{ "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", "dns": "hindsight.grade.anchorterminal.com", "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", "llmsTxt": "https://hindsight.vectorize.io/llms.txt", "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": "2026-11-03", "graded": true, "anchor": { "graded": true, "score": 47.8, "grade": "D", "agentReady": false, "rank": 1141, "ranked": true, "rankOf": 1510, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 57, "maintenance": 80, "payments": 30, "reliability": 27, "schema": 78, "security": 65, "transparency": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": -6, "negativeNotes": [ "2026-10-21 and 2026-11-03. v1.1.0 began rejecting new bank IDs that start with `_` (a 400 on create and import, on Hindsight Cloud as on a server you run), and v1.2.0 stopped sending native tools to any provider (an OpenAI-compatible server must now support structured output) and removed three configuration variables. Both are 1.x minor releases of hindsight-api with the changes listed under Breaking Changes only at release, with no earlier notice found, so this run's -3 for breaking changes in a minor release, folded into one term. https://hindsight.vectorize.io/blog/2026/11/03/version-1-2-0", "2026-10-13. v1.0.0 began rejecting tag-scoped API callers from operations that return a whole memory bank's data, after v0.10.1 (21 September) stopped tenant information leaking between extension requests and isolated file storage between tenants. The changelog describes each flaw, the fixes reach Hindsight Cloud, and OSV lists no advisory for hindsight-api or the clients, so this run's -3 for released fixes without an advisory, folded into one term. https://hindsight.vectorize.io/changelog" ], "verdict": "API keys support bank-level restrictions, expiry and child-key revocation, and prices are published per token and per call. The status page and docs couldn't be read, 1.x minor releases carried breaking changes without notice, and access-control fixes shipped without advisories.", "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 $5 of free credit", "MIT server in one Docker image with an MCP endpoint per bank", "Ten core releases between 7 August and 3 November 2026, with a changelog that lists breaking changes" ], "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", "The status page draws its content by script, and no rate limits were found", "v1.1.0 and v1.2.0 carried breaking changes with no earlier notice", "Prompt-injection blocking and audit trails are Enterprise only" ], "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", "On a 503, wait for Retry-After before retrying recall or reflect, and use async retain rather than retrying a synchronous one", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.8 } ], "editorialScores": { "ergonomics": 57, "maintenance": 80, "payments": 30, "reliability": 27, "schema": 78, "security": 65, "transparency": 30 }, "provenanceScore": 70, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 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": "https://status.hindsight.vectorize.io/", "changelog": "https://docs.hindsight.vectorize.io/whats-new", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "The entity name comes from the copyright line on vectorize.io. The privacy policy and terms are embedded from Termly and load only by script.", "vectorize.io/.well-known/security.txt returns 404. The site footer links a status page at status.hindsight.vectorize.io, which draws its content by script." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hindsight.json", "live": { "slug": "hindsight", "probe": { "target": "https://api.hindsight.vectorize.io", "method": "get", "lastAt": "2026-11-07T19:22:11.866783669Z", "lastOk": true, "lastStatus": 404, "lastMs": 158, "authRequired": false, "uptime24h": 100, "uptime30d": 99.97, "p50ms24h": 140, "p95ms24h": 434, "samples24h": 238, "samples30d": 7920, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 251 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 251 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.hindsight.vectorize.io", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:54.932482644Z" }, "versions": [ { "registry": "github", "name": "vectorize-io/hindsight", "version": "v1.2.0", "released": "2026-11-03", "seenAt": "2026-11-06T23:32:57.112124724Z" }, { "registry": "npm", "name": "@vectorize-io/hindsight-client", "version": "1.2.0", "seenAt": "2026-11-06T23:32:55.261909993Z" }, { "registry": "pypi", "name": "hindsight-api", "version": "1.2.0", "released": "2026-11-03", "seenAt": "2026-11-06T23:32:55.527082914Z" }, { "registry": "pypi", "name": "hindsight-client", "version": "1.2.0", "released": "2026-11-03", "seenAt": "2026-11-06T23:32:54.944345691Z" } ], "githubStars": 59432, "npmWeekly": 58811, "pypiWeekly": 256090, "securityTxt": { "url": "https://vectorize.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T15:59:50.286802374Z" }, "llmsTxt": { "url": "https://hindsight.vectorize.io/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:41.967351296Z" }, "domain": { "domain": "vectorize.io", "checkedAt": "2026-11-01T14:08:29.795893521Z" }, "pages": [ { "url": "https://docs.hindsight.vectorize.io/whats-new", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:06:35.435712738Z", "changedAt": "2026-11-04T08:21:16.781971739Z", "fingerprint": "b587ec39fc4f" }, { "url": "https://hindsight.vectorize.io/blog/2026/10/21/version-1-1-0", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:11:07.699884556Z", "changedAt": "2026-11-07T14:11:07.699884556Z", "fingerprint": "df6edb22bc0a" }, { "url": "https://hindsight.vectorize.io/blog/2026/11/03/version-1-2-0", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:11:09.863064401Z", "changedAt": "2026-11-07T14:11:09.863064401Z", "fingerprint": "1a5b2109af63" }, { "url": "https://vectorize.io/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-07T14:23:11.639474166Z", "changedAt": "2026-11-04T08:35:45.689543412Z", "fingerprint": "d84dd840f26e" }, { "url": "https://vectorize.io/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-11-07T14:23:13.675800462Z", "changedAt": "2026-11-04T08:35:48.039392372Z", "fingerprint": "0cf98240f732" }, { "url": "https://vectorize.io/terms", "kind": "terms", "status": 