Install

Requirements:

• Node.js 20 or newer with npm and npx (node -v, npm -v, and npx -v).
• macOS/Linux automatic iii-engine installation also needs curl, a POSIX sh, and tar. Minimal images such as node:20-slim may not include them.
• Native Windows requires the pinned iii-engine v0.22.1 iii.exe to be installed manually. WSL2 or Docker Desktop are the other supported paths.

Canonical fresh-install command:

npx -y @agentmemory/agentmemory@latest

The first run is an interactive setup: pick the agents to wire (Claude Code, Cursor, Codex, Gemini CLI, OpenCode, ...), pick an LLM provider or stay keyless, and it seeds the config, starts the memory server and its pinned iii engine, and offers to install globally so the bare agentmemory command works everywhere afterward. -y accepts npx's package prompt and @latest avoids a stale cached release. A provider makes LLM features available, but LLM-written observation compression starts only when AGENTMEMORY_AUTO_COMPRESS=true is also set.

Keyless mode disables vector embeddings. memory_recall (the mem::search path) uses BM25, while memory_smart_search can also fuse structural graph matches when graph data already exists. For free on-device semantic recall, set EMBEDDING_PROVIDER=local in ~/.agentmemory/.env and restart. The first embedding request downloads Xenova/all-MiniLM-L6-v2; inference runs locally after that initial model download.

The local runtime uses four ports: 3111 for REST/MCP HTTP, 3112 for iii streams, 3113 for the viewer, and 49134 for the iii worker WebSocket. Persistent iii state lives in ~/Library/Application Support/agentmemory on macOS, $XDG_DATA_HOME/agentmemory or ~/.local/share/agentmemory on Linux, and %APPDATA%\agentmemory on Windows. Use --data-dir <path> or AGENTMEMORY_DATA_DIR to override it, and reuse the same value on every restart. For backward compatibility, an existing ./data/state_store.db or ./data/iii-config.yaml takes precedence over the platform default for instance 0; an explicit flag or environment override still wins.

Then prove recall works and give your agent its skills:

npx -y @agentmemory/agentmemory@latest demo  # seed sample sessions + exercise recall
npx skills add rohitg00/agentmemory -y   # 17 native skills so your agent knows when to reach for memory

The keyword searches should hit in default keyless mode through BM25. The demo's database performance optimization query is intentionally semantic and can return zero until an embedding provider is configured.

Prefer to let a coding agent do the whole thing? Hand it one instruction:

Retrieve and follow the instructions at: https://raw.githubusercontent.com/rohitg00/agentmemory/main/INSTALL_FOR_AGENTS.md

Wire more agents any time with agentmemory connect <agent> — 20 adapters listed at Works with every agent. Full command reference at Quick Start.

The fast path is WSL2. Native Windows engine setup requires the pinned v0.22.1 ZIP to be downloaded and iii.exe extracted manually; the CLI does not auto-extract it. Docker Desktop is also supported. See the Windows notes for the step-by-step.

npm install -g @agentmemory/agentmemory@latest

The npx command above remains the canonical fresh-install path and avoids global-prefix permission issues.

npx caches per version. Force the latest with npx -y @agentmemory/agentmemory@latest, or clear the cache once with rm -rf ~/.npm/_npx (macOS/Linux; on Windows delete %LOCALAPPDATA%\npm-cache\_npx).

agentmemory pins iii-engine v0.22.1 and won't attach to a different version (the worker can't speak another engine's protocol). Stop the other engine, then run npx -y @agentmemory/agentmemory@latest. It installs and runs the pinned v0.22.1 in ~/.agentmemory/bin, leaving your own iii untouched.

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

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, with no re-explaining and no copy-pasting.

npx -y @agentmemory/agentmemory@latest

By default, agentmemory stores iii-engine state outside the repository you start it from: ~/Library/Application Support/agentmemory on macOS, $XDG_DATA_HOME/agentmemory or ~/.local/share/agentmemory on Linux, and %APPDATA%\agentmemory on Windows. An existing legacy ./data/state_store.db or ./data/iii-config.yaml is reused for instance 0 before that platform default. To choose a location explicitly, pass --data-dir <path> or set AGENTMEMORY_DATA_DIR; either explicit setting takes precedence over legacy discovery:

npx -y @agentmemory/agentmemory@latest --data-dir ~/.agentmemory-projects/main
AGENTMEMORY_DATA_DIR=~/.agentmemory-projects/main npx -y @agentmemory/agentmemory@latest

Native and Docker launches use this same resolved host directory; Docker bind-mounts it at /data. --instance 1 appends instance-1 to the resolved directory and selects the separate default port quartet 3211/3212/3213/49234.

