy-memory.json", "title": "Cognee vs Hy Memory", "url": "https://www.anchorterminal.com/compare/cognee-vs-hy-memory" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-langmem.json", "title": "Cognee vs LangMem", "url": "https://www.anchorterminal.com/compare/cognee-vs-langmem" }, { "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-memori.json", "title": "Cognee vs Memori", "url": "https://www.anchorterminal.com/compare/cognee-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-memory-reference-server.json", "title": "Cognee vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/cognee-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-mempalace.json", "title": "Cognee vs MemPalace", "url": "https://www.anchorterminal.com/compare/cognee-vs-mempalace" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-past-dev.json", "title": "Cognee vs past.dev", "url": "https://www.anchorterminal.com/compare/cognee-vs-past-dev" }, { "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-tencentdb-agent-memory.json", "title": "Cognee vs TencentDB Agent Memory", "url": "https://www.anchorterminal.com/compare/cognee-vs-tencentdb-agent-memory" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-tokenrip.json", "title": "Cognee vs Tokenrip", "url": "https://www.anchorterminal.com/compare/cognee-vs-tokenrip" }, { "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/everos-vs-memmachine.json", "title": "EverOS vs MemMachine", "url": "https://www.anchorterminal.com/compare/everos-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/google-memory-bank-vs-memmachine.json", "title": "Agent Platform Memory Bank vs MemMachine", "url": "https://www.anchorterminal.com/compare/google-memory-bank-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-memmachine.json", "title": "Graphiti vs MemMachine", "url": "https://www.anchorterminal.com/compare/graphiti-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-memmachine.json", "title": "Hindsight vs MemMachine", "url": "https://www.anchorterminal.com/compare/hindsight-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/honcho-vs-memmachine.json", "title": "Honcho vs MemMachine", "url": "https://www.anchorterminal.com/compare/honcho-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-memmachine.json", "title": "Hy Memory vs MemMachine", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-memmachine.json", "title": "LangMem vs MemMachine", "url": "https://www.anchorterminal.com/compare/langmem-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/localghost-vs-memmachine.json", "title": "LocalGhost vs MemMachine", "url": "https://www.anchorterminal.com/compare/localghost-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/mem0-vs-memmachine.json", "title": "Mem0 Platform + MCP vs MemMachine", "url": "https://www.anchorterminal.com/compare/mem0-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-memori.json", "title": "MemMachine vs Memori", "url": "https://www.anchorterminal.com/compare/memmachine-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.json", "title": "MemMachine vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-mempalace.json", "title": "MemMachine vs MemPalace", "url": "https://www.anchorterminal.com/compare/memmachine-vs-mempalace" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-past-dev.json", "title": "MemMachine vs past.dev", "url": "https://www.anchorterminal.com/compare/memmachine-vs-past-dev" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-supermemory.json", "title": "MemMachine vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/memmachine-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-tencentdb-agent-memory.json", "title": "MemMachine vs TencentDB Agent Memory", "url": "https://www.anchorterminal.com/compare/memmachine-vs-tencentdb-agent-memory" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-tokenrip.json", "title": "MemMachine vs Tokenrip", "url": "https://www.anchorterminal.com/compare/memmachine-vs-tokenrip" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-zep.json", "title": "MemMachine vs Zep", "url": "https://www.anchorterminal.com/compare/memmachine-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/ai-memory-vs-cognee.json", "title": "AI Memory vs