akitaonrails/ai-memory

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.

Support Matrix

What it is

LLM coding agents lose context when a session ends. ai-memory gives them a shared, persistent wiki compiled from sanitized lifecycle observations. When a session ends, relevant observations become a coherent summary; the next agent receives a bounded handoff. Optional ai-memory run launches add a portable visible-event ledger and native per-harness resume for higher-fidelity cross-harness continuity.

The wiki is plain markdown in a git repo - grep-able, openable in Obsidian, backed up with rsync. No vector database to babysit, no write_note ceremony, no manual context-loading. The full design is in docs/ARCHITECTURE.md; the influences and priors are at the bottom.

Key features

• Zero-friction lifecycle capture. Hooks fire-and-forget bounded, sanitized prompt, tool-lifecycle, and session-boundary observations. Direct launches keep this lightweight path; it is not a complete native transcript. User prompts and post-compaction summaries retain up to 16 KiB; notifications and tool excerpts retain up to 2 KB, with a 16 KiB durable backstop for every observation body.
• Opt-in managed workstreams. ai-memory run claude, then ai-memory run codex --yolo, then ai-memory run command-code, transparently resumes one logical workstream with native per-harness sessions, a portable visible-event ledger, and full-ledger search. Delivered packets are origin-marked; Claude transcript import rejects a packet that Claude persisted and read back through a tool. ai-memory run with no harness continues the newest usable Claude Code, Codex, OpenCode, Pi, Crush, Kimi Code, Command Code, or Kiro CLI v2/v3 session for this checkout. On first explicit use, an interactive launcher can adopt a previous session from the same checkout; later switches cannot select unrelated native history. Native arguments pass through unchanged except the wrapper-owned --yolo and --fresh; direct commands are unaffected. kimi-code and kimi-cli are accepted aliases for the installed kimi command; commandcode, cmdc, and cmd select the cross-platform command-code executable (cmdc on native Windows); and kiro-cli selects the installed kiro-cli command. Kiro defaults to v2; ai-memory run kiro --v3 selects v3, while a returning linked v3 workstream selects its engine transparently.
• Per-repository capture exclusions. A nearest-marker [capture] ignore_paths policy drops matching recognized file-tool events before they reach the local spool or server. See the capture policy reference.
• Optional per-operator memory slots. On shared servers, [slots] per_user = true keeps engine-written _slots/ context in a bounded namespace derived from the authenticated operator. Session briefs and consolidation prompts receive shared slots plus the caller's own; exact wiki reads and searches remain project-wide, so this is context-injection isolation rather than RBAC. See multi-user operation.
• Cross-agent handoffs. Quit Claude Code mid-task, start Codex in the same directory hours later - the next agent sees a "where you left off" block before its first prompt.
• Per-project isolation by construction. Each project lives at <wiki_root>/<workspace_id>/<project_id>/… keyed by stable UUIDs. Workspace defaults to "default". Project is derived from $cwd: CLI subcommands (bootstrap, write-page, lint, …) walk to the main git repo root so all worktrees of the same repo share one project identity; the hook router defaults to basename($cwd) and can opt into the repo-root rule. Drop a .ai-memory.toml marker file in any ancestor directory to override either field explicitly — perfect for multi-client consultancies, work/personal split, mono-repos, or linked git worktrees. Same page path can exist in two projects without collision; a rename is one column update; a purge is one rm -rf.
• Global preferences scope. Standing user/team context — tech choices, code style, durable personal rules — lives in the reserved _global scope (memory_write_page with scope: "global"). Default memory_query reads union it into every project as global_scope_hits, so preferences travel with you into new projects without naming a magic project or paying the all-projects global=true fan-out. Event capture never writes there.
• Entity-assisted recall. Consolidation stores up to 10 specific nouns per page in canonical entities: frontmatter. Exact, prefix, and compound-word matches form a project-scoped RRF stream, so a query can recover a page even when its body uses different wording. The stream is lexical and adds no query-time LLM call.
• Authority-aware recall. FTS5, entity-match RRF, graph-neighbor RRF, and optional vector RRF generate candidates by relevance. Before truncation, a bounded adjustment favors maintained _rules/, decisions/, procedures/, and gotchas/ pages over closely matching episodic session evidence. Tier, pinned, and explicit canonical / active / source-of-truth or superseded / historical / test-fixture / do-not-answer-from tags contribute without becoming absolute filters, so targeted history searches still find session pages. These signals affect retrieval provenance only; retrieved text remains untrusted historical evidence and never gains instruction authority from its namespace, tier, tags, pin, or rank.
• Clear routing alongside code-intelligence tools. Run ai-memory beside a structural MCP server, LSP, or other live-code tool without synchronizing their stores. Use memory for prior decisions, rationale, failed attempts, procedures, and handoffs; use the current checkout and structural provider for symbols, callers, dependencies, and impact analysis. Verify historical code claims against the checkout before acting, and treat source, builds, tests, and observed runtime behavior as operational truth. See Historical memory and live code intelligence.
• Karpathy-style LLM wiki. Pages are compiled from observations at session-end (or PreCompact; clients without a true session-end event can use ai-memory finalize-session --agent <agent> for a manual final close), not retrieved over raw logs. Supersession chain + git-versioned markdown means you can time-travel with ai-memory checkpoints, restore-page, or raw git log.
• Built-in /web browser. Read-only HTML UI for the wiki - project list, folder tree, FTS5 search, markdown rendering, dark mode. Mounted on the same axum server as MCP.
• Server-wide MCP client activity. GET /admin/activity/by-client?since_days=7 shows which MCP clients are calling memory tools, split into reads and writes. Counts use bounded UTC-day buckets, so arbitrary client names cannot grow the database with request volume; shared deployments keep the endpoint root-only. See MCP client activity.
• Multi-agent + multi-machine ready. Supported clients: Claude Code, Codex, Command Code, Devin CLI, OpenCode, Cursor, Claude Desktop (via mcp-remote), Gemini CLI, Antigravity CLI, Grok Build CLI, Kimi Code, OpenClaw, Oh My Pi / OMP (omp / oh-my-pi), Pi via generated bridge extension, VS Code GitHub Copilot agent mode (MCP-only, workspace .vscode/mcp.json), Kiro CLI (MCP + v2 lifecycle hooks), and Zed (MCP-only, user settings.json). Server runs local (loopback) OR on a homelab box (LAN/VPN/cloud) with bearer-token auth. Shared servers can opt into [auto_scope] modes for per-user or session-aware current-project routing; Claude Code has a built-in opt-in bridge via install-mcp --session-aware.
• Thin-client CLI. ai-memory status, bootstrap, checkpoints, restore-page, purge-project, rename-project, move-project, move-session, audit-contamination, lint, curator, auto-improve, auto-improve-report, pending-writes, embed, forget-sweep, backup, finalize-session are all HTTP clients of the running server - never touch SQLite or wiki files directly. status also reports passive LLM/embedding provider health from the last real provider call. Server is the single source of truth. finalize-session lists matching open sessions through GET /admin/open-sessions, then posts synthetic session-end hooks back to the server. On shared deployments it defaults to the caller's own plus unattributed sessions; root can pass --all-owners for explicit cross-operator recovery. When concurrent sessions share an agent and scope, pass --session-id <uuid> to target one exact open session; it cannot be combined with --all.
• LLM is opt-in. Zero-LLM mode still gives you FTS5, manually declared entity, and graph-neighbor search plus rule-based summarisation. Add a provider when you want consolidated pages, lint contradictions, or staged auto-improvement proposals.