304, "checkedAt": "2026-11-07T14:23:18.842999521Z", "changedAt": "2026-11-04T08:35:49.88216235Z", "fingerprint": "cdd8a88976fa" } ], "mcpTools": { "url": "https://api.hindsight.vectorize.io/mcp/my-bank/", "checkedAt": "2026-11-07T01:45:19.646576081Z", "status": "auth", "note": "asks for credentials before listing its tools", "changedAt": "2026-11-07T01:45:19.646576081Z" }, "github": { "repo": "vectorize-io/hindsight", "checkedAt": "2026-11-02T09:35:17.857102846Z", "stars": 57883, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-01T14:13:50Z", "openIssues": 171, "openPRs": 8, "issuesOpened90d": 1068, "issuesClosed90d": 969, "prsMerged90d": 1543, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-30T20:37:17Z" }, "replies": { "sampled": 11, "unanswered": 6, "medianDays": 0.6 } }, "updatedAt": "2026-11-07T19:22:11.866783669Z" } }, "answer": "Hindsight scores 47.8 (D) on agent readiness against Memori's 44.2 (E), and leads in 3 of 7 scored categories. Memori leads on agent ergonomics and payments \u0026 pricing.", "b": { "slug": "memori", "name": "Memori", "vendor": "Memori Labs Inc.", "vendorUrl": "https://memorilabs.ai", "kind": "sdk", "category": "agent-memory", "summary": "Memori is a memory layer from Memori Labs that wraps an app's LLM client to capture conversations and agent traces, extract facts and recall them into later prompts, through Python and TypeScript SDKs, a CLI and a hosted MCP endpoint.", "url": "https://www.anchorterminal.com/tools/memori", "markdownUrl": "https://www.anchorterminal.com/tools/memori.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/memori.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/memori.json", "dns": "memori.grade.anchorterminal.com", "repo": "https://github.com/MemoriLabs/Memori", "license": "Apache-2.0", "transports": [ "streamable-http" ], "remoteUrl": "https://api.memorilabs.ai/mcp/", "packages": [ { "registry": "pypi", "name": "memori" }, { "registry": "npm", "name": "@memorilabs/memori" } ], "auth": "api-key", "authNotes": "Memori Cloud and the MCP endpoint take an API key, `X-Memori-API-Key` on MCP and a bearer token in the SDK. Keys come from app.memorilabs.ai or a CLI signup by email. Own-database mode works without a key, using a built-in anonymous key with a quota of 100 augmentations a month.", "pricing": "freemium", "pricingNotes": "A free key gives 5,000 Advanced Augmentation calls a month, per the docs; 100 a month without a key. Paid plans are sold through sales from $60,000 a year (Team) and $150,000 (Business), with Enterprise priced privately.", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the SDK source, docs, llms.txt or pricing page (checked 2026-11-06).", "endpoints": [] }, "toolCount": 7, "popularity": { "githubStars": 17446, "npmWeekly": 130, "pypiWeekly": 3781, "asOf": "2026-11-06" }, "docsUrl": "https://memorilabs.ai/docs/memori-cloud/", "llmsTxt": "https://memorilabs.ai/llms.txt", "capabilities": [ "memory.store", "memory.search", "memory.user", "memory.graph" ], "tags": [ "open-source", "hosted", "freemium", "free-tier", "apache-2.0", "python", "typescript", "cli", "mcp", "remote-mcp", "llms-txt", "sales-led" ], "lastRelease": "2026-05-27", "graded": true, "anchor": { "graded": true, "score": 44.2, "grade": "E", "agentReady": false, "rank": 1231, "ranked": true, "rankOf": 1510, "categoryRank": 25, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 33, "payments": 45, "reliability": 28, "schema": 68, "security": 45, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": -4, "negativeNotes": [ "-4: misleading claim, confirmed first-hand on 6 November 2026. The BYODB architecture page says \"All data stays on your infrastructure\" and that storage has \"no external dependencies\" (https://memorilabs.ai/docs/memori-byodb/concepts/architecture), but the SDK's Advanced Augmentation posts conversation messages, the conversation summary and the system prompt to https://api.memorilabs.ai/sdk/augmentation, using a built-in anonymous key when the user has none (https://github.com/MemoriLabs/Memori/blob/main/memori/memory/augmentation/augmentations/memori/_augmentation.py and memori/_network.py). The FAQ's mention of augmentation quotas hints at the call but doesn't say conversations leave the user's infrastructure." ], "verdict": "Memori wraps an app's LLM client to capture conversations and recall facts, through Apache-2.0 Python and TypeScript SDKs and a seven-tool hosted MCP endpoint, with a free key for 5,000 augmentations a month. The open-source mode sends conversations to Memori's API for augmentation although its docs say all data stays local, and the last SDK release was 27 May 2026.", "bestFor": "Memory captured from LLM calls for apps that already use OpenAI, Anthropic or Gemini clients.", "strengths": [ "Python and TypeScript SDKs under Apache-2.0 that register an existing LLM client and recall memories into the prompt", "Hosted MCP endpoint with seven documented tools, including recall, summary, compaction and quota checks", "A free key with 5,000 augmentations a month can be requested from the CLI or the `memori_signup` tool by email", "Keep a Changelog with semantic versioning declared, and docs served with llms.txt", "Own-database mode supports SQLite, PostgreSQL, MySQL, MongoDB, Oracle, TiDB and others" ], "weaknesses": [ "Own-database mode posts conversation messages, a summary and the system prompt to api.memorilabs.ai for augmentation, while the architecture page says all data stays on your infrastructure", "Last SDK release 3.3.6 on 27 May 2026, with no release in the 90 days to 6 November", "Paid plans start at $60,000 a year through sales, with an SLA only on