rohitg00__agentmemory/README.md

Your coding agent remembers everything. No more re-explaining.
Persistent memory for Claude Code, Cursor, Gemini CLI, OpenCode, and any MCP client.

The gist extends Karpathy's LLM Wiki pattern with confidence scoring, lifecycle, knowledge graphs, and hybrid search.
agentmemory is the implementation.

Quick Start • Benchmarks • vs Competitors • Agents • How It Works • MCP • Viewer • Config • API

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agentmemory works with any agent that supports hooks, MCP, or REST API. All agents share the same memory server.

Claude Code 12 hooks + MCP + skills	OpenClaw MCP + plugin	Hermes MCP + plugin	Cursor MCP server	Gemini CLI MCP server	OpenCode MCP server	Codex CLI MCP server	Cline MCP server
Goose MCP server	Kilo Code MCP server	Aider REST API	Claude Desktop MCP server	Windsurf MCP server	Roo Code MCP server	Claude SDK AgentSDKProvider	Any agent REST API

_{Works with any agent that speaks MCP or HTTP. One server, memories shared across all of them.}

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You explain the same architecture every session. You re-discover the same bugs. You re-teach the same preferences. Built-in memory (CLAUDE.md, .cursorrules) caps out at 200 lines and goes stale. agentmemory fixes this. It silently captures what your agent does, compresses it into searchable memory, and injects the right context when the next session starts. One command. Works across agents.

What changes: Session 1 you set up JWT auth. Session 2 you ask for rate limiting. The agent already knows your auth uses jose middleware in src/middleware/auth.ts, your tests cover token validation, and you chose jose over jsonwebtoken for Edge compatibility. No re-explaining. No copy-pasting. The agent just knows.

npx @agentmemory/agentmemory

New in v0.8.2 — Security hardening (default localhost, viewer CSP nonces, mesh auth), agentmemory demo command, benchmark comparison vs mem0/Letta/Khoj, OpenClaw gateway plugin, real-time token savings in CLI + viewer.

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Retrieval Accuracy LongMemEval-S (ICLR 2025, 500 questions) System R@5 R@10 MRR agentmemory 95.2% 98.6% 88.2% BM25-only fallback 86.2% 94.6% 71.5%

Token Savings Approach Tokens/yr Cost/yr Paste full context 19.5M+ Impossible (exceeds window) LLM-summarized ~650K ~$500 agentmemory ~170K ~$10 agentmemory + local embeddings ~170K $0

Embedding model: all-MiniLM-L6-v2 (local, free, no API key). Full reports: benchmark/LONGMEMEVAL.md, benchmark/QUALITY.md, benchmark/SCALE.md. Competitor comparison: benchmark/COMPARISON.md — agentmemory vs mem0, Letta, Khoj, claude-mem, Hippo.

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	agentmemory	mem0 (53K ⭐)	Letta / MemGPT (22K ⭐)	Built-in (CLAUDE.md)
Type	Memory engine + MCP server	Memory layer API	Full agent runtime	Static file
Retrieval R@5	95.2%	68.5% (LoCoMo)	83.2% (LoCoMo)	N/A (grep)
Auto-capture	12 hooks (zero manual effort)	Manual add() calls	Agent self-edits	Manual editing
Search	BM25 + Vector + Graph (RRF fusion)	Vector + Graph	Vector (archival)	Loads everything into context
Multi-agent	MCP + REST + leases + signals	API (no coordination)	Within Letta runtime only	Per-agent files
Framework lock-in	None (any MCP client)	None	High (must use Letta)	Per-agent format
External deps	None (SQLite + iii-engine)	Qdrant / pgvector	Postgres + vector DB	None
Memory lifecycle	4-tier consolidation + decay + auto-forget	Passive extraction	Agent-managed	Manual pruning
Token efficiency	~1,900 tokens/session ($10/yr)	Varies by integration	Core memory in context	22K+ tokens at 240 obs
Real-time viewer	Yes (port 3113)	Cloud dashboard	Cloud dashboard	No
Self-hosted	Yes (default)	Optional	Optional	Yes

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Compatibility: this release targets stable iii-sdk ^0.11.0 and iii-engine v0.11.x.

