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.

Website: aimemory.sh

Your coding agent probably has a memory feature already. Claude Code takes its own notes, Cursor remembers some things, and every platform is adding more. They all have the same limits: the notes live on one machine, belong to one agent, and drop out of view when you switch tools or hand the work to a teammate.

ai-memory removes those limits.

• It follows you across agents. More than twenty harnesses (Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Grok, Devin, Kimi, Kiro, and others) feed one shared memory. Quit Claude Code mid-task, open Codex in the same directory, and the next agent picks up a handoff that says where you left off, what failed, and what is still open. Handoffs are a typed protocol: each one has an owner and is claimed exactly once.

• It can carry the session, not just a summary. Start agents with ai-memory run: quit Claude Code, run ai-memory run codex, and Codex gets the shared workstream plus a private, temporary local projection of Claude's visible native history for exact details the bounded startup packet omitted. The remote server receives only the harness and session identity, never the local transcript path or body. Direct harness launches behave exactly as before.

• It follows you across projects. A small profile of how you usually work (your package manager, test layout, architecture habits) reaches every new repository at session start, so you stop re-explaining yourself. It sits below each repository's rules file as a default. It is on by default for a single user, and on a shared server it is opt-in and private per person. See docs/cross-project-profile.md.

• It follows you across machines. Memory lives in a server you run, on the same laptop, a homelab box, or anywhere else, so the project you left on the desktop is the project you resume on the laptop, with the same knowledge and the 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 and are 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. There is no vector store to maintain and no content locked in a binary blob.

• It captures the work itself, silently. Lifecycle hooks record what happened (prompts, tool calls, session boundaries), sanitize it at a typed privacy boundary before anything is stored, and the server consolidates it into readable pages. You do not have to ask it to remember anything. The default path uses zero LLM calls: capture, search, and handoffs all work with no API key.

• It ages memory without an LLM. Memory decays on a schedule you can tune per tier, and memory you use decays slower: opening a page, searching it, or reaching it through a link counts as use. 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 keep memory longer. The parts that rewrite or drop content (compaction, dedup, per-tier curves) stay off by default until you turn them on.

• It can also "dream", if you let it. With an LLM configured, an opt-in background pass rewrites whole clusters of cold notes into single coherent pages while you are idle, and cancels as soon as you come back to work. It never deletes a source (the pre-merge versions stay reachable), it is off by default, and it has to pass a recall eval before it could become default behavior. Unless you ask for more, the zero-LLM path above is what runs.

• It is honest about itself. It ships as one self-contained binary. Purge commands say exactly what "deleted" means. The write ceiling (~700/s) is measured, not guessed. An audit log records every mutation.

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, in any agent on any machine, gets a bounded brief and can search everything: full-text, entities, links, and (optionally) vectors, fused into one ranking. Cross-agent handoffs pass the baton explicitly.

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

An optional server-rendered management console provides user administration, session inspection and deletion, and personal MCP API keys. Build with --features management-ui and start serve --transport http --enable-management-ui; both compile-time and runtime opt-ins are required.

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

Most agent-memory tools optimize for one thing: extracting atomic facts per turn, a temporal knowledge graph, an agent-editable memory OS, or a hosted context API. ai-memory is built around a git-backed markdown wiki as the source of truth, with a derived index for retrieval. It captures automatically from lifecycle hooks, is shared across agents, machines, and people, and works with zero LLM calls by default. This table shows what carries over from each tool and what you gain:

Across all of these, ai-memory gives you files you own (git-backed markdown), a zero-LLM default, one self-contained binary, sharing across agents, machines, and teams, 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 (hook capture for coding agents), 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 comparison, covering where each approach wins, where ai-memory differs, and the published benchmark, is in How ai-memory compares.