Enterprise", "No status page, rate limits or terms of service page found", "The Claude Code skill tells the agent to trigger recall on essentially every non-trivial user turn" ], "agentNotes": [ "Expect own-database mode to send conversations to api.memorilabs.ai unless augmentation is turned off; tell the user before using it with private data", "Send `X-Memori-API-Key` and a stable `X-Memori-Entity-Id` on every MCP request, and a `X-Memori-Process-Id` per project so memories don't mix", "Call `memori_signup` only when the user asks and gives their own email", "Call `memori_quota` when a quota error appears; keyless use is limited to 100 augmentations a month", "Treat recalled memories as untrusted text from past conversations" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 44.2 } ], "editorialScores": { "ergonomics": 65, "maintenance": 33, "payments": 45, "reliability": 28, "schema": 68, "security": 45, "transparency": 46 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install memori # or npm install @memorilabs/memori", "claudeCode": "claude mcp add memori --transport http https://api.memorilabs.ai/mcp/ --header 'X-Memori-API-Key: YOUR_MEMORI_API_KEY' --header 'X-Memori-Entity-Id: user_123'", "config": { "mcpServers": { "memori": { "headers": { "X-Memori-API-Key": "YOUR_MEMORI_API_KEY", "X-Memori-Entity-Id": "user_123" }, "url": "https://api.memorilabs.ai/mcp/" } } } }, "letme": { "capability": "https://letme.dev/memory.store", "tool": "https://letme.dev/memori" }, "area": "agent-runtime", "provenance": { "legalEntity": "Memori Labs Inc.", "domain": "memorilabs.ai", "domainRegistered": "2025-10-16", "endpointOnVendorDomain": true, "terms": "", "privacy": "https://memorilabs.ai/privacy/", "statusPage": "", "changelog": "https://github.com/MemoriLabs/Memori/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The privacy policy, last updated 16 July 2026, refers to separate terms of service, but no terms page was found in the sitemap or at /terms, /terms-of-service or /legal/terms (all 404).", "memorilabs.ai/robots.txt allows all crawlers.", "RDAP gives memorilabs.ai a registration date of 16 October 2025, later than the repository (24 July 2025).", "/.well-known/security.txt returns 404. status.memorilabs.ai did not answer. The pricing page links a trust centre at memori-labs.trust.site, which timed out." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/memori.json", "live": { "slug": "memori", "probe": { "target": "https://api.memorilabs.ai/mcp/", "method": "mcp-initialize", "lastAt": "2026-11-07T19:22:19.067041408Z", "lastOk": true, "lastStatus": 401, "lastMs": 258, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 272, "p95ms24h": 361, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "versions": [ { "registry": "github", "name": "MemoriLabs/Memori", "version": "v3.3.6", "released": "2026-05-28", "seenAt": "2026-11-06T23:44:00.658786718Z" }, { "registry": "npm", "name": "@memorilabs/memori", "version": "0.0.11", "seenAt": "2026-11-06T23:44:00.185501382Z" }, { "registry": "pypi", "name": "memori", "version": "3.3.6", "released": "2026-05-28", "seenAt": "2026-11-06T23:44:00.000273764Z" } ], "githubStars": 17448, "npmWeekly": 130, "pypiWeekly": 3781, "securityTxt": { "url": "https://memorilabs.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T15:59:51.635644364Z" }, "llmsTxt": { "url": "https://memorilabs.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:59.605618502Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/MemoriLabs/Memori/main/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:18:47.202866715Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "4f28fd41f6ed" }, { "url": "https://memorilabs.ai/privacy/", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:14:13.209756014Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "29e786d80880" } ], "mcpTools": { "url": "https://api.memorilabs.ai/mcp/", "checkedAt": "2026-11-07T01:46:13.890822383Z", "status": "auth", "note": "asks for credentials before listing its tools", "changedAt": "2026-11-07T01:46:13.890822383Z" }, "updatedAt": "2026-11-07T19:22:19.067041408Z" } }, "edges": [ { "by": 47, "category": "maintenance", "leads": "hindsight", "name": "Maintenance \u0026 community", "reasons": { "a": "Tag v1.2.0 on 3 November 2026, the day before the check (30). Ten core releases since 6 August, v0.9.0 (7 August), v0.9.1 (14 August), v0.9.2 (25 August), v0.10.0 to v0.10.3 (14 September to 8 October), v1.0.0 (13 October), v1.1.0 (21 October) and v1.2.0 (20). We didn't check issue reply times on GitHub (10 of 25). Current clients for Python, TypeScript and Go, though the hosted MCP isn't in the official registry (15). Python 3.11 or later, and a test workflow in the repository whose result we couldn't read (5 of 10).", "b": "SDK 3.3.6 on 27 May 2026, 163 days before the check (10). No release in the 90 days to 6 November (0). Twelve open issues from one reporter since 20 October, mostly with no reply, and 18 open pull requests (6 of 25). Python and TypeScript SDKs, last released in May 2026 (12 of 15). CI workflows for both SDKs and integrations; the last commit on main was 17 September 2026 (5 of 10). Total 33." }, "scores": { "a": 80, "b": 33 } }, { "by": 20, "category": "security", "leads": "hindsight", "name": "Security \u0026 auth", "reasons": { "a": "Keys can be restricted to named banks, expire after an hour to a year or never, are revocable with child keys revoked alongside, and the docs give a rotation procedure. The hosted MCP uses OAuth with PKCE under RFC 9728, or a Bearer key (30). Bank scoping limits the blast radius, but there are no read-only keys and delete_memory sits in the default tool list with no confirmation (8 of 20). `Memory Defense` screens every retain before storage. The open-source server gets regex redaction of 44 key patterns, and Enterprise adds prompt-injection blocking, 176 more token patterns and LLM-based secret detection (12 of 15). Security audit trails and SIEM