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          "name": "Synap Memory",
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          "url": "https://www.anchorterminal.com/tools/maximem-synap"
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        {
          "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wontopos-mcp.json",
          "kind": "mcp",
          "name": "wontopos.com MCP server",
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          "url": "https://www.anchorterminal.com/tools/wontopos-mcp"
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          "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wuniq.json",
          "kind": "mcp",
          "name": "Wuniq KE (Knowledge Engine)",
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          "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ghosts-wyrm.json",
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          "name": "wyrm",
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          "url": "https://www.anchorterminal.com/tools/ghosts-wyrm"
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        {
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          "kind": "mcp",
          "name": "ZenBrain Memory",
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          "url": "https://www.anchorterminal.com/tools/zensation-zenbrain"
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      "name": "Agent memory",
      "slug": "agent-memory",
      "test": "One user's history fed in over several sessions, then questions that need facts from early on, a changed preference and a deleted fact. We check what comes back, how fast, and whether the deletion sticks.",
      "title": "Memory layers for AI agents",
      "toolCount": 8,
      "tools": [
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        "honcho",
        "supermemory",
        "mem0",
        "cognee",
        "memory-reference-server",
        "graphiti",
        "hindsight"
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      "url": "https://www.anchorterminal.com/categories/agent-memory"
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        "url": "https://www.anchorterminal.com/tools/zep",
        "markdownUrl": "https://www.anchorterminal.com/tools/zep.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zep.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zep.json",
        "repo": "https://github.com/getzep/zep",
        "license": "Apache-2.0 (examples repo). The service is closed-source",
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        "remoteUrl": "https://api.getzep.com/api/v2",
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          {
            "registry": "npm",
            "name": "@getzep/zep-cloud"
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        "auth": "mixed",
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        "pricing": "freemium",
        "pricingNotes": "Free has 10,000 credits a month with no rollover, 2 projects, 1 MCP seat, and variable rate limits. The pricing page doesn't say whether a card is needed. Flex is $125 a month for 50,000 credits, then $25 per 10,000, at 600 requests a minute, with 30-day rollover and auto top-up. Flex Plus is $375 a month for 200,000 credits, then $75 per 40,000, at 1,000 requests a minute, with 60-day rollover. Enterprise is negotiated and adds guaranteed rate limits with an SLA, a HIPAA BAA, BYOK and BYOC. An episode (a chat message, JSON payload or block of text) costs 1 credit for up to 350 bytes and 1 more for each further 350 bytes or part. Storage, retrieval and users aren't metered, and each webhook call costs an eighth of a credit (https://www.getzep.com/pricing/).",
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        "where": "hosted",
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          "verdict": "Facts that new data invalidates keep the time they stopped being true. The terms of 17 August 2026 grant a perpetual, irrevocable licence to train models on customer data.",
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            "Facts that new data invalidates keep the time they stopped being true",
            "API keys can carry ABAC policies, and audit and API logs record what each key did",
            "Published rate limits (600 a minute on Flex, 1,000 on Flex Plus) with 429, Retry-After and X-RateLimit headers",
            "A memory-security guide that treats every context block as untrusted data",
            "SDKs for Python, TypeScript and Go, OpenAPI for v3 and llms.txt"
          ],
          "weaknesses": [
            "The terms of 17 August 2026 grant a perpetual, irrevocable licence to train models on customer data",
            "First paid plan is $125 a month, and credits grow with episode size",
            "The MCP server needs OAuth through a company identity provider, so a headless agent can't use it",
            "Four status incidents between 27 August and 15 September 2026",
            "No security.txt, no subprocessor list and no entry in the MCP registry"
          ],
          "agentNotes": [
            "Create the user and a thread before adding messages, as the quickstart does",
            "Set `return_context` when adding messages to skip a second round trip",
            "Keep episodes short. Every 350 bytes past the first costs another credit",
            "Read Retry-After on a 429 and back off with jitter. Free accounts over quota drop to 5 requests a minute",
            "Expect a delay after a graph add (202 Accepted) before new facts show up in search"
          ],
          "metrics": {
            "kind": "remote",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 4,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "high",
              "grade": "B",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 69.6
            }
          ],
          "editorialScores": {
            "ergonomics": 70,
            "maintenance": 80,
            "payments": 30,
            "reliability": 65,
            "schema": 89,
            "security": 84,
            "transparency": 40
          },
          "provenanceScore": 82
        },
        "connect": {
          "install": "pip install zep-cloud   # or: npm install @getzep/zep-cloud",
          "http": "curl -X POST https://api.getzep.com/api/v2/graph -H \"Authorization: Api-Key $ZEP_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"user_id\":\"user-123\",\"type\":\"text\",\"data\":\"Alex moved to Leeds in March and prefers aisle seats.\"}'",
          "claudeCode": "claude mcp add --transport http zep https://api.getzep.com/mcp",
          "config": {
            "mcpServers": {
              "zep": {
                "type": "http",
                "url": "https://api.getzep.com/mcp"
              }
            }
          }
        },
        "letme": {
          "capability": "https://letme.dev/memory.store",
          "tool": "https://letme.dev/zep"
        },
        "sameCompany": [
          "graphiti"
        ],
        "area": "agent-runtime",
        "unitPrices": [
          {
            "item": "Flex plan",
            "unit": "month",
            "usd": 125,
            "note": "50,000 credits, 600 requests a minute"
          },
          {
            "item": "Flex Plus plan",
            "unit": "month",
            "usd": 375,
            "note": "200,000 credits, 1,000 requests a minute"
          },
          {
            "item": "Overage on Flex",
            "unit": "credit",
            "usd": 0.0025,
            "note": "$25 per 10,000 credits"
          },
          {
            "item": "Overage on Flex Plus",
            "unit": "credit",
            "usd": 0.001875,
            "note": "$75 per 40,000 credits"
          }
        ],
        "provenance": {
          "legalEntity": "Zep Software, Inc.",
          "domain": "getzep.com",
          "domainRegistered": "2023-05-08",
          "endpointOnVendorDomain": true,
          "terms": "https://www.getzep.com/legal/terms/",
          "privacy": "https://www.getzep.com/legal/privacy/",
          "statusPage": "https://status.getzep.com",
          "changelog": "https://help.getzep.com/changelog",
          "securityTxt": "none",
          "checked": "2026-09-30",
          "notes": [
            "The terms, updated 17 August 2026, name Zep Software, Inc., a Delaware corporation, and are governed by California law.",
            "www.getzep.com/.well-known/security.txt returns 404."