Try it in 30 seconds

# Terminal 1: start the server
npx @agentmemory/agentmemory

# Terminal 2: seed sample data and see recall in action
npx @agentmemory/agentmemory demo

demo seeds 3 realistic sessions (JWT auth, N+1 query fix, rate limiting) and runs semantic searches against them. You'll see it find "N+1 query fix" when you search "database performance optimization" — keyword matching can't do that.

Open http://localhost:3113 to watch the memory build live.

Upgrade / Maintenance

Use the maintenance command when you intentionally want to update your local runtime:

npx @agentmemory/agentmemory upgrade

Warning: this command mutates the current workspace/runtime. It can update JavaScript dependencies, may run cargo install iii-engine --force, and may pull Docker images.

Implementation details live in src/cli.ts (see runUpgrade around the src/cli.ts:544-595 region).

Claude Code (one block, paste it)

Install agentmemory: run `npx @agentmemory/agentmemory` in a separate terminal to start the memory server. Then run `/plugin marketplace add rohitg00/agentmemory` and `/plugin install agentmemory` — the plugin registers all 12 hooks, 4 skills, AND auto-wires the `@agentmemory/mcp` stdio server via its `.mcp.json`, so you get 43 MCP tools (memory_smart_search, memory_save, memory_sessions, memory_governance_delete, etc.) without any extra config step. Verify with `curl http://localhost:3111/agentmemory/health`. The real-time viewer is at http://localhost:3113.

OpenClaw (paste this prompt)

Install agentmemory for OpenClaw. Run `npx @agentmemory/agentmemory` in a separate terminal to start the memory server on localhost:3111. Then add this to my OpenClaw MCP config so agentmemory is available with all 43 memory tools:

{
  "mcpServers": {
    "agentmemory": {
      "command": "npx",
      "args": ["-y", "@agentmemory/mcp"]
    }
  }
}

Restart OpenClaw. Verify with `curl http://localhost:3111/agentmemory/health`. Open http://localhost:3113 for the real-time viewer. For deeper 4-hook gateway integration, see integrations/openclaw in the agentmemory repo.

Full guide: integrations/openclaw/

Hermes Agent (paste this prompt)

Install agentmemory for Hermes. Run `npx @agentmemory/agentmemory` in a separate terminal to start the memory server on localhost:3111. Then add this to ~/.hermes/config.yaml so Hermes can use agentmemory as an MCP server with all 43 memory tools:

mcp_servers:
  agentmemory:
    command: npx
    args: ["-y", "@agentmemory/mcp"]

Verify with `curl http://localhost:3111/agentmemory/health`. Open http://localhost:3113 for the real-time viewer. For deeper 6-hook memory provider integration (pre-LLM context injection, turn capture, MEMORY.md mirroring, system prompt block), copy integrations/hermes from the agentmemory repo to ~/.hermes/plugins/memory/agentmemory.

Full guide: integrations/hermes/

Other agents

Start the memory server: npx @agentmemory/agentmemory

Then add the MCP config for your agent:

Agent	Setup
Cursor	Add to ~/.cursor/mcp.json: {"mcpServers": {"agentmemory": {"command": "npx", "args": ["-y", "@agentmemory/mcp"]}}}
OpenClaw	Add to MCP config: {"mcpServers": {"agentmemory": {"command": "npx", "args": ["-y", "@agentmemory/mcp"]}}} or use the gateway plugin
Gemini CLI	gemini mcp add agentmemory -- npx -y @agentmemory/mcp
Codex CLI	Add to .codex/config.yaml: mcp_servers: {agentmemory: {command: npx, args: ["-y", "@agentmemory/mcp"]}}
OpenCode	Add to opencode.json: {"mcp": {"agentmemory": {"type": "local", "command": ["npx", "-y", "@agentmemory/mcp"], "enabled": true}}}
Hermes Agent	Add to ~/.hermes/config.yaml or use the memory provider plugin
Cline / Goose / Kilo Code	Add MCP server in settings
Claude Desktop	Add to claude_desktop_config.json: {"mcpServers": {"agentmemory": {"command": "npx", "args": ["-y", "@agentmemory/mcp"]}}}
Aider	REST API: curl -X POST http://localhost:3111/agentmemory/smart-search -d '{"query": "auth"}'
Any agent (32+)	npx skillkit install agentmemory