webhooks are Enterprise only (5 of 15). These four lines per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page). SECURITY.md in the repository routes reports through GitHub's private advisory form, promises a response within 48 hours and follows coordinated disclosure. No security.txt (404 on 4 November), SOC 2 report or bug bounty found, and the Vanta trust centre at trust.vectorize.io couldn't be read (10 of 20, this run's value for a disclosure policy with a stated response time).", "b": "API keys in a header from the dashboard or CLI, with no scopes found (20 of 30). No read-only mode or per-tool restrictions (4 of 20). Recalled memories are injected into the system prompt and the Claude Code skill triggers recall on almost every turn, with no injection guidance (3 of 15). A dashboard shows memories, retrieval and usage (8 of 15). SECURITY.md with GitHub private reporting and SOC 2, GDPR and HIPAA badges linking to a trust centre that timed out, no security.txt. Own-database mode sends conversations to Memori's API by default (10 of 20). Total 45." }, "scores": { "a": 65, "b": 45 } }, { "by": 15, "category": "payments", "leads": "memori", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402. Calls on an empty balance return 402 with no payment payload (0). Per-unit prices published without a login and re-read on 4 November, retain $10 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 and refresh $0.05 a call, storage $0.25 per million tokens a month after the first 30 days (20). The pricing page says new accounts start with $5 of free credit; whether a card is needed isn't stated (10 of 20). A person signs up in the browser to get a key (0).", "b": "No x402, MPP or L402 (0 of 40). Plan prices are public but sales-led, from $60,000 a year (10). A free key with 5,000 augmentations a month, with no card mentioned (20). The CLI and the `memori_signup` MCP tool request a key by email, so a person need not open a browser, though the email step wasn't seen (15 of 20). Total 45." }, "scores": { "a": 30, "b": 45 } }, { "by": 10, "category": "schema", "leads": "hindsight", "name": "Schema \u0026 documentation", "reasons": { "a": "OpenAPI spec at hindsight.vectorize.io/openapi.json, linked from the open-source docs' llms.txt (25). llms.txt and llms-full.txt on hindsight.vectorize.io, with links to the quick start, SDKs, API reference, OpenAPI file and MCP page (10). Tool names follow the three verbs (retain, recall, reflect) and the docs explain each, but with 27 tools the when-not-to-use guidance is thin (12 of 20). Typed inputs, with an async flag on retain, and we didn't audit enums across the spec (10 of 15). 402 and 403 are documented with their causes, and examples sit in the guides (9 of 15). A changelog at hindsight.vectorize.io/changelog with Breaking Changes sections per release, and versioned /v1 paths (12 of 15). Cloud docs per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page).", "b": "No OpenAPI for the REST API, and the MCP tool schemas couldn't be read without opening a session, so the docs' parameter lists are what was scored (12 of 25). llms.txt with Markdown docs for both modes (10). Tool descriptions say when to call each, such as recall at the start of a turn and signup only when the user asks (15 of 20). Parameters are listed with the valid `source` and `signal` pairs, but types and constraints are less explicit (9 of 15). Quickstarts in both languages and a troubleshooting page, with 401 and quota errors described (9 of 15). Keep a Changelog with semantic versioning (13 of 15). Total 68." }, "scores": { "a": 78, "b": 68 } }, { "by": 8, "category": "ergonomics", "leads": "memori", "name": "Agent ergonomics", "reasons": { "a": "27 MCP tools on a bank-scoped URL and 30 at the root (15), with no documented way to load a subset or a read-only set. List and recall calls exist with bank scoping, and we didn't confirm page-size or token-budget limits (12 of 20). 402 tells an agent it's out of credit and 403 that the key can't reach the bank (12 of 20). No idempotency keys and no readOnlyHint or destructiveHint on the MCP tools, though retain can run asynchronously (3 of 20). Clients for Python, TypeScript and Go, and one URL per bank keeps calls simple (15). MCP and key docs per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page).", "b": "Seven MCP tools with short parameter lists (22 of 25). Recall filters by project, session, date range and source (14 of 20). 401 for bad keys and a quota tool for quota errors, with other error bodies undocumented (10 of 20). No idempotency guidance or tool annotations documented (4 of 20). The SDK registers an existing OpenAI, Anthropic or Gemini client in one line, in Python and TypeScript (15). Total 65." }, "scores": { "a": 57, "b": 65 } } ], "facts": [ { "a": "HTTP API", "b": "SDK + MCP", "name": "Kind" }, { "a": "Vectorize", "b": "Memori Labs Inc.", "name": "Vendor" }, { "a": "https://api.hindsight.vectorize.io", "b": "https://api.memorilabs.ai/mcp/", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "Streamable HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "27", "b": "7", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-03", "b": "2026-05-27", "name": "Last release" }, { "a": "couldn't be read", "b": "no document linked", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "not read yet", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "43k stars, 44k npm/wk, 227k PyPI/wk", "b": "17k stars, 130 npm/wk, 3.8k PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Hindsight scores 47.8 (D) on agent readiness against Memori's 44.2 (E), and leads in 3 of 7 scored categories. Memori leads on agent ergonomics and payments \u0026 pricing.", "question": "Which is better for AI agents, Hindsight or Memori?" }, { "answer": "Yes. Hindsight has a hosted endpoint at https://api.hindsight.vectorize.io and Memori at https://api.memorilabs.ai/mcp/.", "question": "Can an agent call Hindsight and Memori without