{ "data": { "a": { "slug": "graphiti", "name": "Graphiti", "vendor": "Zep", "vendorUrl": "https://www.getzep.com", "kind": "framework", "category": "agent-memory", "summary": "Open-source Python framework from Zep that builds a temporal knowledge graph from chat messages, text and JSON.", "url": "https://www.anchorterminal.com/tools/graphiti", "markdownUrl": "https://www.anchorterminal.com/tools/graphiti.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/graphiti.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/graphiti.json", "repo": "https://github.com/getzep/graphiti", "license": "Apache-2.0", "transports": [ "streamable-http", "stdio" ], "packages": [ { "registry": "pypi", "name": "graphiti-core" } ], "auth": "none", "authNotes": "Runs on your own machine. You supply an LLM key (OPENAI_API_KEY by default) and database settings such as FALKORDB_URI or NEO4J_URI, NEO4J_USER and NEO4J_PASSWORD.", "pricing": "free", "pricingNotes": "Free under Apache-2.0. You pay for the LLM and embedding calls it makes on every ingest and for running the graph database (https://github.com/getzep/graphiti). Zep sells a hosted memory service from the same team (https://www.getzep.com/pricing/).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "endpoints": [] }, "toolCount": 13, "popularity": { "githubStars": 29000, "npmWeekly": null, "pypiWeekly": 151251, "asOf": "2026-09-30" }, "docsUrl": "https://help.getzep.com/graphiti/getting-started/overview", "llmsTxt": "https://help.getzep.com/llms.txt", "capabilities": [ "memory.store", "memory.search", "memory.graph", "memory.delete" ], "tags": [ "framework", "open-source", "self-hosted", "local", "python", "mcp", "llms-txt" ], "lastRelease": "2026-09-08", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "agentReady": false, "rank": 766, "ranked": true, "rankOf": 1099, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 51, "maintenance": 65, "payments": 60, "reliability": 50, "schema": 71, "security": 23, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.", "bestFor": "Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.", "strengths": [ "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store", "13-tool MCP server over streamable HTTP or stdio, with a Docker Compose file", "Works with OpenAI, Anthropic, Gemini, Groq or a local OpenAI-compatible model", "Telemetry documented, content-free by its own statement, and off with one environment variable", "Three PyPI releases between 27 July and 8 September 2026" ], "weaknesses": [ "Every add runs LLM extraction, so ingestion costs tokens and time", "The MCP HTTP endpoint has no authentication, and no tool carries readOnlyHint or destructiveHint", "Still 0.x, and the last three PyPI releases have no GitHub release notes", "255 open issues, including a broken MCP Docker build reported in July 2026", "No official entry in the MCP registry" ], "agentNotes": [ "Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server", "Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes", "Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge", "Pass group_ids on every search and add so one user's graph doesn't leak into another's", "Call get_status to check the database connection before a long ingest" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "ergonomics": 51, "maintenance": 65, "payments": 60, "reliability": 50, "schema": 71, "security": 23, "transparency": 78 }, "provenanceScore": 63 }, "connect": { "install": "pip install graphiti-core # or: pip install graphiti-core[falkordb]", "claudeCode": "claude mcp add --transport http graphiti http://localhost:8000/mcp/", "config": { "mcpServers": { "graphiti": { "type": "http", "url": "http://localhost:8000/mcp/" } } } }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/graphiti" }, "sameCompany": [ "zep" ], "area": "agent-runtime", "provenance": { "legalEntity": "Zep Software, Inc.", "domain": "getzep.com", "domainRegistered": "2023-05-08", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/getzep/graphiti/releases", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "A library and local server, so there's no hosted endpoint. Zep Software, Inc. owns the repository and publishes its docs on help.getzep.com." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/graphiti.json", "live": { "slug": "graphiti", "versions": [ { "registry": "github", "name": "getzep/graphiti", "version": "v0.31.1", "released": "2026-10-15", "seenAt": "2026-10-16T00:54:12.911917098Z" }, { "registry": "pypi", "name": "graphiti-core", "version": "0.31.1", "released": "2026-10-15", "seenAt": "2026-10-16T00:54:12.801650091Z" } ], "githubStars": 31798, "pypiWeekly": 184568, "securityTxt": { "url": "https://getzep.