From source

git clone https://github.com/rohitg00/agentmemory.git && cd agentmemory
npm install && npm run build && npm start

This starts agentmemory with a local iii-engine if iii is already installed, or falls back to Docker Compose if Docker is available. REST, streams, and the viewer bind to 127.0.0.1 by default.

Install iii-engine manually:

• macOS / Linux: curl -fsSL https://install.iii.dev/iii/main/install.sh | sh
• Windows: download iii-x86_64-pc-windows-msvc.zip from iii-hq/iii releases, extract iii.exe, add to PATH

Or use Docker (the bundled docker-compose.yml pulls iiidev/iii:latest). Full docs: iii.dev/docs.

Windows

agentmemory runs on Windows 10/11, but the Node.js package alone isn't enough — you also need the iii-engine runtime (a separate native binary) as a background process. The official upstream installer is a sh script and there is no PowerShell installer or scoop/winget package today, so Windows users have two paths:

Option A — Prebuilt Windows binary (recommended):

# 1. Open https://github.com/iii-hq/iii/releases/latest in your browser
# 2. Download iii-x86_64-pc-windows-msvc.zip
#    (or iii-aarch64-pc-windows-msvc.zip if you're on an ARM machine)
# 3. Extract iii.exe somewhere on PATH, or place it at:
#    %USERPROFILE%\.local\bin\iii.exe
#    (agentmemory checks that location automatically)
# 4. Verify:
iii --version

# 5. Then run agentmemory as usual:
npx -y @agentmemory/agentmemory

Option B — Docker Desktop:

# 1. Install Docker Desktop for Windows
# 2. Start Docker Desktop and make sure the engine is running
# 3. Run agentmemory — it will auto-start the bundled compose file:
npx -y @agentmemory/agentmemory

Option C — standalone MCP only (no engine): if you only need the MCP tools for your agent and don't need the REST API, viewer, or cron jobs, skip the engine entirely:

npx -y @agentmemory/agentmemory mcp
# or via the shim package:
npx -y @agentmemory/mcp

Diagnostics for Windows: if npx @agentmemory/agentmemory fails, re-run with --verbose to see the actual engine stderr. Common failure modes:

Symptom	Fix
iii-engine process started then did not become ready within 15s	Engine crashed on startup — re-run with --verbose, check stderr
Could not start iii-engine	Neither iii.exe nor Docker is installed. See Option A or B above
Port conflict	netstat -ano | findstr :3111 to see what's bound, then kill it or use --port <N>
Docker fallback skipped even though Docker is installed	Make sure Docker Desktop is actually running (system tray icon)

Note: there is no cargo install iii-engine — iii is not published to crates.io. The only supported install methods are the prebuilt binary above, the upstream sh install script (macOS/Linux only), and the Docker image.

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Every coding agent forgets everything when the session ends. You waste the first 5 minutes of every session re-explaining your stack. agentmemory runs in the background and eliminates that entirely.

Session 1: "Add auth to the API"
  Agent writes code, runs tests, fixes bugs
  agentmemory silently captures every tool use
  Session ends -> observations compressed into structured memory

Session 2: "Now add rate limiting"
  Agent already knows:
    - Auth uses JWT middleware in src/middleware/auth.ts
    - Tests in test/auth.test.ts cover token validation
    - You chose jose over jsonwebtoken for Edge compatibility
  Zero re-explaining. Starts working immediately.

vs built-in agent memory

Every AI coding agent ships with built-in memory — Claude Code has MEMORY.md, Cursor has notepads, Cline has memory bank. These work like sticky notes. agentmemory is the searchable database behind the sticky notes.