Use cases

• "Quit Claude Code and continue the same work in Codex." Use the optional managed launcher when you want native session resume plus the portable visible history, not only a summary handoff:

  cd /path/to/project
  ai-memory run claude
  
  # Quit Claude Code, then continue the same workstream in Codex.
  ai-memory run codex --yolo
  
  # Continue in Command Code, preserving its own exact native session.
  ai-memory run command-code
  
  # Later, omit the name to resume the newest usable managed session here.
  ai-memory run
  
  # Start a new Codex session in the same workstream, keeping portable history.
  ai-memory run --fresh codex
  
  # Kiro defaults to v2; select its incompatible v3 engine explicitly once.
  ai-memory run kiro --v3
  

• "Pick the project instead of remembering where it lives." Start from a directory containing your checkouts and choose the checkout before the managed harness:

  ai-memory show
  
  # Machine-readable discovery without launching anything.
  ai-memory show --json
  

  Each successful ai-memory run saves a client-local checkout link keyed by the configured server plus workspace/project. show joins those links with the server's public activity and page-count metadata. A fast, bounded depth-1 scan of the current directory also finds new checkouts carrying a project marker (.git, Cargo.toml, package.json, go.mod, pyproject.toml, and friends), while skipping dependency and build directories. The server never exposes a checkout path, so two client machines can safely use different local paths for the same project on a remote homeserver.

  The list always leads with + New project: type a name and ai-memory validates a portable directory name, stages the new checkout privately, pins its workspace and project in .ai-memory.toml, and installs the routing block and managed Agent Skills for the chosen agent. The final directory appears only after every setup step succeeds, then show launches from it.

  The harness menu only offers agents actually installed on the host, using the same PATH lookup run enforces at launch.

  --no-scan uses only saved links; --workspace filters both sources; --yolo, --fresh, and trailing native arguments are forwarded unchanged. Non-terminal use must pass --json; JSON mode is discovery-only and never launches a harness.

  The first explicit run can offer an existing session from this exact checkout or start a new one. Switching harnesses starts or resumes the native session linked to the shared workstream, so an obsolete local session cannot replace newer cross-harness history. After a normal quit, the next launch waits briefly if the previous launcher is still finalizing; handled failures release the workstream immediately. If a linked native transcript was deleted, ai-memory detects the orphan before launch and starts fresh; --fresh forces that recovery for one harness. Managed mode currently covers Claude Code, Codex, OpenCode, Pi, Crush, Kimi Code, Command Code, Kiro CLI v2/v3, OMP, Grok Build CLI, and Antigravity CLI; direct harness launches remain unchanged. See Managed cross-harness workstreams.