Latest release notes: CHANGELOG.md.

Retrieval Accuracy

coding-agent-life-v1 (in-house corpus, sandbox-reproducible)

| Adapter | P@5 | R@5 | Top-5 hit rate | p50 latency | |---|---|---|---|---| | agentmemory hybrid | 0.240 | 1.000 | 15 / 15 | 14 ms | | grep baseline | 0.227 | 0.967 | 15 / 15 | 0 ms |

100% top-5 hit rate at the P@5 math ceiling for this corpus (0.240, see scorecard). Hybrid retrieves every gold session; grep misses 1 of 2 gold on the multi-session temporal query. Lift is recall + temporal, not aggregate precision. This benchmark is small and gold-sparse; the larger LongMemEval-S below differentiates better. Full per-type breakdown + correction note: docs/benchmarks/2026-05-20-coding-agent-life-v1.md.

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 covering agentmemory vs mem0, Letta, Khoj, supermemory, TencentDB Agent Memory, MemPalace, Zep/Graphiti, Cognee, Hippo.

Reproduce locally: eval/README.md, an adapter-pluggable harness for LongMemEval _s (public 500-Q) + coding-agent-life-v1 (in-house 15-session corpus). Grep / vector / agentmemory adapters score side-by-side, NDJSON output, published scorecards land in docs/benchmarks/.

Pairs with codegraph, Understand Anything, and Graphify. Code-graph indexing, multi-agent build pipelines, and broader knowledge graphs across docs / PDFs / images / videos. agentmemory remembers the work; those three projects light up the rest of the context layer. Recipes + question-routing table: docs/recipes/pairings.md.

Benchmark note: only agentmemory's R@5 is our own measured result (LongMemEval-S, reproducible from benchmark/COMPARISON.md). The mem0 and Letta figures are their published LoCoMo numbers (a different dataset); the MemPalace, supermemory, TencentDB (PersonaMem), and oracleagentmemory figures are vendor self-reported claims we have not independently reproduced (oracleagentmemory's run used GPT-5.5 against an Oracle AI Database). Shown side by side for ballpark only, not a head-to-head on identical data. Star counts are approximate and drift over time.

Newer entrants worth knowing, compared in depth in benchmark/COMPARISON.md:

| System | ⭐ | Angle | |--------|---|-------| | Zep / Graphiti | 30K | Temporal knowledge graph; strongest published temporal-query results (LongMemEval 63.8%), but graph builds asynchronously so fresh facts can lag | | Cognee | 30K | Document-to-knowledge-graph ingestion, Python-only, built for structured entity extraction rather than session capture |

None of these auto-capture from coding-agent hooks, ship a local-first viewer, or run keyless — the combination agentmemory is built around.

Compatibility: this release targets iii-sdk 0.22.1 and pins iii-engine v0.22.1.

Try it in 30 seconds

# Terminal 1: start the server
npx -y @agentmemory/agentmemory@latest

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

demo seeds 3 realistic sessions (JWT auth, N+1 query fix, rate limiting) and runs searches against them. Keyless installs disable vectors, so the mem::search keyword queries should hit through BM25 while database performance optimization can return zero. smart-search may additionally return structural graph matches when graph data exists. To make the semantic query find the N+1 fix through vectors, set EMBEDDING_PROVIDER=local, restart, and allow the first model download to finish.

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

Validate a fresh install and restart persistence

With the server running, validate REST, health, the viewer, and the iii-backed runtime status:

curl -fsS http://localhost:3111/agentmemory/livez
curl -fsS http://localhost:3111/agentmemory/health
curl -fsS -o /dev/null http://localhost:3113/
npx -y @agentmemory/agentmemory@latest status

The startup ready panel accounts for all four ports: REST/MCP HTTP on 3111, iii streams on 3112, the viewer on 3113, and the iii worker WebSocket on 49134. status confirms agentmemory health and the active provider/embedding mode. Save a probe and confirm it is searchable:

curl -fsS -X POST http://localhost:3111/agentmemory/remember \
  -H 'Content-Type: application/json' \
  -d '{"content":"agentmemory restart persistence probe","concepts":["install-check"]}'

curl -fsS -X POST http://localhost:3111/agentmemory/smart-search \
  -H 'Content-Type: application/json' \
  -d '{"query":"restart persistence probe","limit":5}'

Then run npx -y @agentmemory/agentmemory@latest stop, start the canonical command again in Terminal 1, wait for /agentmemory/livez, and repeat the search. The probe must still be returned. If you selected a custom --data-dir, pass the same directory on the restart.

