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Persistent AI memory for Claude Code, OpenClaw, and any MCP-compatible agent. BM25F + vector hybrid, governance-aware, local-first, zero-infrastructure.

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<h1 align="center">
 <img src="https://raw.githubusercontent.com/star-ga/mind-mem/main/assets/logo.png" alt="MIND-Mem logo" width="140"> 
 MIND-Mem
</h1>

 Replayable memory for AI agents. Deterministic recall with a byte-identical audit chain across runs, machines, and substrates.


 Built on the MIND substrate &bull; Governed-write &bull; Deterministic recall &bull; 90 MCP tools 
 MIND Language Profile: <code>default</code> (full tensor stdlib + Q16.16 + heap) &mdash; see <a href="https://github.com/star-ga/mind/blob/main/docs/roadmap.md#phase-106--library-output--c-abi-mindc-026--030">Phase 10.6</a>


 <a href="https://pypi.org/project/mind-mem/"><img src="https://img.shields.io/pypi/v/mind-mem?include_prereleases&style=flat-square&color=blue&label=PyPI" alt="PyPI"></a>
 <a href="https://pypi.org/project/mind-mem/"><img src="https://img.shields.io/pypi/pyversions/mind-mem?style=flat-square" alt="Python Versions"></a>
 <a href="https://github.com/star-ga/mind-mem/blob/main/LICENSE"><img src="https://img.shields.io/pypi/l/mind-mem?style=flat-square" alt="License"></a>
 <a href="https://github.com/star-ga/mind-mem/releases"><img src="https://img.shields.io/github/v/release/star-ga/mind-mem?include_prereleases&style=flat-square&color=green&label=Release" alt="Release"></a>
 <img src="https://img.shields.io/badge/MIND-substrate-orange?style=flat-square" alt="MIND Substrate">
 <img src="https://img.shields.io/badge/deterministic-byte--identical-brightgreen?style=flat-square" alt="Byte-identical Determinism">
 <img src="https://img.shields.io/badge/governed--write-propose→apply-purple?style=flat-square" alt="Governed Write">
 <img src="https://img.shields.io/badge/MCP-compatible-blueviolet?style=flat-square" alt="MCP Compatible">
 <img src="https://img.shields.io/badge/core_deps-zero-brightgreen?style=flat-square" alt="Zero Core Dependencies">
 <a href="https://github.com/star-ga/mind-mem/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/star-ga/mind-mem/ci.yml?branch=main&style=flat-square&label=CI" alt="CI"></a>
 <a href="https://github.com/star-ga/mind-mem/actions/workflows/release.yml"><img src="https://img.shields.io/github/actions/workflow/status/star-ga/mind-mem/release.yml?style=flat-square&label=Release" alt="Release"></a>
 <img src="https://img.shields.io/badge/tests-6000%2B-brightgreen?style=flat-square" alt="Tests: 5900+">
 <img src="https://img.shields.io/badge/MCP_tools-90-blue?style=flat-square" alt="MCP Tools: 90">
 <img src="https://img.shields.io/badge/clients-17-blueviolet?style=flat-square" alt="AI Clients: 17">
 <img src="https://img.shields.io/badge/backends-markdown_%7C_postgres-teal?style=flat-square" alt="Storage: Markdown + Postgres">
 <img src="https://img.shields.io/badge/audit-cross--model_%2B_SAST_%2B_SoW-darkgreen?style=flat-square" alt="Cross-model consensus audit + SAST (CodeQL/bandit/trivy) + external-audit SoW published">

Current release: <code>v4.4.0</code> &mdash; per-run recall attestation, typed knowledge-graph edges (HITL-gated), and a standing LoCoMo recall benchmark &mdash;
 <a href="CHANGELOG.md">see CHANGELOG</a>
 (single source of truth; per-version detail tables below may lag the changelog)

---

MIND-Mem is a deterministic AI memory system: on the same workspace, the same query produces the same ranked results every time, with a Q16.16 fixed-point audit chain — byte-identical across runs, machines, and substrates — embedded in every applied decision. (Recall scoring itself is standard floating-point; the byte-identity guarantee is the Q16.16 audit/replay chain.)

Built on the MIND substrate. Governed-write (`propose → review → approve_apply`). 90 MCP tools as the surface — but the differentiator is the substrate underneath. On the same workspace, recall is deterministic (same query → same ranked results) and every block and audit hash is byte-identical across every architecture mind-mem builds on — the Q16.16 audit chain. (The ranking scores themselves are standard floating-point; the byte-identity guarantee is the audit/replay chain.)

Most memory layers ship tools. That is table-stakes. MIND-Mem ships a substrate: scoring kernels compiled from MIND source with Q16.16 fixed-point encoding in the audit-hash preimage, a governance pipeline that rejects every unreviewed write, and an audit chain where every applied proposal is hash-anchored. The same query on the same workspace produces the same ranked recall, every time; that recall's audit/replay chain is byte-identical whether you replay it on the same machine or a different one that pulls the same workspace. That property is what makes MIND-Mem suitable as a canonical memory layer across heterogeneous agent stacks.

> **If your agent runs for weeks, it will drift. MIND-Mem prevents silent drift.**
>
> MIND-Mem powers the Memory Plane of the [MIND Cognitive Kernel](https://mindlang.dev/docs/cognitive-kernel) — the deterministic AI runtime architecture.

### 30-Second Demo

```bash
pip install mind-mem
mind-mem-init ~/my-workspace        # Create workspace
mind-mem-recall -q "API decisions" --workspace ~/my-workspace  # Hybrid BM25F search
mind-mem-scan ~/my-workspace        # Detect drift & contradictions
```

Output:
```
[1.204] D-20260215-001 (decision) — Use async/await for all API endpoints
        decisions/DECISIONS.md:11
[1.094] D-20260210-003 (decision) — REST over GraphQL for public API
        decisions/DECISIONS.md:20
```

Current release: **v4.4.0** — per-run recall attestation (derivable, never stored), typed knowledge-graph edges (HITL-gated `propose_edge`/`approve_edge`), and a standing LoCoMo recall benchmark — Full per-release notes (issues closed, CI run ids, job counts) live in <a href="./CHANGELOG.md">CHANGELOG.md</a>.

### Substrate Properties

| Property                | What it means                                                                     |
| ----------------------- | --------------------------------------------------------------------------------- |
| **Byte-identical replay** | Recall ranking is a deterministic function of workspace + metadata state — same state + query → same ranked results (A-MEM importance/recency evolves deterministically on access, so ordering shifts as that state updates; no *probabilistic* mutations). The byte-identical guarantee is the audit/replay chain (Q16.16), identical across runs, machines, and substrates. |
| **Governed-write**      | Nothing reaches the source of truth without `propose → review → approve_apply`. No silent mutations. Ever. |
| **Auditable**           | Every apply logged with timestamp, receipt, and DIFF. Full traceability from signal to decision. |
| **Deterministic**       | No ML in the retrieval core. Q16.16 fixed-point encoding in the audit-hash preimage. The same preimage produces the same hash. |
| **Local-first**         | All data stays on disk. No cloud calls, no telemetry, no phoning home.            |
| **No vendor lock-in**   | Plain Markdown files. Move to any system, any time.                               |
| **Zero infrastructure** | Core requires only Python 3.10+ stdlib. Postgres, Redis, Docker, and GPU are opt-in extras. |
| **100% NIAH**           | 250/250 Needle In A Haystack retrieval. Every needle, every depth, every size.     |

---

## Table of Contents

- [Why MIND-Mem](#why-mind-mem)
- [Features](#features)
- [Integrations are the substrate working](#integrations-are-the-substrate-working)
- [Benchmark Results](#benchmark-results)
- [Quick Start](#quick-start)
- [Health Summary](#health-summary)
- [Commands](#commands)
- [Architecture](#architecture)
- [How It Compares](#how-it-compares)
- [Recall](#recall)
- [MIND Kernels](#mind-kernels)
- [Auto-Capture](#auto-capture)
- [Multi-Agent Memory](#multi-agent-memory)
- [Governance Modes](#governance-modes)
- [Block Format](#block-format)
- [Configuration](#configuration)
- [MCP Server](#mcp-server)
- [Security](#security)
- [Troubleshooting](#troubleshooting)
- [Built in MIND lang](#built-in-mind-lang)
- [Contributing](#contributing)
- [License](#license)

### Deep-dive docs

- [`docs/setup.md`](docs/setup.md) — install, configure, wire MCP, opt in to MIND native kernels
- [`docs/usage.md`](docs/usage.md) — every surface (MCP tools by category, `mm` CLI, `mind-mem-verify`, Python library) with worked examples
- [`docs/client-integrations.md`](docs/client-integrations.md) — **18 AI client integrations** (Claude Code, Codex, Grok Build, Vibe, Gemini, Cursor, Windsurf, aider, OpenClaw, NanoClaw, NemoClaw, Continue, Cline, Roo, Zed, Copilot, Cody, Qodo) with `mm install-all` auto-detection
- [`docs/mind-mem-4b-setup.md`](docs/mind-mem-4b-setup.md) — download + run the `star-ga/mind-mem-4b` full-FT model locally (transformers, exllamav2, vLLM, llama.cpp, Ollama, **MindLLM**)
- [`docs/companion-tools.md`](docs/companion-tools.md) — **companion tools** that complement (not compete with) mind-mem: [MindLLM](https://github.com/star-ga/MindLLM) for deterministic + evidence-chained inference, [GitNexus](https://github.com/h4ckf0r0day/GitNexus) for code knowledge-graph
- [`ROADMAP.md`](ROADMAP.md) — feature roadmap (genuinely-open items at the top; bulk of v3.2.0→v4.0.0 shipped)
- [`CHANGELOG.md`](CHANGELOG.md) — release notes for every published version

---

## Why MIND-Mem

Most memory plugins **store and retrieve**. That's table stakes.

MIND-Mem also **detects when your memory is wrong** — contradictions between decisions, drift from informal choices never formalized, dead decisions nobody references, orphan tasks pointing at nothing — and offers a safe path to fix it.

| Problem                  | Without MIND-Mem                  | With MIND-Mem                             |
| ------------------------ | --------------------------------- | ----------------------------------------- |
| Contradicting decisions  | Follows whichever seen last       | Flags, links both, proposes fix           |
| Informal chat decision   | Lost after session ends           | Auto-captured, proposed to formalize      |
| Stale decision           | Zombie confuses future sessions   | Detected as dead, flagged                 |
| Orphan task reference    | Silent breakage                   | Caught in integrity scan                  |
| Scattered recall quality | Single-mode search misses context | Hybrid BM25+Vector+RRF fusion finds it    |
| Ambiguous query intent   | One-size-fits-all retrieval       | 9-type intent router optimizes parameters |

### Novel Contributions

MIND-Mem introduces several techniques not found in existing memory systems:

| Technique | What's new | Why it matters |
|-----------|-----------|----------------|
| **Co-retrieval graph** | PageRank-like score propagation across blocks frequently retrieved together | Surfaces structurally relevant blocks with zero lexical overlap (+2.0pp accuracy) |
| **Fact card sub-block indexing** | Atomic fact extraction → small-to-big retrieval with parent score blending | Catches fine-grained facts that full-block BM25 misses (+2.6pp accuracy) |
| **Adaptive knee cutoff** | Score-drop-based truncation instead of fixed top-K | Eliminates noise that hurts LLM judges — returns 3-15 results adaptively |
| **Hard negative mining** | Logs BM25-high / cross-encoder-low blocks as misleading, penalizes in future queries | Self-improving retrieval: precision increases over time without retraining |
| **Deterministic abstention** | Pre-LLM confidence gate using 5-signal scoring (entity, BM25, speaker, evidence, negation) | Prevents hallucinated answers to unanswerable questions — no ML required |
| **Governance pipeline** | Contradiction detection + drift analysis + safe apply with audit trail | Only memory system that detects when stored knowledge is wrong |
| **Agent-agnostic shared memory** | Single MCP workspace shared across Claude Code, Codex, Gemini, Cursor, Windsurf, Zed | Memory compounds across tools instead of fragmenting |

---

## Features

### Hybrid BM25+Vector Search with RRF Fusion
Thread-parallel BM25 and vector search with Reciprocal Rank Fusion (k=60). Configurable weights per signal. Vector is optional — works with just BM25 out of the box.

### RM3 Dynamic Query Expansion
Pseudo-relevance feedback using JM-smoothed language models. Expands queries with top terms from initial result set. Falls back to static synonyms for adversarial queries. Zero dependencies.

### 9-Type Intent Router
Classifies queries into WHY, WHEN, ENTITY, WHAT, HOW, LIST, VERIFY, COMPARE, or TRACE. Each intent type maps to optimized retrieval parameters (limits, expansion settings, graph traversal depth).

### A-MEM Metadata Evolution
Auto-maintained per-block metadata: access counts, importance scores (clamped to [0.8, 1.5] reranking boost), keyword evolution, and co-occurrence tracking. Importance decays with exponential recency.

### Deterministic Reranking
Four-signal reranking pipeline: negation awareness (penalizes contradicting results), date proximity (Gaussian decay), 20-category taxonomy matching, and recency boosting. No ML required.

### Optional Cross-Encoder
Drop-in ms-marco-MiniLM-L-6-v2 cross-encoder (80MB). Blends 0.6 * CE + 0.4 * original score. Falls back gracefully when unavailable. Enabled via config.

### MIND Kernels (Optional, Native Speed — forward-looking)
26 `.mind` files at `mind/` that tune the scoring pipeline (BM25F, RRF
fusion, reranking, negation penalty, date proximity, category boost,
importance, entity overlap, confidence, top-k, weighted rank, category
affinity, query-category relevance, category assignment, and others). **18
are INI-format declarative configuration** parsed by `mind_ffi.py`; the
**other 8 are already MIND-language tensor sources** (`bm25`, `ranking`,
`reranker`, `rrf`, `prefetch`, `importance`, `category`, `abstention`) that
compile to native `.so` via the [MIND compiler](https://mindlang.dev) — the
forward-looking story. See
[`docs/MIND_CONFIG_VS_MIND_LANG.md`](docs/MIND_CONFIG_VS_MIND_LANG.md) for
the per-file breakdown. The pure-Python scoring logic in
`src/mind_mem/mind_kernels.py` is the authoritative implementation today.

### MIC/MAP — MIND IR graph serialization
Pure-Python codec for the STARGA wire formats: **mic@2** (line-oriented
text, LLM-readable, git-friendly) and **MIC-B** (varint binary, ~4×
smaller). Both encode typed dataflow graphs (symbols + types + values +
output) with byte-identical round-trip. Streaming parser for bounded peak
memory; optional Cython accelerator via `mind-mem[accelerated]`
(+16/+20/+36 % on parse). Two MCP tools (`mic_convert`, `mic_inspect`) and
a `mm mic` CLI surface it for agents and operators. See
[`docs/mic-map.md`](docs/mic-map.md). Note that the canonical IR per
RFC 0021 is `mic@1` text + `mic@3` binary (see
[mindlang.dev/docs/mic](https://mindlang.dev/docs/mic)); the `mic@2`/MIC-B
codec mind-mem ships is the back-compat lineage.

### BM25F Hybrid Recall
BM25F field-weighted scoring (k1=1.2, b=0.75) with per-field weighting (Statement: 3x, Title: 2.5x, Name: 2x, Summary: 1.5x), Porter stemming, bigram phrase matching (25% boost per hit), overlapping sentence chunking (3-sentence windows with 1-sentence overlap), domain-aware query expansion, and optional 2-hop graph-based cross-reference neighbor boosting. Zero dependencies. Fast and deterministic.

### Graph-Based Recall
2-hop cross-reference neighbor boosting — when a keyword match is found, blocks that reference or are referenced by the match get boosted (1-hop: 0.3x decay, 2-hop: 0.1x decay). Surfaces related decisions, tasks, and entities that share no keywords but are structurally connected. Auto-enabled for multi-hop queries.

### Vector Recall (optional)
Pluggable embedding backend — local ONNX (all-MiniLM-L6-v2, no server needed) or cloud (Pinecone). Falls back to BM25 when unavailable.

### Persistent Memory
Structured, validated, append-only decisions / tasks / entities / incidents with provenance and supersede chains. Plain Markdown files — readable by humans, parseable by machines.

### Immune System
Continuous integrity checking: contradictions, drift, dead decisions, orphan tasks, coverage scoring, regression detection. 74+ structural validation rules.

### Safe Governance
All changes flow through graduated modes: `detect_only` → `propose` → `enforce`. Apply engine with snapshot, receipt, DIFF, and automatic rollback on validation failure.

### Adversarial Abstention Classifier
Deterministic pre-LLM confidence gate for adversarial/verification queries. Computes confidence from entity overlap, BM25 score, speaker coverage, evidence density, and negation asymmetry. Below threshold → forces abstention without calling the LLM, preventing hallucinated answers to unanswerable questions.

### Auto-Capture with Structured Extraction
Session-end hook detects decision/task language (26 patterns with confidence classification), extracts structured metadata (subject, object, tags), and writes to `SIGNALS.md` only. Never touches source of truth directly. All signals go through `/apply`.

### Tool-Output Offload (v4.2.0)
A single `cargo test` / `pytest` / build run dumps 10k–50k lines into an agent's context — the biggest single token sink for coding agents. `mm tool-run -- <cmd>` stores the **full output out-of-context** (a `tool_outputs` sibling table; SQLite by default, reuses the Postgres connection with no new DB) and returns only a compact `{handle, summary}`; `mm tool-recall <handle>` returns the full text on demand. The summary is **bounded** regardless of input (a 10 MB line or 100k error lines can't blow it up), **fail-safe** (the full text is always stored and every truncation is explicit and counted — a failure line is never silently dropped), and **deterministic** (pure pattern extraction, no LLM; versioned config). See [docs/tool-output-architecture.md](docs/tool-output-architecture.md).

### Concurrency Safety
Cross-platform advisory file locking (`fcntl`/`msvcrt`/atomic create) protects all concurrent write paths. Stale lock detection with PID-based cleanup. Zero dependencies.

### Compaction & GC
Automated workspace maintenance: archive completed blocks, clean up old snapshots, compact resolved signals, archive daily logs into yearly files. Configurable thresholds with dry-run mode.

### Observability
Structured JSON logging (via stdlib), in-process metrics counters, and timing context managers. All scripts emit machine-parseable events. Controlled via `MIND_MEM_LOG_LEVEL` env var.

### Multi-Agent Namespaces & ACL
Workspace-level + per-agent private namespaces with JSON-based ACL. fnmatch pattern matching for agent policies. Shared fact ledger for cross-agent propagation with dedup and review gate.

### Automated Conflict Resolution
Graduated resolution pipeline: timestamp priority, confidence priority, scope specificity, manual fallback. Generates supersede proposals with integrity hashes. Human veto loop — never auto-applies without review.

### Write-Ahead Log (WAL) + Backup/Restore
Crash-safe writes via journal-based WAL. Full workspace backup (tar.gz), git-friendly JSONL export, selective restore with conflict detection and path traversal protection.

### Transcript JSONL Capture
Scans Claude Code transcript files for user corrections, convention discoveries, bug fix insights, and architectural decisions. 16 transcript-specific patterns with role filtering and confidence classification.

### MCP Server (89 tools, 8 resources)
Full [Model Context Protocol](https://modelcontextprotocol.io/) server with 89 distinct tools and 8 read-only resources (6 static + 2 templated). The server makes 90 `mcp.tool(...)` registrations, but the consolidated `recall` dispatcher intentionally shadows the base `recall`, so the live surface is 89 distinct tool names. Works with Claude Code, Claude Desktop, Cursor, Windsurf, and any MCP-compatible client. HTTP and stdio transports; HTTP requires bearer-token auth (fail-closed) — see [Token Auth (HTTP)](#token-auth-http). v3.8.11 added `mic_convert_tool` / `mic_inspect_tool` (MIC/MAP wire format); v3.9.0 added `compile_truth_walkthrough`, `recall_with_persona`, `pipeline_status`, and `reindex_dirty`; v3.11.0 added `validate_block`, `block_lineage`, and `add_block_edge` (deterministic quality gates + typed lineage edges).

### 74+ Structural Checks + 3024 Unit Tests
`validate.sh` checks schemas, cross-references, ID formats, status values, supersede chains, ConstraintSignatures, and more. Backed by 3024 pytest unit tests covering all core modules.

### Audit Trail
Every applied proposal logged with timestamp, receipt, and DIFF. Full traceability from signal → proposal → decision.

### Calibration Feedback Loop
Per-block quality tracking with Bayesian weight computation. When users provide feedback (thumbs up/down) via `calibration_feedback`, the system maintains a rolling quality score per block over a 30-day window. Bayesian smoothing constrains calibration weights to the 0.5-1.5 range, preventing any single block from dominating or being silenced. Calibration weights integrate directly into the BM25 + FTS5 retrieval pipeline — high-quality blocks rank higher, low-quality blocks are naturally demoted. Use `calibration_stats` to inspect per-block quality distributions and global calibration health.

### LLM-Guided Multi-Query Expansion
Generates semantically diverse query reformulations before search — synonym expansion, specificity shifts, temporal rephrasing, and negation variants. Combines all reformulated queries with Reciprocal Rank Fusion for broader recall without sacrificing precision. Runs locally with zero API calls.

### 4-Layer Search Deduplication
Post-retrieval dedup pipeline: best-chunk-per-source (keeps highest-scoring chunk from each file), cosine similarity dedup (>0.85 threshold), type diversity capping (max 3 results per block type), and per-source chunk limiting. Eliminates redundant results that waste LLM context.

### LLM-Guided Smart Chunking
Content-aware chunking that splits at semantic boundaries (headers, paragraph breaks, list items, code blocks) instead of fixed character counts. Produces variable-size chunks with overlap for continuity. Supports markdown, code, and prose with format-specific splitting rules.

### Compiled Truth Pages
Per-entity knowledge compilation: current-best-understanding on top, timestamped evidence trail below. Contradiction detection across evidence entries with automatic flagging. Entities accumulate knowledge from all sessions — each new evidence entry is checked against existing facts.

### Dream Cycle (Autonomous Memory Enrichment)
Scheduled background enrichment: scans recent memory for missing cross-references, broken citations, orphan entities, and consolidation opportunities. Generates repair proposals for stale links, detects implicit entities not yet formalized, and compacts redundant entries. Runs during idle periods with configurable depth.

### Feature Completeness Matrix

| Capability | MIND-Mem | Mem0 | Zep | Letta | LangMem |
|---|:---:|:---:|:---:|:---:|:---:|
| BM25 lexical search | Y | — | — | — | — |
| Vector semantic search | Y | Y | Y | Y | Y |
| Hybrid BM25+Vector+RRF | Y | — | — | — | — |
| Cross-encoder reranking | Y | — | — | — | — |
| Intent-aware routing (9 types) | Y | — | — | — | — |
| RM3 query expansion | Y | — | — | — | — |
| Co-retrieval graph (PageRank) | Y | — | — | — | — |
| Fact sub-block indexing | Y | — | — | — | — |
| Hard negative mining | Y | — | — | — | — |
| Adaptive knee cutoff | Y | — | — | — | — |
| Contradiction detection | Y | — | — | — | — |
| Drift analysis | Y | — | — | — | — |
| Governance pipeline (propose/apply) | Y | — | — | — | — |
| Multi-agent shared memory (MCP) | Y | — | — | Y | — |
| Zero core dependencies | Y | — | — | — | — |
| Local-only (no cloud required) | Y | — | — | — | — |
| Compiled native kernels (MIND) | Y | — | — | — | — |
| Backup/restore with zip-slip protection | Y | — | — | — | — |
| Multi-query expansion with RRF | Y | — | — | — | — |
| 4-layer search deduplication | Y | — | — | — | — |
| Semantic-aware smart chunking | Y | — | — | — | — |
| Compiled truth pages (per-entity) | Y | — | — | — | — |
| Dream cycle (autonomous enrichment) | Y | — | — | — | — |

---

## Integrations are the substrate working

Because the substrate is deterministic, integrating with 17 different CLIs produces the same answers on each. That is not a coincidence — it is the point. MIND-Mem can be the canonical memory layer across heterogeneous agent stacks precisely because recall is deterministic and its audit/replay chain is byte-identical regardless of which client is asking. The 17-CLI surface is a consequence of the substrate, not a feature in itself.

> Honest positioning: the integrations below are *software-level* —
> the named tool talks to MIND-Mem via the Model Context Protocol.
> They are **not** commercial-customer relationships with any vendor.
> Full positioning policy: [`docs/integrations.md`](docs/integrations.md).

### Native MCP integration with 17 AI development tools

```bash
pip install mind-mem
mm install-all
```

`mm install-all` auto-detects every supported client on your machine
and writes the appropriate config file for each. MIND-Mem speaks the
[Model Context Protocol](https://modelcontextprotocol.io/) — any
MCP-compatible client connects with one command.

| Client | Vendor | Client | Vendor |
|--------|--------|--------|--------|
| Claude Code | Anthropic | Cline | Cline.bot |
| Claude Desktop | Anthropic | Roo | Roo Code |
| Codex CLI | OpenAI | GitHub Copilot | GitHub / Microsoft |
| Grok Build CLI | xAI | Cody | Sourcegraph |
| Gemini CLI | Google | | |
| Vibe (Mistral CLI) | Mistral | Qodo | Qodo |
| Cursor | Anysphere | aider | aider-chat |
| Windsurf | Codeium | OpenClaw | OpenAI (Peter Steinberger) |
| Zed | Zed Industries | NemoClaw / Nemo | NVIDIA |
| Continue | Continue.dev | NanoClaw | Anthropic |

### Compatible with major LLM providers

MIND-Mem's recall pipeline is provider-agnostic. Tested against
Anthropic Claude (3.5 Sonnet, 4.x), OpenAI GPT (4o, 5.4), Google
Gemini (2.0 Flash, 3.1 Pro), Mistral Large, and local endpoints
(Ollama, vLLM, llama.cpp). Compatibility is at the API contract
level — the same MIND-Mem server returns the same answers
regardless of which LLM is asking.

### Production usage at STARGA

MIND-Mem is the daily-driver memory layer across STARGA's active
projects, including `mind`, `mindlang.dev`, `mind-inference`, and
`arch-mind`. First-party, verifiable in our own commit history.

### What we do not claim

- ❌ "OpenAI / Microsoft / Anthropic / Google is a customer" — false.
  These are software-level MCP integrations, not commercial
  relationships.
- ❌ "Used by N production teams outside STARGA" — we have no
  telemetry. PyPI download counts measure installs, not active use.

If a future integration becomes a real commercial relationship
(signed contract, paid pilot, named reference), it will appear in
the press release first — not in the README.

---

## Benchmark Results

MIND-Mem's recall engine evaluated on standard long-term memory benchmarks using multiple configurations — from pure BM25 to full hybrid retrieval with neural reranking.

### Needle In A Haystack (NIAH)

**250/250 — 100% retrieval** across all haystack sizes, burial depths, and needle types.

A single fact is planted at a controlled depth within a haystack of semantically diverse filler blocks. The system must retrieve the needle in its top-5 results using only a natural-language query.

| Haystack Size | Depths Tested | Needles | Passed | Rate |
|---------------|---------------|---------|--------|------|
| 10 blocks | 0/25/50/75/100% | 10 | 50/50 | 100% |
| 50 blocks | 0/25/50/75/100% | 10 | 50/50 | 100% |
| 100 blocks | 0/25/50/75/100% | 10 | 50/50 | 100% |
| 250 blocks | 0/25/50/75/100% | 10 | 50/50 | 100% |
| 500 blocks | 0/25/50/75/100% | 10 | 50/50 | 100% |

**Config:** Hybrid BM25 + BAAI/bge-large-en-v1.5 + RRF (k=60) + sqlite-vec. Full details: [benchmarks/NIAH.md](benchmarks/NIAH.md)

### LoCoMo LLM-as-Judge

Same pipeline as Mem0 and Letta evaluations: retrieve context, generate answer with LLM, score against gold reference with judge LLM. Directly comparable methodology.

**v1.0.7 — Hybrid + top_k=18** (external LLM answerer + judge, conv-0, 199 questions):

| Category        |      N | Acc (>=50) | Mean Score |
| --------------- | -----: | ---------: | ---------: |
| **Overall**     | **199**|  **92.5%** |   **76.7** |
| Adversarial     |     47 |      97.9% |       89.8 |
| Multi-hop       |     37 |      91.9% |       74.3 |
| Open-domain     |     70 |      92.9% |       72.7 |
| Temporal        |     13 |      92.3% |       76.2 |
| Single-hop      |     32 |      84.4% |       68.9 |

> **Pipeline:** BM25 + Qwen3-Embedding-8B (4096d) vector search → RRF fusion (k=60) → top-18 evidence blocks → observation compression → answer → judge. A/B validated: +2.8 mean vs top_k=10 baseline.

**v1.1.1 — BM25 + top_k=18** (external LLM answerer + judge, 10 conversations, 1986 questions):

| Category        |        N | Acc (>=50) | Mean Score |
| --------------- | -------: | ---------: | ---------: |
| **Overall**     | **1986** |  **73.8%** |   **70.5** |
| Adversarial     |      446 |      92.4% |       87.2 |
| Single-hop      |      282 |      80.9% |       68.7 |
| Open-domain     |      841 |      71.2% |       70.3 |
| Temporal        |       96 |      66.7% |       65.9 |
| Multi-hop       |      321 |      50.5% |       51.1 |

> **Pipeline:** BM25 + RM3 query expansion → top-18 evidence blocks → observation compression → answer → judge. Full 10-conversation benchmark with the same external LLM as both answerer and judge.

**v1.0.0 — BM25-only baseline** (external LLM answerer + judge, 10 conversations):

| Category    |        N | Acc (>=50) | Mean Score |
| ----------- | -------: | ---------: | ---------: |
| **Overall** | **1986** |  **67.3%** |   **61.4** |
| Open-domain |      841 |      86.6% |       78.3 |
| Temporal    |       96 |      78.1% |       65.7 |
| Single-hop  |      282 |      68.8% |       59.1 |
| Multi-hop   |      321 |      55.5% |       48.4 |
| Adversarial |      446 |      36.3% |       39.5 |

> **Key improvements since v1.0.0:** Adversarial accuracy tripled from 36.3% to 92.4% via abstention classifier + hybrid retrieval. Overall Acc≥50 improved from 67.3% to 73.8% (+6.5pp).

### Competitive Landscape

| System       |     Score | Approach                                                     |
| ------------ | --------: | ------------------------------------------------------------ |
| **MIND-Mem** | **76.7%** | Hybrid BM25 + Qwen3-8B vector + RRF fusion (local-only)    |
| Memobase     |     75.8% | Specialized extraction                                       |
| **Letta**    |     74.0% | Files + agent tool use                                       |
| **MIND-Mem** |     73.8% | BM25-only, full 10-conv (1986 questions, external LLM judge) |
| **Mem0**     |     68.5% | Graph + LLM extraction                                      |

> MIND-Mem now **surpasses Mem0 and Letta** with **local-only** retrieval — no cloud calls, no graph DB, no LLM in the retrieval loop. MIND-Mem's unique value is **governance** (contradiction detection, drift analysis, audit trails) and **agent-agnostic shared memory** via MCP — areas these benchmarks don't measure.

### Competitive Landscape (LoCoMo)

| System | LoCoMo Acc>=50 | Infrastructure | Dependencies |
| --- | ---: | --- | --- |
| **MIND-Mem** (hybrid) | **76.7%** | **Local-only** | **Zero core (optional: llama.cpp, sentence-transformers)** |
| Memobase | 75.8% | Cloud + GPU | embeddings + vector DB |
| Letta | 74.0% | Cloud | embeddings + vector DB |
| **MIND-Mem** (BM25) | **73.8%** | **Local-only** | **Zero core** |
| full-context | 72.9% | N/A | LLM context window |
| Mem0 | 68.5% | Cloud (managed) | graph DB + embeddings |

> MIND-Mem surpasses Mem0 (68.5%), Letta (74.0%), and Memobase (75.8%) with zero cloud infrastructure. Full 10-conversation benchmark (1986 questions) validates this at scale. Note: benchmarks measure retrieval accuracy. The substrate properties (byte-identical replay, governed-write, audit chain) are not captured by any of these benchmarks — they are properties of the architecture, not the recall scores.

### LongMemEval (held pending reconciliation)

> **Provenance hold active.** The LongMemEval R@5 numbers below are pending reconciliation against a higher-iteration run. They are not part of the MIND-Mem positioning until the hold is resolved. See [`benchmarks/STATUS.md`](benchmarks/STATUS.md) for the current status and methodology.

| Category         |       N |      R@1 |      R@5 |     R@10 |      MRR |
| ---------------- | ------: | -------: | -------: | -------: | -------: |
| **Overall**      | **470** | **73.2** | **(held)** | **88.1** | **.784** |
| Multi-session    |     121 |     83.5 |     (held) |     95.9 |     .885 |
| Temporal         |     127 |     76.4 |     (held) |     92.9 |     .826 |
| Knowledge update |      72 |     80.6 |     (held) |     91.7 |     .844 |
| Single-session   |      56 |     82.1 |     (held) |     89.3 |     .847 |

### Performance (Latency & Throughput)

Measured on a single developer workstation (commodity x86-64, warm cache, single process) against a 65-block workspace (typical personal workspace) with the SQLite FTS5 backend. Absolute latencies are hardware-dependent — the portable claim is the O(log N) scaling noted below, not the millisecond figures:

| Operation | Metric | Value |
|-----------|--------|-------|
| **Query** (FTS5 + rerank) | p50 latency | **2.1 ms** |
| **Query** (FTS5 + rerank) | p95 latency | **4.9 ms** |
| **Query** (FTS5 + rerank) | mean latency | **2.6 ms** |
| **Incremental reindex** | elapsed | **32 ms** (13 blocks indexed) |
| **Full index build** | elapsed | **48 ms** (65 blocks) |
| **MCP tool overhead** | stdio round-trip | **< 15 ms** |
| **Memory footprint** | RSS (idle MCP server) | **~28 MB** |

Query latency scales as O(log N) with SQLite FTS5 (vs O(corpus) for scan backend). The co-retrieval graph adds < 1ms per query. Knee cutoff and fact aggregation add negligible overhead (< 0.5ms).

### Run Benchmarks Yourself

```bash
# Retrieval-only (R@K metrics)

## Install in 3 commands

```bash
pip install mind-mem
mm install-all --force      # auto-wires every detected AI CLI
mm install-model            # downloads mind-mem-4b GGUF + imports to Ollama
```

Full options + Postgres setup + troubleshooting:
[**docs/install-guide.md**](docs/install-guide.md)

python3 benchmarks/locomo_harness.py
python3 benchmarks/longmemeval_harness.py

# LLM-as-judge (accuracy metrics, requires API key)
python3 benchmarks/locomo_judge.py --dry-run
python3 benchmarks/locomo_judge.py --answerer-model <your-answerer-model> --output results.json

# Hybrid retrieval with any model pair (BM25 + vector + cross-encoder)
python3 benchmarks/locomo_judge.py --hybrid --compress --answerer-model <your-answerer-model> --judge-model <your-judge-model> --output results.json

# Selective conversations
python3 benchmarks/locomo_harness.py --conv-ids 4,7,8
```

---

## Quick Start

### One-line install (recommended)

```bash
pipx install "mind-mem[mcp]"
mind-mem-mcp --help          # smoke-test
```

`pipx` keeps MIND-Mem in its own venv, exposes the `mind-mem-mcp` console
script on `PATH`, and avoids polluting your system Python. If you don't have
pipx, `pip install --user "mind-mem[mcp]"` works too.

Then wire it into every AI coding client on your machine:

```bash
git clone https://github.com/star-ga/mind-mem.git
cd mind-mem
./install.sh --all --no-install   # Already installed via pipx, just wire clients
```

Or do both in one shot (the installer will auto-pick pipx if available, else
fall back to pip):

```bash
git clone https://github.com/star-ga/mind-mem.git
cd mind-mem
./install.sh --all
```

This auto-detects every AI coding client on your machine and configures MIND-Mem
for all of them. Each client launches the same `mind-mem-mcp` binary, so all
agents share one workspace. Supported clients:

| Client                  | Config Location                               | Format  |
| ----------------------- | --------------------------------------------- | ------- |
| **Claude Code CLI**     | `~/.claude/mcp.json`                          | JSON    |
| **Claude Desktop**      | `~/.config/Claude/claude_desktop_config.json` | JSON    |
| **Codex CLI** (OpenAI)  | `~/.codex/config.toml`                        | TOML    |
| **Gemini CLI** (Google) | `~/.gemini/settings.json`                     | JSON    |
| **Cursor**              | `~/.cursor/mcp.json`                          | JSON    |
| **Windsurf**            | `~/.codeium/windsurf/mcp_config.json`         | JSON    |
| **Zed**                 | `~/.config/zed/settings.json`                 | JSON    |
| **OpenClaw**            | `~/.openclaw/hooks/mind-mem/`                 | JS hook |

Selective install:

```bash
./install.sh --claude-code --codex --gemini         # Only specific clients
./install.sh --all --workspace ~/my-project/memory  # Custom workspace path
```

Uninstall:

```bash
./uninstall.sh          # Remove from all clients (keeps workspace data)
./uninstall.sh --purge  # Remove everything including workspace data
```

### Manual Setup

For manual or per-project setup:

**1. Clone into your project**

```bash
cd /path/to/your/project
git clone https://github.com/star-ga/mind-mem.git .mind-mem
```

**2. Initialize workspace**

```bash
python3 .mind-mem/src/mind_mem/init_workspace.py .
```

Creates 12 directories, 19 template files, and `mind-mem.json` config. **Never overwrites existing files.**

**3. Validate**

```bash
bash .mind-mem/src/mind_mem/validate.sh .
# or cross-platform:
python3 .mind-mem/src/mind_mem/validate_py.py .
```

Expected: `74 checks | 74 passed | 0 issues`.

**4. First scan**

```bash
python3 .mind-mem/src/mind_mem/intel_scan.py .
```

Expected: `0 critical | 0 warnings` on a fresh workspace.

**5. Verify recall + capture**

```bash
python3 .mind-mem/src/mind_mem/recall.py --query "test" --workspace .
# → No results found. (empty workspace — correct)

python3 .mind-mem/src/mind_mem/capture.py .
# → capture: no daily log for YYYY-MM-DD, nothing to scan (correct)
```

**6. Add hooks (optional)**

**Option A: Claude Code hooks** (recommended)

Merge into your `.claude/hooks.json`:

```json
{
  "hooks": [
    {
      "event": "SessionStart",
      "command": "bash .mind-mem/hooks/session-start.sh"
    },
    {
      "event": "Stop",
      "command": "bash .mind-mem/hooks/session-end.sh"
    }
  ]
}
```

**Option B: OpenClaw hooks** (for OpenClaw 2026.2+)

```bash
cp -r .mind-mem/hooks/openclaw/mind-mem ~/.openclaw/hooks/mind-mem
openclaw hooks enable mind-mem
```

**7. Smoke Test (optional)**

```bash
bash .mind-mem/src/mind_mem/smoke_test.sh
```

Creates a temp workspace, runs init → validate → scan → recall → capture → pytest, then cleans up.

---

## Health Summary

After setup, this is what a healthy workspace looks like:

```
$ python3 -m mind_mem.intel_scan .

mind-mem Intelligence Scan Report v2.0
Mode: detect_only

=== 1. CONTRADICTION DETECTION ===
  OK: No contradictions found among 25 signatures.

=== 2. DRIFT ANALYSIS ===
  OK: All active decisions referenced or exempt.
  INFO: Metrics: active_decisions=17, active_tasks=7, blocked=0,
        dead_decisions=0, incidents=3, decision_coverage=100%

=== 3. DECISION IMPACT GRAPH ===
  OK: Built impact graph: 11 decision(s) with edges.

=== 4. STATE SNAPSHOT ===
  OK: Snapshot saved.

=== 5. WEEKLY BRIEFING ===
  OK: Briefing generated.

TOTAL: 0 critical | 0 warnings | 16 info
```

---

## Commands

| Command | What it does |
| ----------------- | ----------------------------------------------------------------------------------------------- |
| `/scan` | Run integrity scan — contradictions, drift, dead decisions, impact graph, snapshot, briefing |
| `/apply` | Review and apply proposals from scan results (dry-run first, then apply) |
| `/recall <query>` | Search across all memory files with ranked results (add `--graph` for cross-reference boosting) |

---

## Architecture

```
your-workspace/
├── mcp_server.py # MCP server (FastMCP, 89 tools, 8 resources)
├── mind-mem.json # Config
├── MEMORY.md # Protocol rules
│
├── mind/ # 26 INI-style config files (.mind, see docs/MIND_CONFIG_VS_MIND_LANG.md)
│ ├── bm25.mind # BM25F scoring kernel
│ ├── rrf.mind # Reciprocal Rank Fusion kernel
│ ├── reranker.mind # Deterministic reranking
│ ├── abstention.mind # Confidence gating
│ ├── ranking.mind # Evidence ranking
│ ├── importance.mind # A-MEM importance scoring
│ ├── category.mind # Category relevance scoring
│ ├── recall.mind # Combined recall scoring
│ ├── hybrid.mind # BM25 + vector hybrid fusion
│ ├── rm3.mind # RM3 pseudo-relevance feedback
│ ├── rerank.mind # Score combination pipeline
│ ├── adversarial.mind # Adversarial query detection
│ ├── temporal.mind # Time-aware scoring
│ ├── prefetch.mind # Context pre-assembly
│ ├── intent.mind # Intent classification
│ └── cross_encoder.mind # Cross-encoder blending
│
├── lib/ # Compiled MIND kernels (optional)
│ └── libmindmem.so # mindc output — not required for operation
│
├── decisions/
│ └── DECISIONS.md # Formal decisions [D-YYYYMMDD-###]
├── tasks/
│ └── TASKS.md # Tasks [T-YYYYMMDD-###]
├── entities/
│ ├── projects.md # [PRJ-###]
│ ├── people.md # [PER-###]
│ ├── tools.md # [TOOL-###]
│ └── incidents.md # [INC-###]
│
├── memory/
│ ├── YYYY-MM-DD.md # Daily logs (append-only)
│ ├── intel-state.json # Scanner state + metrics
│ └── maint-state.json # Maintenance state
│
├── summaries/
│ ├── weekly/ # Weekly summaries
│ └── daily/ # Daily summaries
│
├── intelligence/
│ ├── CONTRADICTIONS.md # Detected contradictions
│ ├── DRIFT.md # Drift detections
│ ├── SIGNALS.md # Auto-captured signals
│ ├── IMPACT.md # Decision impact graph
│ ├── BRIEFINGS.md # Weekly briefings
│ ├── AUDIT.md # Applied proposal audit trail
│ ├── SCAN_LOG.md # Scan history
│ ├── proposed/ # Staged proposals + resolution proposals
│ │ ├── DECISIONS_PROPOSED.md
│ │ ├── TASKS_PROPOSED.md
│ │ ├── EDITS_PROPOSED.md
│ │ └── RESOLUTIONS_PROPOSED.md
│ ├── applied/ # Snapshot archives (rollback)
│ └── state/snapshots/ # State snapshots
│
├── shared/ # Multi-agent shared namespace
│ ├── decisions/
│ ├── tasks/
│ ├── entities/
│ └── intelligence/
│ └── LEDGER.md # Cross-agent fact ledger
│
├── agents/ # Per-agent private namespaces
│ └── <agent-id>/
│ ├── decisions/
│ ├── tasks/
│ └── memory/
│
├── mind-mem-acl.json # Multi-agent access control
├── .mind-mem-wal/ # Write-ahead log (crash recovery)
│
└── src/mind_mem/
 ├── mind_ffi.py # MIND FFI bridge (ctypes)
 ├── hybrid_recall.py # Hybrid BM25+Vector+RRF orchestrator
 ├── block_metadata.py # A-MEM metadata evolution
 ├── cross_encoder_reranker.py # Optional cross-encoder
 ├── intent_router.py # 9-type intent classification (adaptive)
 ├── recall.py # BM25F + RM3 + graph scoring engine
 ├── recall_vector.py # Vector/embedding backends
 ├── sqlite_index.py # FTS5 + vector + metadata index
 ├── connection_manager.py # SQLite connection pool (WAL read/write separation)
 ├── block_store.py # BlockStore protocol + MarkdownBlockStore
 ├── corpus_registry.py # Central corpus path registry
 ├── abstention_classifier.py # Adversarial abstention
 ├── evidence_packer.py # Evidence assembly and ranking
 ├── intel_scan.py # Integrity scanner
 ├── apply_engine.py # Proposal apply engine (delta-based snapshots)
 ├── block_parser.py # Markdown block parser (typed)
 ├── capture.py # Auto-capture (26 patterns)
 ├── compaction.py # Compaction/GC/archival
 ├── mind_filelock.py # Cross-platform advisory file locking
 ├── observability.py # Structured JSON logging + metrics
 ├── namespaces.py # Multi-agent namespace & ACL
 ├── conflict_resolver.py # Automated conflict resolution
 ├── backup_restore.py # WAL + backup/restore + JSONL export
 ├── transcript_capture.py # Transcript JSONL signal extraction
 ├── validate.sh # Structural validator (74+ checks)
 └── validate_py.py # Structural validator (Python, cross-platform)
```

---

## How It Compares

### Quick Comparison

| Feature | MIND-Mem | Mem0 | Letta | Zep/Graphiti |
|---------|----------|------|-------|--------------|
| Local-only | Yes | No (cloud API) | No (runtime) | No (Neo4j) |
| Zero infrastructure | Yes | No | No | No |
| Hybrid retrieval | BM25F + vector + RRF | Vector only | Hybrid | Graph + vector |
| Governance (propose/review/apply) | Yes | No | No | No |
| Contradiction detection | Yes | No | No | No |
| Tests | 5,465+ | - | - | - |
| LoCoMo benchmark | 86.33 conv-0 (v3.6, external LLM judge) | 66.88 | 74.0% | - |
| MCP tools | 89 distinct (90 `mcp.tool` registrations; `recall` dispatcher shadows base `recall`) | - | - | - |
| Core dependencies | 0 | Many | Many | Many |

### At a Glance

| Tool | Strength | Trade-off |
| ---- | -------- | --------- |
| [**Mem0**](https://github.com/mem0ai/mem0) | Fast managed service, graph memory, multi-user scoping | