{ "data": { "a": { "slug": "hindsight", "name": "Hindsight", "vendor": "Vectorize", "vendorUrl": "https://hindsight.vectorize.io", "kind": "http-api", "category": "agent-memory", "summary": "Memory engine for storing and retrieving information used by agents.", "url": "https://www.anchorterminal.com/tools/hindsight", "markdownUrl": "https://www.anchorterminal.com/tools/hindsight.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hindsight.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hindsight.json", "repo": "https://github.com/vectorize-io/hindsight", "license": "MIT", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://api.hindsight.vectorize.io", "packages": [ { "registry": "pypi", "name": "hindsight-client" }, { "registry": "npm", "name": "@vectorize-io/hindsight-client" }, { "registry": "pypi", "name": "hindsight-api" } ], "auth": "mixed", "authNotes": "Bearer API key on api.hindsight.vectorize.io. Keys can be restricted to named banks and set to expire after an hour to a year, and revoking a parent key revokes its child keys. The hosted MCP uses OAuth with PKCE (RFC 9728), or the same key as a Bearer header. A self-hosted server exposes MCP at /mcp/{bank_id}/ on port 8888.", "pricing": "usage", "pricingNotes": "Hindsight Cloud is pay as you go, with no monthly fee or seat price. Retain $10.00 per million tokens, recall $0.75 per million, reflect $0.05 a call, Iris Extract $7.50 per million, mental model retrieval $0.25 per million, mental model refresh $0.05 a call, storage $0.25 per million tokens a month. New accounts get free credits, amount not stated. Enterprise adds dedicated infrastructure and up to a 99.95 per cent uptime SLA (https://vectorize.io/pricing). Credit is bought in amounts from $5 to $1,000, and calls return 402 once the balance is empty (https://docs.hindsight.vectorize.io/billing/). Self-hosting is free under MIT.", "priceSummary": "$0.05 / call", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": 27, "popularity": { "githubStars": 43400, "npmWeekly": 44381, "pypiWeekly": 227251, "asOf": "2026-09-30" }, "docsUrl": "https://docs.hindsight.vectorize.io", "openapi": "https://hindsight.vectorize.io/openapi.json", "capabilities": [ "memory.store", "memory.search", "memory.delete" ], "tags": [ "hosted", "usage-priced", "mcp", "oauth", "openapi", "python", "typescript", "open-source", "self-hosted", "enterprise" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 50.2, "grade": "D", "agentReady": false, "rank": 690, "ranked": true, "rankOf": 842, "categoryRank": 8, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 57, "maintenance": 80, "payments": 30, "reliability": 25, "schema": 68, "security": 58, "transparency": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "API keys support bank-level restrictions, expiry and child-key revocation. Retain ingestion costs $10 per million tokens.", "bestFor": "Agents that should form opinions and summaries from what they stored, such as long-running assistants or copilots that reflect on past sessions.", "strengths": [ "Keys restricted to named banks, with expiry from an hour to a year and child-key revocation", "`Memory Defense` screens every retain, with regex redaction even in the open-source server", "Published per-token and per-call Cloud prices, with no monthly fee, and a published 99.9 per cent SLA", "MIT server in one Docker image with an MCP endpoint per bank", "Eight PyPI releases between 22 July and 29 September 2026" ], "weaknesses": [ "Retain at $10 per million tokens is the priciest ingestion in this category", "27 MCP tools per bank, with no subset or read-only option", "No status page, no published rate limits and no llms.txt", "Prompt-injection blocking and audit trails are Enterprise only", "Free credit amount not published" ], "agentNotes": [ "Scope the MCP URL to one bank (/mcp/{bank}/) so every call lands in the right memory and three bank-admin tools drop out", "Use recall for lookups and keep reflect ($0.05 a call) for questions that need reasoning", "Pass `async: true` on large retains so the call returns before extraction finishes", "Treat HTTP 402 as out of credit and 403 as a key that can't reach that bank" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.2 } ], "editorialScores": { "ergonomics": 57, "maintenance": 80, "payments": 30, "reliability": 25, "schema": 68, "security": 58, "transparency": 30 }, "provenanceScore": 59 }, "connect": { "install": "pip install hindsight-client # or: npm install @vectorize-io/hindsight-client", "http": "curl -s \"https://api.hindsight.vectorize.io/v1/default/banks/my-bank/memories/list\" -H \"Authorization: Bearer $HINDSIGHT_API_KEY\"", "claudeCode": "claude mcp add --transport http hindsight https://api.hindsight.vectorize.io/mcp/my-bank/", "config": { "mcpServers": { "hindsight": { "headers": { "Authorization": "Bearer ${HINDSIGHT_API_KEY}" }, "type": "http", "url": "https://api.hindsight.vectorize.io/mcp/my-bank/" } } } }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/hindsight" }, "area": "agent-runtime", "unitPrices": [ { "item": "Retain", "unit": "1m-tokens", "usd": 10, "note": "input tokens" }, { "item": "Recall", "unit": "1m-tokens", "usd": 0.75, "note": "output tokens" }, { "item": "Reflect", "unit": "call", "usd": 0.05 }, { "item": "Iris Extract", "unit": "1m-tokens", "usd": 7.5 }, { "item": "Mental model refresh", "unit": "call", "usd": 0.05 }, { "item": "Storage", "unit": "1m-tokens", "usd": 0.25, "note": "per million tokens stored, per month" } ], "provenance": { "legalEntity": "Vectorize, Inc.", "domain": "vectorize.io", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://vectorize.io/terms", "privacy": "https://vectorize.io/privacy", "statusPage": "", "changelog": "https://docs.hindsight.vectorize.io/whats-new", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The entity name comes from the copyright line on vectorize.io. The privacy policy is embedded from Termly and gives no address.", "vectorize.io/.well-known/security.txt returns 404, and we found no status page." ], "score": 59 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hindsight.json", "live": { "slug": "hindsight", "probe": { "target": "https://api.hindsight.vectorize.io", "method": "get", "lastAt": "2026-10-09T11:29:02.591240682Z", "lastOk": true, "lastStatus": 404, "lastMs": 139, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 134, "p95ms24h": 422, "samples24h": 259, "samples30d": 2106, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 122, "ok": 122 } ] }, "versions": [ { "registry": "github", "name": "vectorize-io/hindsight", "version": "v0.10.3", "released": "2026-10-08", "seenAt": "2026-10-08T16:15:48.589909202Z" }, { "registry": "npm", "name": "@vectorize-io/hindsight-client", "version": "0.10.3", "seenAt": "2026-10-08T16:15:46.042936004Z" }, { "registry": "pypi", "name": "hindsight-api", "version": "0.10.3", "released": "2026-10-08", "seenAt": "2026-10-08T16:15:48.571604141Z" }, { "registry": "pypi", "name": "hindsight-client", "version": "0.10.3", "released": "2026-10-08", "seenAt": "2026-10-08T16:15:45.905673909Z" } ], "githubStars": 47181, "npmWeekly": 68722, "pypiWeekly": 225106, "securityTxt": { "url": "https://vectorize.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:36.04417906Z" }, "domain": { "domain": "vectorize.io", "checkedAt": "2026-10-04T13:05:24.522601574Z" }, "pages": [ { "url": "https://docs.hindsight.vectorize.io/whats-new", "kind": "changelog", "status": 304, "checkedAt": "2026-10-08T18:18:50.829567638Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "4b4e1a42fe21" }, { "url": "https://vectorize.io/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-10-08T18:25:34.352322054Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "8914c2c291ed" }, { "url": "https://vectorize.io/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-08T18:25:36.508597709Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "0b650c200752" }, { "url": "https://vectorize.io/terms", "kind": "terms", "status": 304, "checkedAt": "2026-10-08T18:25:38.513182813Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e508ab007e9c" } ], "updatedAt": "2026-10-09T11:29:02.591240682Z" } }, "answer": "Hindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.", "b": { "slug": "langmem", "name": "LangMem", "vendor": "LangChain", "vendorUrl": "https://www.langchain.com", "kind": "sdk", "category": "agent-memory", "summary": "LangMem is LangChain's open-source Python library for long-term agent memory. It extracts facts from conversations with an LLM, stores and searches them in a LangGraph store the owner runs, and includes two memory tools, message summarisation and prompt optimisation.", "url": "https://www.anchorterminal.com/tools/langmem", "markdownUrl": "https://www.anchorterminal.com/tools/langmem.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/langmem.