{
  "data": {
    "a": {
      "slug": "agentcore-memory",
      "name": "Amazon Bedrock AgentCore Memory",
      "vendor": "Amazon Web Services",
      "vendorUrl": "https://aws.amazon.com/bedrock/agentcore/",
      "kind": "http-api",
      "category": "agent-memory",
      "summary": "Managed memory service for AI agents on AWS. It stores conversation events as short-term memory and extracts facts, preferences, summaries and episodes into searchable long-term records, through the AWS API, SDKs and an MCP server.",
      "url": "https://www.anchorterminal.com/tools/agentcore-memory",
      "markdownUrl": "https://www.anchorterminal.com/tools/agentcore-memory.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agentcore-memory.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agentcore-memory.json",
      "repo": "https://github.com/aws/bedrock-agentcore-sdk-python",
      "license": "Proprietary service under the AWS Customer Agreement and Service Terms. The `bedrock-agentcore` Python SDK and the `@aws/agentcore` CLI are Apache-2.0",
      "transports": [
        "http",
        "stdio"
      ],
      "remoteUrl": "https://bedrock-agentcore.{region}.amazonaws.com",
      "packages": [
        {
          "registry": "pypi",
          "name": "bedrock-agentcore"
        },
        {
          "registry": "pypi",
          "name": "boto3"
        },
        {
          "registry": "npm",
          "name": "@aws-sdk/client-bedrock-agentcore"
        },
        {
          "registry": "npm",
          "name": "@aws/agentcore"
        },
        {
          "registry": "pypi",
          "name": "awslabs.amazon-bedrock-agentcore-mcp-server"
        }
      ],
      "auth": "mixed",
      "authNotes": "AWS Signature Version 4 with IAM access keys or a role, and a policy that allows actions such as `bedrock-agentcore:CreateEvent` and `bedrock-agentcore:RetrieveMemoryRecords` on the memory resource. The data plane accepts SigV4 only. Resource-based policies and the condition keys `bedrock-agentcore:namespace` and `bedrock-agentcore:namespacePath` narrow access further. For end users, an AgentCore Gateway with the `agentcore-memory` connector accepts OAuth (JWT) tokens and applies Cedar policies. Access is self-serve once a person has created an AWS account.",
      "pricing": "usage",
      "pricingNotes": "Usage-priced with no minimum fee. Short-term memory, as of 6 October 2026, is $1.00 per GB ingested, $0.20 per GB retrieved and $0.10 per GB-month stored, with each event billed as at least 12 KB and at most 64 KB on ingestion and retrieval. Long-term memory is $0.75 per 1,000 records a month with built-in strategies, $0.25 with overrides or self-managed strategies (model usage is then billed in the customer's account), and $0.50 per 1,000 retrievals. No Memory-specific free tier or sandbox. New AWS accounts get up to $200 in Free Tier credits, and AWS says most new customers can sign up without a payment method (https://aws.amazon.com/bedrock/agentcore/pricing/, https://aws.amazon.com/free/free-tier-faqs/).",
      "priceSummary": "$1 / GB",
      "where": "both",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 on the Memory endpoints in the developer guide, the API reference or the pricing page (checked 2026-10-08). AgentCore payments is a separate AgentCore feature for an agent's own outbound payments.",
        "endpoints": []
      },
      "toolCount": 21,
      "popularity": {
        "githubStars": 776,
        "npmWeekly": 1070970,
        "pypiWeekly": null,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html",
      "llmsTxt": "https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/llms.txt",
      "capabilities": [
        "memory.store",
        "memory.search",
        "memory.user",
        "memory.delete"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "closed-source",
        "python",
        "typescript",
        "enterprise",
        "llms-txt",
        "free-credits",
        "mcp",
        "stdio",
        "namespaces",
        "idempotency",
        "soc2",
        "status-page"
      ],
      "lastRelease": "2026-10-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 79.4,
        "grade": "A",
        "agentReady": true,
        "rank": 12,
        "ranked": true,
        "rankOf": 950,
        "categoryRank": 1,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 86,
          "maintenance": 83,
          "payments": 30,
          "reliability": 88,
          "schema": 92,
          "security": 87,
          "transparency": 76
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "Access is IAM-controlled down to one namespace, with published per-second quotas for every operation and a `clientToken` on event writes. Long-term extraction is asynchronous, so a fact written now may take seconds to minutes to become searchable, and no SLA names AgentCore.",
        "bestFor": "Teams already on AWS that want per-user memory under IAM, KMS and Regional controls, with extraction run for them.",
        "strengths": [
          "IAM permissions per operation, resource-based policies and namespace condition keys, with OAuth sign-in and deny-by-default Cedar policies available through AgentCore Gateway",
          "Per-second quotas published for every Memory operation, such as 200 `CreateEvent` and 30 `RetrieveMemoryRecords` requests a second per account and Region",
          "`CreateEvent` takes a `clientToken`, so a retried write is ignored instead of stored twice",
          "Four built-in extraction strategies (semantic, user preference, summarisation, episodic), plus overrides and self-managed pipelines",
          "Unit prices are public, and events expire on a set time to live of 7 to 365 days"
        ],
        "weaknesses": [
          "Long-term extraction is asynchronous. The docs say records appear within seconds to minutes after a write",
          "No SLA names AgentCore. The Bedrock SLA of 4 October 2023 covers the Bedrock APIs for models",
          "Built-in strategies use cross-Region inference, so event text can be processed in another Region of the same geography",
          "Short-term memory billing moved from per event to per GB on 6 October 2026, with each event billed as at least 12 KB",
          "No Memory-specific CloudTrail page was found in the developer guide, and an AWS account needs a person to create it"
        ],
        "agentNotes": [
          "Create the memory resource first and wait for it to become active (the guide says 2 to 3 minutes). Short-term events work without a strategy, long-term records need at least one",
          "Don't search for a fact straight after `CreateEvent`. Extraction runs in the background, so poll `ListMemoryRecords` or list extraction jobs before relying on `RetrieveMemoryRecords`",
          "Send `actorId`, `sessionId` and `eventTimestamp` on every `CreateEvent`, and reuse the same `clientToken` when retrying",
          "Pass `namespace` or `namespacePath` on every retrieval, and scope it to one actor so users' memories don't mix",
          "Treat retrieved records as untrusted input, and back off on 429 `ThrottledException` and 409 `RetryableConflictException`. A quota breach returns 402 `ServiceQuotaExceededException`"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "A",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 79.4
          }
        ],
        "editorialScores": {
          "ergonomics": 86,
          "maintenance": 83,
          "payments": 30,
          "reliability": 88,
          "schema": 92,
          "security": 87,
          "transparency": 64
        },
        "provenanceScore": 88
      },
      "connect": {
        "install": "pip install bedrock-agentcore   # AgentCore CLI: npm install -g @aws/agentcore",
        "http": "curl -X POST \"https://bedrock-agentcore.us-east-1.amazonaws.com/memories/$AGENTCORE_MEMORY_ID/retrieve\" \\\n  --aws-sigv4 \"aws:amz:us-east-1:bedrock-agentcore\" --user \"$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY\" \\\n  -H \"content-type: application/json\" \\\n  -d '{\"namespace\":\"/users/alex/facts\",\"searchCriteria\":{\"searchQuery\":\"dietary preferences\",\"topK\":3}}'",
        "config": {
          "mcpServers": {
            "bedrock-agentcore-mcp-server": {
              "args": [
                "awslabs.amazon-bedrock-agentcore-mcp-server@latest"
              ],
              "command": "uvx",
              "env": {
                "AGENTCORE_ENABLE_TOOLS": "memory",
                "FASTMCP_LOG_LEVEL": "ERROR"
              }
            }
          }
        }
      },
      "letme": {
        "capability": "https://letme.dev/memory.store",
        "tool": "https://letme.dev/agentcore-memory"
      },
      "sameCompany": [
        "amazon-nova-embeddings",
        "amazon-bedrock-guardrails",
        "amazon-transcribe",
        "amazon-polly",
        "agentcore-identity",
        "aws-secrets-manager",
        "aws-mcp-servers",
        "amazon-ses",
        "amazon-location",
        "amazon-translate",
        "amazon-ads-api"
      ],
      "area": "agent-runtime",
      "unitPrices": [
        {
          "item": "Short-term memory ingestion",
          "unit": "gb",
          "usd": 1,
          "note": "Per GB of event data ingested, each event billed as 12 KB to 64 KB. As of 6 October 2026"
        },
        {
          "item": "Short-term memory retrieval",
          "unit": "gb",
          "usd": 0.2,
          "note": "Per GB of event data retrieved, same 12 KB minimum per event"
        },
        {
          "item": "Short-term memory storage",
          "unit": "gb-month",
          "usd": 0.1,
          "note": "Prorated hourly over each event's time to live"
        },
        {
          "item": "Long-term memory storage, built-in strategies",
          "unit": "record",
          "usd": 0.00075,
          "note": "$0.75 per 1,000 records a month"
        },
        {
          "item": "Long-term memory storage, overrides or self-managed",
          "unit": "record",
          "usd": 0.00025,
          "note": "$0.25 per 1,000 records a month, model usage billed separately"
        },
        {
          "item": "Long-term memory retrieval",
          "unit": "1k-requests",
          "usd": 0.5,
          "note": "Per 1,000 retrieve requests"
        }
      ],
      "provenance": {