          ],
          "score": 82
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/zep.json",
        "live": {
          "slug": "zep",
          "probe": {
            "target": "https://api.getzep.com/api/v2",
            "method": "get",
            "lastAt": "2026-10-04T21:48:39.358774496Z",
            "lastOk": true,
            "lastStatus": 401,
            "lastMs": 264,
            "lastNote": "asks for credentials",
            "authRequired": true,
            "uptime24h": 100,
            "uptime30d": 100,
            "p50ms24h": 194,
            "p95ms24h": 514,
            "samples24h": 272,
            "samples30d": 875,
            "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": 247,
                "ok": 247
              }
            ]
          },
          "vendorStatus": {
            "page": "https://status.getzep.com",
            "indicator": "none",
            "summary": "All Systems Operational",
            "checkedAt": "2026-10-04T21:40:34.944152465Z"
          },
          "versions": [
            {
              "registry": "github",
              "name": "getzep/zep",
              "version": "zep-ingest-v0.3.0",
              "released": "2026-08-28",
              "seenAt": "2026-10-04T16:44:47.058446278Z"
            },
            {
              "registry": "npm",
              "name": "@getzep/zep-cloud",
              "version": "3.30.0",
              "seenAt": "2026-10-04T16:44:46.187964355Z"
            },
            {
              "registry": "pypi",
              "name": "zep-cloud",
              "version": "3.30.0",
              "released": "2026-09-24",
              "seenAt": "2026-10-04T16:44:44.180190065Z"
            }
          ],
          "githubStars": 4948,
          "npmWeekly": 144572,
          "pypiWeekly": 84209,
          "securityTxt": {
            "url": "https://getzep.com/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:15:52.364915515Z"
          },
          "llmsTxt": {
            "url": "https://help.getzep.com/llms.txt",
            "ok": true,
            "status": 200,
            "checkedAt": "2026-10-04T15:18:23.198921048Z"
          },
          "domain": {
            "domain": "getzep.com",
            "registered": "2023-05-08",
            "source": "https://rdap.verisign.com/com/v1/domain/getzep.com",
            "checkedAt": "2026-10-04T13:03:48.930668589Z"
          },
          "pages": [
            {
              "url": "https://help.getzep.com/changelog",
              "kind": "changelog",
              "status": 304,
              "checkedAt": "2026-10-04T15:45:01.696303768Z",
              "changedAt": "2026-10-03T15:33:06.787642615Z",
              "fingerprint": "4bb785bf262b"
            },
            {
              "url": "https://www.getzep.com/legal/privacy/",
              "kind": "privacy",
              "status": 200,
              "checkedAt": "2026-10-04T15:50:29.454925317Z",
              "changedAt": "2026-10-03T15:38:21.106411993Z",
              "fingerprint": "b49841156a51"
            },
            {
              "url": "https://www.getzep.com/legal/terms/",
              "kind": "terms",
              "status": 200,
              "checkedAt": "2026-10-04T15:50:31.622183726Z",
              "changedAt": "2026-10-03T15:38:23.230842634Z",
              "fingerprint": "955360e78c6a"
            }
          ],
          "updatedAt": "2026-10-04T21:48:39.358774496Z"
        }
      },
      {
        "slug": "honcho",
        "name": "Honcho",
        "vendor": "Plastic Labs",
        "vendorUrl": "https://honcho.dev",
        "kind": "http-api",
        "category": "agent-memory",
        "summary": "Memory API that models each participant (a peer) in a conversation.",
        "url": "https://www.anchorterminal.com/tools/honcho",
        "markdownUrl": "https://www.anchorterminal.com/tools/honcho.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/honcho.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/honcho.json",
        "repo": "https://github.com/plastic-labs/honcho",
        "license": "AGPL-3.0",
        "transports": [
          "http",
          "streamable-http"
        ],
        "remoteUrl": "https://api.honcho.dev/v3",
        "packages": [
          {
            "registry": "pypi",
            "name": "honcho-ai"
          },
          {
            "registry": "npm",
            "name": "@honcho-ai/sdk"
          }
        ],
        "auth": "mixed",
        "authNotes": "Bearer API key (`hch-...`) on api.honcho.dev. The create-key endpoint mints further keys scoped to a workspace, peer or session, with an optional expiry, and they're revocable from the API Keys page. The hosted MCP at mcp.honcho.dev takes a key as a Bearer header or an OAuth token, plus an optional workspace header. The AgentCash endpoints take x402 or MPP payment instead of a key, with wallet sign-in for the free ones.",
        "pricing": "usage",
        "pricingNotes": "Ingestion (storage plus background reasoning) is $2.00 per million, and queries through `chat` cost $0.001 (minimal), $0.01 (low), $0.05 (medium), $0.10 (high) or $0.50 (max) each. Context retrieval is listed as unlimited. Startups that have raised under $5 million get $1,000 of credit and 12 months of subsidised pricing (https://honcho.dev/). New organisations get $100 of free credit (https://github.com/plastic-labs/honcho). Over x402 the ingestion price is $2 per million tokens of message content, with a $0.001 minimum and a $5.00 maximum per call (https://agentcash.honcho.dev/openapi.json).",
        "priceSummary": "$0.001 / call",
        "where": "hosted",
        "x402": {
          "level": "yes",
          "evidence": "An AgentCash storefront at agentcash.honcho.dev lists 16 POST endpoints under /api/honcho/. Storing messages and chat are paid over x402 or Tempo MPP on Base, Solana or Tempo. Messages cost $2 per million tokens (minimum $0.001, maximum $5.00), chat $0.001 to $0.50 by reasoning level, and the other endpoints are free (https://agentcash.honcho.dev/openapi.json, checked 2026-09-30).",
          "endpoints": [
            {
              "url": "https://agentcash.honcho.dev/api/honcho/messages",
              "priceUsd": 0.001,
              "network": "eip155:8453"
            },
            {
              "url": "https://agentcash.honcho.dev/api/honcho/chat",
              "priceUsd": 0.001,
              "network": "eip155:8453"
            }
          ]
        },
        "toolCount": null,
        "popularity": {
          "githubStars": 7400,
          "npmWeekly": 17913,
          "pypiWeekly": null,
          "asOf": "2026-09-30"
        },
        "docsUrl": "https://honcho.dev/docs",
        "llmsTxt": "https://honcho.dev/docs/llms.txt",
        "openapi": "https://honcho.dev/docs/v3/openapi.json",
        "registryName": "io.github.plastic-labs/honcho",
        "capabilities": [
          "memory.store",
          "memory.search",
          "memory.user",
          "memory.delete"
        ],
        "tags": [
          "hosted",
          "usage-priced",
          "x402",
          "stablecoin",
          "mcp",
          "oauth",
          "llms-txt",
          "python",
          "typescript",
          "open-source",
          "self-hosted"
        ],
        "lastRelease": "2026-09-24",
        "graded": true,
        "anchor": {
          "graded": true,
          "score": 64.2,
          "grade": "B",
          "agentReady": false,
          "rank": 188,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 2,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 65,
            "maintenance": 73,
            "payments": 80,
            "reliability": 50,
            "schema": 79,
            "security": 48,
            "transparency": 69
          },
          "pending": [
            "performance",
            "tasks"
          ],
          "assessment": {
            "confidence": "medium",
            "date": "2026-10-01"
          },
          "negative": 0,
          "verdict": "Pay per call over x402 or MPP at agentcash.honcho.dev, with free read endpoints. No published rate limits, 429 guidance or SLA.",
          "strengths": [
            "Pay per call over x402 or MPP at agentcash.honcho.dev, with free read endpoints",
            "Keys can be minted per workspace, peer or session, with an expiry, through the API",
            "Context retrieval listed as unlimited, with reasoning billed only when you call chat",
            "Hosted MCP in the official registry, plus OpenAPI specs for v1 to v3",
            "AGPL-3.0 server, the same code that runs the hosted service"
          ],
          "weaknesses": [
            "No published rate limits, 429 guidance or SLA",
            "Changelog entries carry no dates and the repo has no GitHub releases",
            "The MCP tool list is sent on connect, not documented",
            "No security.txt, audit log or prompt-injection guidance found",
            "AGPL-3.0 server licence"
          ],
          "agentNotes": [
            "Get or create the workspace with POST /v3/workspaces before writing sessions and messages",
            "Batch up to 100 messages per request, each under 25,000 characters",
            "Use context retrieval for routine turns and save `chat` at high or max for questions that need it, since max costs 500 times minimal",
            "Mint a peer- or session-scoped key with an expiry for any agent that shouldn't see the whole workspace",
            "Over x402, batch messages rather than sending them singly, as each paid call has a $0.001 floor"
          ],
          "metrics": {
            "kind": "remote",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 3,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "medium",
              "grade": "B",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 64.2
            }
          ],
          "editorialScores": {
            "ergonomics": 65,
            "maintenance": 73,
            "payments": 80,
            "reliability": 50,
            "schema": 79,
            "security": 48,
            "transparency": 56
          },
          "provenanceScore": 82
        },
        "connect": {
          "install": "pip install honcho-ai   # or: npm install @honcho-ai/sdk",
          "http": "curl -X POST https://api.honcho.dev/v3/workspaces -H \"Authorization: Bearer $HONCHO_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"id\":\"workspace-123\",\"metadata\":{}}'",
          "claudeCode": "claude mcp add honcho --transport http \"https://mcp.honcho.dev\" --header \"Authorization: Bearer $HONCHO_API_KEY\"",
          "config": {
            "mcpServers": {
              "honcho": {
                "headers": {
                  "Authorization": "Bearer ${HONCHO_API_KEY}"
                },
                "type": "http",
                "url": "https://mcp.honcho.dev"
              }
            }
          },
          "x402": "curl -s -i -X POST https://agentcash.honcho.dev/api/honcho/messages -H 'content-type: application/json'\n# 402 -\u003e pay over x402 (USDC on eip155:8453, or Solana) or Tempo MPP -\u003e retry. Request schemas at https://agentcash.honcho.dev/openapi.json"
        },
        "letme": {
          "capability": "https://letme.dev/memory.store",
          "tool": "https://letme.dev/honcho"
        },
        "area": "agent-runtime",
        "unitPrices": [
          {
            "item": "Ingestion",
            "unit": "1m-tokens",
            "usd": 2,
            "note": "Storage plus background reasoning. $0.001 minimum per call over x402"
          },
          {
            "item": "chat, minimal reasoning",
            "unit": "call",
            "usd": 0.001
          },
          {
            "item": "chat, low reasoning",
            "unit": "call",
            "usd": 0.01
          },
          {
            "item": "chat, medium reasoning",
            "unit": "call",
            "usd": 0.05
          },
          {
            "item": "chat, high reasoning",
            "unit": "call",
            "usd": 0.1
          },
          {
            "item": "chat, max reasoning",
            "unit": "call",
            "usd": 0.5
          }
        ],
        "provenance": {
          "legalEntity": "Plastic Labs, Inc.",
          "domain": "honcho.dev",
          "domainRegistered": "2022-01-26",
          "endpointOnVendorDomain": true,
          "terms": "https://app.honcho.dev/tos",
          "privacy": "https://app.honcho.dev/privacy",
          "statusPage": "https://status.honcho.dev",
          "changelog": "https://honcho.dev/docs/changelog/introduction",
          "securityTxt": "none",
          "checked": "2026-09-30",
          "notes": [
            "The terms name Plastic Labs, Inc., 169 Madison Avenue, New York, with arbitration in New York. They carry no last-updated date.",
            "honcho.dev/.well-known/security.txt returns 404.",
            "The x402 storefront runs on the AgentCash platform at agentcash.honcho.dev, a subdomain of honcho.dev."
          ],
          "score": 82
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/honcho.json",
        "live": {
          "slug": "honcho",
          "probe": {
            "target": "https://api.honcho.dev/v3",
            "method": "get",
            "lastAt": "2026-10-04T21:48:29.348577618Z",
            "lastOk": true,
            "lastStatus": 401,
            "lastMs": 394,
            "lastNote": "asks for credentials",
            "authRequired": true,
            "uptime24h": 100,
            "uptime30d": 100,
            "p50ms24h": 146,
            "p95ms24h": 420,
            "samples24h": 272,
            "samples30d": 875,
            "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": 247,
                "ok": 247
              }
            ]
          },
          "vendorStatus": {
            "page": "https://status.honcho.dev",
            "indicator": "unknown",
            "summary": "no machine-readable status found",
            "checkedAt": "2026-10-04T21:40:07.625291507Z"
          },
          "versions": [
            {
              "registry": "github",
              "name": "plastic-labs/honcho",
              "version": "v3.2.2",
              "released": "2026-10-01",
              "seenAt": "2026-10-04T16:29:43.003735943Z"
            },
            {
              "registry": "mcp-registry",
              "name": "io.github.plastic-labs/honcho",
              "version": "3.0.0",
              "seenAt": "2026-10-03T23:29:28.630222764Z"
            },
            {
              "registry": "npm",
              "name": "@honcho-ai/sdk",
              "version": "2.5.1",
              "seenAt": "2026-10-04T16:29:42.159609996Z"
            },
            {
              "registry": "pypi",
              "name": "honcho-ai",
              "version": "2.5.1",
              "released": "2026-09-24",
              "seenAt": "2026-10-04T16:29:41.976308274Z"
            }
          ],
          "githubStars": 7458,
          "npmWeekly": 25534,
          "pypiWeekly": 131942,
          "securityTxt": {
            "url": "https://honcho.dev/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:15:39.261197499Z"
          },
          "llmsTxt": {
            "url": "https://honcho.dev/docs/llms.txt",
            "ok": true,
            "status": 200,
            "checkedAt": "2026-10-04T15:17:52.175463718Z"
          },
          "domain": {
            "domain": "honcho.dev",
            "registered": "2022-01-26",
            "source": "https://pubapi.registry.google/rdap/domain/honcho.dev",
            "checkedAt": "2026-10-04T13:09:16.292768042Z"
          },
          "pages": [
            {
              "url": "https://honcho.dev/docs/changelog/introduction",
              "kind": "changelog",
              "status": 200,
              "checkedAt": "2026-10-04T15:45:02.026293636Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "602071e4bfee"
            },
            {
              "url": "https://app.honcho.dev/privacy",
              "kind": "privacy",
              "status": 200,
              "checkedAt": "2026-10-04T15:41:15.347297508Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "9da63f6bcbbd"
            },
            {
              "url": "https://app.honcho.dev/tos",
              "kind": "terms",
              "status": 200,
              "checkedAt": "2026-10-04T15:41:17.505463097Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "f178ff16d893"
            }
          ],
          "updatedAt": "2026-10-04T21:48:29.348577618Z"
        }
      },
      {
        "slug": "supermemory",
        "name": "Supermemory API + MCP",
        "vendor": "Supermemory",
        "vendorUrl": "https://supermemory.ai",
        "kind": "http-api",
        "category": "agent-memory",
        "summary": "Memory and context API that ingests text, URLs, PDFs, images and video, extracts memories into a graph per container tag (usually one per user) and returns them through search or a user profile endpoint.",
        "url": "https://www.anchorterminal.com/tools/supermemory",
        "markdownUrl": "https://www.anchorterminal.com/tools/supermemory.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/supermemory.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/supermemory.json",
        "repo": "https://github.com/supermemoryai/supermemory",
        "license": "MIT",
        "transports": [
          "http",
          "streamable-http"
        ],
        "remoteUrl": "https://api.supermemory.ai",
        "packages": [
          {
            "registry": "npm",
            "name": "supermemory"
          },
          {
            "registry": "pypi",
            "name": "supermemory"
          }
        ],
        "auth": "mixed",
        "authNotes": "Bearer API key (`sm_...`) from console.supermemory.ai. Org keys have full access. Scoped keys are limited to one or more container tags, can't read billing, change settings or mint keys, can expire after 1 to 365 days and are revocable. The hosted MCP at mcp.supermemory.ai/mcp signs in with OAuth in a browser and needs no key.",
        "pricing": "freemium",
        "pricingNotes": "Each plan includes monthly usage credit. Free is $0 with $5 of credit, Pro $19 with $20, Max $100 with $130 (adds the Gmail connector), Scale $399 with $600 (adds PDF extraction, S3 and crawler connectors, SOC 2, a HIPAA BAA and a self-hosted option), and Enterprise is committed spend. Usage is $5 per million SM tokens for plain-text memory and $10 for rich content, $1 and $2 per million for SuperRAG text and rich media, $5 per million search queries and $100 per million operations (https://supermemory.ai/pricing). Paid plans can auto top up, and the top-up limits don't cap the invoice (https://supermemory.ai/docs/overview/billing.md).",
        "priceSummary": "$19 / mo",
        "where": "hosted",
        "x402": {
          "level": "no",
          "endpoints": []
        },
        "toolCount": 8,
        "popularity": {
          "githubStars": 31000,
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        "llmsTxt": "https://supermemory.ai/docs/llms.txt",
        "openapi": "https://api.supermemory.ai/v4/openapi",
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          "memory.store",
          "memory.search",
          "memory.graph",
          "memory.user",
          "memory.delete"
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          "hosted",
          "freemium",
          "free-tier",
          "mcp",
          "oauth",
          "llms-txt",
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          "self-hosted",
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            "tasks"
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            "Scoped keys limited to container tags, with expiry from 1 to 365 days",
            "Status page shows 100 per cent for the API and Console from July to October 2026",
            "Customer content never trains models on any plan, per the security page",
            "One endpoint ingests text, URLs, PDFs, images and video"
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            "No published rate limits or 429 guidance",
            "Ingests web pages and PDFs with no prompt-injection guidance found",
            "v3 and v4 endpoints live side by side, so examples disagree",
            "No security.txt and no official MCP registry entry"
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            "Use one stable `containerTag` per user and a `customId` per session",
            "Poll the document until `status` is `done` before searching for what you just added",
            "Treat a 402 on search or profile as out of credit, not as a bug",
            "Run the prompt-based mass forget with `dryRun` first",
            "Call who_am_i on the MCP to see which spaces you can write to before adding"
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            "note": "$130 of usage credit"
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            "item": "Scale plan",
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            "name": "cognee/cognee-mcp"
          }
        ],
        "auth": "mixed",
        "authNotes": "Cognee Cloud takes an `X-Api-Key` header on a per-tenant host, and keys can be rotated. A local Docker server runs without auth unless you turn it on, then takes a Bearer token. Since 1.6.0 (18 September 2026) the library builds and searches text memory with local models and no LLM key, and LLM-dependent stages skip when none is set. Set `LLM_API_KEY` for the full pipeline with a hosted model. Cognee MCP and Cognee Cloud are separate systems with different auth.",
        "pricing": "freemium",
        "pricingNotes": "Cognee Cloud Free is $0 with 1 million tokens included, 1 workspace, unlimited users and API calls, and no card. Standard is $1 per million tokens processed plus $5 a month for each extra workspace, and adds Slack, Notion, Linear and Google Drive sources. Enterprise is quoted, with bring-your-own-cloud (https://www.cognee.ai/pricing). The billing docs still show older plans (Hobby with 10 million tokens, Growth at $5 a tenant, Enterprise at $2,916 a month), which disagree with the pricing page. Credit is prepaid from $0.50, with optional auto-recharge (https://docs.cognee.ai/cognee-cloud/functionality/account-and-billing). The open-source library is free to run yourself.",
        "priceSummary": "$5 / mo",
        "where": "both",
        "x402": {
          "level": "no",
          "endpoints": []
        },
        "toolCount": 7,
        "popularity": {
          "githubStars": 31200,
          "npmWeekly": null,
          "pypiWeekly": 20830,
          "asOf": "2026-09-30"
        },
        "docsUrl": "https://docs.cognee.ai",
        "llmsTxt": "https://docs.cognee.ai/llms.txt",
        "openapi": "https://docs.cognee.ai/cognee_openapi_spec.json",
        "capabilities": [
          "memory.store",
          "memory.search",
          "memory.graph",
          "memory.delete"
        ],
        "tags": [
          "open-source",
          "self-hosted",
          "hosted",
          "freemium",
          "free-tier",
          "no-card",
          "mcp",
          "llms-txt",
          "python",
          "eu"
        ],
        "lastRelease": "2026-09-29",
        "graded": true,
        "anchor": {
          "graded": true,
          "score": 54.6,
          "grade": "C",
          "agentReady": false,
          "rank": 320,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 5,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 59,
            "maintenance": 82,
            "payments": 35,
            "reliability": 50,
            "schema": 85,
            "security": 39,
            "transparency": 67
          },
          "pending": [
            "performance",
            "tasks"
          ],
          "assessment": {
            "confidence": "medium",
            "date": "2026-10-01"
          },
          "negative": -3,
          "negativeNotes": [
            "2026-05-01: a commit titled as a fix removed 11 MCP tools, including cognify, search, delete and prune, with cognee-mcp at 0.5.4 before and after, and the README the day before listed them as \"still available\" with no deprecation note. The replacements remember, recall and forget had shipped on 10 April and the tools reference now lists what went, so we deduct at the low end (https://github.com/topoteretes/cognee/commit/b52fcc335f6bfc090d1c892afa9c4e81909336fe)"
          ],
          "verdict": "Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.",
          "strengths": [
            "Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0",
            "7-tool MCP server with search_tools and call_tool for reaching the rest on demand",
            "Public OpenAPI 3.1 file with 46 paths and error models",
            "Metered Cloud at $1 per million tokens, 1 million free with no card",
            "Eight stable PyPI releases from 15 August to 29 September 2026, with CI passing on main"
          ],
          "weaknesses": [
            "No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region",
            "Cloud calls hung rather than failing when a tenant ran out of credit (August 2026), and the issue is still open",
            "Billing docs and pricing page disagree on plans and the free allowance",
            "Library telemetry is on by default and sends a persistent machine ID and an ID derived from the LLM key",
            "11 MCP tools were removed in May 2026 without a version bump or notice"
          ],
          "agentNotes": [
            "Use remember, recall and forget. The older cognify, search and delete MCP tools are gone",
            "Always include /api/v1 in REST paths",
            "Check cognify_status before querying data you added with background=true",
            "Never pass everything=true to forget unless you mean to wipe all of the user's memory",
            "Set a client timeout on Cloud calls and treat HTTP 402 as an empty balance, since an empty balance has also shown up as hangs"
          ],
          "metrics": {
            "kind": "remote",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 3,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "medium",
              "grade": "C",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 54.6
            }
          ],
          "editorialScores": {
            "ergonomics": 59,
            "maintenance": 82,
            "payments": 35,
            "reliability": 50,
            "schema": 85,
            "security": 39,
            "transparency": 69
          },
          "provenanceScore": 65
        },
        "connect": {
          "install": "pip install cognee",
          "http": "# COGNEE_URL is your tenant host, e.g. https://\u003ctenant\u003e.aws.cognee.ai\ncurl -X POST \"$COGNEE_URL/api/v1/search\" -H \"X-Api-Key: $COGNEE_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"query\":\"What does the user prefer?\"}'",
          "claudeCode": "docker run -d -e TRANSPORT_MODE=http -e LLM_API_KEY=$LLM_API_KEY -p 8000:8000 cognee/cognee-mcp:main\nclaude mcp add --transport http cognee http://localhost:8000/mcp",
          "config": {
            "mcpServers": {
              "cognee": {
                "args": [
                  "run",
                  "-i",
                  "--rm",
                  "-e",
                  "LLM_API_KEY",
                  "cognee/cognee-mcp:main"
                ],
                "command": "docker",
                "env": {
                  "LLM_API_KEY": "${LLM_API_KEY}"
                }
              }
            }
          }
        },
        "letme": {
          "capability": "https://letme.dev/memory.store",
          "tool": "https://letme.dev/cognee"
        },
        "area": "agent-runtime",
        "unitPrices": [
          {
            "item": "Standard token processing",
            "unit": "1m-tokens",
            "usd": 1,
            "note": "1 million tokens free"
          },
          {
            "item": "Extra workspace",
            "unit": "month",
            "usd": 5
          }
        ],
        "provenance": {
          "legalEntity": "Topoteretes UG (haftungsbeschränkt)",
          "domain": "cognee.ai",
          "domainRegistered": "",
          "endpointOnVendorDomain": true,
          "terms": "https://www.cognee.ai/gtc-eu",
          "privacy": "https://www.cognee.ai/privacy-notice",
          "statusPage": "",
          "changelog": "https://github.com/topoteretes/cognee/releases",
          "securityTxt": "none",
          "checked": "2026-10-02",
          "notes": [
            "The general terms, updated 27 March 2026, name Topoteretes UG (haftungsbeschränkt), Amtsgericht Charlottenburg HRB 252065 B, Paul-Lincke-Ufer 39-40, 10999 Berlin, under German law.",
            "www.cognee.ai/.well-known/security.txt returns 404, and we found no status page.",
            "The privacy notice, current version 20 September 2026, says Cognee Cloud is operated by Cognee Inc. and hosted in AWS us-east-1, while the general terms name Topoteretes UG in Berlin. SECURITY.md in the repository sends reports to security@cognee.ai."
          ],
          "score": 65
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/cognee.json",
        "live": {
          "slug": "cognee",
          "probe": {
            "target": "https://\u003ctenant\u003e.aws.cognee.ai/api/v1",
            "method": "get",
            "lastAt": "2026-10-04T21:48:25.345265121Z",
            "lastOk": false,
            "lastStatus": 0,
            "lastMs": 0,
            "lastNote": "DNS lookup failed",
            "authRequired": false,
            "uptime24h": 0,
            "uptime30d": 0,
            "p50ms24h": 0,
            "p95ms24h": 0,
            "samples24h": 272,
            "samples30d": 875,
            "days": [
              {
                "date": "2026-10-01",
                "probes": 109,
                "ok": 0
              },
              {
                "date": "2026-10-02",
                "probes": 248,
                "ok": 0
              },
              {
                "date": "2026-10-03",
                "probes": 271,
                "ok": 0
              },
              {
                "date": "2026-10-04",
                "probes": 247,
                "ok": 0
              }
            ]
          },
          "versions": [
            {
              "registry": "github",
              "name": "topoteretes/cognee",
              "version": "v1.6.2",
              "released": "2026-09-29",
              "seenAt": "2026-10-04T16:24:05.468766325Z"
            },
            {
              "registry": "pypi",
              "name": "cognee",
              "version": "1.6.2",
              "released": "2026-09-29",
              "seenAt": "2026-10-04T16:24:05.282815483Z"
            }
          ],
          "githubStars": 31352,
          "pypiWeekly": 22752,
          "securityTxt": {
            "url": "https://cognee.ai/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:15:50.060532442Z"
          },
          "llmsTxt": {
            "url": "https://docs.cognee.ai/llms.txt",
            "ok": true,
            "status": 200,
            "checkedAt": "2026-10-04T15:17:26.553412423Z"
          },
          "domain": {
            "domain": "cognee.ai",
            "registered": "2023-12-21",
            "source": "https://rdap.identitydigital.services/rdap/domain/cognee.ai",
            "checkedAt": "2026-10-04T13:09:11.691283649Z"
          },
          "pages": [
            {
              "url": "https://www.cognee.ai/pricing",
              "kind": "pricing",
              "status": 200,
              "checkedAt": "2026-10-04T15:49:51.729470579Z",
              "changedAt": "2026-10-04T15:49:51.729470579Z",
              "fingerprint": "d56cccad38df"
            },
            {
              "url": "https://www.cognee.ai/privacy-notice",
              "kind": "privacy",
              "status": 200,
              "checkedAt": "2026-10-04T15:49:53.709284309Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "c5fb8fa4d461"
            },
            {
              "url": "https://www.cognee.ai/gtc-eu",
              "kind": "terms",
              "status": 304,
              "checkedAt": "2026-10-04T15:49:49.609263653Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "9bd64eccc606"
            }
          ],
          "updatedAt": "2026-10-04T21:48:25.345265121Z"
        }
      },
      {
        "slug": "memory-reference-server",
        "name": "Memory (MCP reference server)",
        "vendor": "MCP project (reference servers)",
        "vendorUrl": "https://modelcontextprotocol.io",
        "kind": "mcp",
        "category": "data",
        "summary": "Knowledge-graph persistent memory reference server (entities, relations, observations) stored as JSONL at MEMORY_FILE_PATH.",
        "url": "https://www.anchorterminal.com/tools/memory-reference-server",
        "markdownUrl": "https://www.anchorterminal.com/tools/memory-reference-server.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/memory-reference-server.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json",
        "repo": "https://github.com/modelcontextprotocol/servers",
        "license": "MIT and Apache-2.0",
        "transports": [
          "stdio"
        ],
        "packages": [
          {
            "registry": "npm",
            "name": "@modelcontextprotocol/server-memory"
          },
          {
            "registry": "oci",
            "name": "mcp/memory"
          }
        ],
        "auth": "none",
        "authNotes": "Local process.",
        "pricing": "free",
        "pricingNotes": "Open source.",
        "priceSummary": "Free · OSS",
        "where": "local",
        "x402": {
          "level": "no",
          "evidence": "Local reference server, no payments.",
          "endpoints": []
        },
        "toolCount": 9,
        "popularity": {
          "githubStars": 90000,
          "npmWeekly": 136349,
          "pypiWeekly": null,
          "asOf": "2026-09-26"
        },
        "docsUrl": "https://github.com/modelcontextprotocol/servers/blob/main/src/memory/README.md",
        "capabilities": [
          "memory.graph"
        ],
        "tags": [
          "reference",
          "local",
          "open-source"
        ],
        "lastRelease": "2026-08-31",
        "graded": true,
        "disclosure": "MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.",
        "anchor": {
          "graded": true,
          "score": 54.4,
          "grade": "C",
          "agentReady": false,
          "rank": 322,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 6,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 67,
            "maintenance": 43,
            "payments": 60,
            "reliability": 52,
            "schema": 60,
            "security": 32,
            "transparency": 74
          },
          "pending": [
            "performance",
            "tasks"
          ],
          "assessment": {
            "confidence": "medium",
            "date": "2026-10-01"
          },
          "negative": 0,
          "verdict": "Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. `read_graph` returns everything and `search_nodes` every match.",
          "disclosure": "MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.",
          "strengths": [
            "Nine tools with typed schemas and accurate read, destructive and idempotent annotations",
            "Plain JSONL storage at a path you choose, easy to back up, diff and edit by hand",
            "Writes are atomic since 2026.8.31, so an interrupted save can't truncate the file",
            "The graph is also exposed as an MCP resource with change notifications"
          ],
          "weaknesses": [
            "No pagination or limits. `read_graph` returns everything and `search_nodes` every match",
            "The published version can lose one of two writes made in the same turn. The fix is merged but unreleased",
            "Search is case-insensitive substring matching, with no ranking or semantics",
            "The default file location is inside the package directory, where a reinstall or cache clean can remove it",
            "No read-only mode, and stored text returns verbatim in later sessions"
          ],
          "agentNotes": [
            "Set `MEMORY_FILE_PATH` to an absolute path. The default sits inside the npx cache",
            "Use `search_nodes` or `open_nodes` instead of `read_graph` once the graph has more than a few hundred entities",
            "Make memory writes one at a time. Parallel calls in one turn can overwrite each other in 2026.8.31",
            "Create both entities before `create_relations`. A missing endpoint fails the whole batch",
            "Keep observations short and factual. They come back verbatim in every later read"
          ],
          "metrics": {
            "kind": "local",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 2.5,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "medium",
              "grade": "C",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 54.4
            }
          ],
          "editorialScores": {
            "ergonomics": 67,
            "maintenance": 43,
            "payments": 60,
            "reliability": 52,
            "schema": 60,
            "security": 32,
            "transparency": 74
          },
          "provenanceScore": 74
        },
        "connect": {
          "claudeCode": "claude mcp add memory -- npx -y @modelcontextprotocol/server-memory",
          "config": {
            "mcpServers": {
              "memory": {
                "args": [
                  "-y",
                  "@modelcontextprotocol/server-memory"
                ],
                "command": "npx",
                "env": {
                  "MEMORY_FILE_PATH": "/data/memory.jsonl"
                }
              }
            }
          }
        },
        "letme": {
          "capability": "https://letme.dev/memory.graph",
          "tool": "https://letme.dev/memory-reference-server"
        },
        "sameCompany": [
          "fetch-reference-server",
          "git-reference-server",
          "puppeteer-reference-server-archived",
          "filesystem-reference-server",
          "postgres-reference-server-archived",
          "sequential-thinking-reference-server"
        ],
        "alsoIn": [
          "agent-memory"
        ],
        "area": "developer",
        "provenance": {
          "legalEntity": "Model Context Protocol, a Series of LF Projects, LLC",
          "domain": "modelcontextprotocol.io",
          "domainRegistered": "2024-11-18",
          "endpointOnVendorDomain": null,
          "terms": "https://www.lfprojects.org/policies/terms-of-use/",
          "privacy": "https://www.lfprojects.org/policies/privacy-policy/",
          "statusPage": "",
          "changelog": "https://github.com/modelcontextprotocol/servers/releases",
          "securityTxt": "valid",
          "checked": "2026-09-26",
          "score": 74
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/memory-reference-server.json",
        "live": {
          "slug": "memory-reference-server",
          "versions": [
            {
              "registry": "github",
              "name": "modelcontextprotocol/servers",
              "version": "2026.8.31",
              "released": "2026-08-31",
              "seenAt": "2026-10-04T16:32:48.571489949Z"
            },
            {
              "registry": "npm",
              "name": "@modelcontextprotocol/server-memory",
              "version": "2026.8.31",
              "seenAt": "2026-10-04T16:32:48.302802106Z"
            }
          ],
          "githubStars": 91001,
          "npmWeekly": 164918,
          "securityTxt": {
            "url": "https://modelcontextprotocol.io/.well-known/security.txt",
            "state": "valid",
            "checkedAt": "2026-10-04T15:15:39.073797817Z"
          },
          "domain": {
            "domain": "modelcontextprotocol.io",
            "checkedAt": "2026-10-04T13:06:56.741922917Z"
          },
          "updatedAt": "2026-10-04T16:32:48.571489949Z"
        }
      },
      {
        "slug": "graphiti",
        "name": "Graphiti",
        "vendor": "Zep",
        "vendorUrl": "https://www.getzep.com",
        "kind": "framework",
        "category": "agent-memory",
        "summary": "Open-source Python framework from Zep that builds a temporal knowledge graph from chat messages, text and JSON.",
        "url": "https://www.anchorterminal.com/tools/graphiti",
        "markdownUrl": "https://www.anchorterminal.com/tools/graphiti.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/graphiti.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/graphiti.json",
        "repo": "https://github.com/getzep/graphiti",
        "license": "Apache-2.0",
        "transports": [
          "streamable-http",
          "stdio"
        ],
        "packages": [
          {
            "registry": "pypi",
            "name": "graphiti-core"
          }
        ],
        "auth": "none",
        "authNotes": "Runs on your own machine. You supply an LLM key (OPENAI_API_KEY by default) and database settings such as FALKORDB_URI or NEO4J_URI, NEO4J_USER and NEO4J_PASSWORD.",
        "pricing": "free",
        "pricingNotes": "Free under Apache-2.0. You pay for the LLM and embedding calls it makes on every ingest and for running the graph database (https://github.com/getzep/graphiti). Zep sells a hosted memory service from the same team (https://www.getzep.com/pricing/).",
        "priceSummary": "Free · OSS",
        "where": "library",
        "x402": {
          "level": "no",
          "endpoints": []
        },
        "toolCount": 13,
        "popularity": {
          "githubStars": 29000,
          "npmWeekly": null,
          "pypiWeekly": 151251,
          "asOf": "2026-09-30"
        },
        "docsUrl": "https://help.getzep.com/graphiti/getting-started/overview",
        "llmsTxt": "https://help.getzep.com/llms.txt",
        "capabilities": [
          "memory.store",
          "memory.search",
          "memory.graph",
          "memory.delete"
        ],
        "tags": [
          "framework",
          "open-source",
          "self-hosted",
          "local",
          "python",
          "mcp",
          "llms-txt"
        ],
        "lastRelease": "2026-09-08",
        "graded": true,
        "anchor": {
          "graded": true,
          "score": 53.3,
          "grade": "D",
          "agentReady": false,
          "rank": 333,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 6,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 51,
            "maintenance": 65,
            "payments": 60,
            "reliability": 50,
            "schema": 71,
            "security": 23,
            "transparency": 71
          },
          "pending": [
            "performance",
            "tasks"
          ],
          "assessment": {
            "confidence": "medium",
            "date": "2026-10-01"
          },
          "negative": 0,
          "verdict": "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.",
          "strengths": [
            "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store",
            "13-tool MCP server over streamable HTTP or stdio, with a Docker Compose file",
            "Works with OpenAI, Anthropic, Gemini, Groq or a local OpenAI-compatible model",
            "Telemetry documented, content-free by its own statement, and off with one environment variable",
            "Three PyPI releases between 27 July and 8 September 2026"
          ],
          "weaknesses": [
            "Every add runs LLM extraction, so ingestion costs tokens and time",
            "The MCP HTTP endpoint has no authentication, and no tool carries readOnlyHint or destructiveHint",
            "Still 0.x, and the last three PyPI releases have no GitHub release notes",
            "255 open issues, including a broken MCP Docker build reported in July 2026",
            "No official entry in the MCP registry"
          ],
          "agentNotes": [
            "Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server",
            "Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes",
            "Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge",
            "Pass group_ids on every search and add so one user's graph doesn't leak into another's",
            "Call get_status to check the database connection before a long ingest"
          ],
          "metrics": {
            "kind": "local",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 2.5,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "medium",
              "grade": "D",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 53.3
            }
          ],
          "editorialScores": {
            "ergonomics": 51,
            "maintenance": 65,
            "payments": 60,
            "reliability": 50,
            "schema": 71,
            "security": 23,
            "transparency": 78
          },
          "provenanceScore": 63
        },
        "connect": {
          "install": "pip install graphiti-core   # or: pip install graphiti-core[falkordb]",
          "claudeCode": "claude mcp add --transport http graphiti http://localhost:8000/mcp/",
          "config": {
            "mcpServers": {
              "graphiti": {
                "type": "http",
                "url": "http://localhost:8000/mcp/"
              }
            }
          }
        },
        "letme": {
          "capability": "https://letme.dev/memory.store",
          "tool": "https://letme.dev/graphiti"
        },
        "sameCompany": [
          "zep"
        ],
        "area": "agent-runtime",
        "provenance": {
          "legalEntity": "Zep Software, Inc.",
          "domain": "getzep.com",
          "domainRegistered": "2023-05-08",
          "endpointOnVendorDomain": null,
          "terms": "",
          "privacy": "",
          "statusPage": "",
          "changelog": "https://github.com/getzep/graphiti/releases",
          "securityTxt": "none",
          "checked": "2026-09-30",
          "notes": [
            "A library and local server, so there's no hosted endpoint. Zep Software, Inc. owns the repository and publishes its docs on help.getzep.com."
          ],
          "score": 63
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/graphiti.json",
        "live": {
          "slug": "graphiti",
          "versions": [
            {
              "registry": "github",
              "name": "getzep/graphiti",
              "version": "v0.30.2",
              "released": "2026-09-08",
              "seenAt": "2026-10-04T16:29:13.398654956Z"
            },
            {
              "registry": "pypi",
              "name": "graphiti-core",
              "version": "0.30.2",
              "released": "2026-09-08",
              "seenAt": "2026-10-04T16:29:13.214213248Z"
            }
          ],
          "githubStars": 31430,
          "pypiWeekly": 142231,
          "securityTxt": {
            "url": "https://getzep.com/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:15:52.364915515Z"
          },
          "llmsTxt": {
            "url": "https://help.getzep.com/llms.txt",
            "ok": true,
            "status": 200,
            "checkedAt": "2026-10-04T15:17:51.568653025Z"
          },
          "domain": {
            "domain": "getzep.com",
            "registered": "2023-05-08",
            "source": "https://rdap.verisign.com/com/v1/domain/getzep.com",
            "checkedAt": "2026-10-04T13:03:48.930668589Z"
          },
          "pages": [
            {
              "url": "https://www.getzep.com/pricing/",
              "kind": "pricing",
              "status": 200,
              "checkedAt": "2026-10-04T15:50:33.53600163Z",
              "changedAt": "2026-10-03T15:38:25.165141175Z",
              "fingerprint": "6ec6dab9051f"
            }
          ],
          "updatedAt": "2026-10-04T16:29:13.398654956Z"
        }
      },
      {
        "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": 50.4,
          "grade": "D",
          "agentReady": false,
          "rank": 359,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 7,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 57,
            "maintenance": 80,
            "payments": 30,
            "reliability": 25,
            "schema": 68,
            "security": 58,
            "transparency": 48
          },
          "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.",
          "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": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 50.4
            }
          ],
          "editorialScores": {
            "ergonomics": 57,
            "maintenance": 80,
            "payments": 30,
            "reliability": 25,
            "schema": 68,
            "security": 58,
            "transparency": 30
          },
          "provenanceScore": 65
        },
        "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": 65
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/hindsight.json",
        "live": {
          "slug": "hindsight",
          "probe": {
            "target": "https://api.hindsight.vectorize.io",
            "method": "get",
            "lastAt": "2026-10-04T21:48:29.176288986Z",
            "lastOk": true,
            "lastStatus": 404,
            "lastMs": 174,
            "authRequired": false,
            "uptime24h": 100,
            "uptime30d": 100,
            "p50ms24h": 130,
            "p95ms24h": 388,
            "samples24h": 272,
            "samples30d": 875,
            "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": 247,
                "ok": 247
              }
            ]
          },
          "versions": [
            {
              "registry": "github",
              "name": "vectorize-io/hindsight",
              "version": "v0.10.2",
              "released": "2026-09-29",
              "seenAt": "2026-10-04T16:29:39.70608693Z"
            },
            {
              "registry": "npm",
              "name": "@vectorize-io/hindsight-client",
              "version": "0.10.2",
              "seenAt": "2026-10-04T16:29:37.810729722Z"
            },
            {
              "registry": "pypi",
              "name": "hindsight-api",
              "version": "0.10.2",
              "released": "2026-09-29",
              "seenAt": "2026-10-04T16:29:38.662862892Z"
            },
            {
              "registry": "pypi",
              "name": "hindsight-client",
              "version": "0.10.2",
              "released": "2026-09-29",
              "seenAt": "2026-10-04T16:29:37.622434193Z"
            }
          ],
          "githubStars": 45387,
          "npmWeekly": 65833,
          "pypiWeekly": 225330,
          "securityTxt": {
            "url": "https://vectorize.io/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:15:46.852372513Z"
          },
          "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": "2026-10-04T15:43:40.455785234Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "4b4e1a42fe21"
            },
            {
              "url": "https://vectorize.io/pricing",
              "kind": "pricing",
              "status": 304,
              "checkedAt": "2026-10-04T15:48:41.9940699Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "8914c2c291ed"
            },
            {
              "url": "https://vectorize.io/privacy",
              "kind": "privacy",
              "status": 304,
              "checkedAt": "2026-10-04T15:48:44.199762911Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "0b650c200752"
            },
            {
              "url": "https://vectorize.io/terms",
              "kind": "terms",
              "status": 304,
              "checkedAt": "2026-10-04T15:48:46.160685664Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "e508ab007e9c"
            }
          ],
          "updatedAt": "2026-10-04T21:48:29.176288986Z"
        }
      }
    ]
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/categories/agent-memory",
    "json": "https://www.anchorterminal.com/categories/agent-memory.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/categories/agent-memory.md",
    "slim": "https://www.anchorterminal.com/categories/agent-memory.min.md"
  },
  "markdown": "Services that store what an agent learns about a user or a task and bring back the right parts later: facts, preferences, conversation summaries and knowledge graphs. Compared on recall quality, latency, what they keep and how you delete it.\n\n- Tools ranked: 8 · agent-ready (BB or better): 0 · accept x402: 1 · hosted endpoints: 6 · desk reviews by the panel: 16\n- JSON: https://www.anchorterminal.com/api/v1/tools.json (list) · https://www.anchorterminal.com/api/v1/rankings.json (ranked) · https://www.anchorterminal.com/api/v1/x402.json (payable) · https://www.anchorterminal.com/api/v1/capabilities.json (by capability)\n- Grades run AA, A, BB, B, C, D, E, F · methodology: https://www.anchorterminal.com/benchmark/\n\n- Capabilities in this category: memory.store, memory.search, memory.graph, memory.user, memory.delete\n- https://letme.dev/memory.store picks the top-graded tool in this list and says how to call it direct; calling through letme comes later (https://www.anchorterminal.com/letme/index.md)\n\n## Ranking\n\n| # | Tool | Vendor | Kind | Category | Grade | Score | Confidence | x402 | Auth | Where | Reviews | Page |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 112 | Zep | Zep | HTTP API | Memory | B | 69.6 | high | no | OAuth or key | hosted | 4/5 (2) | https://www.anchorterminal.com/tools/zep.md |\n| 188 | Honcho | Plastic Labs | HTTP API | Memory | B | 64.2 | medium | yes ($0.001/call) | OAuth or key | hosted | 3/5 (2) | https://www.anchorterminal.com/tools/honcho.md |\n| 201 | Supermemory API + MCP | Supermemory | HTTP API | Memory | B | 63.6 | medium | no | OAuth or key | hosted | 3.5/5 (2) | https://www.anchorterminal.com/tools/supermemory.md |\n| 301 | Mem0 Platform + MCP | Mem0 | HTTP API | Memory | C | 56.6 | medium | no | API key | hosted | 2.5/5 (2) | https://www.anchorterminal.com/tools/mem0.md |\n| 320 | Cognee | Cognee | Model platform | Memory | C | 54.6 | medium | no | OAuth or key | hosted + local | 3/5 (2) | https://www.anchorterminal.com/tools/cognee.md |\n| 322 | Memory (MCP reference server) | MCP project (reference servers) | MCP server | Databases | C | 54.4 | medium | no | None | local | 2.5/5 (2) | https://www.anchorterminal.com/tools/memory-reference-server.md |\n| 333 | Graphiti | Zep | Agent framework | Memory | D | 53.3 | medium | no | None | library | 2.5/5 (2) | https://www.anchorterminal.com/tools/graphiti.md |\n| 359 | Hindsight | Vectorize | HTTP API | Memory | D | 50.4 | medium | no | OAuth or key | hosted | 3/5 (2) | https://www.anchorterminal.com/tools/hindsight.md |\n\nScores are from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/), with Performance and Task success pending. p95 latency and context cost come from our probes, which haven't run yet.\n\n## Summaries\n\n### 112. Zep, B (69.6)\n\nHosted context and memory service built on a temporal graph of facts, relationships and source episodes. Facts that new data invalidates keep the time they stopped being true. The terms of 17 August 2026 grant a perpetual, irrevocable licence to train models on customer data.\n\n- Page: https://www.anchorterminal.com/tools/zep · Markdown: https://www.anchorterminal.com/tools/zep.md · JSON: https://www.anchorterminal.com/api/v1/tools/zep.json\n- Capabilities: memory.store, memory.search, memory.graph, memory.user, memory.delete · endpoint: `https://api.getzep.com/api/v2`\n\n### 188. Honcho, B (64.2)\n\nMemory API that models each participant (a peer) in a conversation. Pay per call over x402 or MPP at agentcash.honcho.dev, with free read endpoints. No published rate limits, 429 guidance or SLA.\n\n- Page: https://www.anchorterminal.com/tools/honcho · Markdown: https://www.anchorterminal.com/tools/honcho.md · JSON: https://www.anchorterminal.com/api/v1/tools/honcho.json\n- Capabilities: memory.store, memory.search, memory.user, memory.delete · endpoint: `https://api.honcho.dev/v3`\n\n### 201. Supermemory API + MCP, B (63.6)\n\nMemory and context API that ingests text, URLs, PDFs, images and video, extracts memories into a graph per container tag (usually one per user) and returns them through search or a user profile endpoint. 8-tool hosted MCP with OAuth and per-space read or write permission. No legal entity named in the terms or privacy policy.\n\n- Page: https://www.anchorterminal.com/tools/supermemory · Markdown: https://www.anchorterminal.com/tools/supermemory.md · JSON: https://www.anchorterminal.com/api/v1/tools/supermemory.json\n- Capabilities: memory.store, memory.search, memory.graph, memory.user, memory.delete · endpoint: `https://api.supermemory.ai`\n\n### 301. Mem0 Platform + MCP, C (56.6)\n\nHosted memory layer that extracts facts from conversations and returns the relevant ones for a user, agent or run on later turns. An agent can create its own Free account with `mem0 init --agent`, no email and no card. Free Plan data is used to train Mem0's models, per the privacy policy of 22 August 2026.\n\n- Page: https://www.anchorterminal.com/tools/mem0 · Markdown: https://www.anchorterminal.com/tools/mem0.md · JSON: https://www.anchorterminal.com/api/v1/tools/mem0.json\n- Capabilities: memory.store, memory.search, memory.graph, memory.user, memory.delete · endpoint: `https://api.mem0.ai`\n\n### 320. Cognee, C (54.6)\n\nOpen-source memory engine that turns documents, conversations and synced sources into a knowledge graph plus a vector index and answers queries over both. Apache-2.0 library, REST server and MCP server, all self-hostable, with local models and no LLM key since 1.6.0. No status page, rate limits, SLA or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region.\n\n- Page: https://www.anchorterminal.com/tools/cognee · Markdown: https://www.anchorterminal.com/tools/cognee.md · JSON: https://www.anchorterminal.com/api/v1/tools/cognee.json\n- Capabilities: memory.store, memory.search, memory.graph, memory.delete · endpoint: `https://\u003ctenant\u003e.aws.cognee.ai/api/v1`\n\n### 322. Memory (MCP reference server), C (54.4)\n\nKnowledge-graph persistent memory reference server (entities, relations, observations) stored as JSONL at MEMORY_FILE_PATH. Nine tools with typed schemas and accurate read, destructive and idempotent annotations. No pagination or limits. `read_graph` returns everything and `search_nodes` every match.\n\n- Page: https://www.anchorterminal.com/tools/memory-reference-server · Markdown: https://www.anchorterminal.com/tools/memory-reference-server.md · JSON: https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json\n- Capabilities: memory.graph\n\n### 333. Graphiti, D (53.3)\n\nOpen-source Python framework from Zep that builds a temporal knowledge graph from chat messages, text and JSON. Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.\n\n- Page: https://www.anchorterminal.com/tools/graphiti · Markdown: https://www.anchorterminal.com/tools/graphiti.md · JSON: https://www.anchorterminal.com/api/v1/tools/graphiti.json\n- Capabilities: memory.store, memory.search, memory.graph, memory.delete\n\n### 359. Hindsight, D (50.4)\n\nMemory engine for storing and retrieving information used by agents. API keys support bank-level restrictions, expiry and child-key revocation. Retain ingestion costs $10 per million tokens.\n\n- Page: https://www.anchorterminal.com/tools/hindsight · Markdown: https://www.anchorterminal.com/tools/hindsight.md · JSON: https://www.anchorterminal.com/api/v1/tools/hindsight.json\n- Capabilities: memory.store, memory.search, memory.delete · endpoint: `https://api.hindsight.vectorize.io`\n\n## How we test this category\n\nOne user's history fed in over several sessions, then questions that need facts from early on, a changed preference and a deleted fact. We check what comes back, how fast, and whether the deletion sticks. This test hasn't run yet, so Task success is pending and the grades here come from the categories assessed from public evidence.\n\n## Indexed, not reviewed (63)\n\nSorted into this category from public catalogues, with facts and our own checks but no score, grade or rank (https://www.anchorterminal.com/indexed/index.md).\n\n| Listing | Kind | What it does | Why it's here |\n| --- | --- | --- | --- |\n| [6DuckLearn MCP](https://www.anchorterminal.com/tools/6ducklearn-mcp.md) | MCP server | Connect agents to 6DuckLearn memory, approvals, and runtime control. | vendor's own |\n| [Achriom](https://www.anchorterminal.com/tools/achriom.md) | MCP server | Media memory for AI agents and their humans: books, movies, music, shows, anime, podcasts, games. | vendor's own |\n| [adrkit decision memory](https://www.anchorterminal.com/tools/adrkit-mcp.md) | MCP server | Deterministic, offline, read-only ADR decision memory for coding agents. No model or network calls. | vendor's own |\n| [AI Context Flow](https://www.anchorterminal.com/tools/plurality-ai-context-flow.md) | MCP server | Universal memory for AI agents and tools. Save, organize and search context anywhere. | vendor's own |\n| [Amber](https://www.anchorterminal.com/tools/ambermem-amber.md) | MCP server | Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics. | vendor's own |\n| [Andish](https://www.anchorterminal.com/tools/andish-mcp.md) | MCP server | Knowledge-graph memory for your team's documents. Answers cite the source, not a summary. | vendor's own |\n| [being-mcp-server](https://www.anchorterminal.com/tools/ruddia-being-mcp-server.md) | MCP server | Personality Runtime for AI agents — persistent memory, personality, and relationships via MCP | vendor's own |\n| [Bluet](https://www.anchorterminal.com/tools/bluet.md) | MCP server | Product memory your agents read before they work: briefs, plan checks against decisions, debriefs. | vendor's own, widely used |\n| [Bourdon](https://www.anchorterminal.com/tools/bourdon.md) | MCP server | Recognition-first cross-agent memory federation. One shared memory across all your agents. | vendor's own |\n| [br8n.io MCP server](https://www.anchorterminal.com/tools/br8n-mcp.md) | MCP server | Owned, portable working memory: a plain-files markdown brain any model can read and cite. | vendor's own |\n| [Brainfeather](https://www.anchorterminal.com/tools/brainfeather-mcp.md) | MCP server | Long-term memory for AI coding agents: durable project facts, recalled by every MCP client. | vendor's own |\n| [Cairn.ink Memory](https://www.anchorterminal.com/tools/cairn-memory.md) | MCP server | Cross-session memory for AI agents with a Source Receipt for every memory, over MCP. | vendor's own |\n| [Citadel](https://www.anchorterminal.com/tools/citadeldb-mcp.md) | MCP server | Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools | vendor's own |\n| [cluster-mcp](https://www.anchorterminal.com/tools/clusteragent-cluster-mcp.md) | MCP server | Self-hostable index of AI financial agents — tokenized stocks + crypto, memory, LLM gateway. | vendor's own |\n| [Compose MCP](https://www.anchorterminal.com/tools/smartmemory-compose-mcp.md) | MCP server | Typed feature management for compose projects: roadmap, changelog, artifacts, journal. | vendor's own, widely used |\n| [ContextStream](https://www.anchorterminal.com/tools/contextstream-mcp.md) | MCP server | Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP). | vendor's own |\n| [deerdawn](https://www.anchorterminal.com/tools/deerdawn.md) | MCP server | AI session memory: the brief your AI reads before every session so no session starts cold. | vendor's own |\n| [Docmancer](https://www.anchorterminal.com/tools/docmancer.md) | MCP server | Local-only MCP server for source-attributed Markdown memory and documentation retrieval. | vendor's own |\n| [Engram](https://www.anchorterminal.com/tools/engram.md) | MCP server | Memory for AI agent teams across tools, sessions, repositories, and teammates. | vendor's own |\n| [Engram](https://www.anchorterminal.com/tools/getengram-engram.md) | MCP server | Persistent, verbatim, searchable memory for AI assistants — one memory across every MCP client. | vendor's own |\n| [engrava](https://www.anchorterminal.com/tools/sovantica-engrava.md) | MCP server | MindQL queries, hybrid search, and a thought graph - read/write memory for AI agents. | vendor's own |\n| [gateway](https://www.anchorterminal.com/tools/wingmanprotocol-gateway.md) | MCP server | Durable self for AI agents: one-call resume, memory, real browser, free chat + hire real humans. | vendor's own |\n| [gjalla](https://www.anchorterminal.com/tools/gjalla-mcp.md) | MCP server | Hosted self-curating shared memory that keeps your agents working like a high-performing team | vendor's own |\n| [Granoflow](https://www.anchorterminal.com/tools/granoflow-mcp-server.md) | MCP server | Connect MCP-capable AI agents to local Granoflow tasks, reviews, cards, imports, and work memory. | vendor's own |\n| [HyperMarrow](https://www.anchorterminal.com/tools/qianshi-hypermarrow.md) | MCP server | Local-first long-term memory for AI coding agents. Stored on your own machine. | vendor's own |\n| [in-memoria](https://www.anchorterminal.com/tools/pi22by7-in-memoria.md) | MCP server | Persistent codebase intelligence that gives AI assistants memory across sessions | vendor's own |\n| [kage](https://www.anchorterminal.com/tools/kage-core-kage.md) | MCP server | Verified memory for coding agents: claims cited against code, stale withheld, savings receipts. | vendor's own |\n| [Kawa Code](https://www.anchorterminal.com/tools/kawacode-mcp.md) | MCP server | Team-aware memory: intent, decisions, real-time conflicts for AI coding assistants. | vendor's own |\n| [Koragraph](https://www.anchorterminal.com/tools/koragraph.md) | MCP server | Local multi-repo code graph over MCP. Blast radius, call graphs, and memory anchored to code. | vendor's own |\n| [Kortexio Memory](https://www.anchorterminal.com/tools/kortexio-memory.md) | MCP server | Hosted ambient memory for AI agents. Retrieve context and save notes via MCP tools with OAuth. | vendor's own |\n| [Lex](https://www.anchorterminal.com/tools/smartergpt-lex.md) | MCP server | Episodic memory, Frames, Policy Neighborhoods, and policy enforcement for AI agents. | vendor's own |\n| [Lithtrix — Identity, Memory \u0026 Trust for AI Agents](https://www.anchorterminal.com/tools/lithtrix-mcp.md) | MCP server | Identity, memory, trust, and swarm primitives for AI agents. MIRC base always free. | vendor's own |\n| [Living Memory](https://www.anchorterminal.com/tools/viibe-living-memory.md) | MCP server | An MCP memory server. One memory your agents share — across models, devices and apps. | vendor's own |\n| [Lodekeep](https://www.anchorterminal.com/tools/lodekeep-memory.md) | MCP server | Memory MCP server for Claude Code. Correct a fact and the old version stops being recalled. | vendor's own |\n| [logic-server](https://www.anchorterminal.com/tools/nocturnus-logic-server.md) | MCP server | Agent reasoning, memory, and token-optimized context for AI applications. | vendor's own |\n| [Magnemo](https://www.anchorterminal.com/tools/magnemo.md) | MCP server | Governed memory for AI agents. Memory with receipts. Local, plain files, zero network calls. | vendor's own |\n| [Marrow](https://www.anchorterminal.com/tools/marrow.md) | MCP server | Evidence-grounded, graph-connected, correctable memory for agents. | vendor's own |\n| [MemCell](https://www.anchorterminal.com/tools/memcell.md) | MCP server | Living memory for AI coding agents: recall before acting, report outcomes so confidence is earned. | vendor's own |\n| [memento](https://www.anchorterminal.com/tools/runmemento-memento.md) | MCP server | Local-first, LLM-agnostic memory layer for AI assistants. | vendor's own |\n| [memgit](https://www.anchorterminal.com/tools/memgit.md) | MCP server | Git for AI memory — version-controlled, searchable context that persists across sessions | vendor's own |\n| [MemoFS MCP Server](https://www.anchorterminal.com/tools/memofs-mcp-server.md) | MCP server | Persistent memory and virtual filesystem MCP server for AI agents. | vendor's own |\n| [memory](https://www.anchorterminal.com/tools/cortex-mem-memory.md) | MCP server | Shared, persistent memory for AI agents — works with Claude, Kiro, Cursor, or any MCP client. | vendor's own |\n| [memory-mcp](https://www.anchorterminal.com/tools/letta-memory-mcp.md) | MCP server | MCP server for AI memory management using Letta - Standard MCP format | vendor's own |\n| [MemoryLayer](https://www.anchorterminal.com/tools/memorylayer.md) | MCP server | Local-first persistent memory for AI agents, with reasoning and formal verification. | vendor's own |\n| [memxus](https://www.anchorterminal.com/tools/memxus.md) | MCP server | Persistent memory layer that saves and recalls your project context and preferences. | vendor's own |\n| [mnemodb](https://www.anchorterminal.com/tools/mnemodb.md) | MCP server | Persistent agent memory as plain Markdown in your repo - readable, diffable, with provenance. | vendor's own |\n| [Neonia: The Cloud Backend for AI Agents](https://www.anchorterminal.com/tools/neonia-cloud-backend.md) | MCP server | Multiple MCP tools, persistent graph memory, token-saving data pointers, and more. | vendor's own |\n| [Operations Pulse](https://www.anchorterminal.com/tools/openlyuseful-operations-pulse.md) | MCP server | Local-first operational pulses, durable ticket memory, and governed connection discovery. | vendor's own |\n| [Pastepile](https://www.anchorterminal.com/tools/pastepile-mcp.md) | MCP server | Durable, versioned memory for AI assistants. Remote memory needs a Pastepile subscription. | vendor's own |\n| [RCLL](https://www.anchorterminal.com/tools/rcll-fleet-memory.md) | MCP server | Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path. | vendor's own |\n| [ReasonGraph Cloud memory](https://www.anchorterminal.com/tools/primaxiom-reasongraph.md) | MCP server | Graph memory for AI agents: entities, cause-effect links, cross-session recall, time travel. | vendor's own |\n| [recall](https://www.anchorterminal.com/tools/clauderecall-recall.md) | MCP server | Persistent memory for Claude Code and Codex. Search and re-inject context from any session. | vendor's own |\n| [Reverie](https://www.anchorterminal.com/tools/knowall-reverie.md) | MCP server | Graph memory that dreams: Neo4j knowledge-graph memory for AI agents with hybrid search | vendor's own |\n| [sdk](https://www.anchorterminal.com/tools/mnemopay-sdk.md) | MCP server | Memory + wallet for AI agents. Real payment rails, Agent FICO 300-850, Merkle audit, identity. | vendor's own |\n| [stdio-proxy](https://www.anchorterminal.com/tools/undisk-stdio-proxy.md) | MCP server | Undisk MCP: safe file memory for AI agents. | vendor's own |\n| [Synap Memory](https://www.anchorterminal.com/tools/maximem-synap.md) | MCP server | Persistent memory for AI agents — log and recall conversation context over MCP. | vendor's own |\n| [Synchronex](https://www.anchorterminal.com/tools/synchronex-mcp-proxy.md) | MCP server | Connect any AI agent to your Synchronex workspace — portable memory, decisions, and workers. | vendor's own |\n| [Trail](https://www.anchorterminal.com/tools/usetrail-trail.md) | MCP server | Issue tracker and durable project memory for your coding agent, scoped to the folder you open. | vendor's own |\n| [Tugra](https://www.anchorterminal.com/tools/tugra-ai-tugra.md) | MCP server | Provenance-aware memory format for AI agents: every claim carries its source, age and boundary. | vendor's own |\n| [wontopos.com MCP server](https://www.anchorterminal.com/tools/wontopos-mcp.md) | MCP server | Wontopos (WOS) long-term memory (beta) - one memory across Claude Code, Cursor, and your agents. | vendor's own |\n\nThe first 60 of 63, most used first; all of them are in https://www.anchorterminal.com/categories/agent-memory.json and https://www.anchorterminal.com/api/v1/indexed.json (`category`).\n\n",
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