agentmemory

Sun, 10 May 2026 17:00:21 +0800

rohitg00/agentmemory

Your coding agent remembers everything. No more re-explaining. Built on iii engine
Persistent memory for Claude Code, Cursor, Gemini CLI, Codex CLI, pi, 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 • iii Console • Powered by iii • Config • API

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.}

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.

`1`	`npx @agentmemory/agentmemory`

New in v0.9.0 — Landing site at agent-memory.dev, filesystem connector (@agentmemory/fs-watcher), standalone MCP now proxies to the running server so hooks and the viewer agree, audit policy codified across every delete path, health stops flagging memory_critical on tiny Node processes. Full notes in CHANGELOG.md.

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.

	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

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.

Session Replay

Every session agentmemory records is replayable. Open the viewer, pick the Replay tab, and scrub through the timeline: prompts, tool calls, tool results, and responses render as discrete events with play/pause, speed control (0.5×–4×), and keyboard shortcuts (space to toggle, arrows to step).

Already have older Claude Code JSONL transcripts you want to bring in?

# Import everything under the default ~/.claude/projects
npx @agentmemory/agentmemory import-jsonl

# Or import a single file
npx @agentmemory/agentmemory import-jsonl ~/.claude/projects/-my-project/abc123.jsonl

Imported sessions show up in the Replay picker alongside native ones. Under the hood each entry routes through the mem::replay::load, mem::replay::sessions, and mem::replay::import-jsonl iii functions — no side-channel servers.

Upgrade / Maintenance

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

`1`	`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 51 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 memory-slot integration, copy `integrations/openclaw` to `~/.openclaw/extensions/agentmemory` and enable `plugins.slots.memory = "agentmemory"` in `~/.openclaw/openclaw.json`.

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"]

memory:
  provider: agentmemory

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/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 memory plugin
Gemini CLI	`gemini mcp add agentmemory npx -y @agentmemory/mcp --scope user`
Codex CLI	`codex mcp add agentmemory -- npx -y @agentmemory/mcp` or add `[mcp_servers.agentmemory]` to `.codex/config.toml`
pi	Copy `integrations/pi` to `~/.pi/agent/extensions/agentmemory` and restart pi
OpenCode	Add to `opencode.json`: `{"mcp": {"agentmemory": {"type": "local", "command": ["npx", "-y", "@agentmemory/mcp"], "enabled": true}}}`
Hermes Agent	Add to `~/.hermes/config.yaml` with `memory.provider: agentmemory` 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

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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. agentmemory currently pins iii-engine to v0.11.2 — v0.11.6 introduces a new sandbox-everything-via-iii worker add model that agentmemory hasn’t been refactored for yet. Pin lifts once the refactor lands. Override with AGENTMEMORY_III_VERSION=<version> if you’ve migrated to the sandbox model manually.

macOS arm64: mkdir -p ~/.local/bin && curl -fsSL https://github.com/iii-hq/iii/releases/download/iii/v0.11.2/iii-aarch64-apple-darwin.tar.gz | tar -xz -C ~/.local/bin && chmod +x ~/.local/bin/iii
macOS x64: swap aarch64-apple-darwin for x86_64-apple-darwin
Linux x64: swap for x86_64-unknown-linux-gnu
Linux arm64: swap for aarch64-unknown-linux-gnu
Windows: download iii-x86_64-pc-windows-msvc.zip from iii-hq/iii releases v0.11.2, extract iii.exe, add to PATH

Or use Docker (the bundled docker-compose.yml pulls iiidev/iii:0.11.2). 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/tag/iii%2Fv0.11.2 in your browser
#    (we pin to v0.11.2 until agentmemory refactors for the new sandbox
#     model that engine v0.11.6+ requires)
# 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
# Should print: 0.11.2

# 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:

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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.

