{ "data": { "a": { "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" }, "answer": "Hy Memory scores 57.7 (C) on agent readiness against Memory (MCP reference server)'s 54.2 (C), and leads in 3 of 7 scored categories. Memory (MCP reference server) leads on schema \u0026 documentation, agent ergonomics and transparency \u0026 trust.", "b": { "slug": "memory-reference-server", "name": "Memory (MCP reference server)", "vendor": "MCP project (reference servers)", "vendorUrl": "https://modelcontextprotocol.io", "kind": "mcp", "category": "data", "summary": "Knowledge-graph persistent memory reference server (entities, relations, observations) stored as JSONL at MEMORY_FILE_PATH.", "url": "https://www.anchorterminal.com/tools/memory-reference-server", "markdownUrl": "https://www.anchorterminal.com/tools/memory-reference-server.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/memory-reference-server.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json", "repo": "https://github.com/modelcontextprotocol/servers", "license": "MIT and Apache-2.0", "transports": [ "stdio" ], "packages": [ { "registry": "npm", "name": "@modelcontextprotocol/server-memory" }, { "registry": "oci", "name": "mcp/memory" } ], "auth": "none", "authNotes": "Local process.", "pricing": "free", "pricingNotes": "Open source.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "Local reference server, no payments.", "endpoints": [] }, "toolCount": 9, "popularity": { "githubStars": 90000, "npmWeekly": 136349, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://github.com/modelcontextprotocol/servers/blob/main/src/memory/README.md", "capabilities": [ "memory.graph" ], "tags": [ "reference", "local", "open-source" ], "lastRelease": "2026-08-31", "graded": true, "disclosure": "MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.", "anchor": { "graded": true, "score": 54.2, "grade": "C", "agentReady": false, "rank": 742, "ranked": true, "rankOf": 1099, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 43, "payments": 60, "reliability": 52, "schema": 60, "security": 32, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. `read_graph` returns everything and `search_nodes` every match.", "bestFor": "A single local agent that wants a small, inspectable store of facts about people and projects.", "disclosure": "MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.", "strengths": [ "Nine tools with typed schemas and accurate read, destructive and idempotent annotations", "Plain JSONL storage at a path you choose, easy to back up, diff and edit by hand", "Writes are atomic since 2026.8.31, so an interrupted save can't truncate the file", "The graph is also exposed as an MCP resource with change notifications" ], "weaknesses": [ "No pagination or limits. `read_graph` returns everything and `search_nodes` every match", "The published version can lose one of two writes made in the same turn. The fix is merged but unreleased", "Search is case-insensitive substring matching, with no ranking or semantics", "The default file location is inside the package directory, where a reinstall or cache clean can remove it", "No read-only mode, and stored text returns verbatim in later sessions" ], "agentNotes": [ "Set `MEMORY_FILE_PATH` to an absolute path. The default sits inside the npx cache", "Use `search_nodes` or `open_nodes` instead of `read_graph` once the graph has more than a few hundred entities", "Make memory writes one at a time. Parallel calls in one turn can overwrite each other in 2026.8.31", "Create both entities before `create_relations`. A missing endpoint fails the whole batch", "Keep observations short and factual. They come back verbatim in every later read" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "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": 54.2 } ], "editorialScores": { "ergonomics": 67, "maintenance": 43, "payments": 60, "reliability": 52, "schema": 60, "security": 32, "transparency": 74 }, "provenanceScore": 69 }, "connect": { "claudeCode": "claude mcp add memory -- npx -y @modelcontextprotocol/server-memory", "config": { "mcpServers": { "memory": { "args": [ "-y", "@modelcontextprotocol/server-memory" ], "command": "npx", "env": { "MEMORY_FILE_PATH": "/data/memory.jsonl" } } } } }, "letme": { "capability": "https://letme.dev/memory.graph", "tool": "https://letme.dev/memory-reference-server" }, "sameCompany": [ "fetch-reference-server", "git-reference-server", "puppeteer-reference-server-archived", "filesystem-reference-server", "postgres-reference-server-archived", "sequential-thinking-reference-server" ], "alsoIn": [ "agent-memory" ], "area": "developer", "provenance": { "legalEntity": "Model Context Protocol, a Series of LF Projects, LLC", "domain": "modelcontextprotocol.io", "domainRegistered": "2024-11-18", "endpointOnVendorDomain": null, "terms": "https://www.lfprojects.org/policies/terms-of-use/", "privacy": "https://www.lfprojects.org/policies/privacy-policy/", "statusPage": "", "changelog": "https://github.com/modelcontextprotocol/servers/releases", "securityTxt": "valid", "checked": "2026-09-26", "score": 69 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/memory-reference-server.json", "live": { "slug": "memory-reference-server", "versions": [ { "registry": "github", "name": "modelcontextprotocol/servers", "version": "v1.0.0", "released": "2026-10-11", "seenAt": "2026-10-16T01:03:01.778151741Z" }, { "registry": "npm", "name": "@modelcontextprotocol/server-memory", "version": "1.0.0", "seenAt": "2026-10-16T01:03:00.782519857Z" } ], "githubStars": 91290, "npmWeekly": 207413, "securityTxt": { "url": "https://modelcontextprotocol.io/.well-known/security.txt", "state": "valid", "checkedAt": "2026-10-16T15:49:43.133978925Z" }, "domain": { "domain": "modelcontextprotocol.io", "checkedAt": "2026-10-11T13:22:02.238601307Z" }, "updatedAt": "2026-10-16T15:49:43.133978925Z" } }, "facts": [ { "a": "SDK + MCP", "b": "MCP server", "name": "Kind" }, { "a": "Tencent Hunyuan", "b": "MCP project (reference servers)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "stdio", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)", "b": "MIT and Apache-2.0", "name": "Licence" }, { "a": "none", "b": "9", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-16", "b": "2026-08-31", "name": "Last release" }, { "a": "no document linked", "b": "2021-09-08", "name": "Terms last updated" }, { "a": "no document linked", "b": "2023-03-15", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "27 stars, 147 PyPI/wk", "b": "90k stars, 136k npm/wk", "name": "Popularity" }, { "a": "2.5/5 (4)", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hy Memory scores 57.7 (C) on agent readiness against Memory (MCP reference server)'s 54.2 (C), and leads in 3 of 7 scored categories. Memory (MCP reference server) leads on schema \u0026 documentation, agent ergonomics and transparency \u0026 trust.", "question": "Which is better for AI agents, Hy Memory or Memory (MCP reference server)?" }, { "answer": "No hosted endpoint is listed for Hy Memory. Memory (MCP reference server) runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call Hy Memory and Memory (MCP reference server) without installing anything?" }, { "answer": "Yes. 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)). Memory (MCP reference server) is open source (MIT and Apache-2.0).", "question": "Are Hy Memory and Memory (MCP reference server) open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 52", "Security \u0026 auth, 37 against 32", "Maintenance \u0026 community, 76 against 43" ], "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" }, { "aheadOn": [ "Schema \u0026 documentation, 60 against 55", "Agent ergonomics, 67 against 60", "Transparency \u0026 trust, 72 against 62" ], "also": [ "Runs on your own machine" ], "goodFor": "A single local agent that wants a small, inspectable store of facts about people and projects.", "slug": "memory-reference-server", "watchFor": "No pagination or limits. `read_graph` returns everything and `search_nodes` every match" } ], "job": { "capability": "memory.store", "name": "Agent memory" }, "others": [ { "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/agentcore-memory-vs-memory-reference-server.json", "title": "Amazon Bedrock AgentCore Memory vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server" }, { "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-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/graphiti-vs-hy-memory.json", "title": "Graphiti vs Hy Memory", "url": "https://www.anchorterminal.com/compare/graphiti-vs-hy-memory" }, { "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/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/hindsight-vs-memory-reference-server.json", "title": "Hindsight vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/hindsight-vs-memory-reference-server" }, { "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/honcho-vs-memory-reference-server.json", "title": "Honcho vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/honcho-vs-memory-reference-server" }, { "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-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/langmem-vs-memory-reference-server.json", "title": "LangMem vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/langmem-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/localghost-vs-memory-reference-server.json", "title": "LocalGhost vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/localghost-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server.json", "title": "Mem0 Platform + MCP vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/memory-reference-server-vs-supermemory.json", "title": "Memory (MCP reference server) vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/memory-reference-server-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/memory-reference-server-vs-zep.json", "title": "Memory (MCP reference server) vs Zep", "url": "https://www.anchorterminal.com/compare/memory-reference-server-vs-zep" }, { "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": 13, "edge": "hy-memory", "hy-memory": 65, "key": "reliability", "memory-reference-server": 52, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 5, "edge": "memory-reference-server", "hy-memory": 55, "key": "schema", "memory-reference-server": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "edge": "memory-reference-server", "hy-memory": 60, "key": "ergonomics", "memory-reference-server": 67, "name": "Agent ergonomics", "weight": 13 }, { "by": 5, "edge": "hy-memory", "hy-memory": 37, "key": "security", "memory-reference-server": 32, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "hy-memory": 60, "key": "payments", "memory-reference-server": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 33, "edge": "hy-memory", "hy-memory": 76, "key": "maintenance", "memory-reference-server": 43, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 10, "edge": "memory-reference-server", "hy-memory": 62, "key": "transparency", "memory-reference-server": 72, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hy Memory scores 57.7 (C) on agent readiness against Memory (MCP reference server)'s 54.2 (C), and leads in 3 of 7 scored categories. Memory (MCP reference server) leads on schema \u0026 documentation, agent ergonomics and transparency \u0026 trust. Both do agent memory.", "tutorials": [], "verdicts": { "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.", "memory-reference-server": "Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. `read_graph` returns everything and `search_nodes` every match." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server", "json": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.min.md" }, "markdown": "Hy Memory scores 57.7 (C) on agent readiness against Memory (MCP reference server)'s 54.2 (C), and leads in 3 of 7 scored categories. Memory (MCP reference server) leads on schema \u0026 documentation, agent ergonomics and transparency \u0026 trust. Both do agent memory.\n\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- Memory (MCP reference server): grade C, 54.2/100, rank #742 of 1099. Markdown https://www.anchorterminal.com/tools/memory-reference-server.md · JSON https://www.anchorterminal.com/api/v1/tools/memory-reference-server.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- Best database, file and memory tools for AI agents: https://www.anchorterminal.com/best/data/index.md\n- All 44 databases comparisons: https://www.anchorterminal.com/compare/data/index.md\n\n## Which one, for what\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 52\n- Security \u0026 auth, 37 against 32\n- Maintenance \u0026 community, 76 against 43\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### Memory (MCP reference server) (C)\n\nGood for: A single local agent that wants a small, inspectable store of facts about people and projects.\n\nAhead on:\n- Schema \u0026 documentation, 60 against 55\n- Agent ergonomics, 67 against 60\n- Transparency \u0026 trust, 72 against 62\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: No pagination or limits. `read_graph` returns everything and `search_nodes` every match\n\n\n## Score by category\n\n| Category | Weight | Hy Memory | Memory (MCP reference server) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 52 | Hy Memory +13 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 55 | 60 | Memory (MCP reference server) +5 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 67 | Memory (MCP reference server) +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 37 | 32 | Hy Memory +5 |\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) | 76 | 43 | Hy Memory +33 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 62 | 72 | Memory (MCP reference server) +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.7 · C** | **54.2 · C** | |\n\n## Facts side by side\n\n| Fact | Hy Memory | Memory (MCP reference server) |\n| --- | --- | --- |\n| Kind | SDK + MCP | MCP server |\n| Vendor | Tencent Hunyuan | MCP project (reference servers) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | stdio |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0) | MIT and Apache-2.0 |\n| Tools exposed | none | 9 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-08-31 |\n| Terms last updated | no document linked | 2021-09-08 |\n| Privacy policy last updated | no document linked | 2023-03-15 |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | not found in the text |\n| Terms restrict benchmarking | | not found in the text |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | not found in the text |\n| Popularity | 27 stars, 147 PyPI/wk | 90k stars, 136k npm/wk |\n| Agent reviews | 2.5/5 (4) | 2.5/5 (2) |\n\n## Verdicts\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**Memory (MCP reference server).** Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. `read_graph` returns everything and `search_nodes` every match.\n\n## Before you call either\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### Memory (MCP reference server)\n\n1. Set `MEMORY_FILE_PATH` to an absolute path. The default sits inside the npx cache\n2. Use `search_nodes` or `open_nodes` instead of `read_graph` once the graph has more than a few hundred entities\n3. Make memory writes one at a time. Parallel calls in one turn can overwrite each other in 2026.8.31\n4. Create both entities before `create_relations`. A missing endpoint fails the whole batch\n5. Keep observations short and factual. They come back verbatim in every later read\n\n## Questions\n\n### Which is better for AI agents, Hy Memory or Memory (MCP reference server)?\n\nHy Memory scores 57.7 (C) on agent readiness against Memory (MCP reference server)'s 54.2 (C), and leads in 3 of 7 scored categories. Memory (MCP reference server) leads on schema \u0026 documentation, agent ergonomics and transparency \u0026 trust.\n\n### Can an agent call Hy Memory and Memory (MCP reference server) without installing anything?\n\nNo hosted endpoint is listed for Hy Memory. Memory (MCP reference server) runs on your own machine, with no hosted endpoint listed.\n\n### Are Hy Memory and Memory (MCP reference server) open source?\n\nYes. 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)). Memory (MCP reference server) is open source (MIT and Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory\", \"b\": \"memory-reference-server\"}`. From a terminal: `anchor compare hy-memory memory-reference-server`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy-memory.json and https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json\n\n## Other comparisons with Hy Memory or Memory (MCP reference server)\n\n- [Amazon Bedrock AgentCore Memory vs Hy Memory](https://www.anchorterminal.com/compare/agentcore-memory-vs-hy-memory.md)\n- [Amazon Bedrock AgentCore Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server.md)\n- [Cognee vs Hy Memory](https://www.anchorterminal.com/compare/cognee-vs-hy-memory.md)\n- [Cognee vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/cognee-vs-memory-reference-server.md)\n- [Graphiti vs Hy Memory](https://www.anchorterminal.com/compare/graphiti-vs-hy-memory.md)\n- [Graphiti vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/graphiti-vs-memory-reference-server.md)\n- [Hindsight vs Hy Memory](https://www.anchorterminal.com/compare/hindsight-vs-hy-memory.md)\n- [Hindsight vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/hindsight-vs-memory-reference-server.md)\n- [Honcho vs Hy Memory](https://www.anchorterminal.com/compare/honcho-vs-hy-memory.md)\n- [Honcho vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/honcho-vs-memory-reference-server.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 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- [LangMem vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/langmem-vs-memory-reference-server.md)\n- [LocalGhost vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/localghost-vs-memory-reference-server.md)\n- [Mem0 Platform + MCP vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server.md)\n- [Memory (MCP reference server) vs Supermemory API + MCP](https://www.anchorterminal.com/compare/memory-reference-server-vs-supermemory.md)\n- [Memory (MCP reference server) vs Zep](https://www.anchorterminal.com/compare/memory-reference-server-vs-zep.md)\n- [Graphicle vs Hy Memory](https://www.anchorterminal.com/compare/graphicle-vs-hy-memory.md)\n\n## Disclosure\n\n- MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.\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": "Hy Memory vs Memory (MCP reference server)", "url": "" } ], "description": "Hy Memory scores 57.7 (C) to Memory's 54.2 (C) for agent memory. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Hy Memory C 57.7", "Memory (MCP reference server) C 54.2", "scores" ], "h1": "Hy Memory vs Memory (MCP reference server)", "image": "https://www.anchorterminal.com/assets/og/compare-hy-memory-vs-memory-reference-server.png", "path": "/compare/hy-memory-vs-memory-reference-server", "published": "2026-10-01", "section": "tools", "title": "Hy Memory vs Memory for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-16", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server" }, "tokens": { "markdown": 2700, "slim": 730 }, "version": 1 }