akitaonrails/ai-memoryPublic

Long-term memory for AI coding agents. Quit Claude Code mid-task, start OpenAI Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions.

Your coding agent already has a memory feature. Claude Code takes its own notes, Cursor remembers some things, and every platform is adding more. All of them share the same walls: the notes live on one machine, belong to one agent, and vanish from view the moment you switch tools — or teammates.

ai-memory is what's on the other side of those walls.

• It follows you across agents. Twenty-plus harnesses — Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Grok, Devin, Kimi, Kiro, and more — feed one shared memory. Quit Claude Code mid-task, open Codex in the same directory, and the next agent picks up a real handoff: where you left off, what failed, what's still open. Handoffs are a protocol here, not a convention — typed, owned, claimed exactly once.

• It follows you across machines. Memory lives in a server you run — on the same laptop, a homelab box, or wherever — so the project you left on the desktop is the project you resume on the laptop. Same knowledge, same open questions.

• It works for a team. Point everyone at one server and what one person's sessions learn, everyone's agents can retrieve. Knowledge is shared per project; personal handoffs stay personal. Multi-user auth, per-person attribution, and an audit log are built in — not a paid tier.

• Your memory is plain markdown. The source of truth is a git-backed wiki of ordinary .md files: grep it, open it in Obsidian, edit it by hand, rsync it. The database is a derived index that can always be rebuilt from the files. No vector store to babysit, nothing held hostage in a binary blob.

• It captures the work itself, silently. Lifecycle hooks record what actually happened — prompts, tool calls, session boundaries — sanitized at a typed privacy boundary before anything is stored, then consolidated into readable pages. No "remember this" ceremony. And the default path uses zero LLM calls: capture, search, and handoffs all work with no API key at all.

• It ages gracefully, without an LLM. Memory decays on a schedule you can tune per tier, and the memory you actually use decays slower — open a page, search it, or reach it through a link and it earns its keep. When episodic notes go cold they can be compacted down to their durable facts (file paths, error codes, decisions) instead of dropped, near-duplicates collapse into one, and likely contradictions get flagged — all with zero API calls. Nothing is hard-deleted: the original stays in git and the version chain (restore-page brings it back). Access-weighted retention is always on because it can only ever keep memory longer; the parts that rewrite or drop content (compaction, dedup, per-tier curves) stay off by default until you turn them on.

• And it can dream, if you let it. Point it at an LLM and an opt-in background pass will, while you're idle, rewrite whole clusters of cold notes into single coherent pages — cancelling the moment you come back to work. It never deletes a source (the pre-merge versions stay reachable), it's off by default, and it's gated on a recall eval before it could ever become default behavior. The zero-LLM path above is what runs unless you ask for more.

• It tells you the truth about itself. One self-contained binary. Purge commands that say exactly what "deleted" means. A measured write ceiling (~700/s) instead of a guessed one. An audit log of every mutation. Boring, in the way infrastructure should be.

capture ──▶ consolidate ──▶ recall ──▶ handoff
 hooks        session-end      search     next agent,
 observe      summaries as     + brief    any harness
 silently     wiki pages       injection

Agents emit sanitized observations through lifecycle hooks as you work. At session end, observations become coherent markdown pages in the project's wiki (optionally LLM-written; useful even without). The next session — any agent, any machine — gets a bounded brief and can search everything: full-text, entities, links, and (optionally) vectors, fused into one ranking. Cross-agent handoffs carry the baton explicitly.

The full design, including the invariants that keep multi-user and multi-session use safe, is in docs/ARCHITECTURE.md.

Every row below is a first-party integration — MCP registration, lifecycle hooks, or both — kept honest by CI. The full matrix with per-agent notes and caveats is in docs/support-matrix.md.

Most agent-memory tools optimize one thing — extracting atomic facts per turn, a temporal knowledge graph, an agent-editable memory OS, or a hosted context API. ai-memory optimizes something different: a git-backed markdown wiki as the source of truth, with a derived index for retrieval, captured automatically from lifecycle hooks, shared across agents, machines, and people, and working with zero LLM calls by default. Here's what carries over from each, and what you gain:

The consistent theme: files you own (git-backed markdown), a zero-LLM default, one self-contained binary, cross-agent + cross-machine + team sharing, automatic lifecycle capture, and typed, claim-once handoffs. Opt-in features (LLM consolidation, vector search, the "dream" consolidation pass, belief-strength in ranking) stay opt-in — and the zero-LLM aging path (per-tier decay, extractive compaction, dedup, contradiction flagging, access-weighted retention) works with no API key at all.

