Agentmemory
Open-source persistent memory runtime for AI coding agents with 95.2% recall
Agentmemory is the most complete open-source memory layer for coding agents we've reviewed—its zero-dependency setup, rich retrieval, and broad MCP integration make it production-ready for serious agent workflows. The 95.2% recall on LongMemEval-S is benchmark-validated, and the active community adds real credibility. If you're building autonomous coding agents, install this today.
- Developers using Claude Code who need persistent memory across sessions
- Teams working on large codebases where agents lose context
- Power users of Cursor, Copilot, and other MCP agents
- Anyone building long-running autonomous coding workflows
- Users who need a cloud-hosted, fully managed solution
- Teams requiring built-in multi-user permissions or RBAC
- Complete beginners unfamiliar with CLI tools and MCP protocols
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Skip Agent Memory if you need a cloud-hosted, managed solution with user permissions and support, or if you're not comfortable with CLI tools and MCP configuration.
Self-hosting requires your own infrastructure and maintenance, which may cost time and server resources.
Agent Memory is free and open-source (MIT licensed), making it ideal for individual developers and teams who prefer self-hosting. There's no per-seat or per-usage fee. By contrast, cloud-managed alternatives like Mem0 or Letta may charge for managed hosting or have paid tiers.
In short
Agentmemory — Open-source persistent memory runtime for AI coding agents with 95.2% recall. Best for Developers using Claude Code who need persistent memory across sessions, Teams working on large codebases where agents lose context, Power users of Cursor, Copilot, and other MCP agents. Free to use.
Viability Score
How likely is Agentmemory to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 12 auto-capture hooks for agent tool calls, prompts, stops
- Triple-stream retrieval: BM25 + vector + knowledge graph
- On-device reranker achieving 95.2% R@5 recall on LongMemEval-S
- ~92% token reduction vs full-context approaches
- Hourly consolidation: compress, merge duplicates, decay stale
- 53 MCP tools for memory operations
- 128 REST endpoints mirroring MCP surface
- Graph extraction and knowledge graph visualization
- Mesh federation: peer-to-peer sync over authenticated HTTPS
- Markdown Obsidian export with frontmatter tags
- OTEL observability with spans and logs (Jaeger, Honeycomb, Tempo)
- JSONL session import from Claude Code transcripts
- Supports 5 LLM providers (Claude, Anthropic, Gemini, MiniMax, OpenRouter)
- Zero external databases: runs on local SQLite/JSON
- Real-time viewer on port 3113 and engine console on port 3114
About Agentmemory
Agentmemory is an open-source persistent memory runtime purpose-built for AI coding agents. It captures every interaction—file edits, commands, decisions, and discoveries—across sessions and recalls relevant context in milliseconds, eliminating the forgetfulness that plagues stateless LLM interactions. Designed for developers using Claude Code, Cursor, GitHub Copilot, and other MCP-compatible agents, it runs as a single Node.js process with zero external databases. The three-layer stack (hooks, recall, consolidation) leverages BM25, vector search, and a knowledge graph with on-device reranking to achieve 95.2% R@5 on LongMemEval-S. It offers 53 MCP tools, 128 REST endpoints, a real-time viewer, OTEL observability, federation for team memory, and Obsidian export. The project is open-source (MIT), backed by the Linux Foundation, and has over 24.6K installations. It reduces input tokens per session by ~92%, saving roughly 170K tokens per year versus full-context approaches. It ships a real-time viewer on port 3113 and an engine console on port 3114. Version 0.9.27 is current. Agentmemory supports six native plugins for Claude Code, Copilot CLI, Codex CLI, OpenClaw, Hermes, Pi, and OpenHuman, and works with every MCP client.
Behind the Verdict
Agentmemory isn't just another vector store—it's a full memory runtime with hooks, recall, consolidation, observability, and federation built in. The zero-external-database promise holds: one Node.js process, JSON on disk, no Redis or Postgres. For solo developers or small teams using Claude Code, Cursor, or Copilot, it solves the biggest pain point—agents forgetting context between sessions. The 95.2% recall benchmark is real and reproducible. On the flip side, it's not for everyone. You need comfort with CLI tools and MCP configuration. There's no hosted cloud version, no built-in multi-user permissions, and the setup requires installing npm packages and starting a server. If you want a plug-and-play SaaS memory layer, look at Mem0 or Letta. Agentmemory is best for developers who want full control and zero external dependencies. The active community and Linux Foundation backing add long-term confidence. We'd reach for it when building autonomous coding agents that need to persist state across days of work.
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Real-world workflow fit
Concrete scenarios for the personas Agentmemory actually fits — and what changes day-one when you adopt it.
Install Agent Memory and connect Claude Code to persist context across sessions.
Outcome: Claude Code remembers past decisions and file edits, reducing repetitive context prompts.
Set up federation to share memory across multiple developers' agents.
Outcome: Team agents collaborate with shared knowledge of codebase decisions and conventions.
Use Cases
- Install Agent Memory via npm and connect Claude Code in under 2 minutes for persistent context across sessions.
- Use the triple-stream recall to instantly retrieve relevant code decisions and commands from past agent sessions.
- Automatically capture every tool call and prompt as observations, then consolidate them into semantic memories hourly.
- Export compressed memories as Obsidian-compatible markdown for human review and knowledge management.
- Federate memories across team members by registering a peer node and syncing over authenticated HTTPS.
- Import a Claude Code JSONL transcript to rehydrate a full session including observations, tool uses, and timeline.
Models Under the Hood
as of 2026-07-17
Limitations
- Agent Memory runs as a single Node process with state on disk (JSON/SQLite), so it may not suit high-throughput multi-server deployments without federation.
- The current release is v0.9.27 (pre-1.0), so stability and breaking changes are possible.
- There is no built-in role-based access control; team memory uses bearer tokens for federation but no user-level permissions.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agentmemory tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (Community)
Free
Ideal for
Solo developers and teams who self-host and want full control over their agent memory without recurring costs.
What this tier adds
Starting tier: completely free, open-source (MIT), includes all features with no usage limits.
Where the pricing makes sense
The company stage and team size where Agentmemory's pricing actually pencils out — and where peers do it cheaper.
Agent Memory is free and open-source (MIT licensed), making it ideal for individual developers and teams who prefer self-hosting. There's no per-seat or per-usage fee. By contrast, cloud-managed alternatives like Mem0 or Letta may charge for managed hosting or have paid tiers.
Setup time & first value
How long it actually takes to get something useful out of Agentmemory — broken out by persona, not the marketing-page minute.
A solo developer can install the npm package, start the server, and connect an agent in under 5 minutes. Team federation may take an additional 15-30 minutes to configure peer nodes.
Switching to or from Agentmemory
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Mem0: Export memories via Mem0's API and import them using Agent Memory's REST endpoints (not automatically supported, but possible programmatically).
- ↗To Mem0 or Letta: Export memories via Agent Memory's REST API or Obsidian markdown, then import into the new system. There is no automated migration tool.
Integrations
Resources & Guides
- Documentationagent-memory.dev
Docs · Agentmemory
Full product docs from agent-memory.dev
- Quickstartagent-memory.dev
Quickstart · Agentmemory
Get up and running fast from agent-memory.dev
- Documentationagent-memory.dev
Connect Agents · Agentmemory
Full product docs from agent-memory.dev
- Guideagent-memory.dev
Guides · Agentmemory
In-depth how-to from agent-memory.dev
- API Referenceagent-memory.dev
Reference · Agentmemory
Methods, params, types from agent-memory.dev
Official links
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