• "Just put me back where I was." From any directory, with no name to type and no list to read:

  ai-memory continue
  

  It picks the checkout whose managed launch is most recent, revalidates the path and its resolved scope, then continues there exactly as bare ai-memory run would. A link whose directory moved, was replaced, now resolves to a different project, or has a corrupt ordering timestamp is reported on stderr and skipped, so a resume never quietly lands in the wrong project. --workspace narrows the search; --yolo and --fresh are forwarded.

• "Quit at 4 PM, pick up at 9 AM in a different agent." The classic. SessionStart hook in the next supported hook client prepends a typed handoff with open questions, next steps, and a session summary. Grok captures lifecycle events but ignores SessionStart stdout, so ask it to call memory_handoff_accept when resuming from a handoff. Zero has the same no-stdout behavior and also must call memory_handoff_accept.

• "What did we decide about X six weeks ago?" Use memory_query X from the agent for FTS5 fused with entity matches and linked-page expansion (plus vector similarity when an embedder is configured). For a quick terminal-only FTS5 lookup, use ai-memory search X; that admin command does not run the hybrid streams. Pages are LLM-consolidated, so the hit is a coherent decision page, not a raw chat log. Pass explain: true to see why each hit ranked where it did in project or explicit-scope retrieval. Cross-project global: true search uses its separate FTS-only ranker and reports that active stream without per-hit RRF details.

• "Remember this permanently." When something is worth keeping beyond auto-captured session logs - a decision, a convention, a gotcha - tell the agent "save a permanent note that we standardised on Postgres for X" or "annotate this as a project rule" and it calls memory_write_page to write a durable, git-versioned wiki page. From a terminal it's ai-memory write-page --path decisions/0007-db.md --body $'# Standardised on Postgres\n\n...' --pinned. --pinned exempts it from the decay sweep; the H1 on the first line of --body becomes the page title (omit --title — it's still accepted, but LLM callers trip over JSON-escaping their way through it, see issue #67). Unlike a handoff (single-use) or an auto-synthesised session page (rewritten on consolidation), a write-page note is yours: it shows up in memory_query, renders in /web, and stays until you change it.

• "That page you found is out of date." The agent calls memory_feedback with the page's path and a signal: helpful / not_helpful tune how strongly retention keeps a sweep-eligible episodic page (they move its salience, which scales the decay formula's time term), while stale / wrong floor the salience and make any current page show up as a feedback_flagged finding in the next memory_lint report. Feedback never deletes anything — it lowers confidence and flags for review — and it attaches to the version current when feedback is recorded, so a later rewrite clears the flag. Retrieved page text is untrusted and never authorizes feedback by itself.

• "Remember this, but only until the sprint ends." Pass expires_at to memory_write_page (RFC3339 or YYYY-MM-DD = end of that day, UTC) — or put expires_at: in a page's frontmatter by hand. Past the TTL the page disappears from search/recent/briefing (pass include_expired: true to memory_query to still see it) and the next forget sweep hard-deletes the file and its rows. A TTL beats a pin; memory_lint warns about pinned+expiring combos.

• "This new project has months of history before ai-memory." cd /path/to/my-project && ai-memory bootstrap collects git log, README, docs/, module headers, project rules and one-shot-summarises them into seed wiki pages. Future sessions build on top.

• "What durable lesson did that session teach?" When an LLM provider is configured, ai-memory runs a background auto-improvement scheduler for newly completed sessions in every project. It records proposed wiki edits in the pending-writes audit trail, then approves them immediately through the normal wiki write path by default. Scheduler ticks are non-overlapping: if reviewing all projects takes longer than the interval, the next tick is delayed until the current one finishes. Scheduling and approval are separate: set [auto_improve.scheduler] enabled = false to stop automatic review, or set [auto_improve] require_approval = true to keep both scheduled and manual proposals pending for human review. ai-memory auto-improve --session-id <uuid> and MCP memory_auto_improve remain available for manual catch-up or targeted reruns. When its session_id is omitted, the MCP tool selects the newest completed session without a persisted auto-improvement run, so repeated calls advance past short preflight-skipped sessions; an explicit ID reruns that session. ai-memory auto-improve-report --workspace <w> --project <p> returns a read-only telemetry report for recent auto-improvement outcomes without staging or creating proposals; add --stage to create one pending report page for audit/approval. On deployments that distinguish operators, pending learning proposals are isolated by qualified operator identity, so one person's proposal for a page does not block another's; unattributed and single-user deployments retain the shared pending queue. See docs/auto-improve-eval-gates.md for example executable eval scorers.