Everyday commands

Install and setup live in Install above (the first run walks you through it). Day to day:

agentmemory                    # start the server
agentmemory stop               # stop it cleanly
agentmemory connect <agent>    # wire another agent
agentmemory doctor             # interactive diagnostics + fix prompts
agentmemory remove             # uninstall everything we created

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 (0.5x to 4x), and keyboard shortcuts (space to toggle, arrows to step).

To bring in older Claude Code JSONL transcripts:

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

# Or import a single file
npx -y @agentmemory/agentmemory@latest 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, with no side-channel servers. Each imported transcript is indexed for search, stamped with origin channel import, and mined for a session crystal and lessons.

Heads-up if you rely on import-jsonl as your primary capture path: Claude Code's cleanupPeriodDays (in ~/.claude/settings.json, default 30) auto-deletes JSONL transcripts older than that window from ~/.claude/projects/. If you install agentmemory fresh on a months-old Claude Code history, anything older than 30 days is already gone before the first import. Either run import-jsonl on a cron, raise cleanupPeriodDays to something higher, or wire the auto-capture hooks (the default plugin install path) so each turn lands in agentmemory while the session is live and the JSONL cleanup stops mattering.

Upgrade / Maintenance

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

npx -y @agentmemory/agentmemory@latest upgrade

Warning: this command mutates the current workspace/runtime. It can update JavaScript dependencies and pull the pinned iiidev/iii:0.22.1 Docker image. It never installs an unpinned or newer iii engine.

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 -y @agentmemory/agentmemory@latest` in a separate terminal to start the memory server and its pinned iii engine. Then run `/plugin marketplace add rohitg00/agentmemory` and `/plugin install agentmemory` — the plugin registers all 12 hooks, 17 skills, AND auto-wires the `@agentmemory/mcp` stdio server via its `.mcp.json`, so you get 54 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. Keyless mode disables vectors: `memory_recall` uses BM25, and `memory_smart_search` can also use existing structural graph data. Set `EMBEDDING_PROVIDER=local` in `~/.agentmemory/.env` and restart to opt into on-device semantic recall.

Claude Code without the plugin install (MCP-standalone path)

If you wire agentmemory's MCP server through ~/.claude.json directly instead of using /plugin install, Claude Code never resolves ${CLAUDE_PLUGIN_ROOT} and you have to point hook scripts at absolute paths in ~/.claude/settings.json. Those paths typically embed the agentmemory version (e.g. ~/.codex/plugins/cache/agentmemory/agentmemory/0.9.22/scripts/…), so the next upgrade silently breaks every hook.

Workaround:

agentmemory connect claude-code --with-hooks

This merges the same hook commands into ~/.claude/settings.json with absolute paths resolved to the bundled plugin/ directory of the currently installed @agentmemory/agentmemory package. Re-run the command after upgrading agentmemory to refresh the paths. User entries in the same file are preserved; only previous agentmemory entries are replaced. Using the /plugin install path remains the recommended approach. For remote or protected deployments, launch Claude Code with AGENTMEMORY_URL and AGENTMEMORY_SECRET set. The plugin passes both values through to its bundled MCP server; when AGENTMEMORY_URL is empty, the MCP shim uses http://localhost:3111.

Codex CLI (Codex plugin platform)

# 1. start the memory server in a separate terminal
npx -y @agentmemory/agentmemory@latest

# 2. register the agentmemory marketplace and install the plugin
codex plugin marketplace add rohitg00/agentmemory
codex plugin add agentmemory@agentmemory

The Codex plugin ships from the same plugin/ directory as the Claude Code plugin. It registers:

• A bundled stdio MCP bridge to the running daemon, with no npm download or fallback store. See the local Codex guide to test an unreleased build.
• 6 lifecycle hooks: SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, PreCompact, Stop
• 9 invocable skills: /recall, /remember, /session-history, /forget, /recap, /handoff, /lesson, /commit-context, /commit-history, plus 8 reference skills the agent loads on demand (memory discipline, MCP tools, REST API, config, agents, hooks, architecture, and the skill-authoring guide)

Codex's hook engine injects CLAUDE_PLUGIN_ROOT into hook subprocesses (per codex-rs/hooks/src/engine/discovery.rs), so the same hook scripts work across both hosts without duplication. Subagent / SessionEnd / Notification / TaskCompleted / PostToolUseFailure events are Claude-Code-only and are not registered for Codex.

Codex hook trust and compatibility

Native plugin hook dispatch is verified with Codex CLI 0.150.1. Trust the plugin hooks before expecting capture. Desktop behavior depends on its bundled runtime; check /hooks and confirm a captured event before enabling a workaround.

If your host requires global hooks, mirror the commands into ~/.codex/hooks.json. When MCP is already wired, the current connector needs --force to reach hook installation:

agentmemory connect codex --with-hooks --force

This merges global hooks and rewrites the agentmemory MCP entry, preserving unrelated entries. Review any custom agentmemory endpoint settings before using --force. Re-run after upgrading to refresh script paths. Enable either native plugin hooks or global copies to avoid duplicate capture.

GitHub Copilot CLI

For VS Code agent mode, use the Copilot MCP and automatic-capture guide. The CLI connector does not configure VS Code.

# MCP-only wiring
agentmemory connect copilot-cli

# Alternatively, full hooks/skills plugin from the GitHub subdir
copilot plugin install rohitg00/agentmemory:plugin

agentmemory connect copilot-cli merges mcpServers.agentmemory into ~/.copilot/mcp-config.json (or $COPILOT_HOME/mcp-config.json when COPILOT_HOME is set) and preserves existing servers. On native Windows this is the only automated connect adapter; configure every other native Windows agent manually. WSL connect is supported only when the target agent is installed in that same WSL environment. Copilot picks up the MCP server on next launch or after /mcp. Install the plugin as well when you want the full hook/skill experience.

Install agentmemory for OpenClaw. Run `npx -y @agentmemory/agentmemory@latest` 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 54 memory tools:

{
  "mcpServers": {
    "agentmemory": {
      "command": "npx",
      "args": ["-y", "@agentmemory/mcp"],
      "env": {
        "AGENTMEMORY_URL": "http://localhost:3111"
      }
    }
  }
}

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/

Install agentmemory for Hermes. Run `npx -y @agentmemory/agentmemory@latest` 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 54 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 -y @agentmemory/agentmemory@latest

Native skills via npx skills add (50+ agents)

agentmemory ships 17 skills in the Claude-Code-style <dir>/SKILL.md format: 9 invocable action skills (remember, recall, recap, handoff, forget, lesson, commit-context, commit-history, session-history) and 8 reference skills the agent loads on demand (memory-discipline, agentmemory-mcp-tools, agentmemory-rest-api, agentmemory-config, agentmemory-agents, agentmemory-hooks, agentmemory-architecture, write-agentmemory-skill). The reference skills carry data tables generated from source, so they never drift. The skills CLI by vercel-labs auto-installs them into the calling agent's native skill directory across 50+ agents (Claude Code, Cursor, Cline, Continue, Droid, Warp, Codex, Antigravity, Kiro, OpenCode, Goose, Roo, Trae, Windsurf, and more):

npx skills add rohitg00/agentmemory -y          # auto-detects the calling agent
npx skills add rohitg00/agentmemory -y -a warp  # explicit agent
npx skills add rohitg00/agentmemory -y -a '*'   # install to every installed agent

This is complementary to agentmemory connect <agent>:

• agentmemory connect <agent> writes the MCP server config so the tools are available.
• npx skills add rohitg00/agentmemory installs the skills so the agent knows when to call them.

For the few agents the skills CLI doesn't cover yet (Zed v1.3.x and below), drop the 17 SKILL.md files under the agent's native skill directory yourself; the same format works everywhere.