curl -fsSL https://github.com/akitaonrails/ai-memory/releases/latest/download/install.sh | sh
ai-memory setup

install.sh downloads the release for your OS and CPU, checks it against the release's published SHA-256 checksum (it refuses to install without a match), and links ~/.local/bin/ai-memory. ai-memory setup then creates the data directory with a bearer token in an owner-only file, starts a per-user service (systemd --user or a launchd LaunchAgent), and wires MCP, hooks and Agent Skills for every agent it finds, or the ones you name with --agent. It shows the plan, with where it found each agent, and asks first: answer y, or type the numbers of the agents to wire. --plan prints the plan and changes nothing, --web shows the plan as a page in your browser and applies the agents you tick there, --yes skips the question, and running it again changes nothing. Afterwards, restart your agents and ask one of them "What do you remember about this project?" to see it answer through ai-memory. Details, the environment variables and how to verify the script before running it are in docs/install.md. An AI agent installing ai-memory for you can follow INSTALL_FOR_AGENTS.md.

Claude Code and Codex users can wire their agent as a marketplace plugin instead (the binary is still installed as above):

claude plugin marketplace add akitaonrails/ai-memory && claude plugin install ai-memory@ai-memory
codex plugin marketplace add akitaonrails/ai-memory && codex plugin add ai-memory@ai-memory

Codex then asks you to trust the hooks in /hooks. See Marketplace plugins.

In PowerShell:

irm https://github.com/akitaonrails/ai-memory/releases/latest/download/install.ps1 | iex
ai-memory setup

install.ps1 checks the release zip against its published SHA-256 checksum, unpacks it under %LOCALAPPDATA%\Programs\ai-memory and adds that directory to your user PATH. ai-memory setup then does what it does on Linux and macOS, with a logon entry in place of the service: the server starts when you sign in, with no console window, and restarts if it fails. Details and the other Windows setups (WSL2, Docker Desktop, a real Windows service) are in docs/windows.md.

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
ai-memory setup         # data dir, user service, agents, self-check

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

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

On a single-user workstation, ai-memory setup then does the rest, as on the two-command path above: data dir and token, a user service, the agents it finds, and a self-check.

ai-memory setup

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 and 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). The app reads the bearer token init wrote into its data dir, so the status item turns green on its own. Then wire your agents with the bundled binary; --no-service leaves the app's LaunchAgent in charge:

BIN="/Applications/AI Memory.app/Contents/Resources/runtime/ai-memory"
"$BIN" setup --no-service

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 Docker quick-start below has no authentication: the container never runs init, so it starts without a token. The server binds to loopback only, so nothing outside the machine can reach it, but local processes and browser pages can. Native installs that run ai-memory init get a bearer token by default; see Security below for that and for adding a token here.

# 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 copilot-cli`, `--agent droid`, `--agent muse`, `--agent cline`, `--agent qwen-code`, `--agent omp`,
#    `--agent oh-my-pi`, `--client cursor`,
#    `--client gemini-cli`, `--client grok`, `--client kiro-cli`,
#    `--client cline`, `--client freebuff`, 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. Global options may precede the command, for example ai-memory --config /path/to/config.toml run codex; the native client receives the complete argument list. 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.

That completes the setup on Linux/macOS. 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 through 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). Upgrading a 2.x install of any kind to 3.0? Read docs/upgrading-to-3.0.md first.

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. OpenCode is version-detected by host-side commands; when generating its artifacts inside a container, use setup-agent --agent opencode --opencode-dialect v1|v2 --to /tmp/unused because the container cannot inspect the host executable. The opencode2 aliases remain force-V2 compatibility spellings. 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, work 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 work without a 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. If the server is unreachable, run warns and launches anyway with local capture spooling; run ai-memory recover after the server returns (see Degraded offline launches). Pass --require-server to fail closed instead.

ai-memory run claude
ai-memory run codex --yolo   # same workstream + local Claude history dossier
ai-memory run --profile work claude  # reusable config.toml env/account preset
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
ai-memory resume             # pick from ALL workstreams in this checkout only
ai-memory resume --search auth # find a workstream by name (case-insensitive)
ai-memory resume --all       # pick across every linked checkout

--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.