  Existing installs do not need per-project migration. The scheduler initializes a per-project first-run watermark so historical sessions are not reviewed automatically on upgrade, then records per-session claims so failed scheduled reviews do not retry forever; use manual auto-improve for old sessions or failed scheduled sessions you want to catch up. Older configs may still contain an [auto_improve] mode = ... line; current ai-memory ignores that legacy key, so you can remove it when convenient.

• "What housekeeping should I consider?" ai-memory curator runs a no-LLM, rule-based maintenance report over cold episodic pages, stale slots, duplicate exact normalized titles, and dangling cross-project links. It is report-only unless --stage is passed; staging queues one report page for approval and still performs no maintenance actions itself. Shared servers can opt into [decay] breadth_weight to give pages reinforced by several identified operators a retention bonus; the default 0.0 leaves existing retention scores unchanged.

• "Run one ai-memory for the whole household." Stand the server up on a homelab box at 0.0.0.0:49374 with a bearer token; every laptop/desktop talks to it. Per-cwd routing keeps each project's pages cleanly separated; the /web UI is reachable from a browser anywhere on the LAN.

• "Audit what landed before sharing with a teammate." Browse the wiki at http://<server>:49374/web - HTTP Basic dialog if auth is on, paste the token as password. Per-project tree view, rendered markdown, supersession chain visible per page.

• "Undo one bad page edit without rolling back the whole server." ai-memory checkpoints shows recent wiki commits, then ai-memory restore-page --path notes/foo.md --from <rev> restores that one markdown file and reindexes it into SQLite. Full backup / restore is still the answer for DB-only state such as sessions, observations, handoffs, users, audit rows, and embeddings.

• "Drop an experiment, keep the rest." ai-memory purge-project --project experimental --confirm. Atomic: that project's DB rows cascade away, its wiki subdir gets rm -rf'd, every sibling project is untouched by construction.

Quick start

Arch Linux (AUR)

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

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.

Docker

You need: Docker + 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 docker 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-... \
    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 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
# Grok Build CLI example:
# ai-memory install-mcp   --client grok --apply
# ai-memory install-hooks --agent  grok --apply
# Kiro CLI v2 example (requires an existing Kiro agent config):
# ai-memory install-mcp   --client kiro-cli --apply
# ai-memory install-hooks --agent  kiro-cli --apply
# Kiro CLI v3 example (standalone hook registration):
# ai-memory install-hooks --agent  kiro-cli-v3 --apply
# Command Code stable MCP + lifecycle example:
# ai-memory install-mcp   --client command-code --apply
# ai-memory install-hooks --agent  command-code --apply

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 release binary is also supported and recommended when you do not need Docker; see docs/macos.md.

If two Claude Code sessions use the same server concurrently, enable per-session routing on the server and replace only the ai-memory MCP entry with the optional bridge:

# <ai-memory data dir>/config.toml
[auto_scope]
mode = "per_session"

ai-memory install-mcp --client claude-code --session-aware --apply

The bridge still connects to the configured local or remote HTTP server and forwards its bearer token, but also attaches Claude's lifecycle session id to every MCP request. Existing static HTTP installs remain the default. See docs/auto-scope.md for Claude's /clear and implicit-resume limitations.

The install-mcp / install-hooks commands use AI_MEMORY_SERVER_URL / AI_MEMORY_AUTH_TOKEN when set; otherwise they default to http://127.0.0.1:49374 (matching the server above) and no bearer token. If hooks are installed after an ai-memory MCP entry already exists, install-hooks reuses that endpoint so a remote MCP setup cannot silently regenerate loopback-only hooks. Both commands are idempotent - re-runs replace ai-memory's entry, preserve every other server / hook you have configured, and write a timestamped .bak-<ts> next to the file before each modifying write. The hook scripts are staged into ~/.local/share/ai-memory/hooks/<agent>/ automatically; re-running overwrites them so future image updates ship updated hooks. Drop --apply to print the snippet instead of mutating. For Claude Code, CLAUDE_CONFIG_DIR relocates MCP registration to $CLAUDE_CONFIG_DIR/.claude.json, hooks to $CLAUDE_CONFIG_DIR/settings.json, and global managed skills to $CLAUDE_CONFIG_DIR/skills. The Docker wrapper forwards this variable when the directory is under its existing $HOME bind mount. Use the native binary when the Claude config root is outside $HOME. Uninstall checks both the active relocated paths and Claude's home defaults, so enabling the variable does not leave an older default-path ai-memory installation behind. If your agent often starts inside repository subdirectories or linked worktrees, add --project-strategy repo-root to install-hooks so captures collapse to the main git repo name; see docs/install.md and docs/marker-file.md for details. Later bare --apply refreshes, including ai-memory upgrade, preserve that choice; pass --project-strategy basename explicitly to remove it.

The Docker wrapper also bridges thin-client commands such as ai-memory status and ai-memory bootstrap back to the host's loopback server. With the local Docker quick start above, no AI_MEMORY_SERVER_URL override is needed.

