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PMB (Personal Memory Brain)

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from oleksiijko

Local-first persistent memory for AI coding agents over MCP: decisions, lessons and facts persist across sessions via hybrid BM25 + vector + graph retrieval, fully offline, no API keys.

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PMB

Local-first memory for your AI coding agent.

SQLite is the source of truth. No cloud, no API keys, no re-explaining.

Local-first memory, visualized. 3,800+ entities and 41,000+ connections, captured automatically as you work.

Website Β· Docs Β· Quickstart Β· Demo Β· Why PMB Β· How it works Β· FAQ

Your AI agent forgets everything between sessions. So you re-explain the same decisions, lessons and constraints over and over. PMB remembers them in one local workspace and feeds them back through MCP - no cloud, no API keys, no LLM call on the read path. And it tells you when memory is actually helping, instead of claiming "+X%".

⭐ Star the repo if PMB saves you a re-explanation.

PMB gives Claude Code, Cursor, Codex and the other MCP-aware agents a real memory: decisions you made last week, lessons you taught them, personal facts, project structure, PDFs. They survive every restart, every model upgrade, every agent switch - because they live in a local workspace you own, with SQLite as the durable source of truth and rebuildable search indexes beside it.

No API keys. No subscription. No LLM call on the read path. Just local files.

Demo

What it feels like - same prompt, with and without memory:

β–Ά Click to play the 2-minute setup

Copy & paste β€” that's it
You: fix that LoadGuard pricing bug we hit last Tuesday

Agent: prepare(message="fix LoadGuard pricing bug")
 ↓ (6 ms)
 β†’ project_context: 12 events about LoadGuard,
 4 lessons (incl. "never lower NEGOTIATE/SKIP under 25%"),
 3 decisions, 2 open goals
 β†’ active_arcs: "LoadGuard verdict-policy refactor (started Tue)"
 β†’ lessons: 1 directly matches "pricing"
 β†’ recent_activity: 8 commits this week

 Looking at src/engine/verdict-policy.ts (you opened it Tuesday
 14:32, last changed Wednesday). The pricing bug is on line 142
 where the bundle fallback tightened below the rate-floor. Per the
 lesson you saved, we never drop the threshold under 25% - restoring
 the guard there.

The agent didn't ask. It didn't guess. It read its memory in 6 ms and showed up already informed.

Why PMB

  • Local-first, zero-config. SQLite holds the durable memory; rebuildable search indexes stay local. No account, API keys, telemetry, or cloud to trust.

  • It actually gets used. On Claude Code / Codex, hooks inject the right memory before the model thinks - you never have to remember to call a tool.

  • Multilingual, no setup. The embedder covers 50+ languages; a Russian query finds an English fact. No per-language config.

  • MCP-native. One pmb connect wires Claude Code, Cursor, Codex, Windsurf, Zed, VS Code, and more.

  • Fast read path. Recall in ~35 ms warm; writes return in under a millisecond - no LLM call to remember.

  • Honest impact. The dashboard shows which lessons actually changed outcomes, instead of claiming "+X%".

  • Your data, in the open. pmb export dumps everything to Markdown/JSON. Apache 2.0.

See your memory

pmb dashboard opens a local, liquid-glass web UI on http://127.0.0.1:8765 over everything PMB captured - written automatically, just by working. It binds to 127.0.0.1 only, so nothing leaves your machine.

Map - every entity and connection in your project, as a live graph.

Timeline - your memory as a journal, newest first.

Nine tabs: Map (entity graph, live), Timeline (git-graph by project), Overview, Entities, Arcs (narrative threads), Lessons (per-rule follow-rate, dead-lesson detection), Duplicates (inline merge), Performance (per-tool latency), Recall (debug ranker).

What you can store

Copy & paste β€” that's it
# Personal facts that change (time-travel: old values archived, never lost)
record_keyed_fact("user", "city", "Warsaw")

# Project structure - symbols, imports, .gitignore-aware
pmb index project .

# Why each file exists + the intent behind every commit (Haiku-summarised, local)
pmb track modules # one-line purpose per indexed file
pmb track changes # new commits: what changed and WHY

# PDFs (research papers, manuals, contracts)
pmb index pdf paper.pdf
pmb index pdf ~/docs --recurse

# Whatever your agent logs as it works: decisions, lessons, completed tasks, goals

PMB is content-agnostic. If it's text the agent will care about later, PMB remembers and retrieves it.

What the agent gets back

A single MCP call - prepare(message) - returns the right things at the right level of detail, in 4-16 ms:

Field What it is project_context Full project overview if the message mentions a project: key facts, lessons (RULES to follow), decisions, open goals, related entities, the project's narrative arc lessons Procedural rules matching the query, each with a surface_id so the agent can confirm it followed the rule later recent_activity Last 24 h of decisions / edits / completions for session continuity open_goals In-progress goals so the agent knows what you're pursuing active_arcs Narrative arcs the project is currently living in

For everything else there's recall(query) (hybrid search, 35 ms warm) and 27 other tools in docs/reference/COMMANDS.md.

How it works

Copy & paste β€” that's it
flowchart LR
 A[Your agent] -->|MCP stdio| B[PMB MCP server]
 B --> C[Engine]
 C -->|read 35 ms| R[Hybrid recall BM25 + vector + graph + rerank]
 C -->|write under 1 ms| W[Async embed queue SQLite first, vectors later]
 R --> D[(SQLite)]
 R --> E[(LanceDB)]
 W --> D
 W --> E
 style A fill:#dbeafe,color:#1e3a8a
 style B fill:#ede9fe,color:#5b21b6
 style C fill:#dcfce7,color:#14532d

  • Storage - every durable event lives in SQLite, the source of truth. Rebuildable vector indexes live in LanceDB beside it. The whole workspace stays on your disk and can be copied or exported anytime.

  • Recall - BM25 (lexical) + dense vector (semantic) + entity graph + optional cross-encoder rerank, fused via Reciprocal-Rank-Fusion.

  • Writes - async. The MCP tool returns in under a millisecond; the embed + LanceDB insert happen on a background thread.

  • Dedup - four layers: exact text match -> cosine >= 0.92 auto-merge -> cosine 0.80-0.92 borderline (LLM verify later) -> manual review in the dashboard. Old values are archived, never deleted; full history via keyed_fact_as_of(t).

  • Multilingual - no language packs. The default embedder (paraphrase-multilingual-MiniLM-L12-v2) covers 50+ languages, so Π³Π΄Π΅ я ΠΆΠΈΠ²Ρƒ finds a keyed-fact stored as user.city = Warsaw . Intent detection rides English semantic anchors that transfer cross-lingually, and the cold lexical path self-compiles from your own traffic. Recall stays strong across ~11 languages (top-3 ~= 0.9 on a 101-query eval; top-1 = 1.00 for en/fr/pt/ru). See docs/contributing/adding-a-language.md.

CLI cheat sheet

Copy & paste β€” that's it
# Memory
pmb stats show counts and storage info
pmb recall "query" search with full debug
pmb dashboard web UI on port 8765 (graph, settings, errors)

# Ingest
pmb index pdf paper.pdf extract + chunk + embed
pmb index pdf ~/docs --recurse entire directory
pmb index project . scan codebase
pmb track changes summarise commit intent (why)
pmb track modules one-line purpose per module
pmb import chatgpt ~/Downloads/export.json bring existing history

# Continuity & efficiency (opt-in)
pmb resume save write .pmb/resume.md (commit it)
pmb resume install refresh resume.md at every turn end
pmb health lessons-impact which lessons actually help outcomes
pmb memory ledger Memory Delta handles this session

# Maintenance
pmb regraph rebuild entity graph
pmb consolidate run sleep pass (optional)
pmb compact archive old events
pmb dedupe resolve borderline duplicates

# Hooks (force-feed PMB at the protocol level - no model cooperation)
pmb hooks install claude-code wire all lifecycle hooks
pmb hooks list show what's installed
pmb hooks capabilities ambient mechanism each agent supports
pmb hooks uninstall claude-code remove them
pmb auto-context "fix bug in PMB" preview per-turn injection
pmb session-restore -m 180 preview post-compaction restore
pmb lesson-followcheck --dry-run preview follow-through scoring

# Ambient memory (the write side - memory journals the agent's work)
pmb autowrite --dry-run preview ambient auto-write for this turn
pmb ambient-watch . ambient auto-write for MCP-only hosts (git observer)
pmb forget-auto drop memory the ambient layer wrote itself

# Config
pmb config list default tier (25 keys you care about)
pmb config list --pro every key, including 80 advanced knobs
pmb config set recall.ppr_enabled true toggle a feature
pmb connect --rules-only refresh CLAUDE.md only

Step-by-step per agent: docs/guide/usage.md. Full reference: docs/reference/COMMANDS.md.

Hooks - memory that doesn't wait to be asked

The hard part of agent memory isn't storing - it's getting the agent to use what's stored. Soft instructions in a rules file get skipped. So PMB wires hooks at the protocol level (pmb hooks install claude-code), each removing a dependency on the model remembering to act:

  • UserPromptSubmit -> auto-recall. Every message is classified (regex, multilingual, sub-ms) and the matching memory - lessons, past decisions, recall hits, project overview - is injected before the model thinks. Trivial messages inject nothing.

  • PostToolUse -> ambient observe. Every tool the agent runs is appended to a lightweight action journal (a single SQLite INSERT, no model). Reads and ls are filtered out; edits, tests and commits are kept.

  • SessionStart -> session-restore. After a context compaction the agent rebuilds "where you left off" from what the session recorded, instead of re-asking you.

  • Stop -> follow-through + ambient auto-write. (a) It checks which surfaced lessons actually showed up in what the agent did and marks them followed, deterministically . (b) If the agent did NOT call a record_* tool, it synthesizes one activity entry from the observed actions - so real work is captured even when the agent stays silent.

Preview any without an agent: pmb auto-context "...", pmb session-restore -m 180, pmb lesson-followcheck --dry-run, pmb autowrite --dry-run.

Ambient memory - the write side

Auto-recall fixed the read side; ambient memory does the same for the write side - the memory journals the agent's work even when it forgets record_batch:

  • Coordinated. If the agent already called a record_* tool this turn, ambient stays silent; it only fills the gap.

  • Outcome-scored, not churn. A turn is journaled only if results clear a quality bar (tests passed, a failure fixed, a deploy ran), not by file count alone.

  • Honest + reversible. Every ambient entry is tagged source=autowrite, shown as auto in the dashboard, and removable with pmb forget-auto. On by default; disable with pmb config set autowrite.enabled false.

  • Works on every host. Claude Code (hooks), Codex (pmb codex-notify), MCP-only hosts like Cursor/Zed/VS Code (git observer, pmb ambient-watch .). Check yours with pmb hooks capabilities.

Synthesis is template-based by default (instant, no model). Opt into a local/CLI model summary with pmb config set autowrite.synthesizer llm:ollama (it has a timeout and falls back to the template).

Self-improvement loop

Every surfaced lesson carries a surface_id. Follow-through is recorded both ways: the agent confirms via mark_lesson_followed(surface_id, True), and the Stop hook infers it from recorded activity. The Lessons tab then shows, per rule: how often it was shown, how often it was followed, β˜… USEFUL (followed >= 2x), ? UNVERIFIED (surfaced but unconfirmed), and πŸ’€ DEAD only when a rule is repeatedly ignored (>= 2). You see which rules help and prune the ones that don't.

Settings - 25 you care about, 80 you don't

PMB has 105 tunables. The 25 that affect day-to-day quality are default-tier (pmb config list). The rest are internal weights and experimental flags, hidden behind --pro so the surface stays scannable. Every pro key still reads with pmb config get and writes with pmb config set - hidden from list, not gated.

Key Default What it does recall.top_k 5 How many results recall returns recall.bm25_weight 0.7 BM25 vs vector mix (1.0 = pure BM25) recall.ppr_enabled true Multi-hop graph diffusion, gated by intent recall.keyed_fact_boost 0.35 How hard personal-attr facts win on personal queries recall.rerank false Always-on cross-encoder (regresses LoCoMo, keep off) embedding.model paraphrase-multilingual-MiniLM-L12-v2 The vector model graph.extractor regex regex / spacy / llm:claude / llm:ollama / llm:codex mcp.record_batch_async true Fire-and-forget writes (sub-ms return) agent.apply_lessons true Agent surfaces lessons before acting dedup.enable true All four dedup layers decay.factor_per_day 0.985 Importance half-life chat.model haiku Default model for pmb-chat

Numbers

Recall p50 / p95 warm 35 ms / 110 ms prepare(message) warm 4-16 ms record_batch_async < 1 ms MCP cold boot 3.7 s LoCoMo recall@10 (n=10) 94.5 % Multilingual mega-stress top-10 (900 q) 99.2 %

Copy & paste β€” that's it
# Reproduce locally
python scripts/benchmarks/benchmark_locomo.py --n-conversations 10
python scripts/benchmarks/mega_stress_test.py

Privacy

  • 100 % offline by default. No network calls from the engine, zero telemetry - there is no PMB server to call home to.

  • Workspace = a directory under ~/.pmb/<name>/. Copy it to Dropbox, push it to git, share it on a USB drive. Your call.

  • Secrets are auto-redacted at write time (OpenAI / Anthropic / AWS / Stripe / GitHub keys; configurable).

  • Apache 2.0 licensed. Forks welcome.

Contributing

Issues and PRs welcome. There's one full-time maintainer; please open a discussion before a large change so we can align on direction.

Copy & paste β€” that's it
git clone https://github.com/oleksiijko/pmb.git && cd pmb
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest # full suite, ~4 minutes
pytest -k recall # fast subset, ~12 s

Dev commands

Copy & paste β€” that's it
bash scripts/test.sh # whole suite (CI-equivalent)
bash scripts/test.sh tests/recall # a subset (any pytest args pass through)
bash scripts/codeql_local.sh # run CI's CodeQL security-extended locally
bash scripts/install-dev-hooks.sh # pre-commit hook: ruff + CodeQL before each commit

scripts/codeql_local.sh auto-installs the CodeQL bundle on first run and runs the exact suite CI uses, so security findings are caught locally instead of on a push. The pre-commit hook bypasses with git commit --no-verify (or skip just the scan with SKIP_CODEQL=1).

License: Apache 2.0.