Built on the shoulders of: the Karpathy LLM Wiki (compile-not-retrieve), agentmemory (this project is its Rust successor), basic-memory (markdown-on-disk truth), cognee (pipeline composition and triplet embeddings), Hermes Agent (the self-improvement loop), and A-MEM (Zettelkasten-style atomic notes).

The full, fair rundown — where each approach wins, where ai-memory differs, the published benchmark — is in How ai-memory compares.

For native Arch installs, use the AUR packages. They install /usr/bin/ai-memory, packaged hook sources, and both system-level and user-level systemd units.

yay -S ai-memory-bin    # prebuilt Linux x86_64/aarch64 binary
yay -S ai-memory        # builds from source

Download the x86_64 or aarch64 RPM from the latest release, then install it:

sudo dnf install ./ai-memory-*.rpm

Then follow the native Linux service instructions in docs/install.md.

Single-user workstation:

mkdir -p ~/.config/ai-memory ~/.local/share/ai-memory
ai-memory --data-dir ~/.local/share/ai-memory \
  --config ~/.config/ai-memory/config.toml init
systemctl --user enable --now ai-memory.service
ai-memory install-mcp --client claude-code --apply
ai-memory install-hooks --agent claude-code --apply

System service installs use /var/lib/ai-memory and /etc/ai-memory/ via the packaged unit. Full user-service, system-service, auth, and provider setup is in docs/install.md#arch-linux-native-packages-aur.

A self-contained .app that bundles the native ai-memory binary and hooks/ tree, starts the existing LaunchAgent, and opens /web, ai-memory status, and config.toml from the menu bar. Wiki, SQLite, config, and models stay in ~/Library/Application Support/ai-memory, so replacing the app is an update — it does not rewrite that tree.

Needs a Rust toolchain and Xcode / Swift 6 (same as a source build):

git clone https://github.com/akitaonrails/ai-memory
cd ai-memory
./companions/ai-memory-macos/build.sh
open "companions/ai-memory-macos/dist/AI Memory.app"

Drag AI Memory.app to /Applications, then Install & Start Server from the menu extra (no Dock icon). When the status item is green, wire an agent with the bundled binary:

BIN="/Applications/AI Memory.app/Contents/Resources/runtime/ai-memory"
"$BIN" install-mcp --client claude-code --apply
"$BIN" install-hooks --agent claude-code --apply

Prebuilt tarball and launchd-without-the-app paths: docs/macos.md. Companion details: companions/ai-memory-macos.

You need: Docker or Podman + an agent CLI from the Support Matrix, or anything else that speaks MCP.

The published Docker image includes linux/amd64 and linux/arm64 variants, so Apple Silicon Macs and ARM64 Linux hosts can pull akitaonrails/ai-memory without --platform linux/amd64 emulation.

The default quick-start has no authentication - the server binds to loopback only, so on a single-user laptop nothing else can reach it. Adding a bearer token is a one-line change once you're ready to expose the server on the LAN; see Security below.

# 1. Install the ai-memory CLI wrapper (a small shell script that
#    runs the binary inside a container with your $HOME mounted). This is
#    the only thing that needs to live on the host filesystem.
mkdir -p ~/.local/bin
wrapper_tmp="$(mktemp -d)"
trap 'rm -rf "$wrapper_tmp"' EXIT
wrapper_base=https://github.com/akitaonrails/ai-memory/releases/latest/download/ai-memory-wrapper
curl -fsSL "$wrapper_base" -o "$wrapper_tmp/ai-memory-wrapper"
curl -fsSL "$wrapper_base.sha256" -o "$wrapper_tmp/ai-memory-wrapper.sha256"
expected="$(awk 'NR == 1 { print $1 }' "$wrapper_tmp/ai-memory-wrapper.sha256")"
if command -v sha256sum >/dev/null 2>&1; then
    actual="$(sha256sum "$wrapper_tmp/ai-memory-wrapper" | awk '{ print $1 }')"
else
    actual="$(shasum -a 256 "$wrapper_tmp/ai-memory-wrapper" | awk '{ print $1 }')"
fi
[ -n "$expected" ] && [ "$actual" = "$expected" ] || { echo "wrapper checksum mismatch" >&2; exit 1; }
install -m 0755 "$wrapper_tmp/ai-memory-wrapper" ~/.local/bin/ai-memory
rm -rf "$wrapper_tmp"
trap - EXIT
# Most distros put ~/.local/bin on PATH automatically. If `which
# ai-memory` comes up empty, add this to ~/.bashrc / ~/.zshrc:
#     export PATH="$HOME/.local/bin:$PATH"

