AGENTS.md · git:20261004.5baa412 · 2026-10-04 · sha256 3e4f6ba078d38e71
AGENTS.md git:20261004.5baa412A
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# mempalace — Agent Guide ## 0. Rule Zero — smallest justified solution Rule Zero is owned by the "Rule Zero — smallest justified solution" section of the maintainer's user-level `~/.codex/AGENTS.md`; Claude Code reads the identical section from `~/.claude/CLAUDE.md`. It is not restated here; every rule in this file is a local addition to it. **Release credential boundary.** Release preparation, qualification, admission, and publication gates must never execute external AI clients such as `codex`, `claude`, or `gemini`; run their authentication commands or provider/model calls; or read, copy, inspect, require, or transmit user credentials, API keys, OAuth tokens, keychains, or paid-account state. Verify MemPalace with its local CLI, credential-free stdio MCP protocol checks, installed-package behavior, and credential-free hosted workflows. An optional interoperability exercise with an AI client requires separate explicit owner authorization, runs outside the release gate, and cannot block a release. **Commit attribution boundary.** Commits in this repository must not contain a Claude `Co-Authored-By` trailer. Configure automation to omit it; do not add it and clean it up later. ## Audience and default scope - The default audience is developers asking questions about repository code. Ground answers in current source, tests, repository documentation, and Git history. - Users preserve valuable project results in repository artifacts and commits. Use those version-controlled artifacts as durable project context. - Inspecting or mining stored user conversations, maintaining diaries, and adding, enabling, or repairing conversation-save reminders or persistence guarantees require an explicit request for that workflow. An audit finding about an optional conversation workflow does not bring it into a code-question task. ## Stack - **Python** 3.11+ (supports 3.11–3.14) - **Storage**: LanceDB (core, crash-safe vector DB — no server required) - **Embeddings**: FastEmbed/ONNX by default; SentenceTransformer only in `[custom-models]` - **MCP**: the official `mcp` Python SDK - **Config**: PyYAML - **Runtime dependencies**: declared once in `[project].dependencies` of `pyproject.toml` - **Linting / formatting / typing**: Ruff + Pyright - **Tests**: pytest - **Package manager**: uv (preferred) or pip ## Dev Setup ```bash # With uv (preferred) uv pip install -e ".[dev,spellcheck,treesitter]" # With pip pip install -e ".[dev,spellcheck,treesitter]" ``` These are the extras the CI type check installs; with `.[dev]` alone Pyright reports the Tree-sitter imports as missing. No Docker required. Everything runs locally in a venv or with pipx. Optional extras: - `.[custom-models]` — arbitrary SentenceTransformer models; follow `docs/OFFLINE_USAGE.md` - `.[dev]` — test, lint, type-check, build, and release tooling - `.[spellcheck]` — autocorrect helper API (`mempalace_code.spellcheck`); mining never applies it - `.[treesitter]` — Tree-sitter AST parsing - `.[watch]` — automatic mining on file changes ## Running Tests **Important**: Use the Python from your mempalace virtualenv (pipx venv, `.venv`, etc.), not the system Python which may lack dev deps. ```bash # Full suite (stop on first failure) python -m pytest tests/ -x -q -m "not needs_network" # Per-module python -m pytest tests/test_storage.py -v python -m pytest tests/test_mcp_server.py -v python -m pytest tests/test_miner.py -v python -m pytest tests/test_convo_miner.py -v ``` ## Linting / Formatting ```bash # Check ruff check mempalace_code/ tests/ scripts/ # Format check ruff format --check mempalace_code/ tests/ scripts/ # Type check (gating in CI — must exit 0) python -m pyright --pythonpath "$(python -c 'import sys; print(sys.executable)')" python -m pyright -p pyrightconfig.strict.json # Auto-fix lint ruff check --fix mempalace_code/ tests/ scripts/ # Auto-fix format ruff format mempalace_code/ tests/ scripts/ ``` Line length: 100. Target: py311. Quote style: double. The complete gate set the **Tests** workflow runs on every pull request is listed once, in `CONTRIBUTING.md` under "Running Tests". ## Key Modules The main owners under `mempalace_code/`. The full module guide is `mempalace_code/README.md`. | Module | Purpose | |--------|---------| | `storage.py` | LanceDB vector storage — add, search, delete, health_check, recover | | `backup.py` | Tarball backup/restore — `mempalace-code backup`, scheduled backups | | `mining/` | Project miner package — scanning, language detection, chunking, symbols, batching, orchestration; `miner.py` is a compatibility shim | | `convo_miner.py` | Conversation miner — ingests Claude/ChatGPT/Slack exports | | `searcher.py` | Semantic search — query palace with optional wing/room filters | | `knowledge_graph.py` | Temporal KG — entity-relationship triples with validity windows | | `layers.py` | Tiered context loading — L0/L1/L2/L3 wake-up layers for local models | | `palace_graph.py` | Graph traversal and tunnel detection across wings/rooms | | `mcp/` | MCP server package — `registry.py` (the `TOOLS` dict), `dispatch.py`, `runtime.py`, `tools/`; `mcp_server.py` is the public entrypoint shim and `mcp_launcher.py` backs `mempalace-code-mcp` | | `watcher.py` | File watcher — `watch_and_mine`, `watch_all`, launchd/cron schedule rendering | | `cli_commands/` | CLI command handlers; `cli.py` is the thin `mempalace-code` entry point and compatibility facade | ## Architecture Principles 1. **Verbatim-first** — store content exactly as provided; do not summarize or compress drawers. 2. **Local-first** — no external APIs required; default embeddings run on-device via CPU FastEmbed/ONNX. 3. **Zero-API-by-default** — LanceDB and FastEmbed work offline after explicit model setup. ## Storage Backend - **LanceDB** is the core backend (installed by default via `lancedb>=0.20`). - **ChromaDB** support is retired from current packages because every available release is advisory-affected. Back up a legacy source palace before upgrading, then use the last public bridge release in isolation: `uvx --python 3.12 --from 'mempalace-code[chroma]==1.13.4' mempalace-code migrate-storage SRC DST --verify`. ## Embedding Model Policy - **Current default**: `all-MiniLM-L6-v2` (384d, normalized CPU FastEmbed/ONNX). - **Cache authority**: `$HF_HOME/mempalace-fastembed/all-MiniLM-L6-v2-v1/` plus its immutable `.mempalace-model.json` provenance. Recovery is `mempalace-code fetch-model` while online. - **Custom-model boundary**: arbitrary Hugging Face names and local SentenceTransformer paths use the explicit `[custom-models]` extra. On CPU-only Linux, follow the ordered installation and recovery contour in `docs/OFFLINE_USAGE.md`; only that explicit custom-model path may use `trust_remote_code=True`, and canonical MiniLM aliases never do. - **No code-only models**: CodeBERT, UniXcoder, etc. improve code at the expense of prose. Only general-purpose sentence-transformers that handle both are candidates. ### Text gate An embedding model change is gated behind A/B benchmark results (`benchmarks/embed_ab_bench.py`) and must pass both the code gate (the A/B code-retrieval results) and the text gate: match or beat MiniLM on LongMemEval R@5 (text retrieval). Text quality is non-negotiable — this is a code-first fork but natural language search (conversations, commits, decisions) must not degrade. The only LongMemEval harness, `benchmarks/longmemeval_bench.py`, is the legacy upstream ChromaDB-based script. It imports `chromadb`, which current packages do not install and whose available releases carry known advisories, so it runs only in an isolated, disposable legacy environment. No text baseline has been recorded, so a model change must first record one there. The gate cannot be skipped: without that environment and baseline it has not run, and the model change is blocked. ### Historical Benchmark Results — 2026-04-09 (BENCH-EMBED-AB) Historical record, measured with the fixed 20-query set that `benchmarks/embed_ab_bench.py` used at the time. It is not comparable with the current `benchmarks/code_retrieval_bench.py` dataset (symbol and file-only truth, schema-2 report), and no current baseline has been recorded. Record a fresh dated baseline on the current dataset before the next model decision. Code retrieval on the mempalace repo (20 known-answer queries, 469 chunks): | Model | R@5 | R@10 | Embed(s) | Query(ms) | Index(MB) | |--------------------|-------|-------|----------|-----------|-----------| | all-MiniLM-L6-v2 | 0.950 | 1.000 | 15.2 | 15.9 | 17.0 | | all-mpnet-base-v2 | 0.900 | 1.000 | 47.5 | 30.5 | 17.7 | | nomic-embed-text-v1.5 | 0.950 | 1.000 | 85.4 | 45.8 | 17.7 | Per-category R@5: | Model | architecture | class_lookup | cross_file | function_lookup | |--------------------|:---:|:---:|:---:|:---:| | all-MiniLM-L6-v2 | 0.800 | 1.000 | 1.000 | 1.000 | | all-mpnet-base-v2 | 0.600 | 1.000 | 1.000 | 1.000 | | nomic-embed-text-v1.5 | 1.000 | 1.000 | 1.000 | 0.833 | **Recommendation: minilm remains default.** - mpnet regresses on code R@5 (0.900 vs 0.950) while being 3× slower to embed and 2× slower at query. Eliminated. - nomic ties minilm on code R@5 (0.950) but is 5.6× slower to embed and 2.9× slower at query, with a 550 MB model vs 80 MB. No net gain. - [Text-gate](#text-gate) (LongMemEval) evidence was not collected — prerequisites missing (`benchmarks/data/longmemeval_s_cleaned.json` not present, `fastembed` not installed). - Full results: `benchmarks/results_embed_ab_2026-04-09.json`. ## Git Workflow - **Two histories**: local `main` is the development trunk; work reaches it from topic branches. Public `main` carries one squashed commit per release and advances only by fast-forwarding a release candidate built and admitted per `docs/RELEASING.md`. - **Branch naming**: `feat/<slug>`, `fix/<slug>`, `chore/<slug>`, `docs/<slug>` for development; `release/vX.Y.Z` (rebuilds `release/vX.Y.Z-rc2`, `-rc3`, …) for public release candidates only. - **Conventional commits**: - `feat:` — new feature or capability - `fix:` — bug fix - `docs:` — documentation only - `test:` — test additions or changes - `bench:` — benchmarks - `chore:` — maintenance, deps, tooling - Release candidate commits use the subject `release vX.Y.Z`. - **No force-push to `main`**. - Pull requests on GitHub, including Dependabot updates, are never merged on public `main`: the next release candidate would silently revert them. Port an accepted change into the development trunk, ship it in the next release, then close the pull request with a note naming that release. ## Maintainer-Local Tools The `autopilot` and `backlog-utility` tools are maintainer-local; they are not distributed with this repository. `/task-plan` and `/task-hardening` admit executor state only through `autopilot` and stop when it is unavailable. Current task contracts and planning are owned by `.backlog` (backlog-files/v2). Read `backlog_context`, `backlog_workset_context`, and `backlog_workset_queue` through the verified project-bound MCP connection. CLI fallback uses a verified absolute `backlog-utility` binary with `call --store .backlog --project mempalace-code --principal <principal> --name <tool> --arguments-file <json>`. Unavailable or mismatched access means unknown backlog state; never fall back to active YAML or edit canonical JSON by hand. `docs/BACKLOG.yaml` retains section metadata; `docs/BACKLOG-archived.yaml` retains historical completion evidence. Recovery: `<absolute-backlog-utility> validate --store .backlog`. Edits still require admitted authority and no conflicting writer. ## Operational Lessons - **Keep this file and public docs public-safe.** `AGENTS.md` is the canonical public instruction file; `CLAUDE.md` is only its pointer. Public docs may name package ranges, workflow categories, advisory IDs, and reproducible commands. Private remotes, hostnames, tokens, credentials, local machine paths, customer/project details, incident specifics, and non-public operational history never go here or in other public docs; put private or machine-local lessons only in an ignored local-only note outside the published tree, such as `.codex-local/LESSONS.md`. - **Record reusable lessons only when they are public knowledge.** When a session exposes a project gotcha, publish step, verification boundary, or agent-behavior correction that is safe for public readers and useful to future contributors, rewrite the matching lesson here in place, or add one when none matches. - **Verify on the surface that will run the change, and name what was not run.** Python tests alone do not prove GitHub Actions runtime changes: use local YAML/static checks such as `actionlint`, then verify the real hosted workflow run when action runtime behavior matters. Direct handler calls are useful for MCP compatibility but do not prove a separate stdio MCP client, and CLI help does not prove the command executes. A release-readiness summary must distinguish unit tests, focused integration tests, direct API smoke, real CLI execution, and hosted/daemon behavior that was not run; a tag-only or release-only workflow without a real trigger run is reported as syntax-checked and version-checked, not execution-tested. Never imply full local coverage for hosted-only behavior. - **Check the intended public release target.** Before publishing, verify the repository, branch, tag, and workflow that public users will see. Do not assume local remote names or private mirrors represent public release truth. - **Treat release status as multiple independent facts.** Branch Tests, tag-triggered PyPI publish, GitHub Release creation, PyPI version visibility, and any deployment/release-environment status can diverge. Check all of them before calling a release published or latest; if one is red or missing, either fix it now or record the explicit remaining blocker. - **Test dependency drift with a fresh resolver.** Local `.venv` and `uv.lock` success can hide what GitHub Actions or users get from an unlocked `pip install`. For dependency-sensitive failures, reproduce in a clean pip environment matching the hosted workflow before declaring the tests fixed. - **Audit dependency targets before raising bounds.** For runtime, dev, and optional extras, check current and target versions against OSV or an equivalent advisory source, then run a resolver-level audit on a fresh environment. Do not raise optional legacy backends into advisory-affected ranges; hold or cap them and backlog the upgrade gate instead. - **Keep benchmark gates tied to measured baselines.** If a release benchmark fails, reproduce it locally against the pinned fixture, update the CI threshold only to the observed stable baseline, and backlog any desired quality increase separately. - **Do not call tests "local feature testing."** When asked to test new features locally, run the public CLI/MCP/API behavior itself, not only pytest. For each new feature, exercise at least one success path and one important failure/guard path when safe, record the exact command or request, and name any behavior that was covered only by tests. - **Clean up smoke-test artifacts immediately.** Real backup, cleanup, and benchmark smokes can create archives, temp palaces, and result JSON. Put them under disposable temp paths, verify success/failure, then remove artifacts or explicitly report what remains. - **Treat palace disk growth as storage forensics first.** Compare backup size, live storage stats, row counts, and cleanup output before deleting anything. Preserve non-regenerable manual drawers and KG data, stop active writers when needed, use supported cleanup APIs, and verify health/status afterward. ## LDR adoption and problem handoff - LDR (`ldr/v1`) is the default format for current durable architectural decisions. Canonical accepted records live under `docs/decisions` when present. Historical ADRs remain evidence; do not migrate them or invent records for routine tasks. Reuse unchanged applicable accepted decisions without a new proposal or repeated record approval. No applicable records means an explicit empty selection with its applicability reason in the existing task context. - Apply this rule before architecture, durable-contract, lifecycle, ownership or recovery changes. Start with one decision needed by the current task; keep task state and execution evidence with their existing owners. - Discover applicability from the task and repository-owned references. Resolve the exact accepted key/revision; inspect status first and apply only active records. Check current committed bytes, owner, local references and fitness. Search results and historical prose cannot accept a decision. - A missing or stale binding blocks only the dependent change. Propose a record outside the canonical accepted root; only the operator accepts active records. Same-key changes advance revision by one and preserve lifecycle/recovery. - Use the existing adopter and pinned validator when present. Do not replace a pin, install a utility, add a runtime gate, or migrate historical decisions as part of this instruction. Run the current task's existing relevant checks. - For a reproduced LDR problem, prepare and deliver the targeted handoff below during the same work session without waiting for a separate request. Identify whether the failure belongs to LDR or the adopter; record uncertainty. Continue independent safe work while the affected action remains blocked. - Private maintainer routing and the first scoped adoption candidate are in .codex-local/LDR.md when present. Read that note before sending an LDR finding. If no route is configured, retain the sanitized draft and report missing_route. Keep machine paths, private transport and incident evidence out of public docs.