Managed workstreams are optional. They execute the harness on the host while the server may remain local or remote:

ai-memory run claude
# later, continue the same workstream in another harness
ai-memory run codex --yolo
# omit the name to continue the newest usable local harness session
ai-memory run
# or resume the newest managed checkout without changing directories first
ai-memory continue

To remove ai-memory later, run ai-memory uninstall --apply from the same host environment. It removes ai-memory-owned config entries, instruction blocks, default-root managed skill files, and generated plugin files only after matching their ai-memory signatures; custom skill roots installed with --target-dir are cleaned up manually. Use --mcp-url if you installed MCP with a custom endpoint, and --mcp-name only when you need to narrow removal to one matching entry.

Install Notes

• SELinux: on enforcing Linux hosts, the wrapper automatically adds --security-opt label=disable only to short-lived helper commands that touch bind-mounted host files. It does not alter the long-lived server container or relabel $HOME; do not add :z/:Z to the whole home bind. Rootless engines also get -u 0:0 for those commands. Docker and podman report rootless mode and SELinux support under different info keys; both are read. The same treatment applies whenever AI_MEMORY_DATA_DIR selects a host directory or an explicit --config reads a host file. See docs/install.md.
• Windows: use the Linux path inside WSL2, or the native Windows wrapper from PowerShell/cmd. Local supported profiles default to host-native commands: Claude Code may use its supported ai-memory.exe exec form, while other agents use native single command strings matching their hook schema. The Docker wrapper protects .ps1 fallback commands from nested PowerShell expansion with -EncodedCommand; rerun install-hooks --agent <agent> --apply after upgrading so existing hook entries receive the current form. PowerShell/Git Bash script bundles are compatibility fallbacks and do not enforce capture-policy v1. Do not mix path worlds. See docs/windows.md.
• Docker compose: docker compose -f docker/docker-compose.yml up -d is supported; agent setup is the same as step 3 above.
• Remote server: set AI_MEMORY_SERVER_URL=http://<server-ip>:49374 and AI_MEMORY_AUTH_TOKEN=<token> on the client before installing MCP/hooks. Explicit --server-url flags still work, but are no longer required when the env vars are set. Any non-loopback server should use bearer auth.
• Managed-launch wrapper: ai-memory run, ai-memory show, and ai-memory continue must be intercepted by the current host wrapper so local checkouts, native harnesses, and session stores remain accessible. An old wrapper may pass these commands into Docker and fail to find a checkout or host executable. Run ai-memory upgrade on the agent machine to refresh it. The host-native runner inherits AI_MEMORY_SERVER_URL, AI_MEMORY_AUTH_TOKEN, and the host PATH.
• Upgrades: for Docker-wrapper installs, run ai-memory upgrade on each agent machine. It refreshes the local wrapper, pulls the latest image, and re-stages hook scripts under ~/.local/share/ai-memory/hooks/<agent>/. Native package/source installs should rerun ai-memory install-hooks --agent <agent> --apply after upgrading the binary. Remote/homelab servers must still be redeployed separately; local wrapper upgrade only updates the client machine. Existing project prompt files keep working. Refresh the managed ai-memory routing package (ai-memory install-instructions, or --target AGENTS.md for AGENTS-based projects) when you want new tool guidance. The refresh writes the slim markered snippet and managed Agent Skills from the same binary-owned assets.

For every client in the Support Matrix, plus curl-based hook installs, source builds, CLI environment variables, and the full subcommand reference, see docs/install.md.

Tab completion for the CLI is available in bash, zsh, fish, PowerShell, and elvish:

ai-memory completions fish > ~/.config/fish/completions/ai-memory.fish

See docs/shell-completions.md for the other shells' install paths.

Security

Loopback-only (127.0.0.1:49374) with no auth is the default because it is safe for a single-user laptop: no process outside the machine can reach the server.

Unauthenticated non-loopback HTTP now fails closed. Set AI_MEMORY_AUTH_TOKEN or bind loopback; --allow-insecure-no-auth is an intentional, dangerous exception for plain HTTP only. Authentication does not encrypt bearer tokens: for LAN or remote access, use the ready Caddy or Cloudflare Tunnel templates described in the HTTPS reverse-proxy guide.

Enable bearer auth when the server is exposed beyond loopback, when untrusted local processes share the machine, or when the data dir holds sensitive project history:

TOKEN=$(ai-memory generate-auth-token)

docker run -d --name ai-memory \
    --restart unless-stopped \
    -p 0.0.0.0:49374:49374 \
    -v ai-memory-data:/data \
    -e AI_MEMORY_AUTH_TOKEN="$TOKEN" \
    -e AI_MEMORY_ALLOWED_HOSTS="<server-ip>,localhost,127.0.0.1" \
    akitaonrails/ai-memory:latest

ai-memory install-mcp   --client claude-code --apply \
    --server-url "http://<server-ip>:49374/mcp" --auth-token "$TOKEN"
ai-memory install-hooks --agent  claude-code --apply \
    --server-url "http://<server-ip>:49374" --auth-token "$TOKEN"

Bearer auth protects /mcp, /hook, /handoff, /admin/*, and /web/*. Browser access to /web uses HTTP Basic auth with the token as the password. When /web is exposed through an HTTPS reverse proxy, set AI_MEMORY_AUTH__SECURE_COOKIE=true; it makes the browser cookie HTTPS-only. Close or redirect direct HTTP access to that hostname. Non-loopback binds should also set AI_MEMORY_ALLOWED_HOSTS to guard against DNS rebinding.