	Built-in (CLAUDE.md)	agentmemory
Scale	200-line cap	Unlimited
Search	Loads everything into context	BM25 + vector + graph (top-K only)
Token cost	22K+ at 240 observations	~1,900 tokens (92% less)
Cross-agent	Per-agent files	MCP + REST (any agent)
Coordination	None	Leases, signals, actions, routines
Observability	Read files manually	Real-time viewer on :3113

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Memory Pipeline

PostToolUse hook fires
  -> SHA-256 dedup (5min window)
  -> Privacy filter (strip secrets, API keys)
  -> Store raw observation
  -> LLM compress -> structured facts + concepts + narrative
  -> Vector embedding (6 providers + local)
  -> Index in BM25 + vector + knowledge graph

SessionStart hook fires
  -> Load project profile (top concepts, files, patterns)
  -> Hybrid search (BM25 + vector + graph)
  -> Token budget (default: 2000 tokens)
  -> Inject into conversation

4-Tier Memory Consolidation

Inspired by how human brains process memory — not unlike sleep consolidation.

Tier	What	Analogy
Working	Raw observations from tool use	Short-term memory
Episodic	Compressed session summaries	"What happened"
Semantic	Extracted facts and patterns	"What I know"
Procedural	Workflows and decision patterns	"How to do it"

Memories decay over time (Ebbinghaus curve). Frequently accessed memories strengthen. Stale memories auto-evict. Contradictions are detected and resolved.

What Gets Captured

Hook	Captures
SessionStart	Project path, session ID
UserPromptSubmit	User prompts (privacy-filtered)
PreToolUse	File access patterns + enriched context
PostToolUse	Tool name, input, output
PostToolUseFailure	Error context
PreCompact	Re-injects memory before compaction
SubagentStart/Stop	Sub-agent lifecycle
Stop	End-of-session summary
SessionEnd	Session complete marker

Key Capabilities

Capability	Description
Automatic capture	Every tool use recorded via hooks — zero manual effort
Semantic search	BM25 + vector + knowledge graph with RRF fusion
Memory evolution	Versioning, supersession, relationship graphs
Auto-forgetting	TTL expiry, contradiction detection, importance eviction
Privacy first	API keys, secrets, <private> tags stripped before storage
Self-healing	Circuit breaker, provider fallback chain, health monitoring
Claude bridge	Bi-directional sync with MEMORY.md
Knowledge graph	Entity extraction + BFS traversal
Team memory	Namespaced shared + private across team members
Citation provenance	Trace any memory back to source observations
Git snapshots	Version, rollback, and diff memory state

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Triple-stream retrieval combining three signals:

Stream	What it does	When
BM25	Stemmed keyword matching with synonym expansion	Always on
Vector	Cosine similarity over dense embeddings	Embedding provider configured
Graph	Knowledge graph traversal via entity matching	Entities detected in query

Fused with Reciprocal Rank Fusion (RRF, k=60) and session-diversified (max 3 results per session).

Embedding providers

agentmemory auto-detects your provider. For best results, install local embeddings (free):

npm install @xenova/transformers

Provider	Model	Cost	Notes
Local (recommended)	all-MiniLM-L6-v2	Free	Offline, +8pp recall over BM25-only
Gemini	text-embedding-004	Free tier	1500 RPM
OpenAI	text-embedding-3-small	$0.02/1M	Highest quality
Voyage AI	voyage-code-3	Paid	Optimized for code
Cohere	embed-english-v3.0	Free trial	General purpose
OpenRouter	Any model	Varies	Multi-model proxy

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43 tools, 6 resources, 3 prompts, and 4 skills — the most comprehensive MCP memory toolkit for any agent.