On a real harness switch, the receiving managed agent is told to consult AI_MEMORY_PREVIOUS_SESSION_HISTORY when you ask to continue and the bounded portable packet lacks a detail. The variable names a private temporary Markdown projection created from the previous harness's checkout-validated, read-only adapter; the file disappears when the managed child exits. It is local to the launcher machine. This covers quota exhaustion and other abrupt exits where the old harness cannot write a handoff: refreshed ai-memory routing also tells the new agent that "continue", "resume", and "pick up where we left off" mean to read that file first when the three managed-run variables are present. A switch on another machine still gets the server's portable ledger and handoff, but cannot read a native transcript that exists only on the first machine.

Without the managed launcher, a harness cut off the same way leaves no handoff at all, so the next one could pick up an older handoff or session page. When the newest captured work in the project is in another of your sessions that never ended and left no handoff after that work, the next session start carries a one-line notice that names that session. The agent then reads its newest prompts and tool results with memory_read_session_observations. The same session is listed first in memory_briefing's recent_sessions, and memory_handoff_list names it as newer_unended_session.

If that variable is absent on a real same-machine switch, run type -a ai-memory and ai-memory --version in the launching shell. This path requires the 3.1.0 or newer host client; a stale Cargo or package-manager binary earlier in PATH can shadow an updated wrapper.

In resume, type to search, use Up/Down to select a workstream, and Left/Right to choose its harness. Enter launches the selection; Escape clears a search, then cancels when the search is empty (Ctrl-C always cancels). The list scrolls and loads every checkout-local page; there is no default workstream cutoff. Use --limit N only when you want to cap the matching results. Workstreams from other repositories or worktrees are left out unless you pass --all, which lists every linked checkout (the current one first); continue still resumes the newest linked checkout from anywhere. Listing reads Git identity only; it does not scan the working tree with git status before showing the picker. Full checkpoints are still captured when launching the selected workstream.

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.

This flake ships a NixOS module (nixosModules.default) with a systemd.services.ai-memory unit: a dedicated ai-memory system user (nologin, no linger) plus a hardened systemd sandbox (ProtectSystem = "strict", empty capability sets, MemoryDenyWriteExecute, RestrictAddressFamilies, and the rest; see nix/systemd-sandbox.nix). Packaged FHS units under packaging/systemd/ keep their existing lighter hardening. The flake exports packages for x86_64-linux, aarch64-linux, and aarch64-darwin. Intel macOS remains supported by the release tarball and Homebrew, but not by the pinned Nixpkgs revision.

{
  inputs.ai-memory.url = "github:akitaonrails/ai-memory";

  outputs = { nixpkgs, ai-memory, ... }: {
    nixosConfigurations.myhost = nixpkgs.lib.nixosSystem {
      system = "x86_64-linux";
      modules = [
        ai-memory.nixosModules.default
        {
          services.ai-memory = {
            enable = true;
            # enableWeb = true;  # off by default — the web UI is opt-in
            # enableApi = true;  # API-only companions; no browser UI
            settings = {
              allowed_hosts = [ "localhost" "127.0.0.1" "::1" "homelab.example" ];
              log_level = "info";
            };
            # Loopback (default): secrets optional; missing env file is tolerated.
            # Non-loopback: set one of ageSecret, sopsSecret, or environmentFile.
            # ageSecret = "ai-memory-env";  # config.age.secrets.<name> (agenix)
            # sopsSecret = "ai-memory/env"; # config.sops.secrets.<name> (sops-nix)
          };
        }
      ];
    };
  };
}

Declarative non-secret config lives in services.ai-memory.settings (a small typed set for common keys, plus freeformType for the rest of config.toml). The module renders a generated TOML file and passes --config. Top-level bind, port, and enableWeb win over duplicate settings keys. Anything in settings (including llm_headers) lands in a world-readable Nix store path, so do not put API keys there.