Standard MCP block

The agentmemory entry is the same MCP server block across every host that uses the mcpServers shape (Cursor, Claude Desktop, Cline, Roo Code, Gemini CLI, OpenClaw):

"agentmemory": {
  "command": "npx",
  "args": ["-y", "@agentmemory/mcp"],
  "env": {
    "AGENTMEMORY_URL": "${AGENTMEMORY_URL}",
    "AGENTMEMORY_SECRET": "${AGENTMEMORY_SECRET}"
  }
}

Merge this entry into the existing mcpServers object in the host's config file; don't replace the file. If the file already has other servers, add agentmemory next to them as another key inside mcpServers. If mcpServers is missing entirely, paste the block inside { "mcpServers": { ... } }. The ${VAR} placeholders inherit AGENTMEMORY_URL / AGENTMEMORY_SECRET from the shell at MCP-server launch; unset vars pass empty strings and the shim falls back to http://localhost:3111. One wired entry covers both local and remote (k8s / reverse-proxied) deployments.

| Agent | Config file | Notes | |---|---|---| | Cursor (MCP only) | ~/.cursor/mcp.json | Merge into mcpServers, or agentmemory connect cursor. One-click deeplink also available on the website. | | Cursor (full plugin) | .cursor-plugin/ | Cursor Marketplace listing (submission in review) or Cursor Settings → Plugins → local checkout. Registers 7 auto-capture hooks (sessionStart, beforeSubmitPrompt, preToolUse, postToolUse, postToolUseFailure, stop, sessionEnd) + 17 skills + the MCP server, with AGENTMEMORY_URL / AGENTMEMORY_SECRET managed in Cursor's plugin dashboard. Works in the Cursor IDE and cursor-agent CLI; CLI print-mode prompts are backfilled from the session transcript at session end. | | Claude Desktop | claude_desktop_config.json (Application Support) | Merge into mcpServers. Restart Claude Desktop after editing. | | Cline / Roo Code / Kilo Code | Cline MCP settings (Settings UI → MCP Servers → Edit) | Same mcpServers block. | | Devin CLI (MCP + hooks) | ~/.config/devin/config.json | agentmemory connect devin merges the MCP entry; --with-hooks adds six native auto-capture hooks (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SessionEnd) with Devin'"'"'s lowercase tool matchers. Verify with devin mcp list and /hooks inside devin. | | Devin CLI (full plugin) | plugin/.devin-plugin/ | devin plugins install ./plugin from a checkout registers all 17 skills as /agentmemory:<skill> slash commands plus the MCP server. Devin plugin hooks cannot fire SessionStart/SessionEnd, so pair it with connect devin --with-hooks for full session capture. | | Devin (cloud) | Settings → Connections → MCP servers | Add a custom MCP (STDIO): command npx, args -y @agentmemory/mcp@latest, env AGENTMEMORY_URL pointing at a network-reachable agentmemory deployment plus AGENTMEMORY_SECRET (cloud sessions cannot reach localhost — see deploy/). Store the secret in Devin Secrets, then use "Test listing tools" to verify all 54 tools appear. | | Gemini CLI | ~/.gemini/settings.json | gemini mcp add agentmemory npx -y @agentmemory/mcp --scope user (auto-merges). | | GitHub Copilot CLI (MCP only) | ~/.copilot/mcp-config.json | agentmemory connect copilot-cli merges mcpServers.agentmemory; Copilot picks it up on next launch or /mcp. | | GitHub Copilot CLI (full plugin) | Copilot plugin install | copilot plugin install rohitg00/agentmemory:plugin for the plugin from the GitHub subdir. | | OpenClaw | OpenClaw MCP config | Same mcpServers block. Deeper: openclaw plugins install ./integrations/openclaw claims OpenClaw's memory slot (auto-switches from memory-core); set plugins.entries.agentmemory.hooks.allowConversationAccess=true or turn capture is silently blocked. See integrations/openclaw. | | Codex CLI (MCP only) | .codex/config.toml | TOML shape: codex mcp add agentmemory -- npx -y @agentmemory/mcp, or add [mcp_servers.agentmemory] manually. | | Codex CLI (full plugin) | Codex plugin marketplace | codex plugin marketplace add rohitg00/agentmemory then codex plugin add agentmemory@agentmemory. Registers MCP + 6 lifecycle hooks + 17 skills. Trust hooks and verify capture in your host; see Codex setup and validation. | | OpenCode (MCP only) | opencode.json | Different shape: top-level mcp key, command as array: {"mcp": {"agentmemory": {"type": "local", "command": ["npx", "-y", "@agentmemory/mcp"], "enabled": true}}}. | | OpenCode (full plugin) | plugin/opencode/ | 22 auto-capture hooks covering session lifecycle, messages, tools, errors. Project attribution is per-session, so one OpenCode process spanning several repositories files each session under its own project. Two slash commands (/recall, /remember). Copy plugin/opencode/ into your OpenCode workspace and add the plugin entry to opencode.json. See plugin/opencode/README.md for the full hook table + gap analysis. | | pi | ~/.pi/agent/extensions/agentmemory | agentmemory connect pi installs the bundled extension into pi's auto-discovery directory (recall on agent start, capture on agent end, memory_search / memory_save / memory_health tools, /agentmemory-status). /reload in a running pi picks it up. integrations/pi is also a pi package (pi install ./integrations/pi from a checkout). | | Hermes Agent | ~/.hermes/config.yaml | cp -r integrations/hermes ~/.hermes/plugins/agentmemory + memory.provider: agentmemory gives the 6-hook memory provider (prefetch, turn capture, session end, pre-compress, MEMORY.md mirroring, system prompt block). Validate with hermes plugins doctor and hermes memory status. See integrations/hermes. | | Qwen Code | ~/.qwen/settings.json | agentmemory connect qwen writes the standard mcpServers block. Hook payload is field-compatible with Claude Code, so the existing 12-hook scripts work without modification; wire them via the hooks section in the same settings.json. | | Antigravity IDE / 2.0 | ~/.gemini/config/mcp_config.json | agentmemory connect antigravity --with-hooks installs MCP and capture hooks in the shared customization directory. See Antigravity setup and limits. | | Antigravity CLI (agy) | ~/.gemini/config/mcp_config.json | agentmemory connect antigravity-cli --with-hooks uses the same MCP and hook configuration as current IDE versions. Existing installations should refresh with --force; see the upgrade notes. | | Kiro | ~/.kiro/settings/mcp.json | agentmemory connect kiro writes the user-level config. Workspace overrides go in .kiro/settings/mcp.json next to your code. | | Warp | ~/.warp/.mcp.json | agentmemory connect warp writes the standard mcpServers block. Warp also auto-discovers skills from .claude/skills/; once the Claude Code plugin is installed the 8 agentmemory skills (remember, recall, recap, handoff, forget, commit-context, commit-history, session-history) appear natively in Warp's slash-command palette. | | Cline (CLI) | ~/.cline/mcp.json | agentmemory connect cline writes the standard mcpServers block. VS Code extension users: paste the same block via Cline Settings → MCP Servers → Edit JSON. | | Continue.dev | ~/.continue/config.yaml (preferred) or config.json (legacy) | agentmemory connect continue creates config.yaml from scratch when neither exists, or modifies existing config.json. If you already have config.yaml the adapter prints the exact block to paste under mcpServers:; it won't silently rewrite your yaml because preserving comments and anchors safely needs a YAML parser the package doesn't ship. Continue uses array form (not object) for mcpServers. | | Zed | ~/.config/zed/settings.json | agentmemory connect zed writes under context_servers (Zed's key, NOT mcpServers). Remote MCP servers can be wired via {"url": "..."} instead. | | Droid (Factory.ai) | ~/.factory/mcp.json | agentmemory connect droid writes the standard mcpServers block. Project-scoped overrides go in <repo>/.factory/mcp.json. Pass --with-hooks for native auto-capture. | | DeepSeek Harness | $DSH_HOME/cordis.patch.yml | agentmemory connect dsh appends an @deepseek-ai/dsh-mcp-client row to the home-level patch layer every Harness profile loads; tools register as mcp__agentmemory__*. Pass --with-hooks to also wire auto-capture: the bundled Claude Code hook scripts run through Harness's first-party @deepseek-ai/dsh-hooks-claude-code bridge (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop) via a manifest written to $DSH_HOME/agentmemory.hooks.json. Defaults to ~/.dsh when DSH_HOME is unset. | | Goose | Goose MCP settings UI | Same mcpServers block; use goose configure → Add Extension → MCP. Direct YAML edit at ~/.config/goose/config.yaml is supported but the schema uses extensions: + cmd (not mcpServers: + command). | | Aider | n/a | Talk to the REST API directly: curl -X POST http://localhost:3111/agentmemory/smart-search -d '{"query": "auth"}'. | | Any agent (32+) | n/a | npx skillkit install agentmemory auto-detects the host and merges. |

Sandboxed MCP clients (Flatpak / Snap / restrictive containers) that can't reach the host's localhost: also set "AGENTMEMORY_FORCE_PROXY": "1" in the env block, and point AGENTMEMORY_URL at a route the sandbox can actually reach (e.g. your LAN IP).

Programmatic access (Python / Rust / Node)

agentmemory registers its core operations as iii functions (mem::remember, mem::observe, mem::context, mem::smart-search, mem::forget). Any language with an iii SDK can call them directly over ws://localhost:49134, with no separate REST client per language.

pip install iii-sdk         # Python
cargo add iii-sdk           # Rust
npm  install iii-sdk        # Node

from iii import register_worker

iii = register_worker("ws://localhost:49134")
iii.connect()

iii.trigger({
    "function_id": "mem::smart-search",
    "payload": {"project": "demo", "query": "how do tokens refresh"},
})

Worked example: examples/python/ (quickstart + observation/recall flow). REST on :3111 remains available for hosts without an iii runtime.

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 the pinned binary is already installed, or uses Docker Compose when selected. REST, streams, and the viewer bind to 127.0.0.1 by default. The automatic macOS/Linux binary path requires curl, a POSIX sh, and tar.

Install iii-engine manually. agentmemory currently pins iii-engine to v0.22.1, the same release as its iii-sdk dependency; the worker speaks that engine's wire protocol, and 0.20.0 reorganized the SDK surface, so the two move together in agentmemory releases. Override with AGENTMEMORY_III_VERSION=<version> if you run your own engine and know it matches.

• macOS arm64: mkdir -p ~/.local/bin && curl -fsSLo iii.tar.gz https://github.com/iii-hq/iii/releases/download/iii/v0.22.1/iii-aarch64-apple-darwin.tar.gz && echo "2b309019b909a896cae874dc947e2cdf877b4f3c51dd026b79850af858517fa4 iii.tar.gz" | shasum -a 256 -c - && tar -xzf iii.tar.gz -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 v0.22.1 and extract iii.exe to %USERPROFILE%\.agentmemory\bin\iii.exe

Every archive has a matching .sha256 file on the release page; when you swap the platform, use that file's hash in the check above (on Windows: Get-FileHash). The automatic installer in npx @agentmemory/agentmemory pins these hashes and refuses an archive that does not match.

Or use Docker (the bundled docker-compose.yml pulls iiidev/iii:0.22.1). 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 pinned iii-engine v0.22.1 runtime as a background process. The CLI does not auto-extract the Windows ZIP, so native Windows users must install iii.exe manually, use WSL2, or choose Docker Desktop.

Native Windows automated MCP wiring supports only agentmemory connect copilot-cli. For Claude Code, Codex, Cursor, and every other native Windows agent, copy the manual MCP block from Other agents into that agent's Windows config. Running connect in WSL is appropriate only when the target agent is also installed in the same WSL environment; it does not edit a Windows-host agent's configuration.

Option A: prebuilt Windows binary (recommended)

# 1. Open https://github.com/iii-hq/iii/releases/tag/iii%2Fv0.22.1 in your browser
#    (agentmemory pins the engine to the same release as its iii-sdk;
#     v0.22.1 is the current pair)
# 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 to agentmemory's private engine directory:
New-Item -ItemType Directory -Force "$HOME\.agentmemory\bin"
# Copy iii.exe to $HOME\.agentmemory\bin\iii.exe
# 4. Verify:
& "$HOME\.agentmemory\bin\iii.exe" --version
# Should print: 0.22.1

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

Option B: Docker Desktop

# 1. Install Docker Desktop for Windows
# 2. Start Docker Desktop and make sure the engine is running
# 3. Select Docker explicitly and run agentmemory:
$env:AGENTMEMORY_USE_DOCKER = "1"
npx -y @agentmemory/agentmemory@latest

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

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

| Symptom | Fix | |---|---| | The engine process started but the REST API never responded. | Confirm all four derived ports are free, verify the pinned iii.exe stayed alive, then re-run with --verbose and inspect the captured engine 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: the iii engine is a prebuilt binary, not a cargo crate, so don't try to cargo install it. (The iii SDKs are published on crates.io, npm, and PyPI, but agentmemory doesn't need them.) Supported engine install methods are all pinned to v0.22.1: the prebuilt binary above, agentmemory's macOS/Linux auto-install path (curl, POSIX sh, and tar required), and the Docker image iiidev/iii:0.22.1. A bare upstream install.sh | sh installs the latest engine, which agentmemory does not support. Use npx -y @agentmemory/agentmemory@latest; on macOS/Linux it fetches the pinned engine into ~/.agentmemory/bin.

One-click templates for managed hosts. Each one ships a self-contained Dockerfile that pulls @agentmemory/agentmemory from npm and copies the iii engine binary in from the official iiidev/iii Docker Hub image; no pre-built agentmemory image required. Persistent storage mounts at /data; the first-boot entrypoint overwrites the npm-bundled iii config (which binds 127.0.0.1) with a deploy-tuned one that binds 0.0.0.0 and uses absolute /data paths, generates the HMAC secret, then drops privileges from root to node via gosu before exec'ing the agentmemory CLI.

Render's one-click deploy button requires render.yaml at the repository root, which we deliberately keep clean. Use the Render Blueprint flow documented in deploy/render/ to point at the in-repo blueprint manually.

Full setup details (HMAC capture, viewer SSH tunnel, rotation, backup, cost floors) live in deploy/:

• deploy/fly: single machine with auto_stop_machines = "stop"; cheapest idle.
• deploy/railway: Hobby plan flat fee, volume in the dashboard.
• deploy/render: Blueprint flow, automatic disk snapshots on paid plans.
• deploy/coolify: self-hosted on your own VPS via Coolify; same Docker Compose stack, you own the host and the data.

Only port 3111 is published. The viewer on 3113 stays bound to loopback inside the container; every template's README documents the SSH-tunnel pattern for reaching it.

Every coding agent forgets everything when the session ends, and each new session starts with you re-explaining your stack. agentmemory runs in the background and removes that step.

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
  -> Synthetic compression by default
     (LLM-written compression only with a provider + AGENTMEMORY_AUTO_COMPRESS=true)
  -> Vector embedding when an embedding provider is active
  -> Index in BM25, plus vectors when enabled

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

Modeled on how human brains process memory, including 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, no manual effort | | Semantic search | BM25 + vector + knowledge graph with RRF fusion | | Memory evolution | Versioning, supersession, relationship graphs | | Recall hygiene | Superseded memory versions leave the search indexes; the version chain in KV keeps full history | | Near-duplicate hints | Saves report an advisory similarTo match when new content closely resembles an existing memory | | Per-agent scoping | agentId threads through save and recall across REST, MCP, and the search index, in shared or isolated mode | | Write-time provenance | Every observation and memory carries an immutable origin channel (user, agent, tool, import, or shared) stamped at capture, save, and import | | 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).

When a vector index is populated, mem::search (behind memory_recall) uses the hybrid BM25 + vector ranker. Without embeddings it uses BM25. smart-search can additionally fuse structural graph matches when graph data exists, including in keyless mode. Lesson recall runs on a dedicated in-memory BM25 index instead of scanning the whole corpus per query. Superseded memory versions are excluded from every recall path; the version chain keeps their history.

Vectors survive a crash or force-kill. The vector index is saved in buckets at most every AGENTMEMORY_INDEX_SAVE_INTERVAL_MS (10 minutes). Every vector added or removed in between is also written right away to a small pending log in the state store, and the next start replays it without calling the embedding provider. Each successful save empties the log. Documents that still have no vector after the replay are re-embedded in the background in batches of AGENTMEMORY_VECTOR_BACKFILL_MAX (500) until none are left, and a backfill that is stopped continues at the next start. /agentmemory/status and the viewer show the pending log size and the backfill state. Keyless installs write nothing.

BM25 tokenizes Greek, Cyrillic, Hebrew, Arabic, and accented Latin out of the box. For Chinese / Japanese / Korean memories, install the optional segmenters (npm install @node-rs/jieba tiny-segmenter) to split CJK runs into word-level tokens; without them, agentmemory soft-falls to whole-run tokenization and prints a one-time hint on stderr.

Embedding providers

Keyless installs disable vector embeddings: mem::search uses BM25, while smart-search can also use existing structural graph data. To opt into free on-device semantic embeddings, add this to ~/.agentmemory/.env and restart agentmemory: