{ "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": 842, "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": "2026-10-09T10:14:06.863477896Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "invalid character \"{\" in host name", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 28, "samples30d": 28, "days": [ { "date": "2026-10-09", "probes": 28, "ok": 0 } ], "outages": [ { "start": "2026-10-09T07:40:18.334133541Z", "end": "0001-01-01T00:00:00Z", "note": "invalid character \"{\" in host name" } ] }, "updatedAt": "2026-10-09T10:14:06.863477896Z" } }, "answer": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing.", "b": { "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": 632, "ranked": true, "rankOf": 842, "categoryRank": 7, "methodology": "0.4", "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.", "bestFor": "Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.", "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": "0.4", "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": "2026-10-08T16:14:56.048781026Z" }, { "registry": "pypi", "name": "graphiti-core", "version": "0.30.2", "released": "2026-09-08", "seenAt": "2026-10-08T16:14:55.855085919Z" } ], "githubStars": 31558, "pypiWeekly": 156133, "securityTxt": { "url": "https://getzep.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:46.378005865Z" }, "llmsTxt": { "url": "https://help.getzep.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:30.305602164Z" }, "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": "2026-10-08T18:28:02.500956978Z", "changedAt": "2026-10-03T15:38:25.165141175Z", "fingerprint": "6ec6dab9051f" } ], "updatedAt": "2026-10-08T18:28:02.500956978Z" } }, "facts": [ { "a": "HTTP API", "b": "Agent framework", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Zep", "name": "Vendor" }, { "a": "https://bedrock-agentcore.{region}.amazonaws.com", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, stdio", "b": "Streamable HTTP, stdio", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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": "13", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-06", "b": "2026-09-08", "name": "Last release" }, { "a": "2026-10-01", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "", "name": "Customer content may train models" }, { "a": "yes", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "776 stars, 1.1M npm/wk", "b": "29k stars, 151k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Amazon Bedrock AgentCore Memory or Graphiti?" }, { "answer": "Amazon Bedrock AgentCore Memory has a hosted endpoint at https://bedrock-agentcore.{region}.amazonaws.com. Graphiti runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call Amazon Bedrock AgentCore Memory and Graphiti without installing anything?" }, { "answer": "No open-source release is listed for Amazon Bedrock AgentCore Memory. Graphiti is open source (Apache-2.0).", "question": "Are Amazon Bedrock AgentCore Memory and Graphiti open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 88 against 50", "Schema \u0026 documentation, 92 against 71", "Agent ergonomics, 86 against 51", "Security \u0026 auth, 87 against 23", "Maintenance \u0026 community, 83 against 65", "Transparency \u0026 trust, 76 against 71" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install" ], "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, 60 against 30" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.", "slug": "graphiti", "watchFor": "Every add runs LLM extraction, so ingestion costs tokens and time" } ], "job": { "capability": "memory.store", "name": "Memory store" }, "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-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-mem0.json", "title": "Amazon Bedrock AgentCore Memory vs Mem0 Platform + MCP", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0" }, { "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-graphiti.json", "title": "Cognee vs Graphiti", "url": "https://www.anchorterminal.com/compare/cognee-vs-graphiti" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-hindsight.json", "title": "Graphiti vs Hindsight", "url": "https://www.anchorterminal.com/compare/graphiti-vs-hindsight" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-honcho.json", "title": "Graphiti vs Honcho", "url": "https://www.anchorterminal.com/compare/graphiti-vs-honcho" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-langmem.json", "title": "Graphiti vs LangMem", "url": "https://www.anchorterminal.com/compare/graphiti-vs-langmem" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-localghost.json", "title": "Graphiti vs LocalGhost", "url": "https://www.anchorterminal.com/compare/graphiti-vs-localghost" }, { "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/graphiti-vs-supermemory.json", "title": "Graphiti vs Supermemory API + MCP", "url": "https://www.anchorterminal.com/compare/graphiti-vs-supermemory" }, { "json": "https://www.anchorterminal.com/compare/graphiti-vs-zep.json", "title": "Graphiti vs Zep", "url": "https://www.anchorterminal.com/compare/graphiti-vs-zep" } ], "scores": [ { "agentcore-memory": 88, "by": 38, "edge": "agentcore-memory", "graphiti": 50, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "agentcore-memory": 92, "by": 21, "edge": "agentcore-memory", "graphiti": 71, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "agentcore-memory": 86, "by": 35, "edge": "agentcore-memory", "graphiti": 51, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "agentcore-memory": 87, "by": 64, "edge": "agentcore-memory", "graphiti": 23, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "agentcore-memory": 30, "by": 30, "edge": "graphiti", "graphiti": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "agentcore-memory": 83, "by": 18, "edge": "agentcore-memory", "graphiti": 65, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "agentcore-memory": 76, "by": 5, "edge": "agentcore-memory", "graphiti": 71, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing. Both do memory store.", "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.", "graphiti": "Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti", "json": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.md", "slim": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.min.md" }, "markdown": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing. Both do memory store.\n\n- Amazon Bedrock AgentCore Memory: grade A, 79.4/100, rank #12 of 842. Markdown https://www.anchorterminal.com/tools/agentcore-memory.md · JSON https://www.anchorterminal.com/api/v1/tools/agentcore-memory.json\n- Graphiti: grade D, 53.3/100, rank #632 of 842. Markdown https://www.anchorterminal.com/tools/graphiti.md · JSON https://www.anchorterminal.com/api/v1/tools/graphiti.json\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 50\n- Schema \u0026 documentation, 92 against 71\n- Agent ergonomics, 86 against 51\n- Security \u0026 auth, 87 against 23\n- Maintenance \u0026 community, 83 against 65\n- Transparency \u0026 trust, 76 against 71\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n\nWatch for: Long-term extraction is asynchronous. The docs say records appear within seconds to minutes after a write\n\n### Graphiti (D)\n\nGood for: Teams that want Zep's temporal graph model on their own infrastructure and can run a graph database.\n\nAhead on:\n- Payments \u0026 pricing, 60 against 30\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Every add runs LLM extraction, so ingestion costs tokens and time\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock AgentCore Memory | Graphiti | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 88 | 50 | Amazon Bedrock AgentCore Memory +38 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 92 | 71 | Amazon Bedrock AgentCore Memory +21 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 51 | Amazon Bedrock AgentCore Memory +35 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 23 | Amazon Bedrock AgentCore Memory +64 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | Graphiti +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 65 | Amazon Bedrock AgentCore Memory +18 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 76 | 71 | Amazon Bedrock AgentCore Memory +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **79.4 · A** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock AgentCore Memory | Graphiti |\n| --- | --- | --- |\n| Kind | HTTP API | Agent framework |\n| Vendor | Amazon Web Services | Zep |\n| Hosted endpoint | `https://bedrock-agentcore.{region}.amazonaws.com` | no (local only) |\n| Transports | HTTP, stdio | Streamable HTTP, stdio |\n| Auth | OAuth or key | None |\n| Pricing | Pay per use | Free |\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 | 13 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-06 | 2026-09-08 |\n| Terms last updated | 2026-10-01 | no document linked |\n| Privacy policy last updated | 2026-05-18 | no document linked |\n| Customer content may train models | yes, with an opt-out | |\n| Terms restrict automated access | yes | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | yes | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 776 stars, 1.1M npm/wk | 29k stars, 151k 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**Graphiti.** 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## 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### Graphiti\n\n1. Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server\n2. Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes\n3. Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge\n4. Pass group_ids on every search and add so one user's graph doesn't leak into another's\n5. Call get_status to check the database connection before a long ingest\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock AgentCore Memory or Graphiti?\n\nAmazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing.\n\n### Can an agent call Amazon Bedrock AgentCore Memory and Graphiti without installing anything?\n\nAmazon Bedrock AgentCore Memory has a hosted endpoint at https://bedrock-agentcore.{region}.amazonaws.com. Graphiti runs on your own machine, with no hosted endpoint listed.\n\n### Are Amazon Bedrock AgentCore Memory and Graphiti open source?\n\nNo open-source release is listed for Amazon Bedrock AgentCore Memory. Graphiti 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-graphiti.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agentcore-memory\", \"b\": \"graphiti\"}`. From a terminal: `anchor compare agentcore-memory graphiti`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agentcore-memory.json and https://www.anchorterminal.com/api/v1/tools/graphiti.json\n\n## Other comparisons with Amazon Bedrock AgentCore Memory or Graphiti\n\n- [Amazon Bedrock AgentCore Memory vs Cognee](https://www.anchorterminal.com/compare/agentcore-memory-vs-cognee.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 Mem0 Platform + MCP](https://www.anchorterminal.com/compare/agentcore-memory-vs-mem0.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 Graphiti](https://www.anchorterminal.com/compare/cognee-vs-graphiti.md)\n- [Graphiti vs Hindsight](https://www.anchorterminal.com/compare/graphiti-vs-hindsight.md)\n- [Graphiti vs Honcho](https://www.anchorterminal.com/compare/graphiti-vs-honcho.md)\n- [Graphiti vs LangMem](https://www.anchorterminal.com/compare/graphiti-vs-langmem.md)\n- [Graphiti vs LocalGhost](https://www.anchorterminal.com/compare/graphiti-vs-localghost.md)\n- [Graphiti vs Mem0 Platform + MCP](https://www.anchorterminal.com/compare/graphiti-vs-mem0.md)\n- [Graphiti vs Supermemory API + MCP](https://www.anchorterminal.com/compare/graphiti-vs-supermemory.md)\n- [Graphiti vs Zep](https://www.anchorterminal.com/compare/graphiti-vs-zep.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Amazon Bedrock AgentCore Memory vs Graphiti", "url": "" } ], "description": "Amazon Bedrock AgentCore Memory scores 79.4 (A) on agent readiness against Graphiti's 53.3 (D), and leads in 6 of 7 scored categories. Graphiti leads on payments \u0026 pricing. Both do memory store. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Amazon Bedrock AgentCore Memory A 79.4", "Graphiti D 53.3", "scores" ], "h1": "Amazon Bedrock AgentCore Memory vs Graphiti", "image": "https://www.anchorterminal.com/assets/og/compare-agentcore-memory-vs-graphiti.png", "path": "/compare/agentcore-memory-vs-graphiti", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock AgentCore Memory vs Graphiti for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/agentcore-memory-vs-graphiti" }, "tokens": { "markdown": 2300, "slim": 780 }, "version": 1 }