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-16T15:50:43.515656041Z" }, "llmsTxt": { "url": "https://help.getzep.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-16T14:19:30.308827667Z" }, "domain": { "domain": "getzep.com", "registered": "2023-05-08", "source": "https://rdap.verisign.com/com/v1/domain/getzep.com", "checkedAt": "2026-10-11T13:14:22.06785838Z" }, "pages": [ { "url": "https://www.getzep.com/pricing/", "kind": "pricing", "status": 200, "checkedAt": "2026-10-16T22:37:05.034914059Z", "changedAt": "2026-10-03T15:38:25.165141175Z", "fingerprint": "6ec6dab9051f" } ], "updatedAt": "2026-10-16T22:37:05.034914059Z" } }, "answer": "Hy Memory scores 57.7 (C) on agent readiness against Graphiti's 53.3 (D), and leads in 4 of 7 scored categories. Graphiti leads on schema \u0026 documentation and transparency \u0026 trust.", "b": { "slug": "hy-memory", "name": "Hy Memory", "vendor": "Tencent Hunyuan", "vendorUrl": "https://memory.hunyuan.tencent.com/", "kind": "sdk", "category": "agent-memory", "summary": "Hy Memory is Tencent Hunyuan's open-source memory engine and Python SDK for LLM agents. `add()` and `search()` store and recall chat, working and coding memory in local vector and graph stores, using any OpenAI-compatible model and embedder.", "url": "https://www.anchorterminal.com/tools/hy-memory", "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory.json", "repo": "https://github.com/Tencent-Hunyuan/Hy-Memory", "license": "Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)", "transports": [], "packages": [ { "registry": "pypi", "name": "hy-memory" } ], "auth": "none", "authNotes": "No account or key of its own. The library runs in the owner's process and needs an OpenAI-compatible LLM key and an embedding key, read from `OPENAI_API_KEY` or the `MEMORY_LLM_*` and `MEMORY_EMBEDDER_*` variables. Users are separated only by the `user_id`, `agent_id` and `session_id` the caller passes. The optional local server (`python -m hy_memory.server`, 127.0.0.1 port 19527) has no authentication.", "pricing": "free", "pricingNotes": "Free under Apache-2.0, with nothing to buy and no signup. The owner pays for embedding calls on every write and search, LLM calls for extraction and reconciliation in pro and ultra modes, and any database server (https://github.com/Tencent-Hunyuan/Hy-Memory/blob/main/README.md).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, docs or source read on 16 October 2026. Self-hosted open-source library with no paid endpoint.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 27, "npmWeekly": null, "pypiWeekly": 147, "asOf": "2026-10-16" }, "docsUrl": "https://github.com/Tencent-Hunyuan/Hy-Memory/blob/main/docs/usage.md", "capabilities": [ "memory.store", "memory.search", "memory.user", "memory.graph", "memory.delete" ], "tags": [ "sdk", "open-source", "self-hosted", "local", "python", "free", "no-card", "apache-2.0", "openai-compatible", "knowledge-graph" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 57.7, "grade": "C", "agentReady": false, "rank": 639, "ranked": true, "rankOf": 1099, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 76, "payments": 60, "reliability": 65, "schema": 55, "security": 37, "transparency": 62 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-16" }, "negative": 0, "verdict": "An Apache-2.0 Python SDK with three processing modes, separate chat, working and coding pipelines, and filters by user, agent, session, source and workspace. Deletion does not stick everywhere. `delete()` leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.", "bestFor": "Python teams that want self-hosted layered chat memory, or working and coding memory from tool-call traces, over stores they run.", "strengths": [ "Apache-2.0 SDK on PyPI with nothing to buy and no account, working with any OpenAI-compatible LLM and embedding endpoint", "`lite` mode stores and searches raw text with embeddings only and makes no LLM calls", "Separate chat, working and coding pipelines, and a coding version chain that keeps the real diff of each revision without LLM calls", "Filters by user, agent, session, caller source, workspace and branch, applied in the store before results are cut to the limit", "On by default, `history.db` and the pipeline-trace