installing anything?" }, { "answer": "Yes. Hindsight is open source (MIT). Memori is open source (Apache-2.0).", "question": "Are Hindsight and Memori open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 78 against 68", "Security \u0026 auth, 65 against 45", "Maintenance \u0026 community, 80 against 33" ], "also": null, "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": [ "Agent ergonomics, 65 against 57", "Payments \u0026 pricing, 45 against 30" ], "also": null, "goodFor": "Memory captured from LLM calls for apps that already use OpenAI, Anthropic or Gemini clients.", "slug": "memori", "watchFor": "Own-database mode posts conversation messages, a summary and the system prompt to api.memorilabs.ai for augmentation, while the architecture page says all data stays on your infrastructure" } ], "history": { "hindsight": [ { "causes": [ "product", "evidence" ], "date": "2026-11-04", "delta": -2.4, "from": 50.2, "fromGrade": "D", "to": 47.8, "toGrade": "D" }, { "causes": [ "evidence" ], "date": "2026-10-28", "delta": -0.1, "from": 50.3, "fromGrade": "D", "to": 50.2, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 50.2, "fromGrade": "D", "to": 50.3, "toGrade": "D" } ], "memori": null }, "job": { "capability": "memory.store", "name": "Agent memory" }, "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-memori.json", "title": "Amazon Bedrock AgentCore Memory vs Memori", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-memori" }, { "json": 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"https://www.anchorterminal.com/compare/honcho-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-vs-memori.json", "title": "Hy Memory vs Memori", "url": "https://www.anchorterminal.com/compare/hy-memory-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/langmem-vs-memori.json", "title": "LangMem vs Memori", "url": "https://www.anchorterminal.com/compare/langmem-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/localghost-vs-memori.json", "title": "LocalGhost vs Memori", "url": "https://www.anchorterminal.com/compare/localghost-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/mem0-vs-memori.json", "title": "Mem0 Platform + MCP vs Memori", "url": "https://www.anchorterminal.com/compare/mem0-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/memmachine-vs-memori.json", "title": "MemMachine vs Memori", "url": "https://www.anchorterminal.com/compare/memmachine-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-memory-reference-server.json", "title": "Memori vs Memory (MCP reference server)", "url": "https://www.anchorterminal.com/compare/memori-vs-memory-reference-server" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-mempalace.json", "title": "Memori vs MemPalace", "url": "https://www.anchorterminal.com/compare/memori-vs-mempalace" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-past-dev.json", "title": "Memori vs past.dev", "url": "https://www.anchorterminal.com/compare/memori-vs-past-dev" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-supermemory.json", "title": "Memori vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/memori-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-tencentdb-agent-memory.json", "title": "Memori vs TencentDB Agent Memory", "url": "https://www.anchorterminal.com/compare/memori-vs-tencentdb-agent-memory" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-tokenrip.json", "title": "Memori vs Tokenrip", "url": "https://www.anchorterminal.com/compare/memori-vs-tokenrip" }, { "json": "https://www.anchorterminal.com/compare/memori-vs-zep.json", "title": "Memori vs Zep", "url": "https://www.anchorterminal.com/compare/memori-vs-zep" }, { "json": "https://www.anchorterminal.com/compare/ai-memory-vs-hindsight.json", "title": "AI Memory vs Hindsight", "url": "https://www.anchorterminal.com/compare/ai-memory-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/ai-memory-vs-memori.json", "title": "AI Memory vs Memori", "url": "https://www.anchorterminal.com/compare/ai-memory-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/context-mode-vs-hindsight.json", "title": "Context Mode Engine vs Hindsight", "url": "https://www.anchorterminal.com/compare/context-mode-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/context-mode-vs-memori.json", "title": "Context Mode Engine vs Memori", "url": "https://www.anchorterminal.com/compare/context-mode-vs-memori" }, { "json": "https://www.anchorterminal.com/compare/graphicle-vs-hindsight.json", "title": "Graphicle vs Hindsight", "url": "https://www.anchorterminal.com/compare/graphicle-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/graphicle-vs-memori.json", "title": "Graphicle vs Memori", "url": "https://www.anchorterminal.com/compare/graphicle-vs-memori" } ], "panel": null, "scores": [ { "by": 1, "edge": "memori", "hindsight": 27, "key": "reliability", "memori": 28, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 10, "edge": "hindsight", "hindsight": 78, "key": "schema", "memori": 68, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 8, "edge": "memori", "hindsight": 57, "key": "ergonomics", "memori": 65, "name": "Agent ergonomics", "weight": 13 }, { "by": 20, "edge": "hindsight", "hindsight": 65, "key": "security", "memori": 45, "name": "Security \u0026 auth", "weight": 14 }, { "by": 15, "edge": "memori", "hindsight": 30, "key": "payments", "memori": 45, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 47, "edge": "hindsight", "hindsight": 80, "key": "maintenance", "memori": 33, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 2, "edge": "memori", "hindsight": 50, "key": "transparency", "memori": 52, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hindsight scores 47.8 (D) on agent readiness against Memori's 44.2 (E), and leads in 3 of 7 scored categories. Memori leads on agent ergonomics and payments \u0026 pricing. Both do agent memory.", "tasks": null, "tutorials": [], "verdicts": { "hindsight": "API keys support bank-level restrictions, expiry and child-key revocation, and prices are published per token and per call. The status page and docs couldn't be read, 1.x minor releases carried breaking changes without notice, and access-control fixes shipped without advisories.", "memori": "Memori wraps an app's LLM client to capture conversations and recall facts, through Apache-2.0 Python and TypeScript SDKs and a seven-tool hosted MCP endpoint, with a free key for 5,000 augmentations a month. The open-source mode sends conversations to Memori's API for augmentation although its docs say all data stays local, and the last SDK release was 27 May 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hindsight-vs-memori", "json": "https://www.anchorterminal.com/compare/hindsight-vs-memori.