Busy shared hook servers can also set AI_MEMORY_HOOK_RATE_PER_SEC (tokens per second per actor/session source) and optionally AI_MEMORY_HOOK_RATE_BURST to bound one runaway session without blocking unrelated hook sources. Unset or 0 rate leaves the limiter disabled.

For shared servers where each developer should authenticate their own hook writes, native Claude Code hooks can use a stored OIDC device token instead of embedding a shared static token:

ai-memory auth login oidc-device \
    --issuer "https://issuer.example.com/realms/team" \
    --client-id "ai-memory-cli"

ai-memory install-hooks --agent claude-code --apply \
    --server-url "http://<server-ip>:49374"

OIDC hook auth requires the native ai-memory hook ... command path. The Docker wrapper keeps shell-script hooks by default; set up OIDC from a native release binary or source install. Thin-client HTTP commands such as ai-memory status and ai-memory search also use the stored OIDC access token when no static AI_MEMORY_AUTH_TOKEN / [auth].bearer_token is configured; the static bearer still wins when present. This is for OIDC-aware gateways/bridges; native ai-memory server auth still accepts static root bearer / DB-user tokens, and /admin/* remains root-only unless a gateway translates accepted OIDC auth into upstream auth that ai-memory accepts.

OIDC/Keycloak session ids are login-provider sessions, not ai-memory agent sessions. Shared servers that rely on [auto_scope] session isolation still need explicit workspace + project / scopes, or a bridge that forwards the real lifecycle-hook session id on MCP requests.

Want HTTPS? ai-memory deliberately does not terminate TLS itself — the right answer is a battle-tested reverse proxy in front of it. docs/https-via-proxy.md is the deployment guide, with copy-paste docker compose templates in docker/compose.tls.caddy.yml (Caddy with Let's Encrypt or internal CA) and docker/compose.tls.cloudflared.yml (Cloudflare Tunnel — no open ports). Both are recommended once you turn on multi-user or bind beyond loopback. The Quick Start happy path of single-user on loopback doesn't need TLS — that case is called out explicitly in the guide so you don't add ceremony where it doesn't earn its keep.

Multi-user attribution (v0.8, optional). When more than one human shares a server, ai-memory can attribute each write to a named user. The bearer token continues to authenticate at the wire level; users created via ai-memory user add get their own tokens that resolve to their identity in audit logs, page frontmatter, /api/v1 responses, and the page view UI. Data stays single-tenant — there is no per-page RBAC. A [auth].token_pepper is required for DB-user authentication, but creating the first user row is what immediately switches every /admin/* endpoint to root-only, including status/search/read-page and user-management routes. ai-memory init generates a pepper for new installs without changing single-user behavior until a user is added. An SSO gateway can instead use a dedicated [auth].actor_proxy_bearer_token and trusted X-Memory-Actor-* headers; its credential is deliberately separate from the root bearer so a missing identity cannot become root. See docs/users.md for the full walkthrough and the four-rung auth ladder.

See docs/deploy.md for the full homelab pattern with bearer auth, host allowlisting, and TLS/reverse-proxy options.

Using Memory

Day to day, you mostly do not think about ai-memory. Lifecycle hooks capture prompts, tool calls, compaction checkpoints, and session boundaries. SessionStart hooks fetch pending handoffs before your first prompt in the next agent.

Useful entry points:

• 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 ai-memory in an existing project with months of history.

• Start the server with --enable-web and visit /web for a read-only browser view of the markdown wiki. --enable-web also mounts a read-only JSON frontend API at /api/v1 (workspaces, projects, pages, recent, briefing, search) so custom web UIs can read the memory without opening SQLite or wiki files directly:

  GET  /api/v1/workspaces
  GET  /api/v1/projects?workspace=...
  GET  /api/v1/workspaces/{workspace}/projects/{project}/pages
  GET  /api/v1/workspaces/{workspace}/projects/{project}/pages/{path}
  GET  /api/v1/workspaces/{workspace}/projects/{project}/recent?limit=...
  GET  /api/v1/workspaces/{workspace}/projects/{project}/briefing?limit=...
  GET  /api/v1/workspaces/{workspace}/overview?limit=...
  GET  /api/v1/workspaces/{workspace}/projects/{project}/overview?limit=...
  GET  /api/v1/workspaces/{workspace}/projects/{project}/handoffs?state=...&limit=...
  GET  /api/v1/search?q=...&workspace=...&project=...&limit=...
  POST /api/v1/search   { "q": "...", "scopes": [{ "workspace": "...", "project": "..." }] }
  

  overview bundles the open handoff + briefing + memory-health for a workspace or project in one call (the data a project overview screen needs). The handoff history defaults to the caller's own plus shared rows; root can use all_owners=true for recovery across operators.