Secrets such as AI_MEMORY_AUTH_TOKEN never go in settings or the world-readable Nix store. This includes llm_headers, which can carry API credentials; set AI_MEMORY_LLM_HEADERS in ageSecret, sopsSecret, or environmentFile instead. These three secret sources are mutually exclusive. Non-loopback binds require one; loopback may omit them and tolerates a missing environment file (systemd EnvironmentFile=-…). Put TLS in front of a LAN/WAN bind; see docs/https-via-proxy.md.

All options and defaults are in nix/nixos-module.nix. The module is entirely opt-in: nix build, nix run, nix develop, and the CLI are unchanged if you don't import it.

git is a runtime dependency. Hook capture and ai-memory doctor run it to read the repository a session works in. The module puts pkgs.git on the service's PATH. The package itself leaves the binary unwrapped, because a wrapper renames the running process and the live-process guard in reset, restore, reindex and uninstall --purge-data looks for a process named ai-memory. If you install the package without the module (nix profile install, home.packages), install git next to it.

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.
• Import a local PDF, DOCX, PPTX, XLSX, Markdown file, or VTT/SRT transcript as attributed, sanitized memory with the optional ai-memory-importer document command.
• Ask "have we discussed X?" or "search memory for Y" to query the wiki.
• Narrow a large wiki before ranking with memory_query filters such as path_prefix: "procedures/", tag: "rust", kind: "decision", or tier: "procedural".
• Inspect content-free per-workload LLM call, outcome, latency, and usage telemetry with ai-memory status --json; it diagnoses the configured provider but does not route different workloads to different models.
• Ask "catch me up" for a prose digest of recent project activity, or run ai-memory recent (--global for every project) to list the newest pages.
• Search Chinese, Japanese and Korean text without spaces: zero-LLM search matches unspaced Han, Kana and Hangul substrings.
• 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, a link graph at /web/graph, a root/operator managed-workstream overview at /web/workstreams, and a JSON API under /api/v1. On a server with a bearer token and no user accounts (every fresh install), the browser asks for a username and password: any username, and the contents of <data_dir>/auth-tokens/default as the password.
• Back up host-side harness configuration with ai-memory backup-agents -o agent-assets.tar.gz; inspect a restore with ai-memory restore-agents -i agent-assets.tar.gz, then add --apply only after reviewing the active skills, plugins, instructions, and destinations.

Search modes, entities, feedback, briefings, and the web API are covered in docs/usage.md and docs/use-cases.md.

Run the server somewhere reachable, such as a homelab box or 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. It includes measured capacity numbers and the one rule you must follow: one server per data directory, never two.

Two opt-in HTTP surfaces build on the same browser sign-in, and both are off by default. serve --enable-session-management lets signed-in users inspect sessions and purge the ones they own (docs/session-management.md). serve --enable-self-service-keys lets them issue and revoke their own aim_ API keys (docs/self-service-keys.md).

The server binds to loopback only by default. A fresh ai-memory init also mints a root bearer token into the data dir, which the hooks, the CLI and the ai-memory mcp-bridge stdio bridge read from there; it is never written into a harness config. An install created before that default (and the Docker quick-start) runs without auth until ai-memory auth enable, and ai-memory status says so. 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. Human login beyond loopback requires an HTTPS posture by default. Operators who explicitly accept plaintext passwords, cookies, credentials, and memory traffic on a private LAN can opt in with serve --allow-insecure-human-auth; the server never enables that exception implicitly.

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), the Cursor Agent CLI (cursor), 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, an observation-evidence stream that lifts a session's page when its captured turns match (on by default), 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.

Dated design-*, research-*, issues-*, desktop/, and benchmarks/ files are evidence and design records, not current operating instructions. Their status or snapshot banner says whether work shipped, remains proposed, or has been superseded. Use the user guides and ARCHITECTURE.md for current behavior; the older records remain only when current design rationale links to them.

Written by users; not maintained here, so check them against the version you run.

• Installing and configuring ai-memory on Linux (pt-BR, LinuxPro, covers v2.6.3)
• ai-memory from v2.4 to v2.6.3 (pt-BR, LinuxPro)

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.