Cognee", "url": "https://www.anchorterminal.com/compare/ai-memory-vs-cognee" }, { "json": "https://www.anchorterminal.com/compare/ai-memory-vs-memmachine.json", "title": "AI Memory vs MemMachine", "url": "https://www.anchorterminal.com/compare/ai-memory-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-context-mode.json", "title": "Cognee vs Context Mode Engine", "url": "https://www.anchorterminal.com/compare/cognee-vs-context-mode" }, { "json": "https://www.anchorterminal.com/compare/context-mode-vs-memmachine.json", "title": "Context Mode Engine vs MemMachine", "url": "https://www.anchorterminal.com/compare/context-mode-vs-memmachine" }, { "json": "https://www.anchorterminal.com/compare/graphicle-vs-memmachine.json", "title": "Graphicle vs MemMachine", "url": "https://www.anchorterminal.com/compare/graphicle-vs-memmachine" } ], "panel": null, "scores": [ { "by": 8, "cognee": 43, "edge": "memmachine", "key": "reliability", "memmachine": 51, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 6, "cognee": 85, "edge": "cognee", "key": "schema", "memmachine": 79, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 6, "cognee": 59, "edge": "memmachine", "key": "ergonomics", "memmachine": 65, "name": "Agent ergonomics", "weight": 13 }, { "by": 2, "cognee": 39, "edge": "memmachine", "key": "security", "memmachine": 41, "name": "Security \u0026 auth", "weight": 14 }, { "by": 5, "cognee": 35, "edge": "memmachine", "key": "payments", "memmachine": 40, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 1, "cognee": 82, "edge": "memmachine", "key": "maintenance", "memmachine": 83, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 15, "cognee": 72, "edge": "cognee", "key": "transparency", "memmachine": 57, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "MemMachine scores 58 (C) on agent readiness against Cognee's 47.7 (D), and leads in 5 of 7 scored categories. Cognee leads on schema \u0026 documentation and transparency \u0026 trust. Both do agent memory.", "tasks": null, "tutorials": [], "verdicts": { "cognee": "Apache-2.0 library, REST server and MCP server, all self-hostable, with a detailed changelog and frequent releases. Cloud has no status page, rate limits or SOC 2, and runs in AWS us-east-1. Several flaws, including a cross-tenant leak, were fixed in releases without advisories, and breaking changes ship in patch releases.", "memmachine": "MemMachine is an Apache-2.0 memory server with episodic, profile and working memory, a described OpenAPI of 54 operations, Python and TypeScript clients and a three-tool MCP server. It is version 0.4.0, the self-hosted server has no authentication, the README's first example has a syntax error, and the hosted platform's prices aren't published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cognee-vs-memmachine", "json": "https://www.anchorterminal.com/compare/cognee-vs-memmachine.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cognee-vs-memmachine.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cognee-vs-memmachine.min.md" }, "markdown": "MemMachine scores 58 (C) on agent readiness against Cognee's 47.7 (D), and leads in 5 of 7 scored categories. Cognee leads on schema \u0026 documentation and transparency \u0026 trust. Both do agent memory.\n\n- Cognee: grade D, 47.7/100, rank #1133 of 1486. Markdown https://www.anchorterminal.com/tools/cognee.md · JSON https://www.anchorterminal.com/api/v1/tools/cognee.json\n- MemMachine: grade C, 58/100, rank #767 of 1486. Markdown https://www.anchorterminal.com/tools/memmachine.md · JSON https://www.anchorterminal.com/api/v1/tools/memmachine.json\n- Best memory layers for AI agents: https://www.anchorterminal.com/best/agent-memory/index.md\n- All 353 memory comparisons: https://www.anchorterminal.com/compare/agent-memory/index.md\n\n## Which one, for what\n\n### Cognee (D)\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- Schema \u0026 documentation, 85 against 79\n- Transparency \u0026 trust, 72 against 57\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n- Free to start without a card\n\nWatch for: No status page, rate limits, SLA figure or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region\n\n### MemMachine (C)\n\nGood for: Self-hosted memory for agent frameworks that want episodic and profile memory with many integrations.\n\nAhead on:\n- Reliability, 51 against 43\n- Agent ergonomics, 65 against 59\n- Payments \u0026 pricing, 40 against 35\n\nAlso in its favour:\n- No key needed to call it\n- No incidents deducted, where Cognee loses 9 points for them\n\nWatch for: Version 0.4.0 of 6 October 2026, pre-1.0, with no changelog file in the repository\n\n\n## Why the edge\n\n### Transparency \u0026 trust: Cognee +15\n\n- Cognee (72): The library, REST server and MCP server are Apache-2.0, and Cloud is closed (25 of 30). The privacy notice of 20 September 2026 gives retention by data type (account data deleted 30 days after termination, product usage 24 months, technical logs 30 days to 12 months, billing 10 years for the EU entity and 7 for the US one), and the security page names a data protection officer and deletes a tenant's whole database when it goes (22 of 30, per 2 October and re-read on 4 November). Deprecations now come as dated changelog notices. v1.6.5 (16 October) deprecates `cognee.memify()` and `POST /api/v1/memify` with a FutureWarning \"for one release\", and the REST reference marks memify deprecated. No written policy with a notice period, so 15 of 20 (6 on 1 October). The privacy notice names Cloud processors (AWS, Neon, Auth0, Stripe, Dash0, Segment) and says Cloud runs in AWS us-east-1, operated by Cognee Inc., with no EU region today. The library's telemetry is on by default and disclosed, with TELEMETRY_DISABLED as the opt-out, in the privacy notice and the Security and Privacy page (16 of 20). The \"anonymous\" wording is charged once as a negative event, not here. 25 + 22 + 15 + 16 = 78.\n- MemMachine (57): Apache-2.0 (30). The terms say hosted customer data is destroyed on termination, with no period, and use usage data only in aggregate; the privacy policy wasn't read (12 of 30). No deprecation policy or notices found (3 of 20). No telemetry calls found in the server or client source, and the docs don't address it (10 of 20). Total 55.\n\n### Reliability: MemMachine +8\n\n- Cognee (43): We departed from the checklist here. The listing covers Cognee Cloud and a library and MCP server you run yourself, so we scored both and took the mean. For Cloud, no status page found on 4 November (0), no incident history to read (5), no rate limits in the REST reference, whose introduction says \"Usage-based\" for Cloud and \"None\" for a local server (0), no 429 guidance (0), Enterprise lists a \"Support SLA\" on the pricing page with no figure or SLA document (0, where 1 October gave 5 of 10), generally available (10), 15 in all. For the local package, cognee on PyPI with Python 3.10 to 3.14 stated in SECURITY.md and pyproject.toml (20). 64 GitHub Actions workflows, and test_suites.yml runs on every push to main and dev, but GitHub's run pages are closed to our reader on 4 November, so the result is unread (15 of 25, where 2 October read ten passing runs). Open regressions not counted, per the 2 October check (10 of 25). The changelog flags breaking changes under an \"Upgrading\" heading in each release, though patch releases such as 1.6.3, 1.6.5 and 1.6.6 carry them and 11 MCP tools went in May without a version bump (10 of 15). Version 1.6.6 (15). 70 in all. Mean 42.5, rounded half up to 43.\n- MemMachine (51): Local-software lines, as a server the owner runs. `memmachine-server` and `memmachine-client` on PyPI with Python versions stated, a Docker image and a compose script (20). pytest, integration and installation-test workflows exist, but GitHub's API answered with a rate limit, so run results were unread (15 of 25, unchecked). 292 open issues and pull requests; the newest bug reports, including a 429 from a provider surfacing as a 500 and a 0.4.0 configuration that no longer starts, have zero to two replies (10 of 25). No changelog file, release notes only on GitHub, which weren't read, and pre-1.0 tags (6 of 15). Version 0.4.0 (0). Total 51.\n\n### Schema \u0026 documentation: Cognee +6\n\n- Cognee (85): Each REST reference page publishes its OpenAPI 3.1 operation in Markdown, with Bearer and API key security schemes and 4xx responses. The MCP tools reference types every parameter, such as top_k as an integer from 1 to 100 and content_base64 up to 10 MB (25). llms.txt plus separate llms-mcp.md and llms-api.md indexes (10). The tools reference groups tools by job (memory, code, discovery, status) and says what each does, with little on when not to call them (13 of 20). Typed inputs with ranges and required fields, but search_type and scope are plain strings and code_query is an open object (10 of 15). Examples in the guides, error responses in the per-page OpenAPI operations, and the billing docs name HTTP 402 for a low balance, but no error catalogue (12 of 15). Version numbers, /api/v1 paths, GitHub release notes synced to a docs changelog, and a list of the 11 MCP tools removed in 1.x (15). Re-read on 4 November, the reference lists 144 pages. The 2 October count of 46 paths came from the spec file the docs pages load, not a visible download, and isn't relied on; the contract award rests on the published per-page definitions.\n- MemMachine (79): OpenAPI documents for the server (54 operations) and the hosted platform (12) (25). llms.txt on docs.memmachine.ai with Markdown pages (10). Every operation has a description, and the docs explain memory types and scoping (15 of 20). Typed request models generated from the server, with search options for limits and filters (12 of 15). Examples for each client and the MCP tools, but only 422 is documented on the server and the README's first example doesn't run (8 of 15). `/api/v2` paths and tagged releases, no changelog (9 of 15). Total 79.\n\n### Agent ergonomics: MemMachine +6\n\n- Cognee (59): The tools reference lists 7 MCP tools, remember, recall, forget, code_search, search_tools, call_tool and cognify_status, with search_tools and call_tool reaching further tools on demand and COGNEE_MCP_TOOL_MODE=minimal cutting the list to the memory tools (25). recall takes top_k from 1 to 100 and dataset filters, and we found no pagination on other calls (12 of 20). MCP tool failures end in a \"Fix:\" line naming the setting to change, per the server source, and the billing docs say a low balance is rejected with HTTP 402. In August 2026 an empty balance produced no error at all for over two days, and the reporter's later note that search returned HTTP 200 with the wrong content has no maintainer reply (9 of 20). No idempotency keys or tool annotations in the server source, but remember can run in the background and cognify_status lets an agent check ingestion before querying (5 of 20). Python only, and the MCP server starts with one docker run (8 of 15). On 4 November the tools reference and cognee-mcp 0.5.7 source still register the same seven tools, with no tool annotations.\n- MemMachine (65): Searches take limit and filter options for episodic and semantic results (20 of 25). Memories can be listed and filtered by scope (15 of 20). Validation errors are typed; other failures are less clear, as the 429-to-500 issue shows (10 of 20). No idempotency keys or safe-retry guidance found (5 of 20). Python and TypeScript clients and a five-line quick start, once the import is fixed (15). Total 65.\n\n### Payments \u0026 pricing: MemMachine +5\n\n- Cognee (35): No x402, MPP or L402 (0). Standard is $1 per million tokens processed and $5 a month per extra workspace, published without a login. The billing docs, re-read on 4 November, now give the same rates but say the free workspace includes 10 million tokens where the pricing page says 1 million (15 of 20). Free includes a workspace, \"no card required\" (20). A person signs up for a Cloud tenant in the browser (0). Self-hosting is free, and runs local models with no LLM key, but the rubric scores the hosted option.\n- MemMachine (40): Self-hosted rule, with the price lines read for the hosted platform. No x402, MPP or L402 (0 of 40). No hosted prices found on the site or docs (0). Free to self-host with no card (20). An agent can install it from PyPI with no account (20). Total 40.