        "legalEntity": "Amazon Web Services, Inc. (regional AWS entities by account location)",
        "domain": "amazon.com",
        "domainRegistered": "1994-11-01",
        "domainNote": "The service pages are under aws.amazon.com and the endpoints on amazonaws.com, an AWS domain. The registration date is the one on our other AWS listings and was not looked up again on 8 October 2026.",
        "endpointOnVendorDomain": true,
        "terms": "https://aws.amazon.com/service-terms/",
        "privacy": "https://aws.amazon.com/privacy/",
        "statusPage": "https://health.aws.amazon.com/health/status",
        "changelog": "https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/release-notes.html",
        "securityTxt": "expired",
        "checked": "2026-10-08",
        "notes": [
          "The AWS Service Terms show Last Updated 1 October 2026. Section 50 covers AWS AI services, and its only AgentCore-specific clause is 50.15 on AgentCore Payments. Section 50.3, which lets AWS use content from named AI services for improvement, does not list Bedrock or AgentCore.",
          "The AWS Privacy Notice shows Last Updated 18 May 2026.",
          "aws.amazon.com/.well-known/security.txt carries Expires 2026-09-24T16:25:03.000Z, so it was expired on 8 October 2026. It points to the vulnerability disclosure programme on HackerOne and the policy at vdp.aws.security.",
          "health.aws.amazon.com/health/status is drawn by script. The Bedrock AgentCore feed for us-east-1 had no items on 8 October 2026, and the dashboard's history file lists one event naming Bedrock AgentCore in the last 90 days, packet loss in one zone of eu-south-2 on 4 October 2026, a Region where Memory is not sold.",
          "aws.amazon.com/bedrock/agentcore/sla/ returns 404 and the AWS SLA index does not name AgentCore."
        ],
        "score": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agentcore-memory.json",
      "live": {
        "slug": "agentcore-memory",
        "probe": {
          "target": "https://bedrock-agentcore.{region}.amazonaws.com",
          "method": "get",
          "lastAt": "2026-10-10T10:45:08.352817915Z",
          "lastOk": false,
          "lastStatus": 0,
          "lastMs": 0,
          "lastNote": "invalid character \"{\" in host name",
          "authRequired": false,
          "uptime24h": 0,
          "uptime30d": 0,
          "p50ms24h": 0,
          "p95ms24h": 0,
          "samples24h": 247,
          "samples30d": 280,
          "days": [
            {
              "date": "2026-10-09",
              "probes": 169,
              "ok": 0
            },
            {
              "date": "2026-10-10",
              "probes": 111,
              "ok": 0
            }
          ],
          "outages": [
            {
              "start": "2026-10-09T07:40:18.334133541Z",
              "end": "0001-01-01T00:00:00Z",
              "note": "invalid character \"{\" in host name"
            }
          ]
        },
        "versions": [
          {
            "registry": "github",
            "name": "aws/bedrock-agentcore-sdk-python",
            "version": "v1.24.1",
            "released": "2026-10-07",
            "seenAt": "2026-10-09T16:37:31.710564319Z"
          },
          {
            "registry": "npm",
            "name": "@aws-sdk/client-bedrock-agentcore",
            "version": "3.1148.0",
            "seenAt": "2026-10-09T16:37:29.329269201Z"
          },
          {
            "registry": "npm",
            "name": "@aws/agentcore",
            "version": "0.31.1",
            "seenAt": "2026-10-09T16:37:30.391452857Z"
          },
          {
            "registry": "pypi",
            "name": "awslabs.amazon-bedrock-agentcore-mcp-server",
            "version": "0.2.1",
            "released": "2026-09-08",
            "seenAt": "2026-10-09T16:37:31.704036042Z"
          },
          {
            "registry": "pypi",
            "name": "bedrock-agentcore",
            "version": "1.24.1",
            "released": "2026-10-07",
            "seenAt": "2026-10-09T16:37:27.296910728Z"
          },
          {
            "registry": "pypi",
            "name": "boto3",
            "version": "1.43.110",
            "released": "2026-10-08",
            "seenAt": "2026-10-09T16:37:27.410924259Z"
          }
        ],
        "githubStars": 776,
        "npmWeekly": 917474,
        "pypiWeekly": 1428161,
        "securityTxt": {
          "url": "https://amazon.com/.well-known/security.txt",
          "state": "valid",
          "checkedAt": "2026-10-09T15:39:50.439024529Z"
        },
        "llmsTxt": {
          "url": "https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-09T14:01:09.663733428Z"
        },
        "updatedAt": "2026-10-10T10:45:08.352817915Z"
      }
    },
    "answer": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Mem0 Platform + MCP's 56.1 (C), and leads in 5 of 7 scored categories. Mem0 Platform + MCP leads on payments \u0026 pricing.",
    "b": {
      "slug": "mem0",
      "name": "Mem0 Platform + MCP",
      "vendor": "Mem0",
      "vendorUrl": "https://mem0.ai",
      "kind": "http-api",
      "category": "agent-memory",