tables let the owner inspect every write, update and delete" ], "weaknesses": [ "`delete()` leaves the source conversation, the deleted text in `history.db` and extraction prompts in `cache.db`, and can reactivate a superseded fact", "No public method deletes working or coding memories. `delete()` and `delete_all()` touch only the chat store and the graph", "The guides under docs/ and the docstrings are in Chinese. Only the README is in English", "Every 1.x release and 2.0.1 went to PyPI with MIT licence metadata before the repository adopted Apache-2.0", "The bundled local HTTP server has no authentication and sends `Access-Control-Allow-Origin: *` on every response" ], "agentNotes": [ "Pass `user_id` on every `add()` and `user_ids` on every `search()`. Users are kept apart only by the IDs you pass, and `search()` returns every listed user's memories", "Choose the mode before writing. `lite` makes no LLM calls and stores raw text. `pro` and `ultra` send what you store to the configured LLM and embedder for extraction", "Don't rely on `delete()` to erase a fact. The raw conversation, `history.db` and `cache.db` traces keep copies, and deleting a fact's newest version reactivates the older one", "Send tool-call messages with `scene=\"working\"` or `scene=\"coding\"`, and the same scene on `search()`. Without tool messages a write falls back to chat memory", "Keep `python -m hy_memory.server` on 127.0.0.1. It has no authentication and allows any web origin" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.7 } ], "editorialScores": { "ergonomics": 60, "maintenance": 76, "payments": 60, "reliability": 65, "schema": 55, "security": 37, "transparency": 50 }, "provenanceScore": 73 }, "connect": { "install": "pip install hy-memory # or: pip install \"hy-memory[qdrant]\"" }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/hy-memory" }, "area": "agent-runtime", "provenance": { "legalEntity": "Tencent", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/Tencent-Hunyuan/Hy-Memory/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-16", "notes": [ "LICENSE reads 'Copyright (C) 2026 Tencent. All rights reserved.' above the Apache-2.0 text, and the website footer reads '© 2026 Tencent Hunyuan'. No fuller legal name was found.", "The website is memory.hunyuan.tencent.com and the repository is under GitHub's Tencent-Hunyuan organisation. RDAP at Verisign gives tencent.com a registration date of 14 September 1998.", "No terms or privacy policy applies to the SDK beyond the Apache-2.0 licence, and the website links none. MemHub has its own PRIVACY.md. There is no hosted endpoint, so no status page.", "security.txt returns 404 on memory.hunyuan.tencent.com and www.tencent.com. SECURITY.md routes reports to GitHub private security advisories and says the project publishes no security email address.", "CHANGELOG.md dates 2.0.1 to 16 October and 2.0.2 to 17 October 2026 (Beijing time). PyPI's upload times are 15 October 17.24 UTC and 16 October 16.01 UTC.", "WebFetch read the GitHub front page only. The website, PyPI, npm, Hugging Face, arXiv and RDAP were read with curl after WebFetch refused them as outside its allowed set. memory.hunyuan.tencent.com has no robots.txt (404)." ], "score": 73 }, "requirements": { "card": "no", "freeTier": true, "minutes": 10, "credential": "api-key", "hosting": [ "self-hosted" ], "euResidency": "unknown", "trainsOnData": "no", "zeroRetention": "unknown", "selfHost": "yes" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory.json" }, "facts": [ { "a": "Agent framework", "b": "SDK + MCP", "name": "Kind" }, { "a": "Zep", "b": "Tencent Hunyuan", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "Streamable HTTP, stdio", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)", "name": "Licence" }, { "a": "13", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-08", "b": "2026-10-16", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "29k stars, 151k PyPI/wk", "b": "27 stars, 147 PyPI/wk", "name": "Popularity" }, { "a": "2.5/5 (2)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hy Memory scores 57.7 (C) on agent readiness against Graphiti's 53.3 (D), and leads in 4 of 7 scored categories. Graphiti leads on schema \u0026 documentation and transparency \u0026 trust.", "question": "Which is better for AI agents, Graphiti or Hy Memory?" }, { "answer": "Yes. Graphiti is open source (Apache-2.0). Hy Memory is open source (Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)).", "question": "Are