# 2. Start the server. `--restart unless-stopped` makes it come back
#    on docker daemon restart and on machine boot (provided your
#    docker service is enabled at boot — `sudo systemctl enable
#    docker` on most distros). Loopback-only bind (`127.0.0.1:49374`)
#    so nothing outside this machine can reach it. Omit the LLM /
#    EMBEDDING lines for zero-LLM mode — FTS5 search still works
#    without any keys.
docker run -d --name ai-memory \
    --restart unless-stopped \
    -p 127.0.0.1:49374:49374 \
    -v ai-memory-data:/data \
    -e AI_MEMORY_LLM_PROVIDER=anthropic \
    -e ANTHROPIC_API_KEY=sk-ant-... \
    -e AI_MEMORY_EMBEDDING_PROVIDER=openai \
    -e OPENAI_API_KEY=sk-... \
    docker.io/akitaonrails/ai-memory:latest

# 3. Wire your agent CLI in two commands. The wrapper takes care of
#    mounts and each client's config-path detection. Re-run with
#    `--agent codex`, `--agent command-code`, `--agent devin`, `--agent opencode`, `--agent opencode2`, `--agent gemini-cli`,
#    `--agent grok`, `--agent kimi-code`, `--agent kiro-cli`, `--agent omp`,
#    `--agent oh-my-pi`, `--client cursor`,
#    `--client gemini-cli`, `--client grok`, `--client kiro-cli`, etc.
#    for additional agents; full list in docs/install.md.
ai-memory install-mcp   --client claude-code --apply
ai-memory install-hooks --agent  claude-code --apply

On Linux and macOS, the Docker wrapper runs install-hooks through its checksum-verified native host client. The installed hooks therefore enforce client-side capture controls such as [capture] ignore_paths and allowlist mode before an event reaches the spool or network. Set AI_MEMORY_HOOK_PLATFORM=posix explicitly only when you need the legacy shell compatibility path; the installer warns that path cannot enforce capture policy v1.

The examples use docker; replace it with podman on a Podman host. The wrapper automatically uses Podman when Docker is not installed. Set AI_MEMORY_DOCKER=podman to force Podman when both engines are available.

On Linux/macOS, that's it. Start a Claude Code session as usual - every prompt and tool call now lands in ai-memory, and the next session you open in this project will see a handoff with where you left off. On macOS the native binary is the recommended path when you do not need Docker — either the menu bar app above or a release tarball / launchd agent. Later updates for that path use ai-memory upgrade (checksum-verified GitHub release replace + hook refresh) — see docs/install.md#keeping-ai-memory-up-to-date. The same native upgrade path covers Windows x86_64 zip installs under a writable prefix (see docs/windows.md Scenario C).

Wiring another agent is the same two commands with a different name — --client codex, --agent codex, and so on for every row of the support matrix. The full per-agent guide, including Windows and remote servers, is docs/install.md.

Two agents in the same project at once, or teammates on one server? That works out of the box: the "current project" pointer is isolated per caller by default (v1.39+). See docs/auto-scope.md for the optional session-aware Claude Code bridge and the details.

If in doubt, start your harness with ai-memory run. It is the preferred way to launch: the first time it runs a harness it auto-installs that harness's ai-memory hooks + MCP if they are missing (so capture and recall just work — no separate install-hooks/install-mcp step to forget), it wires the right project scope by construction, and it adds cross-harness session continuity on top of shared memory. Everything is idempotent and one-time per harness and config home.

ai-memory run claude
ai-memory run codex --yolo   # later: same workstream, different harness
ai-memory continue           # resume the newest managed checkout
# after a dead launcher left its lease behind (same operator only)
ai-memory run --force-unlock codex

--force-unlock immediately expires the selected workstream's active lease; use it only when you know the previous launcher is gone. It does not kill a native process, and it cannot evict another authenticated operator's run. See the managed-workstream recovery notes for the full safety contract.

Auto-wiring is on by default; opt out with ai-memory run --no-autowire or AI_MEMORY_RUN_AUTOWIRE=false. You can still wire agents by hand with install-hooks / install-mcp (e.g. for a harness you never launch through ai-memory run).

ai-memory uninstall --apply removes everything ai-memory installed, and only what it installed. It also clears ai-memory run's auto-wire record, so the next managed launch wires that harness again; to keep it unwired, launch with --no-autowire or set AI_MEMORY_RUN_AUTOWIRE=false. Install commands are idempotent and write timestamped backups next to any file they touch.

Day to day, you mostly do not think about ai-memory. Hooks capture prompts, tool calls, and session boundaries; session end turns them into readable wiki pages; the next session starts with a handoff.

• Ask "where did we leave off?" to continue from the pending handoff.
• Ask "have we discussed X?" or "search memory for Y" to query the wiki.
• Ask "catch me up" for a prose digest of recent project activity.
• Run ai-memory bootstrap once when adopting an existing project with months of history.
• Start the server with --enable-web for a read-only browser view of the wiki and a JSON API under /api/v1.

The full tour — search modes, entities, feedback, briefings, the web API — is in docs/usage.md and docs/use-cases.md.

Run the server somewhere reachable — a homelab box, a LAN host — and point every machine and every teammate at it. Knowledge is shared per project; personal handoffs stay personal; every write is attributed and audited. Multi-user auth (passwords, API credentials) is built in.

Start with docs/users.md for accounts and ownership, and docs/deploy.md for the server itself — including capacity numbers measured rather than guessed, and the one rule that matters: one server per data directory, never two.

The quick-start default is loopback-only with no auth — nothing outside your machine can reach it. From there, hardening is incremental: a bearer token for the LAN, per-user accounts, OIDC device auth for hooks, TLS via a reverse proxy. Capture is sanitized at a typed privacy boundary before anything is stored, and per-repository [capture] rules can exclude paths or invert to allowlist mode. A repository can also route its capture to a different server than the one the hooks were installed against.

The full model is in docs/security.md, docs/users.md, and docs/https-via-proxy.md. For data-flow, identity/SSO, and offline-install questions specifically, see DATA_HANDLING.md, docs/sso.md, and docs/airgapped-install.md.

Optional. Everything works with zero LLM calls; adding a provider upgrades session summaries and enables semantic search. Anthropic, OpenAI (including OAuth), Codex CLI credential reuse, GitHub Copilot, Gemini, OpenCode (Go and Zen), and any OpenAI-compatible endpoint (Ollama, LM Studio, vLLM) are supported for consolidation; OpenAI, Voyage, Gemini, and keyless OpenAI-compatible endpoints for embeddings. Configuration lives in docs/llm-providers.md.

One Rust binary runs an MCP/HTTP server and owns one data directory:

<data_dir>/
├── wiki/    # markdown source of truth, git-versioned
├── raw/     # immutable sanitized managed-workstream transcript segments
├── db/      # SQLite indexes, including FTS5, entities, and embeddings
├── models/  # reserved for local embedding models
└── logs/    # rolling tracing output

Hooks POST observations to the server. The server serializes writes through one SQLite writer, compiles session observations into markdown pages, and serves retrieval through FTS5, entity-match and graph-neighbor RRF, optional vector RRF, bounded source-authority adjustment, and bounded raw-observation fallback for non-global searches.

See docs/ARCHITECTURE.md for the data-flow diagram, crate breakdown, schema notes, and invariants.

MIT - see LICENSE.

This codebase is being built collaboratively with Claude Code (Anthropic Claude Opus 4.7) following the plan documented in docs/design-decisions.md.