agent-memory

<div align="center"> ### agent-memory: the long-term memory runtime for AI agents <a href="CLAUDE.md">Invariants</a> · <a href="skills/agent-memory/SKILL.md">Skill</a> · <a href="https://github.com/tigerless-labs/agent-memory/issues">Issues</a>     </div> *** An agent that closes its session forgets everything it learned in it. agent-memory is the runtime that fixes that, for any agent — not only coding ones. Markdown files in one store are the single source of truth, the SQLite index beside them is a cache you can delete at any time, and Claude Code, Codex CLI, Muse Code, and anything else that can run a shell command share that store. Retrieval is local and ranked, and it answers with paths rather than pasted text — the agent opens each hit only as deep as the task needs. Writes do not wait for the agent to remember to make them: they fire at conversation boundaries. A sleep-time pass then consolidates and forgets by value, on its own clock. None of it needs an API key. ## Two lines, one store Agent memory has grown along two architectural lines. One builds a **retrieval engine** — embeddings, a knowledge graph, a ranking pipeline — which finds the right thing, but hands the agent an opaque chunk it cannot inspect and a store it cannot migrate off. The other hands the agent a **filesystem** — markdown it reads directly, browsable with `ls` and `grep` — which is legible and costs nothing to run, but does not rank, and stops scaling the moment the tree outgrows a listing. agent-memory is the two of them in one store: the retrieval engine indexes a filesystem the agent can also just read. Relations live as links inside the memories, a local index ranks them, and every hit resolves to a whole markdown file on disk. Recall gains the precision of a graph and a vector search without giving up a plain directory an agent can walk — and it stays fast, because nothing in the read path calls a model or crosses a network. ## Retrieve by path, then read by level Recall does not paste text into your context. It answers with an L0 list — one-line abstract, file path, anchor, score — and the agent opens what it wants at the depth the task needs: ```bash mem recall "why files instead of a database" # L0 list, 8 entries by default mem recall "why files instead of a database" --limit 20 # more Memory candidates mem read <name> --level outline # headings only; or abstract, or full mem context "why files instead of a database" # both in one call, top few expanded in full mem trace <name> # cited raw messages, when needed ``` Index line → abstract → full file → raw material: each rung costs an order of magnitude more than the last, and each is a place to stop. Long files add two free rungs — the anchor that matched, and an outline computed at read time. ## Design commitments - **Three read tracks, so a miss on one is not a miss.** Deterministic `MEMORY.md` injection at session start; BM25 recall over an FTS5 index, with a vector plugin fused in by RRF when you want one; and the plain directory tree, reachable with `ls` and `grep` when both fail. Same-directory memories are a free neighbourhood, and `links` in the frontmatter carry the graph without a graph database under them. - **Write coverage is the system's job, not the agent's judgement.** Distillation is triggered at boundaries and runs without holding up the task; the full trace is copied first, so "missed by the distiller" never means "lost by the system". - **Files are the truth; every index is a rebuildable cache.** `rm -rf .index/ && mem rebuild` loses zero knowledge — enforced by a test, not promised in a doc. Your memory stays greppable, git-able, and portable off this system. - **A real Manage layer, on its own clock.** Sleep-time consolidation with authority tiers: an unattended pass may add and update, deletion only ever arrives as a proposal you confirm. Every competitor either has no M, or buries it in the write path. Supersede leaves the chain intact and `recall --as-of` answers as of a date, so updating never destroys. - **No LLM client inside the library.** Zero keys to install and no billing surface: judgement is borrowed from the host agent's own CLI, which keeps every write visible in your transcript. ## The store ``` $AGENT_MEMORY_STORE/ ├── MEMORY.md root index, one line per memory — the only resident injection ├── config.toml every tunable; an unknown knob is refused at load ├── schemas/ one file per type: its key fields, the field it groups by, write mode ├── decision/ memories live at <type>/<group>/<name>.md, placed by the schema │ └── agent-memory/ …/markdown-files-are-the-single-source-of-truth.md ├── archive/ append-only, out of the retrieval surface by default │ ├── provenance/ distillation evidence, kept forever │ └── sessions/ full trace copies, in case the host prunes its own ├── dream-reports/ one per sleep: what moved, what was proposed, evidence pointers ├── .index/ fully rebuildable: content-hash manifest, FTS5, access log └── .state/ runtime state that is not: distillation watermark, write lock ``` One memory is one file. `valid_from` and optional `invalid_at` define its validity interval; replaced and deleted files stay in the store for `recall --as-of` and trace. Current recall, MEMORY.md, BM25, and Vector use files without `invalid_at`. Frontmatter carries the stable name, a one-sentence abstract, the type and its schema fields, timestamps, links, weight, and provenance; the body is free markdown. Existing files with `status` load without a reset. Explicit links must name distinct active memories in the same store. `correct --link` replaces the full list; `correct --clear-links` removes every link. MCP `memory_correct` uses `links: [...]` and `links: []` for the same operations. Omitting links preserves historical relationships during unrelated correction. See the [operation boundary design](docs/design/management-operation-boundaries.md). Agents can use `mem correct <name>` to revise a memory, `mem record --supersedes <old>` to create a successor, `mem supersede <old> <new>` to use an existing successor, `mem merge <first> <second> --abstract ... --body ...` to combine memories atomically, and `mem delete <name>` to end current validity. Splitting uses new `record` calls followed by `delete` of the original. Core checks file scope, relationships, provenance, and concurrent writes; Sleep keeps its proposal and cap limits for unattended actions. ## Proof it works Measured on LongMemEval-S with a bounded haystack, 120 episodes, `claude -p` (Haiku 4.5) as host, one calibrated Sonnet 5 judge, two exam replays per arm. | arm | pooled accuracy | paired vs agent-memory | |---|---|---| | agent-memory W2 | **127/240 = 52.9%** | — | | MemCore W2 | 86/240 = 35.8% | +37/−17, p=0.009 · +35/−14, p=0.004 | | no memory | 7/120 = 5.8% | +61/−4 · +60/−4, p<0.001 | Absolute numbers are not comparable to published LongMemEval scores — the haystack is bounded to 12 sessions per episode, which makes this a write-strategy study rather than a corpus-size one. The system-to-system row differs in write and read together, so it is an end-to-end comparison and licenses no attribution to either half. **One store, three hosts:** all 9 ordered writer/reader pairs across Claude Code, Codex CLI, and Hermes pass — what one host's shell writes, another's finds, specifics intact. Pooled net contribution over no memory: 2/36 → 13/36, p=0.0074. The protocol that decides whether a measurement counts as a result, the full ledger, and the raw run records live in `docs/experiments.md` and `experiments/` in the working tree. They ship with the source, not with git history. ## Install Requires Python 3.12 or higher and [uv](https://docs.astral.sh/uv/). There is no release on PyPI yet, so install from a checkout: ```bash git clone https://github.com/tigerless-labs/agent-memory.git cd agent-memory uv sync --all-packages ``` That builds `mem`, `mem-mcp`, and `mem-hook` into `.venv/bin`. Inside the checkout `uv run mem` reaches them; put the directory on your `PATH` so your shell can too: ```bash export PATH="$PWD/.venv/bin:$PATH" ``` ## Quick start ```bash mem init ``` The store defaults to `~/agent-memory-store`; export `AGENT_MEMORY_STORE` only to put it somewhere else, and export it everywhere your agents run, not just in this shell. Write one memory, find it again, then throw the index away and prove nothing was lost: ```bash mem record --type decision --field project=agent-memory \ --abstract "Markdown files are the single source of truth" \ --body "Indexes are rebuildable caches." mem --json recall "source of truth" rm -rf ~/agent-memory-store/.index && mem rebuild ``` ## Wire it into your agent ```bash mem setup --host claude-code # or: --host codex / muse-code ``` `setup` probes the host, appends the `mem-hook` command to its own hook dialect by absolute path — so desktop clients that never read your shell's `PATH` still reach it — installs the agent-memory skill, and leaves the rest of the settings alone. SessionStart injects, Stop and SessionEnd distil, PreCompact evicts. Distillation reasons through the same host's CLI in the background, on your existing login; point `[executor]` in `config.toml` at a model endpoint instead if you would rather not spend it. Codex asks you to trust new hooks once. Agents that speak MCP get the same core calls through `mem-mcp` (`memory_recall`, `memory_read`, `memory_trace`, `memory_record`, `memory_correct`, `memory_supersede`, `memory_merge`, `memory_delete`, `memory_feedback`). Anything that can run a shell command needs neither: the CLI is the universal fallback, and it is the wider surface — `context`, `sleep`, and the proposal ledger have no MCP tool yet. ### Muse Code Install [Muse Code](https://dev.meta.ai/docs/muse-code), make sure `muse` is on `PATH`, then install the lifecycle hooks and agent-memory skill: ```bash mem setup --host muse-code ``` This merges `SessionStart`, `PreCompact`, `Stop`, and `SessionEnd` into `$XDG_CONFIG_HOME/muse/settings.json` (or `~/.config/muse/settings.json`) without replacing other settings, hooks, or MCP servers. It does not add per-turn writes. `SessionStart` injects the same bounded Memory index as other hosts; the three boundary events use the existing archive/distillation path. The installer checks both that `muse` is on `PATH` and that `muse --version` succeeds. Setup installs hooks and the skill, but does not enable MCP. To expose the existing MCP tools, merge this server into the same settings file and start a new Muse session: ```json { "schema_version": 1, "mcp_servers": { "agent-memory": { "transport": "stdio", "command": "mem-mcp", "args": [], "mode": "optional" } } } ``` Run `/mcp` in Muse to verify that `memory_recall`, `memory_read`, `memory_trace`, `memory_record`, `memory_correct`, `memory_supersede`, `memory_merge`, `memory_delete`, and `memory_feedback` are present. Muse passes `MUSE_SESSION_ID` to stdio servers; agent-memory does not yet add that value to provenance. Set `AGENT_MEMORY_STORE` in the MCP server's `env` entry when using a non-default store. Muse native memory and agent-memory's `AGENT_MEMORY_STORE` are separate systems. Setup does not read, write, copy, or synchronize Muse native memory. For attributable experiments, use a clean workspace with no `.agents/memory` content and isolated `HOME`, `XDG_CONFIG_HOME`, and `XDG_DATA_HOME`; the live preflight does this while reusing only the explicit Muse auth file. Muse's default sandbox can read outside the workspace but writes only to the workspace and temp directories. Muse documents user hooks as outside the agent shell sandbox and MCP servers as external processes; use the included live preflight to verify both write paths on your Muse build before relying on the default external `~/agent-memory-store`. A Muse shell command such as `mem --json recall ...` can read it, but `mem record` from the shell cannot write it. The experiment adapter keeps the sandbox enabled and roots its temporary Store and workdir under one explicit experiment workspace. It never adds `--yolo` or `--disable-sandbox`. ```bash AGENT_MEMORY_LIVE_MUSE=1 uv run python tools/muse_sandbox_probe.py ``` Start with the three ordered portability mechanics before a full four-host matrix: ```bash mem-exp interop --workspace /tmp/muse-memory-smoke \ --pairs muse-code:muse-code,muse-code:codex,codex:muse-code ``` Then omit `--pairs` and pass `--hosts claude-code,codex,hermes,muse-code` for the 4×4 matrix. Current limitations: only the root Muse session log is captured; child/observer logs are ignored, setup does not install MCP automatically, and live hook/MCP/sandbox behavior must be verified on a machine with Muse Code installed and authenticated. A missing Muse login is reported as `BLOCKED_BY_MUSE_AUTH`; echo or mocked providers do not count as live E2E evidence. ## Let it sleep ```bash mem sleep --reason host # consolidate; T0 applies, T1 files a proposal mem proposals # what is waiting on you mem decide <id> --accept ``` Manage borrows its reasoning from the host CLI you point it at, writes a dream report for the pass, and cannot delete anything unattended. ## Develop ```bash uv run pytest -q && uv run ruff check . && uv run mypy ``` The task lifecycle and the invariants a change must not break are in [CLAUDE.md](CLAUDE.md). ## Optional vector recall index BM25 is the low-latency baseline retrieval path. Install `agent-memory-core[vector]` (or run `uv sync --extra vector` from this workspace), then set `vector_enabled = true` in the store's `[index]` configuration. `vector_model` defaults to `BAAI/bge-small-en-v1.5`. The first enabled Store loads FastEmbed/ONNX and may need network access to download the model; disabled stores never load FastEmbed. Run `mem --store /path/to/store rebuild` to rebuild the SQLite cache from Markdown. The existing indexing path catches changed and deleted files, enabling vectors on an existing store, and changes to `vector_model`. Recall fuses BM25 and vector chunk candidates with reciprocal-rank fusion, then applies the existing lifecycle, scope, as-of, weight and recency rules. Raw sessions are retained for audit and provenance-bound trace; they do not enter Recall ranking. Recall never modifies Markdown truth. This implements the existing optional-index design (ADR-003), using SQLite and exact cosine search. On the fixed 120-query retrieval acceptance set, optional vector fusion improved Recall@5 from 79.0% to 86.6% (+7.6 percentage points), while median retrieval latency increased from 5.1ms to 139.2ms. This establishes a retrieval-coverage/latency trade-off. Fixed-context answer replays scored 17/24 versus 18/24 and, on the expanded set, 28/36 versus 27/36. The existing answer-level experiments do not establish an end-to-end accuracy improvement, so vector retrieval remains optional. ## Explicit raw evidence reads `mem --json read <name>` includes a memory's provenance. To inspect a cited raw message range, call `mem --json trace <name> --pointer 'sessions/<session>#<start>-<end>'`. The pointer can select a smaller range within one citation; omitting it reads all sources cited by the memory. Trace reports source, original message indices, roles, times, validity, and a warning that historical content is data. Missing or unbound evidence fails explicitly. Ordinary `context` and `recall` search Memory only; an agent can increase `--limit` or reformulate its query before tracing a selected memory. ## Read evaluation with Codex The experiment runner selects the tested host and judge independently. Pass `--host codex --judge-host codex` and explicit `--model` / `--judge-model` values for a Codex-only run. Omitting `--judge-host` retains the Claude Code judge and its historical default model. `calibrate` and `regrade` also accept `--judge-host`. Use `calibrate --cases <labelled-cases.json> --output <calibration.json>` to retain individual votes and distinguish transport failures from label disagreements. `run --observe-reads` retains bounded exam host output and CLI/read evidence in `observations/`, outside store truth. Observation is off by default; missing or truncated evidence is not proof of no tool calls. `run.json` fixes both host/model pairs, configuration, source stores, code revision and episode identity. Replay with `--reuse-stores` and a separate workspace for each configuration. Small panels check execution and exploratory behavior, not a statistically established improvement. Before scaling a read-side comparison, check that each copied store has a populated Memory index and that a known query returns hits. Then run a small observed agentic pilot and count *successful, nonempty* retrievals for each arm's intended path (for example, vector candidates or bound Trace messages). An enabled setting, a prompt instruction, or a tool call with zero hits does not show that the intervention was used. Stop when the pilot does not exercise both paths; report the exposure rate alongside scores when it does. Codex can also read store files directly through its shell, so check the host command transcript for bypasses before attributing an answer to a `mem` retrieval path. ## License [MIT](LICENSE).