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/langmem.json", "repo": "https://github.com/langchain-ai/langmem", "license": "MIT", "transports": [], "packages": [ { "registry": "pypi", "name": "langmem" } ], "auth": "none", "authNotes": "The library has no account or key of its own. It runs in the owner's process and needs a key for the chosen LLM provider (the README uses `ANTHROPIC_API_KEY`) and, for semantic search, an embedding provider. Access to memories is whatever the owner's LangGraph store and namespace settings allow.", "pricing": "free", "pricingNotes": "Free under the MIT licence, with nothing to buy and no signup. The owner pays for the LLM calls that extract memories, the embedding calls behind search, and the database behind the store (https://github.com/langchain-ai/langmem).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 1698, "npmWeekly": null, "pypiWeekly": 214379, "asOf": "2026-10-08" }, "docsUrl": "https://langchain-ai.github.io/langmem/", "capabilities": [ "memory.store", "memory.search", "memory.user", "memory.delete" ], "tags": [ "sdk", "open-source", "self-hosted", "local", "python", "free", "langgraph", "pre-1.0" ], "lastRelease": "2025-10-27", "graded": true, "anchor": { "graded": true, "score": 47.9, "grade": "D", "agentReady": false, "rank": 734, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 66, "maintenance": 15, "payments": 60, "reliability": 32, "schema": 55, "security": 45, "transparency": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply.", "bestFor": "Teams already on LangGraph that want memory tools and background extraction over a store they run.", "strengths": [ "MIT licence, installed with `pip install -U langmem`, with no account, key or fee of its own", "Two agent tools, `manage_memory` and `search_memory`, with typed inputs, an action enum and `limit`, `offset` and `filter` on search", "`actions_permitted` limits the manage tool to any subset of create, update and delete, and `create_memory_store_manager` leaves deletes off by default", "Namespace templates such as `(\"memories\", \"{langgraph_user_id}\")` keep each user's memories apart at run time", "Works with any LangGraph `BaseStore`, including `InMemoryStore` for tests and `AsyncPostgresStore` for durable storage" ], "weaknesses": [ "The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that", "No changelog, GitHub releases or tags, and versions are still 0.0.x", "The repository's two workflows deploy docs and publish to PyPI. Neither runs the tests", "54 open issues, 23 with no comment, and 19 open pull requests, the oldest from June 2025", "No guidance on memory poisoning or prompt injection found, and two open issues asking for it (May 2026) have no reply" ], "agentNotes": [ "Pass a store with an embedding index (`index={\"dims\": 1536, \"embed\": \"openai:text-embedding-3-small\"}`), or `search_memory` cannot rank by meaning", "Use a database-backed store such as `AsyncPostgresStore` for anything that must survive a restart. `InMemoryStore` loses everything", "Put a per-user placeholder in the namespace and set it in `config[\"configurable\"]` on every call, or users share one memory space", "Send the memory `id` with `update` and `delete`, and never with `create`. A retried `create` writes a duplicate under a new UUID", "Run on Python 3.11 or later. `src/langmem/knowledge/extraction.py` uses `typing.NotRequired`, which Python 3.10 lacks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.9 } ], "editorialScores": { "ergonomics": 66, "maintenance": 15, "payments": 60, "reliability": 32, "schema": 55, "security": 45, "transparency": 63 }, "provenanceScore": 55 }, "connect": { "install": "pip install -U langmem" }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/langmem" }, "sameCompany": [ "langgraph", "langsmith" ], "area": "agent-runtime", "provenance": { "legalEntity": "LangChain (the licence file gives no legal form)", "domain": "langchain.com", "domainRegistered": "2019-12-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "A library the owner runs, with no hosted endpoint. The code is on github.com under the langchain-ai organisation and the docs are on langchain-ai.github.io/langmem.", "The LICENSE file reads Copyright (c) 2025 LangChain. PyPI metadata names no author.", "No terms or privacy document governs the library, so both fields are left out. The MIT licence is the only agreement.", "No changelog file, GitHub releases or tags exist in the repository. PyPI's release history is the only record of versions.", "www.langchain.com/.well-known/security.txt returns 404. The organisation's SECURITY.md points to two Intigriti disclosure programmes.", "RDAP for langchain.com gives a registration date of 2019-12-03." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/langmem.json" }, "facts": [ { "a": "HTTP API", "b": "SDK + MCP", "name": "Kind" }, { "a": "Vectorize", "b": "LangChain", "name": "Vendor" }, { "a": "https://api.hindsight.vectorize.io", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "MIT", "name": "Licence" }, { "a": "27", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-29", "b": "2025-10-27", "name": "Last release" }, { "a": "couldn't be read", "b": "no document linked", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "43k stars, 44k npm/wk, 227k PyPI/wk", "b": "1.7k stars, 214k PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Hindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.", "question": "Which is better for AI agents, Hindsight or LangMem?" }, { "answer": "Hindsight has a hosted endpoint at https://api.hindsight.vectorize.io. No hosted endpoint is listed for LangMem.", "question": "Can an agent call Hindsight and LangMem without installing anything?" }, { "answer": "Yes. Hindsight is open source (MIT). LangMem is open source (MIT).", "question": "Are Hindsight and LangMem open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 68 against 55", "Security \u0026 auth, 58 against 45", "Maintenance \u0026 community, 80 against 15" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Agents that should form opinions and summaries from what they stored, such as long-running assistants or copilots that reflect on past sessions.", "slug": "hindsight", "watchFor": "Retain at $10 per million tokens is the priciest ingestion in this category" }, { "aheadOn": [ "Reliability, 32 against 25", "Agent ergonomics, 66 against 57", "Payments \u0026 pricing, 60 against 30", "Transparency \u0026 trust, 59 against 45" ], "also": [ "No key needed to call it" ], "goodFor": "Teams already on LangGraph that want memory tools and background extraction over a store they run.", "slug": "langmem", "watchFor": "The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that" } ], "job": { "capability": "memory.store", "name": "Memory store" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight.json", "title": "Amazon Bedrock AgentCore Memory vs Hindsight", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.json", "title": "Amazon Bedrock AgentCore Memory vs LangMem", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/cognee-vs-hindsight.json", "title": "Cognee vs Hindsight", "url": "https://www.anchorterminal.com/compare/cognee-vs-hindsight" }, { "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/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-langmem.json", "title": "Graphiti vs LangMem", "url": "https://www.anchorterminal.com/compare/graphiti-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-honcho.json", "title": "Hindsight vs Honcho", "url": "https://www.anchorterminal.com/compare/hindsight-vs-honcho" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-localghost.json", "title": "Hindsight vs LocalGhost", "url": "https://www.anchorterminal.com/compare/hindsight-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-mem0.json", "title": "Hindsight vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/hindsight-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-supermemory.json", "title": "Hindsight vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/hindsight-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/hindsight-vs-zep.json", "title": "Hindsight vs Zep", "url": "https://www.anchorterminal.com/compare/hindsight-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/honcho-vs-langmem.json", "title": "Honcho vs LangMem", "url": "https://www.anchorterminal.com/compare/honcho-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-localghost.json", "title": "LangMem vs LocalGhost", "url": "https://www.anchorterminal.com/compare/langmem-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-mem0.json", "title": "LangMem vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/langmem-vs-mem0" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-supermemory.json", "title": "LangMem vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/langmem-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-zep.json", "title": "LangMem vs Zep", "url": "https://www.anchorterminal.com/compare/langmem-vs-zep" } ], "scores": [ { "by": 7, "edge": "langmem", "hindsight": 25, "key": "reliability", "langmem": 32, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 13, "edge": "hindsight", "hindsight": 68, "key": "schema", "langmem": 55, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 9, "edge": "langmem", "hindsight": 57, "key": "ergonomics", "langmem": 66, "name": "Agent ergonomics", "weight": 13 }, { "by": 13, "edge": "hindsight", "hindsight": 58, "key": "security", "langmem": 45, "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "edge": "langmem", "hindsight": 30, "key": "payments", "langmem": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 65, "edge": "hindsight", "hindsight": 80, "key": "maintenance", "langmem": 15, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 14, "edge": "langmem", "hindsight": 45, "key": "transparency", "langmem": 59, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. Both do memory store.", "verdicts": { "hindsight": "API keys support bank-level restrictions, expiry and child-key revocation. Retain ingestion costs $10 per million tokens.", "langmem": "Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hindsight-vs-langmem", "json": "https://www.anchorterminal.com/compare/hindsight-vs-langmem.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hindsight-vs-langmem.md", "slim": "https://www.anchorterminal.com/compare/hindsight-vs-langmem.min.md" }, "markdown": "Hindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. Both do memory store.\n\n- Hindsight: grade D, 50.2/100, rank #690 of 842. Markdown https://www.anchorterminal.com/tools/hindsight.md · JSON https://www.anchorterminal.com/api/v1/tools/hindsight.json\n- LangMem: grade D, 47.9/100, rank #734 of 842. Markdown https://www.anchorterminal.com/tools/langmem.md · JSON https://www.anchorterminal.com/api/v1/tools/langmem.json\n\n## Which one, for what\n\n### Hindsight (D)\n\nGood for: Agents that should form opinions and summaries from what they stored, such as long-running assistants or copilots that reflect on past sessions.\n\nAhead on:\n- Schema \u0026 documentation, 68 against 55\n- Security \u0026 auth, 58 against 45\n- Maintenance \u0026 community, 80 against 15\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Retain at $10 per million tokens is the priciest ingestion in this category\n\n### LangMem (D)\n\nGood for: Teams already on LangGraph that want memory tools and background extraction over a store they run.\n\nAhead on:\n- Reliability, 32 against 25\n- Agent ergonomics, 66 against 57\n- Payments \u0026 pricing, 60 against 30\n- Transparency \u0026 trust, 59 against 45\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The newest PyPI release, 0.0.30, is from 27 October 2025, and every commit to `src` on main is older than that\n\n\n## Score by category\n\n| Category | Weight | Hindsight | LangMem | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 25 | 32 | LangMem +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 55 | Hindsight +13 |\n| Agent ergonomics | 13% (16.2 this run) | 57 | 66 | LangMem +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 58 | 45 | Hindsight +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | LangMem +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 15 | Hindsight +65 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 45 | 59 | LangMem +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.2 · D** | **47.9 · D** | |\n\n## Facts side by side\n\n| Fact | Hindsight | LangMem |\n| --- | --- | --- |\n| Kind | HTTP API | SDK + MCP |\n| Vendor | Vectorize | LangChain |\n| Hosted endpoint | `https://api.hindsight.vectorize.io` | no (local only) |\n| Transports | HTTP, Streamable HTTP | |\n| Auth | OAuth or key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | MIT | MIT |\n| Tools exposed | 27 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-29 | 2025-10-27 |\n| Terms last updated | couldn't be read | no document linked |\n| Privacy policy last updated | couldn't be read | no document linked |\n| Customer content may train models | couldn't be read | |\n| Terms restrict automated access | couldn't be read | |\n| Terms restrict benchmarking | couldn't be read | |\n| Terms or service can change without notice | couldn't be read | |\n| Arbitration or class-action waiver | couldn't be read | |\n| Popularity | 43k stars, 44k npm/wk, 227k PyPI/wk | 1.7k stars, 214k PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## Verdicts\n\n**Hindsight.** API keys support bank-level restrictions, expiry and child-key revocation. Retain ingestion costs $10 per million tokens.\n\n**LangMem.** Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest PyPI release, 0.0.30, dates from 27 October 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues have no reply.\n\n## Before you call either\n\n### Hindsight\n\n1. Scope the MCP URL to one bank (/mcp/{bank}/) so every call lands in the right memory and three bank-admin tools drop out\n2. Use recall for lookups and keep reflect ($0.05 a call) for questions that need reasoning\n3. Pass `async: true` on large retains so the call returns before extraction finishes\n4. Treat HTTP 402 as out of credit and 403 as a key that can't reach that bank\n\n### LangMem\n\n1. Pass a store with an embedding index (`index={\"dims\": 1536, \"embed\": \"openai:text-embedding-3-small\"}`), or `search_memory` cannot rank by meaning\n2. Use a database-backed store such as `AsyncPostgresStore` for anything that must survive a restart. `InMemoryStore` loses everything\n3. Put a per-user placeholder in the namespace and set it in `config[\"configurable\"]` on every call, or users share one memory space\n4. Send the memory `id` with `update` and `delete`, and never with `create`. A retried `create` writes a duplicate under a new UUID\n5. Run on Python 3.11 or later. `src/langmem/knowledge/extraction.py` uses `typing.NotRequired`, which Python 3.10 lacks\n\n## Questions\n\n### Which is better for AI agents, Hindsight or LangMem?\n\nHindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.\n\n### Can an agent call Hindsight and LangMem without installing anything?\n\nHindsight has a hosted endpoint at https://api.hindsight.vectorize.io. No hosted endpoint is listed for LangMem.\n\n### Are Hindsight and LangMem open source?\n\nYes. Hindsight is open source (MIT). LangMem is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hindsight-vs-langmem.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hindsight-vs-langmem.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hindsight\", \"b\": \"langmem\"}`. From a terminal: `anchor compare hindsight langmem`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hindsight.json and https://www.anchorterminal.com/api/v1/tools/langmem.json\n\n## Other comparisons with Hindsight or LangMem\n\n- [Amazon Bedrock AgentCore Memory vs Hindsight](https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight.md)\n- [Amazon Bedrock AgentCore Memory vs LangMem](https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.md)\n- [Cognee vs Hindsight](https://www.anchorterminal.com/compare/cognee-vs-hindsight.md)\n- [Cognee vs LangMem](https://www.anchorterminal.com/compare/cognee-vs-langmem.md)\n- [Graphiti vs Hindsight](https://www.anchorterminal.com/compare/graphiti-vs-hindsight.md)\n- [Graphiti vs LangMem](https://www.anchorterminal.com/compare/graphiti-vs-langmem.md)\n- [Hindsight vs Honcho](https://www.anchorterminal.com/compare/hindsight-vs-honcho.md)\n- [Hindsight vs LocalGhost](https://www.anchorterminal.com/compare/hindsight-vs-localghost.md)\n- [Hindsight vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/hindsight-vs-mem0.md)\n- [Hindsight vs Supermemory API + MCP](https://www.anchorterminal.com/compare/hindsight-vs-supermemory.md)\n- [Hindsight vs Zep](https://www.anchorterminal.com/compare/hindsight-vs-zep.md)\n- [Honcho vs LangMem](https://www.anchorterminal.com/compare/honcho-vs-langmem.md)\n- [LangMem vs LocalGhost](https://www.anchorterminal.com/compare/langmem-vs-localghost.md)\n- [LangMem vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/langmem-vs-mem0.md)\n- [LangMem vs Supermemory API + MCP](https://www.anchorterminal.com/compare/langmem-vs-supermemory.md)\n- [LangMem vs Zep](https://www.anchorterminal.com/compare/langmem-vs-zep.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Hindsight vs LangMem", "url": "" } ], "description": "Hindsight scores 50.2 (D) on agent readiness against LangMem's 47.9 (D), and leads in 3 of 7 scored categories. LangMem leads on reliability, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. Both do memory store. Category scores, facts, verdicts and agent notes…", "facts": [ "Hindsight D 50.2", "LangMem D 47.9", "scores" ], "h1": "Hindsight vs LangMem", "image": "https://www.anchorterminal.com/assets/og/compare-hindsight-vs-langmem.png", "path": "/compare/hindsight-vs-langmem", "published": "2026-10-01", "section": "tools", "title": "Hindsight vs LangMem for AI agents, D 50.2 vs D 47.9 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/hindsight-vs-langmem" }, "tokens": { "markdown": 2100, "slim": 730 }, "version": 1 }