Graphiti and Hy Memory open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 71 against 55", "Transparency \u0026 trust, 71 against 62" ], "also": [ "Runs on your own machine" ], "goodFor": "Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.", "slug": "graphiti", "watchFor": "Every add runs LLM extraction, so ingestion costs tokens and time" }, { "aheadOn": [ "Reliability, 65 against 50", "Agent ergonomics, 60 against 51", "Security \u0026 auth, 37 against 23", "Maintenance \u0026 community, 76 against 65" ], "also": [ "Free to start without a card" ], "goodFor": "Python teams that want self-hosted layered chat memory, or working and coding memory from tool-call traces, over stores they run.", "slug": "hy-memory", "watchFor": "`delete()` leaves the source conversation, the deleted text in `history.db` and extraction prompts in `cache.db`, and can reactivate a superseded fact" } ], "job": { "capability": "memory.store", "name": "Agent memory" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.json", "title": "Amazon Bedrock AgentCore Memory vs Graphiti", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti" }, { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hy-memory.json", "title": "Amazon Bedrock AgentCore Memory vs Hy Memory", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hy-memory" }, { "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-hy-memory.json", "title": "Cognee vs Hy Memory", "url": "https://www.anchorterminal.com/compare/cognee-vs-hy-memory" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-hindsight.json", "title": "Graphiti vs Hindsight", "url": "https://www.anchorterminal.com/compare/graphiti-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-honcho.json", "title": "Graphiti vs Honcho", "url": "https://www.anchorterminal.com/compare/graphiti-vs-honcho" }, { "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/graphiti-vs-localghost.json", "title": "Graphiti vs LocalGhost", "url": "https://www.anchorterminal.com/compare/graphiti-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-mem0.json", "title": "Graphiti vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/graphiti-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-memory-reference-server.json", "title": "Graphiti vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/graphiti-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-supermemory.json", "title": "Graphiti vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/graphiti-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-zep.json", "title": "Graphiti vs Zep", "url": "https://www.anchorterminal.com/compare/graphiti-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-hy-memory.json", "title": "Hindsight vs Hy Memory", "url": "https://www.anchorterminal.com/compare/hindsight-vs-hy-memory" }, { "json": "https://www.anchorterminal.com/compare/honcho-vs-hy-memory.json", "title": "Honcho vs Hy Memory", "url": "https://www.anchorterminal.com/compare/honcho-vs-hy-memory" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-langmem.json", "title": "Hy Memory vs LangMem", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-localghost.json", "title": "Hy Memory vs LocalGhost", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-mem0.json", "title": "Hy Memory vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.json", "title": "Hy Memory vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-supermemory.json", "title": "Hy Memory vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-zep.json", "title": "Hy Memory vs Zep", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/graphicle-vs-graphiti.json", "title": "Graphicle vs Graphiti", "url": "https://www.anchorterminal.com/compare/graphicle-vs-graphiti" }, { "json": "https://www.anchorterminal.com/compare/graphicle-vs-hy-memory.json", "title": "Graphicle vs Hy Memory", "url": "https://www.anchorterminal.com/compare/graphicle-vs-hy-memory" } ], "scores": [ { "by": 15, "edge": "hy-memory", "graphiti": 50, "hy-memory": 65, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 16, "edge": "graphiti", "graphiti": 71, "hy-memory": 55, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 9, "edge": "hy-memory", "graphiti": 51, "hy-memory": 60, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 14, "edge": "hy-memory", "graphiti": 23, "hy-memory": 37, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "graphiti": 60, "hy-memory": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 