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hindsight-vs-memori.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hindsight-vs-memori.min.md" }, "markdown": "Hindsight scores 47.8 (D) on agent readiness against Memori's 44.2 (E), and leads in 3 of 7 scored categories. Memori leads on agent ergonomics and payments \u0026 pricing. Both do agent memory.\n\n- Hindsight: grade D, 47.8/100, rank #1141 of 1510. Markdown https://www.anchorterminal.com/tools/hindsight.md · JSON https://www.anchorterminal.com/api/v1/tools/hindsight.json\n- Memori: grade E, 44.2/100, rank #1231 of 1510. Markdown https://www.anchorterminal.com/tools/memori.md · JSON https://www.anchorterminal.com/api/v1/tools/memori.json\n- Best memory layers for AI agents: https://www.anchorterminal.com/best/agent-memory/index.md\n- All 353 memory comparisons: https://www.anchorterminal.com/compare/agent-memory/index.md\n\n## Which one, for what\n\n### 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, 78 against 68\n- Security \u0026 auth, 65 against 45\n- Maintenance \u0026 community, 80 against 33\n\nWatch for: Retain at $10 per million tokens is the priciest ingestion in this category\n\n### Memori (E)\n\nGood for: Memory captured from LLM calls for apps that already use OpenAI, Anthropic or Gemini clients.\n\nAhead on:\n- Agent ergonomics, 65 against 57\n- Payments \u0026 pricing, 45 against 30\n\nWatch for: Own-database mode posts conversation messages, a summary and the system prompt to api.memorilabs.ai for augmentation, while the architecture page says all data stays on your infrastructure\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Hindsight +47\n\n- Hindsight (80): Tag v1.2.0 on 3 November 2026, the day before the check (30). Ten core releases since 6 August, v0.9.0 (7 August), v0.9.1 (14 August), v0.9.2 (25 August), v0.10.0 to v0.10.3 (14 September to 8 October), v1.0.0 (13 October), v1.1.0 (21 October) and v1.2.0 (20). We didn't check issue reply times on GitHub (10 of 25). Current clients for Python, TypeScript and Go, though the hosted MCP isn't in the official registry (15). Python 3.11 or later, and a test workflow in the repository whose result we couldn't read (5 of 10).\n- Memori (33): SDK 3.3.6 on 27 May 2026, 163 days before the check (10). No release in the 90 days to 6 November (0). Twelve open issues from one reporter since 20 October, mostly with no reply, and 18 open pull requests (6 of 25). Python and TypeScript SDKs, last released in May 2026 (12 of 15). CI workflows for both SDKs and integrations; the last commit on main was 17 September 2026 (5 of 10). Total 33.\n\n### Security \u0026 auth: Hindsight +20\n\n- Hindsight (65): Keys can be restricted to named banks, expire after an hour to a year or never, are revocable with child keys revoked alongside, and the docs give a rotation procedure. The hosted MCP uses OAuth with PKCE under RFC 9728, or a Bearer key (30). Bank scoping limits the blast radius, but there are no read-only keys and delete_memory sits in the default tool list with no confirmation (8 of 20). `Memory Defense` screens every retain before storage. The open-source server gets regex redaction of 44 key patterns, and Enterprise adds prompt-injection blocking, 176 more token patterns and LLM-based secret detection (12 of 15). Security audit trails and SIEM webhooks are Enterprise only (5 of 15). These four lines per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page). SECURITY.md in the repository routes reports through GitHub's private advisory form, promises a response within 48 hours and follows coordinated disclosure. No security.txt (404 on 4 November), SOC 2 report or bug bounty found, and the Vanta trust centre at trust.vectorize.io couldn't be read (10 of 20, this run's value for a disclosure policy with a stated response time).\n- Memori (45): API keys in a header from the dashboard or CLI, with no scopes found (20 of 30). No read-only mode or per-tool restrictions (4 of 20). Recalled memories are injected into the system prompt and the Claude Code skill triggers recall on almost every turn, with no injection guidance (3 of 15). A dashboard shows memories, retrieval and usage (8 of 15). SECURITY.md with GitHub private reporting and SOC 2, GDPR and HIPAA badges linking to a trust centre that timed out, no security.txt. Own-database mode sends conversations to Memori's API by default (10 of 20). Total 45.\n\n### Payments \u0026 pricing: Memori +15\n\n- Hindsight (30): No x402, MPP or L402. Calls on an empty balance return 402 with no payment payload (0). Per-unit prices published without a login and re-read on 4 November, retain $10 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 and refresh $0.05 a call, storage $0.25 per million tokens a month after the first 30 days (20). The pricing page says new accounts start with $5 of free credit; whether a card is needed isn't stated (10 of 20). A person signs up in the browser to get a key (0).\n- Memori (45): No x402, MPP or L402 (0 of 40). Plan prices are public but sales-led, from $60,000 a year (10). A free key with 5,000 augmentations a month, with no card mentioned (20). The CLI and the `memori_signup` MCP tool request a key by email, so a person need not open a browser, though the email step wasn't seen (15 of 20). Total 45.