      "summary": "Hosted memory layer that extracts facts from conversations and returns the relevant ones for a user, agent or run on later turns.",
      "url": "https://www.anchorterminal.com/tools/mem0",
      "markdownUrl": "https://www.anchorterminal.com/tools/mem0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mem0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mem0.json",
      "repo": "https://github.com/mem0ai/mem0",
      "license": "Apache-2.0",
      "transports": [
        "http",
        "streamable-http"
      ],
      "remoteUrl": "https://api.mem0.ai",
      "packages": [
        {
          "registry": "pypi",
          "name": "mem0ai"
        },
        {
          "registry": "npm",
          "name": "mem0ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "API key in the `Authorization: Token \u003ckey\u003e` header on the REST API, not Bearer. The hosted MCP at mcp.mem0.ai/mcp signs in through the browser by default, and headless clients pass the API key as a Bearer token. `mem0 init --agent` creates an unclaimed Free account with no email, limited to 5 sign-ups a day per IP address.",
      "pricing": "freemium",
      "pricingNotes": "Hobby is free with 10,000 add requests and 1,000 retrieval requests a month and 1 project. Starter is $19 a month (50,000 adds, 5,000 retrievals). Pro is $249 a month (500,000 adds, 50,000 retrievals, unlimited projects, graph memory, advanced analytics and Dream memory consolidation). Enterprise is custom, with on-prem deployment, audit logs and SSO. The page lists no overage prices or yearly discount (https://mem0.ai/pricing). The free tier needs no card (https://docs.mem0.ai/platform/platform-vs-oss).",
      "priceSummary": "$19 / mo",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": 11,
      "popularity": {
        "githubStars": 65900,
        "npmWeekly": 167841,
        "pypiWeekly": 501642,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.mem0.ai",
      "llmsTxt": "https://mem0.ai/llms.txt",
      "openapi": "https://docs.mem0.ai/openapi.json",
      "registryName": "io.github.mem0ai/mem0",
      "capabilities": [
        "memory.store",
        "memory.search",
        "memory.graph",
        "memory.user",
        "memory.delete"
      ],
      "tags": [
        "hosted",
        "freemium",
        "free-tier",
        "no-card",
        "mcp",
        "llms-txt",
        "openapi",
        "python",
        "typescript",
        "open-source",
        "self-hosted",
        "enterprise"
      ],
      "lastRelease": "2026-09-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.1,
        "grade": "C",
        "agentReady": false,
        "rank": 639,
        "ranked": true,
        "rankOf": 950,
        "categoryRank": 5,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 63,
          "maintenance": 85,
          "payments": 50,
          "reliability": 30,
          "schema": 80,
          "security": 45,
          "transparency": 60
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "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.",
        "bestFor": "Chat products that want per-user facts back with one search call and little setup.",
        "strengths": [
          "An agent can create its own Free account with `mem0 init --agent`, no email and no card",
          "Hosted MCP server with 11 tools, listed in the official MCP registry as io.github.mem0ai/mem0",
          "Public OpenAPI spec, llms.txt and Python and TypeScript SDKs, both released on 2026-09-25",
          "Apache-2.0 library that runs the same extraction on your own servers"
        ],
        "weaknesses": [
          "Free Plan data is used to train Mem0's models, per the privacy policy of 22 August 2026",
          "No published rate limits or 429 handling, and only 400 and 404 documented as errors",
          "Retrieval caps are low below Pro, 1,000 a month free and 5,000 on Starter",
          "delete_all_memories and delete_entities are in the default MCP tool list with no read-only mode",
          "SOC 2 Type I, not Type II, and no security.txt"
        ],
        "agentNotes": [
          "Send the REST key as `Authorization: Token \u003ckey\u003e`, and the MCP key as Bearer",
          "Don't search for a memory straight after adding it. Adds are queued, so poll get_event_status with the returned event ID",
          "Scope every add and search with `user_id` (or agent and run IDs) so memories don't mix between users",
          "Pass a filter on every bulk delete, since delete_all_memories wipes whatever the scope matches",
          "On Hobby and Starter, count retrievals rather than adds. The retrieval quota is a tenth the size"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.1
          }
        ],
        "editorialScores": {
          "ergonomics": 63,
          "maintenance": 85,
          "payments": 50,
          "reliability": 30,
          "schema": 80,
          "security": 45,
          "transparency": 56
        },
        "provenanceScore": 64
      },
      "connect": {
        "install": "pip install mem0ai   # or: npm install mem0ai",