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

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

Stop / SessionEnd hook fires
  -> Summarize session
  -> Knowledge graph extraction (if GRAPH_EXTRACTION_ENABLED=true)
  -> Slot reflection (if SLOT_REFLECT_ENABLED=true)

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

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):

`1`	`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

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

50 Tools

Core tools (always available)

Tool	Description
`memory_recall`	Search past observations
`memory_compress_file`	Compress markdown files while preserving structure
`memory_save`	Save an insight, decision, or pattern
`memory_patterns`	Detect recurring patterns
`memory_smart_search`	Hybrid semantic + keyword search
`memory_file_history`	Past observations about specific files
`memory_sessions`	List recent sessions
`memory_timeline`	Chronological observations
`memory_profile`	Project profile (concepts, files, patterns)
`memory_export`	Export all memory data
`memory_relations`	Query relationship graph

Extended tools (50 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:

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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
    }
  }
}

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

`1`	`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').

The viewer at :3113 shows what your agent remembered. The iii console shows what your agent did — every memory op as an OpenTelemetry trace, every KV entry editable, every function invocable, every stream tappable. Two windows on the same memory: one product-shaped, one engine-shaped.

Watch a memory_smart_search fire and see the BM25 scan → embedding lookup → RRF fusion → reranker as a waterfall. Edit a stuck consolidation timer in the KV browser. Replay a PostToolUse hook with a tweaked payload. Pin the WebSocket stream and watch observations land live.

agentmemory ships this for free because every function, trigger, state scope, and stream is an iii primitive — nothing custom, nothing to instrument.

Workers page: every connected worker — including agentmemory itself — with PID, function count, runtime, and last-seen.

Already installed. The console ships with iii — no separate installer.

Launch alongside agentmemory:

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# agentmemory viewer holds port 3113, so run the console on 3114.
# Engine REST (3111), WebSocket (3112), and bridge (49134) defaults match agentmemory.
iii console --port 3114

Then open http://localhost:3114. Add --enable-flow for the experimental architecture-graph page.

Override engine endpoints only if you’ve moved them:

iii console --port 3114 \
  --engine-port 3111 \
  --ws-port 3112 \
  --bridge-port 49134

What you can do from the console:

Page	Use it to
Workers	See every connected worker and its live metrics — including the agentmemory worker itself.
Functions	Invoke any of agentmemory’s functions directly with a JSON payload — handy for testing `memory.recall`, `memory.consolidate`, `graph.query` without wiring a client.
Triggers	Replay HTTP, cron, event, and state triggers — fire the consolidation cron manually, retry an HTTP route, emit a state change.
States	KV browser with full CRUD — sessions, memory slots, lifecycle timers, embeddings index — edit values in place.
Streams	Live WebSocket monitor for memory writes, hook events, and observation updates as they flow through iii streams.
Queues	Durable queue topics + dead-letter management. Replay or drop failed embedding / compression jobs.
Traces	OpenTelemetry waterfall / flame / service-breakdown views. Filter by `trace_id` to see exactly which functions, DB calls, and embedding requests a single `memory.search` produced.
Logs	Structured OTEL logs filtered and correlated to trace/span IDs.
Config	Runtime configuration — see exactly which workers, providers, and ports your engine is running with.
Flow	(Optional, `--enable-flow`) Interactive architecture graph of every worker, trigger, and stream.

Traces: waterfall / flame / service breakdown for every memory operation.

Traces are already on:

iii-config.yaml ships with the iii-observability worker enabled (exporter: memory, sampling_ratio: 1.0, metrics + logs). No extra config needed — the moment agentmemory starts, every memory operation emits a trace span and a structured log the console can read.

If you want to export to Jaeger/Honeycomb/Grafana Tempo instead, change exporter: memory to exporter: otlp and set the collector endpoint per iii’s observability docs.

Heads-up: no auth is enforced on the console itself — keep it bound to 127.0.0.1 (the default) and never expose it publicly.

agentmemory is already a running iii instance. Functions, triggers, KV state, streams, OTEL traces — all of it is iii primitives. You didn’t install Postgres, Redis, Express, pm2, or Prometheus, because iii replaces them.

That means one more command extends agentmemory with an entire new capability.

Extend agentmemory with one command

iii worker add iii-pubsub          # fan memory writes out to every connected instance
iii worker add iii-cron            # scheduled consolidation, decay sweeps, snapshot rotation
iii worker add iii-queue           # durable retries for embedding + compression jobs
iii worker add iii-observability   # OTEL traces on every memory op (default on)
iii worker add iii-sandbox         # run recalled code inside an isolated microVM
iii worker add iii-database        # swap in a SQL-backed state adapter
iii worker add mcp                 # generic MCP host alongside the agentmemory MCP

Each iii worker add registers new functions and triggers into the same engine agentmemory is already running on. The viewer and console pick them up immediately — no reload, no new integration, no new container.

`iii worker add`	What you get on top of agentmemory
`iii-pubsub`	Multi-instance memory: every `remember` fans out, every `search` reads the union
`iii-cron`	Scheduled lifecycle — nightly consolidation, weekly snapshots, decay on a fixed clock
`iii-queue`	Durable retries: failed embedding + compression jobs survive restart, no lost observations
`iii-observability`	OTEL traces, metrics, logs on every function — wired in `iii-config.yaml` from day one
`iii-sandbox`	Code that came out of `memory_recall` runs inside a throwaway VM, not your shell
`iii-database`	SQL-backed state adapter when you outgrow the in-memory KV defaults
`mcp`	Stand up extra MCP servers next to agentmemory’s, share the same engine

Full registry: workers.iii.dev. Every worker there composes through the same primitives agentmemory uses — and the agentmemory you already have is one of them.

What iii 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 supervision
Prometheus / Grafana	iii OTEL + health monitor
Custom plugin systems	`iii worker add <name>`

118 source files · ~21,800 LOC · 800 tests · 123 functions · 34 KV scopes — all on three primitives. No agentmemory plugin install. The plugin system is iii itself.

LLM Providers

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

Provider	Config	Notes
No-op (default)	No config needed	LLM-backed compress/summarize is DISABLED. Synthetic BM25 compression + recall still work. See `AGENTMEMORY_ALLOW_AGENT_SDK` below if you used to rely on the Claude-subscription fallback.
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
Claude subscription fallback	`AGENTMEMORY_ALLOW_AGENT_SDK=true`	Opt-in only. Spawns `@anthropic-ai/claude-agent-sdk` sessions — used to cause unbounded Stop-hook recursion (#149 follow-up) so it is no longer the default.

Environment Variables

Create ~/.agentmemory/.env:

# LLM provider (pick one — default is the no-op provider: no LLM calls)
# ANTHROPIC_API_KEY=sk-ant-...
# ANTHROPIC_BASE_URL=...              # Optional: Anthropic-compatible proxy / Azure
# GEMINI_API_KEY=...
# OPENROUTER_API_KEY=...
# MINIMAX_API_KEY=...
# Opt-in Claude-subscription fallback (spawns @anthropic-ai/claude-agent-sdk);
# leave OFF unless you understand the Stop-hook recursion risk (#149 follow-up):
# AGENTMEMORY_ALLOW_AGENT_SDK=true

# Embedding provider (auto-detected, or override)
# EMBEDDING_PROVIDER=local
# VOYAGE_API_KEY=...
# OPENAI_API_KEY=sk-...
# OPENAI_BASE_URL=https://api.openai.com   # Override for Azure / vLLM / LM Studio / proxies
# OPENAI_EMBEDDING_MODEL=text-embedding-3-small
# OPENAI_EMBEDDING_DIMENSIONS=1536        # Required when the model is not in the known-models table

# 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_SLOTS=false          # OFF by default. Editable pinned
                                   # memory slots — persona,
                                   # user_preferences, tool_guidelines,
                                   # project_context, guidance,
                                   # pending_items, session_patterns,
                                   # self_notes. Size-limited; agent
                                   # edits via memory_slot_* tools.
                                   # Pinned slots addressable for
                                   # SessionStart injection.
# AGENTMEMORY_REFLECT=false        # OFF by default. Requires SLOTS=on.
                                   # Stop hook fires mem::slot-reflect:
                                   # scans recent observations, auto-
                                   # appends TODOs to pending_items,
                                   # counts patterns in
                                   # session_patterns, records touched
                                   # files in project_context. Fire-
                                   # and-forget; does not block.
# 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" (8 tools) or "all" (51 tools)
# AGENTMEMORY_TOOLS=core

107 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

npm run dev               # Hot reload
npm run build             # Production build
npm test                  # 800 tests (~1.7s)
npm run test:integration  # API tests (requires running services)

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

Apache-2.0

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