\n\n### Schema \u0026 documentation: Hindsight +10\n\n- Hindsight (78): OpenAPI spec at hindsight.vectorize.io/openapi.json, linked from the open-source docs' llms.txt (25). llms.txt and llms-full.txt on hindsight.vectorize.io, with links to the quick start, SDKs, API reference, OpenAPI file and MCP page (10). Tool names follow the three verbs (retain, recall, reflect) and the docs explain each, but with 27 tools the when-not-to-use guidance is thin (12 of 20). Typed inputs, with an async flag on retain, and we didn't audit enums across the spec (10 of 15). 402 and 403 are documented with their causes, and examples sit in the guides (9 of 15). A changelog at hindsight.vectorize.io/changelog with Breaking Changes sections per release, and versioned /v1 paths (12 of 15). Cloud docs per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page).\n- Memori (68): No OpenAPI for the REST API, and the MCP tool schemas couldn't be read without opening a session, so the docs' parameter lists are what was scored (12 of 25). llms.txt with Markdown docs for both modes (10). Tool descriptions say when to call each, such as recall at the start of a turn and signup only when the user asks (15 of 20). Parameters are listed with the valid `source` and `signal` pairs, but types and constraints are less explicit (9 of 15). Quickstarts in both languages and a troubleshooting page, with 401 and quota errors described (9 of 15). Keep a Changelog with semantic versioning (13 of 15). Total 68.\n\n### Agent ergonomics: Memori +8\n\n- Hindsight (57): 27 MCP tools on a bank-scoped URL and 30 at the root (15), with no documented way to load a subset or a read-only set. List and recall calls exist with bank scoping, and we didn't confirm page-size or token-budget limits (12 of 20). 402 tells an agent it's out of credit and 403 that the key can't reach the bank (12 of 20). No idempotency keys and no readOnlyHint or destructiveHint on the MCP tools, though retain can run asynchronously (3 of 20). Clients for Python, TypeScript and Go, and one URL per bank keeps calls simple (15). MCP and key docs per the 1 October check, not re-read on 4 November (docs.hindsight.vectorize.io answers robots.txt with its app page).\n- Memori (65): Seven MCP tools with short parameter lists (22 of 25). Recall filters by project, session, date range and source (14 of 20). 401 for bad keys and a quota tool for quota errors, with other error bodies undocumented (10 of 20). No idempotency guidance or tool annotations documented (4 of 20). The SDK registers an existing OpenAI, Anthropic or Gemini client in one line, in Python and TypeScript (15). Total 65.\n\n## Score by category\n\n| Category | Weight | Hindsight | Memori | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 27 | 28 | Memori +1 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 68 | Hindsight +10 |\n| Agent ergonomics | 13% (16.2 this run) | 57 | 65 | Memori +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 45 | Hindsight +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 45 | Memori +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 33 | Hindsight +47 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 50 | 52 | Memori +2 |\n| Negative events | ≤15 | -6 | -4 | |\n| **Total** | | **47.8 · D** | **44.2 · E** | |\n\n## Facts side by side\n\n| Fact | Hindsight | Memori |\n| --- | --- | --- |\n| Kind | HTTP API | SDK + MCP |\n| Vendor | Vectorize | Memori Labs Inc. |\n| Hosted endpoint | `https://api.hindsight.vectorize.io` | `https://api.memorilabs.ai/mcp/` |\n| Transports | HTTP, Streamable HTTP | Streamable HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Tools exposed | 27 | 7 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-03 | 2026-05-27 |\n| Terms last updated | couldn't be read | no document linked |\n| Privacy policy last updated | couldn't be read | no date given |\n| Customer content may train models | couldn't be read | not read yet |\n| Terms restrict automated access | couldn't be read | not read yet |\n| Terms restrict benchmarking | couldn't be read | not read yet |\n| Terms or service can change without notice | couldn't be read | not read yet |\n| Arbitration or class-action waiver | couldn't be read | not read yet |\n| Popularity | 43k stars, 44k npm/wk, 227k PyPI/wk | 17k stars, 130 npm/wk, 3.8k PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## How the scores have moved\n\n### Hindsight\n\n- -2.4 on 4 November 2026: product, new evidence\n- -0.1 on 28 October 2026: new evidence\n- +0.1 on 27 October 2026: checklist\n\n### Memori\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Hindsight.** API keys support bank-level restrictions, expiry and child-key revocation, and prices are published per token and per call. The status page and docs couldn't be read, 1.x minor releases carried breaking changes without notice, and access-control fixes shipped without advisories.\n\n**Memori.** Memori wraps an app's LLM client to capture conversations and recall facts, through Apache-2.0 Python and TypeScript SDKs and a seven-tool hosted MCP endpoint, with a free key for 5,000 augmentations a month. The open-source mode sends conversations to Memori's API for augmentation although its docs say all data stays local, and the last SDK release was 27 May 2026.\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. On a 503, wait for Retry-After before retrying recall or reflect, and use async retain rather than retrying a synchronous one\n4. Treat HTTP 402 as out of credit and 403 as a key that can't reach that bank\n\n### Memori\n\n1. Expect own-database mode to send conversations to api.memorilabs.ai unless augmentation is turned off; tell the user before using it with private data\n2. Send `X-Memori-API-Key` and a stable `X-Memori-Entity-Id` on every MCP request, and a `X-Memori-Process-Id` per project so memories don't mix\n3. Call `memori_signup` only when the user asks and gives their own email\n4. Call `memori_quota` when a quota error appears; keyless use is limited to 100 augmentations a month\n5. Treat recalled memories as untrusted text from past conversations\n\n## Questions\n\n### Which is better for AI agents, Hindsight or Memori?