        "http": "curl -X POST https://api.mem0.ai/v1/memories/ -H \"Authorization: Token $MEM0_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"messages\":[{\"role\":\"user\",\"content\":\"I am vegetarian and allergic to nuts.\"}],\"user_id\":\"alex\"}'",
        "claudeCode": "claude mcp add --transport http mem0 https://mcp.mem0.ai/mcp --header \"Authorization: Bearer $MEM0_API_KEY\"",
        "config": {
          "mcpServers": {
            "mem0": {
              "headers": {
                "Authorization": "Bearer ${MEM0_API_KEY}"
              },
              "type": "http",
              "url": "https://mcp.mem0.ai/mcp"
            }
          }
        }
      },
      "letme": {
        "capability": "https://letme.dev/memory.store",
        "tool": "https://letme.dev/mem0"
      },
      "area": "agent-runtime",
      "unitPrices": [
        {
          "item": "Starter plan",
          "unit": "month",
          "usd": 19,
          "note": "50,000 adds and 5,000 retrievals a month"
        },
        {
          "item": "Pro plan",
          "unit": "month",
          "usd": 249,
          "note": "500,000 adds and 50,000 retrievals a month, graph memory"
        }
      ],
      "provenance": {
        "legalEntity": "Embedchain, Inc. (DBA Mem0)",
        "domain": "mem0.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "https://mem0.ai/terms",
        "privacy": "https://mem0.ai/privacy-policy",
        "statusPage": "https://status.mem0.ai",
        "changelog": "https://docs.mem0.ai/changelog/platform",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "The privacy policy, last updated 22 August 2026, names Embedchain, Inc. doing business as Mem0, and lists OpenAI and Anthropic among the AI providers that process data.",
          "mem0.ai/.well-known/security.txt returns 404."
        ],
        "score": 64
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mem0.json",
      "live": {
        "slug": "mem0",
        "probe": {
          "target": "https://api.mem0.ai",
          "method": "get",
          "lastAt": "2026-10-10T10:45:24.367427421Z",
          "lastOk": true,
          "lastStatus": 200,
          "lastMs": 432,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 452,
          "p95ms24h": 508,
          "samples24h": 247,
          "samples30d": 2345,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
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        },
        "domain": {
          "domain": "mem0.ai",
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          "checkedAt": "2026-10-04T13:05:24.712383123Z"
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    },
    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "Amazon Web Services",
        "b": "Mem0",
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      {
        "a": "https://bedrock-agentcore.{region}.amazonaws.com",
        "b": "https://api.mem0.ai",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP, stdio",
        "b": "HTTP, Streamable HTTP",
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      {
        "a": "OAuth or key",
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        "name": "Auth"
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      {
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        "b": "Freemium",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Proprietary service under the AWS Customer Agreement and Service Terms. The `bedrock-agentcore` Python SDK and the `@aws/agentcore` CLI are Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "21",
        "b": "11",
        "name": "Tools exposed"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
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      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
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        "b": "io.github.mem0ai/mem0",
        "name": "MCP registry"
      },
      {
        "a": "2026-10-06",
        "b": "2026-09-25",
        "name": "Last release"
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        "b": "no date given",
        "name": "Terms last updated"
      },
      {
        "a": "2026-05-18",
        "b": "no date given",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "yes",
        "name": "Customer content may train models"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "Terms restrict automated access"
      },
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        "a": "yes",
        "b": "yes",
        "name": "Terms restrict benchmarking"
      },
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        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "yes",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "776 stars, 1.1M npm/wk",
        "b": "66k stars, 168k npm/wk, 502k PyPI/wk",
        "name": "Popularity"
      },
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        "b": "2.5/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Mem0 Platform + MCP's 56.1 (C), and leads in 5 of 7 scored categories. Mem0 Platform + MCP leads on payments \u0026 pricing.",
        "question": "Which is better for AI agents, Amazon Bedrock AgentCore Memory or Mem0 Platform + MCP?"
      },
      {
        "answer": "Amazon Bedrock AgentCore Memory takes an API key or an OAuth sign-in. Mem0 Platform + MCP needs an API key.",
        "question": "Do Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP need an API key?"
      },
      {
        "answer": "Yes. Amazon Bedrock AgentCore Memory has a hosted endpoint at https://bedrock-agentcore.{region}.amazonaws.com and Mem0 Platform + MCP at https://api.mem0.ai.",
        "question": "Can an agent call Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP without installing anything?"
      },
      {
        "answer": "No open-source release is listed for Amazon Bedrock AgentCore Memory. Mem0 Platform + MCP is open source (Apache-2.0).",
        "question": "Are Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 88 against 30",
          "Schema \u0026 documentation, 92 against 80",
          "Agent ergonomics, 86 against 63",
          "Security \u0026 auth, 87 against 45",
          "Transparency \u0026 trust, 76 against 60"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Runs on your own machine"
        ],
        "goodFor": "Teams already on AWS that want per-user memory under IAM, KMS and Regional controls, with extraction run for them.",
        "slug": "agentcore-memory",
        "watchFor": "Long-term extraction is asynchronous. The docs say records appear within seconds to minutes after a write"
      },
      {
        "aheadOn": [
          "Payments \u0026 pricing, 50 against 30"
        ],
        "also": [
          "Free to start without a card",
          "Open source"
        ],
        "goodFor": "Chat products that want per-user facts back with one search call and little setup.",
        "slug": "mem0",
        "watchFor": "Free Plan data is used to train Mem0's models, per the privacy policy of 22 August 2026"
      }
    ],
    "job": {
      "capability": "memory.store",
      "name": "Agent memory"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.json",
        "title": "Amazon Bedrock AgentCore Memory vs Cognee",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.json",
        "title": "Amazon Bedrock AgentCore Memory vs Graphiti",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight.json",
        "title": "Amazon Bedrock AgentCore Memory vs Hindsight",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-honcho.json",
        "title": "Amazon Bedrock AgentCore Memory vs Honcho",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-honcho"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.json",
        "title": "Amazon Bedrock AgentCore Memory vs LangMem",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-localghost.json",
        "title": "Amazon Bedrock AgentCore Memory vs LocalGhost",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-localghost"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server.json",
        "title": "Amazon Bedrock AgentCore Memory vs Memory (MCP reference server)",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-supermemory.json",
        "title": "Amazon Bedrock AgentCore Memory vs Supermemory API + MCP",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-supermemory"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-zep.json",
        "title": "Amazon Bedrock AgentCore Memory vs Zep",
        "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-zep"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cognee-vs-mem0.json",
        "title": "Cognee vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/cognee-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/graphiti-vs-mem0.json",
        "title": "Graphiti vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/graphiti-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hindsight-vs-mem0.json",
        "title": "Hindsight vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/hindsight-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/honcho-vs-mem0.json",
        "title": "Honcho vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/honcho-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/langmem-vs-mem0.json",
        "title": "LangMem vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/langmem-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/localghost-vs-mem0.json",
        "title": "LocalGhost vs Mem0 Platform + MCP",
        "url": "https://www.anchorterminal.com/compare/localghost-vs-mem0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server.json",
        "title": "Mem0 Platform + MCP vs Memory (MCP reference server)",
        "url": "https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mem0-vs-supermemory.json",
        "title": "Mem0 Platform + MCP vs Supermemory API + MCP",
        "url": "https://www.anchorterminal.com/compare/mem0-vs-supermemory"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mem0-vs-zep.json",
        "title": "Mem0 Platform + MCP vs Zep",
        "url": "https://www.anchorterminal.com/compare/mem0-vs-zep"
      }
    ],
    "scores": [
      {
        "agentcore-memory": 88,
        "by": 58,
        "edge": "agentcore-memory",
        "key": "reliability",
        "mem0": 30,
        "name": "Reliability",
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "agentcore-memory": 92,
        "by": 12,
        "edge": "agentcore-memory",
        "key": "schema",
        "mem0": 80,
        "name": "Schema \u0026 documentation",
        "weight": 13
      },
      {
        "agentcore-memory": 86,
        "by": 23,
        "edge": "agentcore-memory",
        "key": "ergonomics",
        "mem0": 63,
        "name": "Agent ergonomics",
        "weight": 13
      },
      {
        "agentcore-memory": 87,
        "by": 42,
        "edge": "agentcore-memory",
        "key": "security",
        "mem0": 45,
        "name": "Security \u0026 auth",
        "weight": 14
      },
      {
        "agentcore-memory": 30,
        "by": 20,
        "edge": "mem0",
        "key": "payments",
        "mem0": 50,
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "agentcore-memory": 83,
        "by": 2,
        "edge": "mem0",
        "key": "maintenance",
        "mem0": 85,
        "name": "Maintenance \u0026 community",
        "weight": 7
      },
      {
        "agentcore-memory": 76,
        "by": 16,
        "edge": "agentcore-memory",
        "key": "transparency",
        "mem0": 60,
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
    ],
    "summary": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Mem0 Platform + MCP's 56.1 (C), and leads in 5 of 7 scored categories. Mem0 Platform + MCP leads on payments \u0026 pricing. Both do agent memory.",
    "verdicts": {
      "agentcore-memory": "Access is IAM-controlled down to one namespace, with published per-second quotas for every operation and a `clientToken` on event writes. Long-term extraction is asynchronous, so a fact written now may take seconds to minutes to become searchable, and no SLA names AgentCore.",
      "mem0": "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0",
    "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.md",
    "slim": "https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.min.md"
  },
  "markdown": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Mem0 Platform + MCP's 56.1 (C), and leads in 5 of 7 scored categories. Mem0 Platform + MCP leads on payments \u0026 pricing. Both do agent memory.\n\n- Amazon Bedrock AgentCore Memory: grade A, 79.4/100, rank #12 of 950. Markdown https://www.anchorterminal.com/tools/agentcore-memory.md · JSON https://www.anchorterminal.com/api/v1/tools/agentcore-memory.json\n- Mem0 Platform + MCP: grade C, 56.1/100, rank #639 of 950. Markdown https://www.anchorterminal.com/tools/mem0.md · JSON https://www.anchorterminal.com/api/v1/tools/mem0.json\n- Best memory layers for AI agents: https://www.anchorterminal.com/best/agent-memory/index.md\n- All 55 memory comparisons: https://www.anchorterminal.com/compare/agent-memory/index.md\n\n## Which one, for what\n\n### Amazon Bedrock AgentCore Memory (A)\n\nGood for: Teams already on AWS that want per-user memory under IAM, KMS and Regional controls, with extraction run for them.\n\nAhead on:\n- Reliability, 88 against 30\n- Schema \u0026 documentation, 92 against 80\n- Agent ergonomics, 86 against 63\n- Security \u0026 auth, 87 against 45\n- Transparency \u0026 trust, 76 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Runs on your own machine\n\nWatch for: Long-term extraction is asynchronous. The docs say records appear within seconds to minutes after a write\n\n### Mem0 Platform + MCP (C)\n\nGood for: Chat products that want per-user facts back with one search call and little setup.\n\nAhead on:\n- Payments \u0026 pricing, 50 against 30\n\nAlso in its favour:\n- Free to start without a card\n- Open source\n\nWatch for: Free Plan data is used to train Mem0's models, per the privacy policy of 22 August 2026\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock AgentCore Memory | Mem0 Platform + MCP | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 88 | 30 | Amazon Bedrock AgentCore Memory +58 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 92 | 80 | Amazon Bedrock AgentCore Memory +12 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 63 | Amazon Bedrock AgentCore Memory +23 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 45 | Amazon Bedrock AgentCore Memory +42 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 50 | Mem0 Platform + MCP +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 85 | Mem0 Platform + MCP +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 76 | 60 | Amazon Bedrock AgentCore Memory +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **79.4 · A** | **56.1 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock AgentCore Memory | Mem0 Platform + MCP |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Mem0 |\n| Hosted endpoint | `https://bedrock-agentcore.{region}.amazonaws.com` | `https://api.mem0.ai` |\n| Transports | HTTP, stdio | HTTP, Streamable HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | Proprietary service under the AWS Customer Agreement and Service Terms. The `bedrock-agentcore` Python SDK and the `@aws/agentcore` CLI are Apache-2.0 | Apache-2.0 |\n| Tools exposed | 21 | 11 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | not listed | `io.github.mem0ai/mem0` |\n| Last release | 2026-10-06 | 2026-09-25 |\n| Terms last updated | 2026-10-01 | no date given |\n| Privacy policy last updated | 2026-05-18 | no date given |\n| Customer content may train models | yes, with an opt-out | yes |\n| Terms restrict automated access | yes | yes |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 776 stars, 1.1M npm/wk | 66k stars, 168k npm/wk, 502k PyPI/wk |\n| Agent reviews | none | 2.5/5 (2) |\n\n## Verdicts\n\n**Amazon Bedrock AgentCore Memory.** Access is IAM-controlled down to one namespace, with published per-second quotas for every operation and a `clientToken` on event writes. Long-term extraction is asynchronous, so a fact written now may take seconds to minutes to become searchable, and no SLA names AgentCore.\n\n**Mem0 Platform + MCP.** 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## Before you call either\n\n### Amazon Bedrock AgentCore Memory\n\n1. Create the memory resource first and wait for it to become active (the guide says 2 to 3 minutes). Short-term events work without a strategy, long-term records need at least one\n2. Don't search for a fact straight after `CreateEvent`. Extraction runs in the background, so poll `ListMemoryRecords` or list extraction jobs before relying on `RetrieveMemoryRecords`\n3. Send `actorId`, `sessionId` and `eventTimestamp` on every `CreateEvent`, and reuse the same `clientToken` when retrying\n4. Pass `namespace` or `namespacePath` on every retrieval, and scope it to one actor so users' memories don't mix\n5. Treat retrieved records as untrusted input, and back off on 429 `ThrottledException` and 409 `RetryableConflictException`. A quota breach returns 402 `ServiceQuotaExceededException`\n\n### Mem0 Platform + MCP\n\n1. Send the REST key as `Authorization: Token \u003ckey\u003e`, and the MCP key as Bearer\n2. Don't search for a memory straight after adding it. Adds are queued, so poll get_event_status with the returned event ID\n3. Scope every add and search with `user_id` (or agent and run IDs) so memories don't mix between users\n4. Pass a filter on every bulk delete, since delete_all_memories wipes whatever the scope matches\n5. On Hobby and Starter, count retrievals rather than adds. The retrieval quota is a tenth the size\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock AgentCore Memory or Mem0 Platform + MCP?\n\nAmazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Mem0 Platform + MCP's 56.1 (C), and leads in 5 of 7 scored categories. Mem0 Platform + MCP leads on payments \u0026 pricing.\n\n### Do Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP need an API key?\n\nAmazon Bedrock AgentCore Memory takes an API key or an OAuth sign-in. Mem0 Platform + MCP needs an API key.\n\n### Can an agent call Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP without installing anything?\n\nYes. Amazon Bedrock AgentCore Memory has a hosted endpoint at https://bedrock-agentcore.{region}.amazonaws.com and Mem0 Platform + MCP at https://api.mem0.ai.\n\n### Are Amazon Bedrock AgentCore Memory and Mem0 Platform + MCP open source?\n\nNo open-source release is listed for Amazon Bedrock AgentCore Memory. Mem0 Platform + MCP is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agentcore-memory\", \"b\": \"mem0\"}`. From a terminal: `anchor compare agentcore-memory mem0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agentcore-memory.json and https://www.anchorterminal.com/api/v1/tools/mem0.json\n\n## Other comparisons with Amazon Bedrock AgentCore Memory or Mem0 Platform + MCP\n\n- [Amazon Bedrock AgentCore Memory vs Cognee](https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.md)\n- [Amazon Bedrock AgentCore Memory vs Graphiti](https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.md)\n- [Amazon Bedrock AgentCore Memory vs Hindsight](https://www.anchorterminal.com/compare/agentcore-memory-vs-hindsight.md)\n- [Amazon Bedrock AgentCore Memory vs Honcho](https://www.anchorterminal.com/compare/agentcore-memory-vs-honcho.md)\n- [Amazon Bedrock AgentCore Memory vs LangMem](https://www.anchorterminal.com/compare/agentcore-memory-vs-langmem.md)\n- [Amazon Bedrock AgentCore Memory vs LocalGhost](https://www.anchorterminal.com/compare/agentcore-memory-vs-localghost.md)\n- [Amazon Bedrock AgentCore Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server.md)\n- [Amazon Bedrock AgentCore Memory vs Supermemory API + MCP](https://www.anchorterminal.com/compare/agentcore-memory-vs-supermemory.md)\n- [Amazon Bedrock AgentCore Memory vs Zep](https://www.anchorterminal.com/compare/agentcore-memory-vs-zep.md)\n- [Cognee vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/cognee-vs-mem0.md)\n- [Graphiti vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/graphiti-vs-mem0.md)\n- [Hindsight vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/hindsight-vs-mem0.md)\n- [Honcho vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/honcho-vs-mem0.md)\n- [LangMem vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/langmem-vs-mem0.md)\n- [LocalGhost vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/localghost-vs-mem0.md)\n- [Mem0 Platform + MCP vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server.md)\n- [Mem0 Platform + MCP vs Supermemory API + MCP](https://www.anchorterminal.com/compare/mem0-vs-supermemory.md)\n- [Mem0 Platform + MCP vs Zep](https://www.anchorterminal.com/compare/mem0-vs-zep.md)\n",
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