  Full integration guide: see docs/frontend-api.md for auth setup, response schemas, error model, limits/pagination, custom-UI hosting, a worked fetch/curl example, and the canonical source-of-truth files. Read that first if you're building a frontend.

  To serve your own static frontend instead of the built-in UI, point --web-ui-dir at the frontend's build output (same-origin with /api/v1, /mcp, /admin/*, so the existing auth applies):

  ai-memory serve --transport http --bind 127.0.0.1:49374 \
    --enable-web --web-ui-dir ../ai-memory-ui/dist
  

  A reference implementation — a SolidJS knowledge browser with screenshots and e2e tests — lives at djalmajr/ai-memory-ui.

  Richer products such as import/migration pipelines and write-capable browser chat/editors should live as optional companion crates or projects that call ai-memory's public HTTP/MCP surfaces. The first implemented companion is the standalone OMC wiki importer at companions/ai-memory-importer, which is intentionally not a root workspace member and is not included in root cargo test --workspace. See docs/companion-crates.md for the boundary.

  When a reverse proxy hosts ai-memory under a URL subpath, set --base-path (or AI_MEMORY_BASE_PATH) so every HTTP surface moves together. Example: --base-path /wiki serves MCP at /wiki/mcp, hooks at /wiki/hook, the API at /wiki/api/v1, and the default browser at /wiki/web. Set --web-slug / if you want the browser or custom SPA at /wiki itself.

Install the managed routing package once so agents proactively call the right MCP tool for those prompts:

ai-memory install-instructions

That command writes or updates the slim <!-- ai-memory:start --> block and the managed ai-memory Agent Skills that carry the detailed routing guidance. See docs/usage.md for handoff examples, proactive query routing, bootstrap details, web UI screenshots, and the raw-wiki inspection commands. CLI URL/auth configuration lives in docs/install.md.

Entity retrieval

Consolidation extracts specific technologies, components, services, files, and domain nouns into each page's canonical frontmatter. Hand-edited wiki pages can declare the same bounded index explicitly:

---
title: Queue choice
entities:
  - nats jetstream
  - delivery guarantees
---

Names are lowercased, whitespace-normalized, de-duplicated, capped at 10 per page and 64 characters each, and rebuilt from Markdown during a clean-store ai-memory reindex. Entity lookup is project-scoped, ignores expired pages by default, and reports entity_rank, its raw inverse-frequency entity_weight, matched_entities, and its RRF contribution under memory_query(..., explain: true).

LLM Providers

ai-memory runs without an LLM: hooks still capture sessions, search uses FTS5 + declared entities + graph neighbors, and summaries fall back to rule-based output. Add an LLM provider when you want LLM consolidation (on PreCompact, on demand via memory_consolidate, or opt-in at session end with AI_MEMORY_CONSOLIDATE_ON_SESSION_END), richer linting, and bootstrap. Substantive session ends always write a rule-based summary page + handoff either way. A session containing only SessionStart / SessionEnd boundaries is closed without a page, handoff, or provider job. When that empty session had accepted startup context, its session-bound handoff is returned to the open pool for the next receiver instead of being lost. When the session-end opt-in is enabled, provider work is durably queued after those deterministic writes and handled by one bounded server worker, so hook drain latency does not cancel it. Failed jobs retry with backoff and survive a server restart. A resumed native session is ended again only after its observation generation advances; the persisted generation watermark makes duplicate SessionEnd delivery and system clock skew converge without repeated provider work. The end watermark and automatic handoff commit atomically, and an interrupted keyed replay finishes the wiki commit, queue insert, and key completion without duplicating that handoff. On the next SessionStart, the newest cwd-eligible automatic handoff wins; accepting it expires older eligible automatic handoffs without consuming manual or sibling-directory work. A new automatic handoff also expires prior open automatic handoffs from its exact cwd, so repeated SessionEnds cannot accumulate there before a receiver starts.

To keep consolidation style project-specific, write _prompts/consolidation.md in that project's wiki. Its body can express preferences such as "prefer Portuguese titles" or "omit routine CI noise". Automatic, single-page, and multi-page consolidation use the page; a manual memory_consolidate call can pass instructions to override it once. ai-memory sanitizes and caps the value at 2,000 characters, JSON-encodes it in the user message, and treats it as untrusted advisory data. It cannot supply facts, request tool use or disclosure, or override the consolidation schema and faithfulness rules. TTL-expired preference pages are ignored. With no active page or argument, no preference block is appended.

