https://github.com/bnimit/memor-ai
Measured memory layer for coding agents — store, distill, and retrieve past session context instead of re-sending full history to the LLM. 99.7% token savings, 11ms retrieval, built-in eval harness.
https://github.com/bnimit/memor-ai
agent ai claude coding-agent context-management developer-tools distillation embeddings llm memory rag retrieval sqlite
Last synced: 17 days ago
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Measured memory layer for coding agents — store, distill, and retrieve past session context instead of re-sending full history to the LLM. 99.7% token savings, 11ms retrieval, built-in eval harness.
- Host: GitHub
- URL: https://github.com/bnimit/memor-ai
- Owner: bnimit
- License: mit
- Created: 2026-06-03T17:32:20.000Z (22 days ago)
- Default Branch: main
- Last Pushed: 2026-06-03T18:41:52.000Z (22 days ago)
- Last Synced: 2026-06-03T20:09:39.285Z (22 days ago)
- Topics: agent, ai, claude, coding-agent, context-management, developer-tools, distillation, embeddings, llm, memory, rag, retrieval, sqlite
- Language: Python
- Size: 122 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
```
_
_ __ ___ ___ _ __ ___ ___ _ __ __ _(_)
| '_ ` _ \ / _ \ '_ ` _ \ / _ \| '__|____ / _` | |
| | | | | | __/ | | | | | (_) | | |_____| (_| | |
|_| |_| |_|\___|_| |_| |_|\___/|_| \__,_|_|
Measured memory for coding agents.
```
[](LICENSE)
[]()
[]()
[](https://pypi.org/project/memor-cli/)
**Automatic background memory for Claude Code.** Fire and forget — no API keys needed.
Memor watches your coding sessions, extracts decisions and patterns, and recalls relevant context on every prompt. Zero configuration. One install. Your agent remembers everything.
---
## Quick Start
```bash
# Install globally (recommended)
pipx install memor-cli
# Install the Claude Code hook + download embedding model (~60MB)
memor install-hook
# Start as a background service (macOS/Linux)
memor service install
# Or run in the foreground
memor daemon
```
That's it. Every Claude Code prompt now gets automatic context recall. Open the dashboard to see it working:
```bash
memor dashboard
# Opens http://localhost:8420
```
> **Alternative install:** `pip install memor-cli` works too — just make sure `~/.local/bin` is on your PATH so the `memor` command is available.
---
## How It Works
```
You type a prompt in Claude Code
|
v
Hook fires (UserPromptSubmit)
|
v
Embed query locally (model2vec, ~2ms)
|
v
Hybrid retrieval: dense vectors + lexical BM25, fused (RRF)
|
v
Relevance gate drops off-topic matches (inject nothing if nothing fits)
|
v
Rank: similarity + recency + kind weight + quality
|
v
Inject relevant context into prompt
|
v
Claude sees your past decisions, bugfixes,
architecture choices — without you re-explaining
```
**Two background processes:**
1. **Daemon** — polls `~/.claude/projects/` for transcripts, embeds chunks, runs distillation, analyzes feedback (positive and negative), promotes cross-project patterns to global scope, compacts duplicates, tracks session-level token usage. All local.
2. **Hook** — fires on every prompt, recalls relevant memories, injects them as context. Sub-15ms.
**No API keys required.** Embeddings run locally via [model2vec](https://github.com/MinishLab/model2vec) (potion-base-8M, 256-dim). Vectors stored in [sqlite-vec](https://github.com/asg017/sqlite-vec). Everything runs on your machine.
---
## Hybrid Retrieval
Memor retrieves over two channels and fuses them, so it catches both semantic matches and exact terms:
- **Dense** — local vector similarity (model2vec) for semantic recall.
- **Lexical** — SQLite FTS5 / BM25 over the raw text, to recover exact identifiers, error strings, and API names that static embeddings blur together.
The two rankings are combined with **Reciprocal Rank Fusion (RRF)**. A **relevance gate** drops anti-correlated (off-topic) candidates *before* ranking, so an unrelated prompt injects nothing rather than the least-bad guess. The lexical channel only activates when the dense channel finds the query on-topic, preventing generic words from pulling in noise.
> Tunable via `MEMOR_MIN_SIMILARITY` (relevance floor, default 0.0) and `MEMOR_MAX_TOKENS` (injection budget, default 1500).
## Scoring
Surviving candidates are ranked by four signals:
| Signal | Weight | How it works |
|---|---|---|
| **Semantic similarity** | 50% | Dense + lexical relevance, fused via RRF |
| **Recency** | 25% | Exponential decay with 14-day half-life — recent decisions rank higher |
| **Kind weight** | 15% | Distilled memories (1.3x) rank above raw session chunks (1.0x) |
| **Quality** | 10% | Bayesian score from implicit feedback — memories the agent actually uses rank higher |
This means a relevant decision from yesterday beats a vaguely-related chunk from a month ago — even if the raw embedding similarity is similar.
### Feedback Loop
Memor tracks whether recalled memories actually get used by the agent — and whether they actively hurt. After each session, the daemon analyzes the transcript in both directions:
- **Positive signal** — n-gram overlap or semantic similarity between recalled content and the agent's response. Memories that consistently prove useful get quality boosts.
- **Negative signal** — user rejection ("no that's wrong", "we switched to X") or assistant contradiction ("however, looking at the current code, we actually use Y"). Memories that get corrected receive a quality penalty, making them less likely to be recalled next time.
The quality formula is Bayesian: `(uses - negatives + 1) / (recalls + 2)`. One correction weighs as much as one positive use, so harmful memories drop fast. Memories never recalled in 30+ days get automatically deactivated. Near-duplicate memories are compacted into one.
---
## What Gets Stored
| Kind | Source | Description |
|---|---|---|
| `session_chunk` | Daemon auto-ingest | Filtered turns from Claude Code transcripts |
| `memory` | Extractive distillation | Key decisions, patterns, bugfixes per session |
Memories are automatically classified as `decision`, `bugfix`, `lesson`, `snippet`, or generic `extract` based on content patterns. The daemon runs a signal filter that keeps decisions, bugfixes, lessons, and code rationale while skipping noise (tool calls, file listings, boilerplate).
---
## Global Memories
Some patterns aren't project-specific — they're yours. "Always use type hints." "Structure FastAPI apps with a `routes/` directory." "Prefer composition over inheritance."
Memor detects these automatically. When the same pattern appears in **3 or more projects** (measured by embedding similarity), the daemon promotes it to a `_global` scope:
- **Global memories are recalled everywhere** — they show up in every project's search results alongside project-specific memories.
- **Source duplicates are deactivated** — the per-project copies get superseded by the single global version, reducing clutter.
- **No manual tagging** — promotion is fully automatic, based on cross-project clustering.
This means your coding habits and preferences follow you into new projects from the first prompt, without you having to re-explain anything.
---
## Dashboard
```bash
memor dashboard
```
Dark fintech-inspired UI showing:
- **Hero metrics** — total memories, recall count, avg latency, coverage — with sparkline bars
- **Daily recall activity** — stacked bar chart of hits vs misses over time
- **Session efficiency** — real token savings measured from API usage data (avg tokens/turn with vs without recall)
- **Per-project breakdown** — artifact counts, token totals, last activity
- **Recent recalls** — every hook event with scores, latency, and status
---
## Commands
```
memor help Print the full manual
memor install-hook Install Claude Code hook + download model
memor daemon Auto-ingest + distill (background watcher)
memor dashboard Web dashboard on localhost:8420
memor version Print installed version
memor service install Run daemon as background service (launchd/systemd)
memor service stop Stop the background service
memor service uninstall Remove the background service
memor service status Check if the service is running
memor query Search memories from the CLI
memor reingest Wipe DB and re-ingest everything
memor reingest --project Re-ingest only one project
memor forget-stale Deactivate memories unused for 30+ days
memor scan Audit DB for leaked secrets
memor scan --purge Redact secrets in place
memor setup-model Download/retry the embedding model
memor ingest-cc Ingest a single transcript
memor ingest-project Bulk ingest a project directory
memor ingest-doc Ingest a markdown document
memor distill --project Run distillation manually
memor eval Run eval suite
memor eval-counterfactual --project Win/tie/loss vs no-memory baseline
memor bench-embed --project Compare embedding models
```
---
## Architecture
```
memor/
+-- types.py Core dataclasses: Artifact, Scope, Hit, RetrievalTrace
+-- interfaces.py Protocols: Embedder, LLM, MemoryStore
+-- cli.py Typer CLI entry point
+-- daemon.py Auto-ingest + auto-distill + compaction watcher
+-- project.py Git-root project resolver (filesystem-aware)
+-- recall.py Shared recall core (used by hook + skill)
+-- redact.py Secret detection and redaction at ingest
+-- feedback.py Feedback analyzer (positive usage + negative signals)
+-- global_memories.py Cross-project promotion to _global scope
|
+-- retrieve/
| +-- retriever.py Hybrid retrieval (dense + BM25, RRF) + relevance gate + scoring
|
+-- store/
| +-- sqlite_store.py SQLite + sqlite-vec + FTS5 (WAL mode, dimension safety)
|
+-- embed/
| +-- local.py model2vec (potion-base-8M, 256-dim, ~60MB)
| +-- api.py OpenAI-compatible embedding API (optional)
| +-- fake.py Deterministic SHA-256 embedder (tests)
|
+-- service.py Background service management (launchd/systemd)
+-- dashboard/
| +-- server.py FastAPI dashboard backend
| +-- static/index.html Self-contained dashboard (no CDN deps)
|
+-- distill/
| +-- extractive.py TF-IDF + clustering + auto-classification
| +-- distiller.py Extractive + optional LLM abstractive
|
+-- eval/
+-- runner.py 4-baseline eval runner
+-- judge.py LLM-as-judge evaluation
+-- embed_benchmark.py Embedding model comparison
memor/hook_cli.py Claude Code hook entry point (thin client)
skill/recall.py Standalone recall script
```
---
## Security
**Nothing leaves your machine.** In the default configuration:
- **No telemetry, no analytics, no phone-home.** Zero outbound network calls.
- **Embeddings run locally** via model2vec static token embeddings — no inference runtime, no GPU (one-time model download from HuggingFace — no user data sent).
- **Hook transport is a Unix socket** (`~/.memor/hook.sock`), not a network port.
- **Dashboard binds localhost only.**
The only optional network paths are the LLM-based abstractive distiller (requires explicitly setting `ANTHROPIC_API_KEY`) and the API embedding backend — both off by default.
### Secret redaction
Memor automatically redacts secrets **at ingest**, before anything is embedded or stored:
- API keys (AWS `AKIA...`, OpenAI `sk-...`, Anthropic `sk-ant-...`, GitHub `ghp_...`, Stripe, Slack)
- JWTs, PEM private key blocks
- Connection strings (`postgres://`, `mongodb://`, `redis://`, etc.)
- `.env`-style assignments (`DB_PASSWORD=...`, `API_KEY=...`)
- High-entropy tokens (Shannon entropy > 4.0, length > 20)
Redacted content is replaced with `[REDACTED]` in place, preserving surrounding context. To audit and clean an existing database: `memor scan` (audit) or `memor scan --purge` (redact in place).
### Contradiction handling
When a new memory contradicts an older one in the same project (detected via replacement cues like "switched from X to Y", "no longer", "ripped out"), the older memory is automatically deactivated. This prevents stale decisions from being recalled and misleading the agent.
### Local storage
The memory database (`~/.memor/memor.db`) is stored as plaintext SQLite on disk. For at-rest protection, we recommend enabling OS-level full-disk encryption (FileVault on macOS, LUKS on Linux) which covers all local files with zero performance overhead.
---
## Development
```bash
git clone https://github.com/bnimit/memor-ai.git
cd memor-ai
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest # 251 tests
```
---
## License
MIT. See [LICENSE](LICENSE) for the full text.