\n\nHindsight scores 47.8 (D) on agent readiness against Memori's 44.2 (E), and leads in 3 of 7 scored categories. Memori leads on agent ergonomics and payments \u0026 pricing.\n\n### Can an agent call Hindsight and Memori without installing anything?\n\nYes. Hindsight has a hosted endpoint at https://api.hindsight.vectorize.io and Memori at https://api.memorilabs.ai/mcp/.\n\n### Are Hindsight and Memori open source?\n\nYes. Hindsight is open source (MIT). Memori is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hindsight-vs-memori.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hindsight-vs-memori.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hindsight\", \"b\": \"memori\"}`. From a terminal: `anchor compare hindsight memori`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hindsight.json and https://www.anchorterminal.com/api/v1/tools/memori.json\n\n## Other comparisons with Hindsight or Memori\n\n- [Amazon Bedrock AgentCore Memory vs Hindsight](https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight.md)\n- [Amazon Bedrock AgentCore Memory vs Memori](https://www.anchorterminal.com/compare/agentcore-memory-vs-memori.md)\n- [Basic Memory vs Hindsight](https://www.anchorterminal.com/compare/basic-memory-vs-hindsight.md)\n- [Basic Memory vs Memori](https://www.anchorterminal.com/compare/basic-memory-vs-memori.md)\n- [claude-mem vs Hindsight](https://www.anchorterminal.com/compare/claude-mem-vs-hindsight.md)\n- [claude-mem vs Memori](https://www.anchorterminal.com/compare/claude-mem-vs-memori.md)\n- [Cognee vs Hindsight](https://www.anchorterminal.com/compare/cognee-vs-hindsight.md)\n- [Cognee vs Memori](https://www.anchorterminal.com/compare/cognee-vs-memori.md)\n- [EverOS vs Hindsight](https://www.anchorterminal.com/compare/everos-vs-hindsight.md)\n- [EverOS vs Memori](https://www.anchorterminal.com/compare/everos-vs-memori.md)\n- [Agent Platform Memory Bank vs Hindsight](https://www.anchorterminal.com/compare/google-memory-bank-vs-hindsight.md)\n- [Agent Platform Memory Bank vs Memori](https://www.anchorterminal.com/compare/google-memory-bank-vs-memori.md)\n- [Graphiti vs Hindsight](https://www.anchorterminal.com/compare/graphiti-vs-hindsight.md)\n- [Graphiti vs Memori](https://www.anchorterminal.com/compare/graphiti-vs-memori.md)\n- [Hindsight vs Honcho](https://www.anchorterminal.com/compare/hindsight-vs-honcho.md)\n- [Hindsight vs Hy Memory](https://www.anchorterminal.com/compare/hindsight-vs-hy-memory.md)\n- [Hindsight vs LangMem](https://www.anchorterminal.com/compare/hindsight-vs-langmem.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 MemMachine](https://www.anchorterminal.com/compare/hindsight-vs-memmachine.md)\n- [Hindsight vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/hindsight-vs-memory-reference-server.md)\n- [Hindsight vs MemPalace](https://www.anchorterminal.com/compare/hindsight-vs-mempalace.md)\n- [Hindsight vs past.dev](https://www.anchorterminal.com/compare/hindsight-vs-past-dev.md)\n- [Hindsight vs Supermemory API + MCP](https://www.anchorterminal.com/compare/hindsight-vs-supermemory.md)\n- [Hindsight vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/hindsight-vs-tencentdb-agent-memory.md)\n- [Hindsight vs Tokenrip](https://www.anchorterminal.com/compare/hindsight-vs-tokenrip.md)\n- [Hindsight vs Zep](https://www.anchorterminal.com/compare/hindsight-vs-zep.md)\n- [Honcho vs Memori](https://www.anchorterminal.com/compare/honcho-vs-memori.md)\n- [Hy Memory vs Memori](https://www.anchorterminal.com/compare/hy-memory-vs-memori.md)\n- [LangMem vs Memori](https://www.anchorterminal.com/compare/langmem-vs-memori.md)\n- [LocalGhost vs Memori](https://www.anchorterminal.com/compare/localghost-vs-memori.md)\n- [Mem0 Platform + MCP vs Memori](https://www.anchorterminal.com/compare/mem0-vs-memori.md)\n- [MemMachine vs Memori](https://www.anchorterminal.com/compare/memmachine-vs-memori.md)\n- [Memori vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/memori-vs-memory-reference-server.md)\n- [Memori vs MemPalace](https://www.anchorterminal.com/compare/memori-vs-mempalace.md)\n- [Memori vs past.dev](https://www.anchorterminal.com/compare/memori-vs-past-dev.md)\n- [Memori vs Supermemory API + MCP](https://www.anchorterminal.com/compare/memori-vs-supermemory.md)\n- [Memori vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/memori-vs-tencentdb-agent-memory.md)\n- [Memori vs Tokenrip](https://www.anchorterminal.com/compare/memori-vs-tokenrip.md)\n- [Memori vs Zep](https://www.anchorterminal.com/compare/memori-vs-zep.md)\n- [AI Memory vs Hindsight](https://www.anchorterminal.com/compare/ai-memory-vs-hindsight.md)\n- [AI Memory vs Memori](https://www.anchorterminal.com/compare/ai-memory-vs-memori.md)\n- [Context Mode Engine vs Hindsight](https://www.anchorterminal.com/compare/context-mode-vs-hindsight.md)\n- [Context Mode Engine vs Memori](https://www.anchorterminal.com/compare/context-mode-vs-memori.md)\n- [Graphicle vs Hindsight](https://www.anchorterminal.com/compare/graphicle-vs-hindsight.md)\n- [Graphicle vs Memori](https://www.anchorterminal.com/compare/graphicle-vs-memori.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": "Hindsight vs Memori", "url": "" } ], "description": "Hindsight scores 47.8 (D) to Memori's 44.2 (E) for agent memory. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Hindsight D 47.8", "Memori E 44.2", "scores" ], "h1": "Hindsight vs Memori", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/hindsight-vs-memori", "published": "2026-10-01", "section": "tools", "title": "Hindsight vs Memori for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/hindsight-vs-memori" }, "tokens": { "markdown": 4550, "slim": 2330 }, "version": 1 }