Recommended defaults:

openai-oauth stores a refresh token in <data_dir>/auth.json and talks to the ChatGPT/Codex Responses backend, not api.openai.com. For Docker quick starts, run ai-memory auth login openai-oauth with the wrapper so the token lands in the same ai-memory-data volume as the server.

anthropic-oauth hits the same /v1/messages endpoint as anthropic but authenticates with an OAuth bearer token instead of an API key. Run claude setup-token once, then set AI_MEMORY_LLM_PROVIDER=anthropic-oauth and ANTHROPIC_OAUTH_TOKEN=<token> (or CLAUDE_CODE_OAUTH_TOKEN, which claude setup-token writes automatically). No ANTHROPIC_API_KEY is needed. The Docker wrappers forward either token by name to short-lived helper commands such as llm-test; configure the long-lived server container separately as shown in the installation guide.

For both Anthropic providers, ai-memory omits temperature for Claude 4.7 and later models and Claude Mythos Preview because those models reject non-default sampling parameters. llm-test sends the same representative 0.2 value as the normal pipeline before the provider applies that compatibility rule.

⚠️ Unofficial and against Anthropic's usage policies — use at your own risk; it may get your account rate-limited or banned. See the warning in docs/install.md.

copilot stores a GitHub user token in the same auth file, exchanges it for a short-lived Copilot API token via GitHub's /copilot_internal/v2/token, and uses the Copilot Chat endpoint with vscode-chat integration headers. You can also set COPILOT_GITHUB_TOKEN, GH_TOKEN, or GITHUB_TOKEN on the server.

[!TIP] For the OAuth/subscription backends (anthropic-oauth, openai-oauth, copilot), pick a small, fast model via AI_MEMORY_LLM_MODEL — e.g. claude-haiku-4-5 or gpt-5-mini. ai-memory's LLM work (consolidation, lint, explore) is summarisation, not hard reasoning, so a Haiku/mini-class model is plenty and is much easier on subscription rate limits. Save the high-effort thinking models for your coding agent.

[!TIP] OpenAI-compatible structured output is schema-constrained by default. ai-memory sends each operation's JSON Schema through response_format=json_schema, which recent Ollama, vLLM, LM Studio, and llama.cpp releases honour. It falls back to the tolerant parser when an endpoint explicitly rejects that field or returns a malformed shape. Set AI_MEMORY_LLM_COMPAT_STRICT=false only for an incompatible endpoint.

For small-context local models, configure both consolidation limits. The input target accounts for the complete rendered prompt, including bounded slot and current-page context plus the structured-output schema; the output limit is sent to the provider. Their sum must fit the model context window, with extra headroom because provider tokenizers differ:

[consolidation]
max_input_tokens = 6500
max_output_tokens = 1000

The equivalent environment variables are AI_MEMORY_CONSOLIDATION__MAX_INPUT_TOKENS and AI_MEMORY_CONSOLIDATION__MAX_OUTPUT_TOKENS. Provider failures during an automatic PreCompact/PostCompaction checkpoint fall back to the deterministic rule-based page; admission, storage, and scope errors still fail closed. The validated minimums are 6,000 input and 1,000 output tokens.

Reranking is optional and off by default. With an LLM provider configured, AI_MEMORY_RERANKER=llm makes project and explicit-scope memory_query calls over-fetch from the hybrid stage, fuse scopes, and make at most one LLM call to reorder the best candidates. This can promote a relevant page that RRF ranked below the requested cut, at the cost of LLM latency and usage. The request sends the query plus at most 30 bounded page titles and search snippets to the configured provider; all values are JSON-encoded and treated as untrusted data. A timeout, provider error, or incomplete/invalid score set preserves the normal order. global=true and supplemental global-preference hits keep their existing non-RRF ranking. Concurrent provider calls are capped at four; saturated queries keep their local ranking without waiting.

Embeddings are optional and separate from the LLM provider. Set AI_MEMORY_EMBEDDING_PROVIDER=openai, voyage, google/gemini, or openai-compat when you want vector retrieval in addition to FTS5 + entity + graph-neighbor retrieval. openai-compat targets self-hosted engines (Ollama, LM Studio, vLLM): it needs no API key and requires explicit AI_MEMORY_EMBEDDING_BASE_URL, AI_MEMORY_EMBEDDING_MODEL, and AI_MEMORY_EMBEDDING_DIM. Both the FTS-only and hybrid paths apply the same bounded page-authority adjustment after candidate generation; embeddings improve relevance recall but do not decide which source is canonical.

See docs/install.md#llm-provider-tiers for env vars and Ollama/OpenRouter/Atlas Cloud examples, and docs/llm-provider-comparison.md for the empirical model comparison.

Architecture

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.

Docs

Influences and prior art

• Karpathy LLM Wiki - the compile-not-retrieve pattern.
• agentmemory - most of the right ideas; this project is the Rust successor.
• basic-memory - the markdown-on-disk source-of-truth model.
• cognee - pipeline composition and triplet embeddings.
• Hermes Agent - the self-improvement loop: post-turn review, approval gates, and curator boundaries.
• A-MEM - Zettelkasten-style atomic notes with link evolution.

License

MIT - see LICENSE.

Acknowledgements

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