\n\n## Score by category\n\n| Category | Weight | Cognee | MemMachine | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 43 | 51 | MemMachine +8 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 79 | Cognee +6 |\n| Agent ergonomics | 13% (16.2 this run) | 59 | 65 | MemMachine +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 39 | 41 | MemMachine +2 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 40 | MemMachine +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 83 | MemMachine +1 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 72 | 57 | Cognee +15 |\n| Negative events | ≤15 | -9 | 0 | |\n| **Total** | | **47.7 · D** | **58 · C** | |\n\n## Facts side by side\n\n| Fact | Cognee | MemMachine |\n| --- | --- | --- |\n| Kind | Platform | HTTP API |\n| Vendor | Cognee | MemVerge, Inc. |\n| Hosted endpoint | `https://\u003ctenant\u003e.aws.cognee.ai/api/v1` | no (local only) |\n| Transports | HTTP, stdio, SSE (legacy) | HTTP, stdio, Streamable HTTP |\n| Auth | OAuth or key | None |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Tools exposed | 7 | 3 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-10-08 |\n| Terms last updated | 2026-03-27 | no date given |\n| Privacy policy last updated | no date given | 2026-05-20 |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | not found in the text | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 31k stars | 3.1k stars, 28 npm/wk, 581 PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## How the scores have moved\n\n### Cognee\n\n- -6.9 on 4 November 2026 (C to D): product, correction, negative event\n- no change on 27 October 2026: checklist\n- no change on 8 October 2026: checklist\n\n### MemMachine\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Cognee.** Apache-2.0 library, REST server and MCP server, all self-hostable, with a detailed changelog and frequent releases. Cloud has no status page, rate limits or SOC 2, and runs in AWS us-east-1. Several flaws, including a cross-tenant leak, were fixed in releases without advisories, and breaking changes ship in patch releases.\n\n**MemMachine.** MemMachine is an Apache-2.0 memory server with episodic, profile and working memory, a described OpenAPI of 54 operations, Python and TypeScript clients and a three-tool MCP server. It is version 0.4.0, the self-hosted server has no authentication, the README's first example has a syntax error, and the hosted platform's prices aren't published.\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. Treat HTTP 402 as an empty Cloud balance and set a client timeout, since a run that exhausts credit part-way reports Failed in dataset status instead\n6. Read the \"Upgrading\" list in each release's notes before moving to a new patch version\n\n### MemMachine\n\n1. Import with `from memmachine_client import MemMachineClient`; the README line has a doubled `import`\n2. Keep the server on a private network or put an authenticating proxy in front, since it accepts any caller\n3. Scope every call with org, project, group, agent, user and session ids so memories don't mix\n4. Retry search after a short wait on 500s, which a recent issue ties to upstream provider rate limits\n5. Treat recalled episodes as untrusted text from past conversations\n\n## Questions\n\n### Which is better for AI agents, Cognee or MemMachine?\n\nMemMachine scores 58 (C) on agent readiness against Cognee's 47.7 (D), and leads in 5 of 7 scored categories. Cognee leads on schema \u0026 documentation and transparency \u0026 trust.\n\n### Can an agent call Cognee and MemMachine without installing anything?\n\nCognee has a hosted endpoint at https://\u003ctenant\u003e.aws.cognee.ai/api/v1. MemMachine runs on your own machine, with no hosted endpoint listed.\n\n### Are Cognee and MemMachine open source?\n\nYes. Cognee is open source (Apache-2.0). MemMachine is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cognee-vs-memmachine.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cognee-vs-memmachine.