43 Tools

Core tools (always available)

Tool	Description
memory_recall	Search past observations
memory_save	Save an insight, decision, or pattern
memory_smart_search	Hybrid semantic + keyword search
memory_file_history	Past observations about specific files
memory_sessions	List recent sessions
memory_profile	Project profile (concepts, files, patterns)
memory_export	Export all memory data

Extended tools (43 total — set AGENTMEMORY_TOOLS=all)

Tool	Description
memory_patterns	Detect recurring patterns
memory_timeline	Chronological observations
memory_relations	Query relationship graph
memory_graph_query	Knowledge graph traversal
memory_consolidate	Run 4-tier consolidation
memory_claude_bridge_sync	Sync with MEMORY.md
memory_team_share	Share with team members
memory_team_feed	Recent shared items
memory_audit	Audit trail of operations
memory_governance_delete	Delete with audit trail
memory_snapshot_create	Git-versioned snapshot
memory_action_create	Create work items with dependencies
memory_action_update	Update action status
memory_frontier	Unblocked actions ranked by priority
memory_next	Single most important next action
memory_lease	Exclusive action leases (multi-agent)
memory_routine_run	Instantiate workflow routines
memory_signal_send	Inter-agent messaging
memory_signal_read	Read messages with receipts
memory_checkpoint	External condition gates
memory_mesh_sync	P2P sync between instances
memory_sentinel_create	Event-driven watchers
memory_sentinel_trigger	Fire sentinels externally
memory_sketch_create	Ephemeral action graphs
memory_sketch_promote	Promote to permanent
memory_crystallize	Compact action chains
memory_diagnose	Health checks
memory_heal	Auto-fix stuck state
memory_facet_tag	Dimension:value tags
memory_facet_query	Query by facet tags
memory_verify	Trace provenance

6 Resources · 3 Prompts · 4 Skills

Type	Name	Description
Resource	agentmemory://status	Health, session count, memory count
Resource	agentmemory://project/{name}/profile	Per-project intelligence
Resource	agentmemory://memories/latest	Latest 10 active memories
Resource	agentmemory://graph/stats	Knowledge graph statistics
Prompt	recall_context	Search + return context messages
Prompt	session_handoff	Handoff data between agents
Prompt	detect_patterns	Analyze recurring patterns
Skill	/recall	Search memory
Skill	/remember	Save to long-term memory
Skill	/session-history	Recent session summaries
Skill	/forget	Delete observations/sessions

Standalone MCP

Run without the full server — for any MCP client. Either of these works:

npx -y @agentmemory/agentmemory mcp   # canonical (always available)
npx -y @agentmemory/mcp                # shim package alias

Or add to your agent's MCP config:

Most agents (Cursor, Claude Desktop, Cline, etc.):

{
  "mcpServers": {
    "agentmemory": {
      "command": "npx",
      "args": ["-y", "@agentmemory/mcp"]
    }
  }
}

OpenCode (opencode.json):

{
  "mcp": {
    "agentmemory": {
      "type": "local",
      "command": ["npx", "-y", "@agentmemory/mcp"],
      "enabled": true
    }
  }
}

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Auto-starts on port 3113. Live observation stream, session explorer, memory browser, knowledge graph visualization, and health dashboard.

open http://localhost:3113

The viewer server binds to 127.0.0.1 by default. The REST-served /agentmemory/viewer endpoint follows the normal AGENTMEMORY_SECRET bearer-token rules. CSP headers use a per-response script nonce and disable inline handler attributes (script-src-attr 'none').

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LLM Providers

agentmemory auto-detects from your environment. No API key needed if you have a Claude subscription.

Provider	Config	Notes
Claude subscription (default)	No config needed	Uses @anthropic-ai/claude-agent-sdk
Anthropic API	ANTHROPIC_API_KEY	Per-token billing
MiniMax	MINIMAX_API_KEY	Anthropic-compatible
Gemini	GEMINI_API_KEY	Also enables embeddings
OpenRouter	OPENROUTER_API_KEY	Any model

Environment Variables

Create ~/.agentmemory/.env:

# LLM provider (pick one, or leave empty for Claude subscription)
# ANTHROPIC_API_KEY=sk-ant-...
# GEMINI_API_KEY=...
# OPENROUTER_API_KEY=...

# Embedding provider (auto-detected, or override)
# EMBEDDING_PROVIDER=local
# VOYAGE_API_KEY=...

# Search tuning
# BM25_WEIGHT=0.4
# VECTOR_WEIGHT=0.6
# TOKEN_BUDGET=2000

# Auth
# AGENTMEMORY_SECRET=your-secret

# Ports (defaults: 3111 API, 3113 viewer)
# III_REST_PORT=3111

# Features
# AGENTMEMORY_AUTO_COMPRESS=false  # OFF by default (#138). When on,
                                   # every PostToolUse hook calls your
                                   # LLM provider to compress the
                                   # observation — expect significant
                                   # token spend on active sessions.
# AGENTMEMORY_INJECT_CONTEXT=false # OFF by default (#143). When on:
                                   # - SessionStart may inject ~1-2K
                                   #   chars of project context into
                                   #   the first turn of each session
                                   #   (this is what actually reaches
                                   #   the model — Claude Code treats
                                   #   SessionStart stdout as context)
                                   # - PreToolUse fires /agentmemory/enrich
                                   #   on every file-touching tool call
                                   #   (resource cleanup, not a token
                                   #   fix — PreToolUse stdout is debug
                                   #   log only per Claude Code docs)
                                   # Observations are still captured via
                                   # PostToolUse regardless of this flag.
# GRAPH_EXTRACTION_ENABLED=false
# CONSOLIDATION_ENABLED=true
# LESSON_DECAY_ENABLED=true
# OBSIDIAN_AUTO_EXPORT=false
# AGENTMEMORY_EXPORT_ROOT=~/.agentmemory
# CLAUDE_MEMORY_BRIDGE=false
# SNAPSHOT_ENABLED=false

# Team
# TEAM_ID=
# USER_ID=
# TEAM_MODE=private

# Tool visibility: "core" (7 tools) or "all" (43 tools)
# AGENTMEMORY_TOOLS=core

---

109 endpoints on port 3111. The REST API binds to 127.0.0.1 by default. Protected endpoints require Authorization: Bearer <secret> when AGENTMEMORY_SECRET is set, and mesh sync endpoints require AGENTMEMORY_SECRET on both peers.

Key endpoints

Method	Path	Description
GET	/agentmemory/health	Health check (always public)
POST	/agentmemory/session/start	Start session + get context
POST	/agentmemory/session/end	End session
POST	/agentmemory/observe	Capture observation
POST	/agentmemory/smart-search	Hybrid search
POST	/agentmemory/context	Generate context
POST	/agentmemory/remember	Save to long-term memory
POST	/agentmemory/forget	Delete observations
POST	/agentmemory/enrich	File context + memories + bugs
GET	/agentmemory/profile	Project profile
GET	/agentmemory/export	Export all data
POST	/agentmemory/import	Import from JSON
POST	/agentmemory/graph/query	Knowledge graph query
POST	/agentmemory/team/share	Share with team
GET	/agentmemory/audit	Audit trail

Full endpoint list: src/triggers/api.ts

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Built on iii-engine's three primitives — no Express, no Postgres, no Redis.

118 source files · ~21,800 LOC · 646 tests · 123 functions · 34 KV scopes

What iii-engine replaces

Traditional stack	agentmemory uses
Express.js / Fastify	iii HTTP Triggers
SQLite / Postgres + pgvector	iii KV State + in-memory vector index
SSE / Socket.io	iii Streams (WebSocket)
pm2 / systemd	iii-engine worker management
Prometheus / Grafana	iii OTEL + health monitor

npm run dev               # Hot reload
npm run build             # Production build
npm test                  # 646 tests (~1.7s)
npm run test:integration  # API tests (requires running services)

Prerequisites: Node.js >= 20, iii-engine or Docker

Apache-2.0