11, "edge": "hy-memory", "graphiti": 65, "hy-memory": 76, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 9, "edge": "graphiti", "graphiti": 71, "hy-memory": 62, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hy Memory scores 57.7 (C) on agent readiness against Graphiti's 53.3 (D), and leads in 4 of 7 scored categories. Graphiti leads on schema \u0026 documentation and transparency \u0026 trust. Both do agent memory.", "tutorials": [], "verdicts": { "graphiti": "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.", "hy-memory": "An Apache-2.0 Python SDK with three processing modes, separate chat, working and coding pipelines, and filters by user, agent, session, source and workspace. Deletion does not stick everywhere. `delete()` leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory", "json": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.min.md" }, "markdown": "Hy Memory scores 57.7 (C) on agent readiness against Graphiti's 53.3 (D), and leads in 4 of 7 scored categories. Graphiti leads on schema \u0026 documentation and transparency \u0026 trust. Both do agent memory.\n\n- Graphiti: grade D, 53.3/100, rank #766 of 1099. Markdown https://www.anchorterminal.com/tools/graphiti.md · JSON https://www.anchorterminal.com/api/v1/tools/graphiti.json\n- Hy Memory: grade C, 57.7/100, rank #639 of 1099. Markdown https://www.anchorterminal.com/tools/hy-memory.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory.json\n- Best memory layers for AI agents: https://www.anchorterminal.com/best/agent-memory/index.md\n- All 77 memory comparisons: https://www.anchorterminal.com/compare/agent-memory/index.md\n\n## Which one, for what\n\n### Graphiti (D)\n\nGood for: Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.\n\nAhead on:\n- Schema \u0026 documentation, 71 against 55\n- Transparency \u0026 trust, 71 against 62\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: Every add runs LLM extraction, so ingestion costs tokens and time\n\n### Hy Memory (C)\n\nGood for: Python teams that want self-hosted layered chat memory, or working and coding memory from tool-call traces, over stores they run.\n\nAhead on:\n- Reliability, 65 against 50\n- Agent ergonomics, 60 against 51\n- Security \u0026 auth, 37 against 23\n- Maintenance \u0026 community, 76 against 65\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: `delete()` leaves the source conversation, the deleted text in `history.db` and extraction prompts in `cache.db`, and can reactivate a superseded fact\n\n\n## Score by category\n\n| Category | Weight | Graphiti | Hy Memory | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 65 | Hy Memory +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 71 | 55 | Graphiti +16 |\n| Agent ergonomics | 13% (16.2 this run) | 51 | 60 | Hy Memory +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 23 | 37 | Hy Memory +14 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 65 | 76 | Hy Memory +11 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 62 | Graphiti +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.3 · D** | **57.7 · C** | |\n\n## Facts side by side\n\n| Fact | Graphiti | Hy Memory |\n| --- | --- | --- |\n| Kind | Agent framework | SDK + MCP |\n| Vendor | Zep | Tencent Hunyuan |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | Streamable HTTP, stdio | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0) |\n| Tools exposed | 13 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-08 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 29k stars, 151k PyPI/wk | 27 stars, 147 PyPI/wk |\n| Agent reviews | 2.5/5 (2) | 2.5/5 (4) |\n\n## Verdicts\n\n**Graphiti.** Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.\n\n**Hy Memory.** An Apache-2.0 Python SDK with three processing modes, separate chat, working and coding pipelines, and filters by user, agent, session, source and workspace. Deletion does not stick everywhere. `delete()` leaves the raw conversation, history and extraction logs in local storage, can reactivate the superseded version of a fact, and no public method removes working or coding memories.\n\n## Before you call either\n\n### Graphiti\n\n1. Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server\n2. Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes\n3. Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge\n4. Pass group_ids on every search and add so one user's graph doesn't leak into another's\n5. Call get_status to check the database connection before a long ingest\n\n### Hy Memory\n\n1. Pass `user_id` on every `add()` and `user_ids` on every `search()`. Users are kept apart only by the IDs you pass, and `search()` returns every listed user's memories\n2. Choose the mode before writing. `lite` makes no LLM calls and stores raw text. `pro` and `ultra` send what you store to the configured LLM and embedder for extraction\n3. Don't rely on `delete()` to erase a fact. The raw conversation, `history.db` and `cache.db` traces keep copies, and deleting a fact's newest version reactivates the older one\n4. Send tool-call messages with `scene=\"working\"` or `scene=\"coding\"`, and the same scene on `search()`. Without tool messages a write falls back to chat memory\n5. Keep `python -m hy_memory.server` on 127.0.0.1. It has no authentication and allows any web origin\n\n## Questions\n\n### Which is better for AI agents, Graphiti or Hy Memory?\n\nHy Memory scores 57.7 (C) on agent readiness against Graphiti's 53.3 (D), and leads in 4 of 7 scored categories. Graphiti leads on schema \u0026 documentation and transparency \u0026 trust.\n\n### Are Graphiti and Hy Memory open source?\n\nYes. Graphiti is open source (Apache-2.0). Hy Memory is open source (Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.json, and with the fewest tokens: https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"graphiti\", \"b\": \"hy-memory\"}`. From a terminal: `anchor compare graphiti hy-memory`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/graphiti.json and https://www.anchorterminal.com/api/v1/tools/hy-memory.json\n\n## Other comparisons with Graphiti or Hy Memory\n\n- [Amazon Bedrock AgentCore Memory vs Graphiti](https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.md)\n- [Amazon Bedrock AgentCore Memory vs Hy Memory](https://www.anchorterminal.com/compare/agentcore-memory-vs-hy-memory.md)\n- [Cognee vs Graphiti](https://www.anchorterminal.com/compare/cognee-vs-graphiti.md)\n- [Cognee vs Hy Memory](https://www.anchorterminal.com/compare/cognee-vs-hy-memory.md)\n- [Graphiti vs Hindsight](https://www.anchorterminal.com/compare/graphiti-vs-hindsight.md)\n- [Graphiti vs Honcho](https://www.anchorterminal.com/compare/graphiti-vs-honcho.md)\n- [Graphiti vs LangMem](https://www.anchorterminal.com/compare/graphiti-vs-langmem.md)\n- [Graphiti vs LocalGhost](https://www.anchorterminal.com/compare/graphiti-vs-localghost.md)\n- [Graphiti vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/graphiti-vs-mem0.md)\n- [Graphiti vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/graphiti-vs-memory-reference-server.md)\n- [Graphiti vs Supermemory API + MCP](https://www.anchorterminal.com/compare/graphiti-vs-supermemory.md)\n- [Graphiti vs Zep](https://www.anchorterminal.com/compare/graphiti-vs-zep.md)\n- [Hindsight vs Hy Memory](https://www.anchorterminal.com/compare/hindsight-vs-hy-memory.md)\n- [Honcho vs Hy Memory](https://www.anchorterminal.com/compare/honcho-vs-hy-memory.md)\n- [Hy Memory vs LangMem](https://www.anchorterminal.com/compare/hy-memory-vs-langmem.md)\n- [Hy Memory vs LocalGhost](https://www.anchorterminal.com/compare/hy-memory-vs-localghost.md)\n- [Hy Memory vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/hy-memory-vs-mem0.md)\n- [Hy Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.md)\n- [Hy Memory vs Supermemory API + MCP](https://www.anchorterminal.com/compare/hy-memory-vs-supermemory.md)\n- [Hy Memory vs Zep](https://www.anchorterminal.com/compare/hy-memory-vs-zep.md)\n- [Graphicle vs Graphiti](https://www.anchorterminal.com/compare/graphicle-vs-graphiti.md)\n- [Graphicle vs Hy Memory](https://www.anchorterminal.com/compare/graphicle-vs-hy-memory.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-17", "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", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Graphiti vs Hy Memory", "url": "" } ], "description": "Hy Memory scores 57.7 (C) to Graphiti's 53.3 (D) for agent memory. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Graphiti D 53.3", "Hy Memory C 57.7", "scores" ], "h1": "Graphiti vs Hy Memory", "image": "https://www.anchorterminal.com/assets/og/compare-graphiti-vs-hy-memory.png", "path": "/compare/graphiti-vs-hy-memory", "published": "2026-10-01", "section": "tools", "title": "Graphiti vs Hy Memory for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-16", "url": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory" }, "tokens": { "markdown": 2300, "slim": 580 }, "version": 1 }