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cognee\", \"b\": \"memmachine\"}`. From a terminal: `anchor compare cognee memmachine`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cognee.json and https://www.anchorterminal.com/api/v1/tools/memmachine.json\n\n## Other comparisons with Cognee or MemMachine\n\n- [Amazon Bedrock AgentCore Memory vs Cognee](https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.md)\n- [Amazon Bedrock AgentCore Memory vs MemMachine](https://www.anchorterminal.com/compare/agentcore-memory-vs-memmachine.md)\n- [Basic Memory vs Cognee](https://www.anchorterminal.com/compare/basic-memory-vs-cognee.md)\n- [Basic Memory vs MemMachine](https://www.anchorterminal.com/compare/basic-memory-vs-memmachine.md)\n- [claude-mem vs Cognee](https://www.anchorterminal.com/compare/claude-mem-vs-cognee.md)\n- [claude-mem vs MemMachine](https://www.anchorterminal.com/compare/claude-mem-vs-memmachine.md)\n- [Cognee vs EverOS](https://www.anchorterminal.com/compare/cognee-vs-everos.md)\n- [Cognee vs Agent Platform Memory Bank](https://www.anchorterminal.com/compare/cognee-vs-google-memory-bank.md)\n- [Cognee vs Graphicle](https://www.anchorterminal.com/compare/cognee-vs-graphicle.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 Hy Memory](https://www.anchorterminal.com/compare/cognee-vs-hy-memory.md)\n- [Cognee vs LangMem](https://www.anchorterminal.com/compare/cognee-vs-langmem.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 Memori](https://www.anchorterminal.com/compare/cognee-vs-memori.md)\n- [Cognee vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/cognee-vs-memory-reference-server.md)\n- [Cognee vs MemPalace](https://www.anchorterminal.com/compare/cognee-vs-mempalace.md)\n- [Cognee vs past.dev](https://www.anchorterminal.com/compare/cognee-vs-past-dev.md)\n- [Cognee vs Supermemory API + MCP](https://www.anchorterminal.com/compare/cognee-vs-supermemory.md)\n- [Cognee vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/cognee-vs-tencentdb-agent-memory.md)\n- [Cognee vs Tokenrip](https://www.anchorterminal.com/compare/cognee-vs-tokenrip.md)\n- [Cognee vs Zep](https://www.anchorterminal.com/compare/cognee-vs-zep.md)\n- [EverOS vs MemMachine](https://www.anchorterminal.com/compare/everos-vs-memmachine.md)\n- [Agent Platform Memory Bank vs MemMachine](https://www.anchorterminal.com/compare/google-memory-bank-vs-memmachine.md)\n- [Graphiti vs MemMachine](https://www.anchorterminal.com/compare/graphiti-vs-memmachine.md)\n- [Hindsight vs MemMachine](https://www.anchorterminal.com/compare/hindsight-vs-memmachine.md)\n- [Honcho vs MemMachine](https://www.anchorterminal.com/compare/honcho-vs-memmachine.md)\n- [Hy Memory vs MemMachine](https://www.anchorterminal.com/compare/hy-memory-vs-memmachine.md)\n- [LangMem vs MemMachine](https://www.anchorterminal.com/compare/langmem-vs-memmachine.md)\n- [LocalGhost vs MemMachine](https://www.anchorterminal.com/compare/localghost-vs-memmachine.md)\n- [Mem0 Platform + MCP vs MemMachine](https://www.anchorterminal.com/compare/mem0-vs-memmachine.md)\n- [MemMachine vs Memori](https://www.anchorterminal.com/compare/memmachine-vs-memori.md)\n- [MemMachine vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.md)\n- [MemMachine vs MemPalace](https://www.anchorterminal.com/compare/memmachine-vs-mempalace.md)\n- [MemMachine vs past.dev](https://www.anchorterminal.com/compare/memmachine-vs-past-dev.md)\n- [MemMachine vs Supermemory API + MCP](https://www.anchorterminal.com/compare/memmachine-vs-supermemory.md)\n- [MemMachine vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/memmachine-vs-tencentdb-agent-memory.md)\n- [MemMachine vs Tokenrip](https://www.anchorterminal.com/compare/memmachine-vs-tokenrip.md)\n- [MemMachine vs Zep](https://www.anchorterminal.com/compare/memmachine-vs-zep.md)\n- [AI Memory vs Cognee](https://www.anchorterminal.com/compare/ai-memory-vs-cognee.md)\n- [AI Memory vs MemMachine](https://www.anchorterminal.com/compare/ai-memory-vs-memmachine.md)\n- [Cognee vs Context Mode Engine](https://www.anchorterminal.com/compare/cognee-vs-context-mode.md)\n- [Context Mode Engine vs MemMachine](https://www.anchorterminal.com/compare/context-mode-vs-memmachine.md)\n- [Graphicle vs MemMachine](https://www.anchorterminal.com/compare/graphicle-vs-memmachine.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from 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Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Cognee D 47.7", "MemMachine C 58", "scores" ], "h1": "Cognee vs MemMachine", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/cognee-vs-memmachine", "published": "2026-10-01", "section": "tools", "title": "Cognee vs MemMachine for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/cognee-vs-memmachine" }, "tokens": { "markdown": 4900, "slim": 2580 }, "version": 1 }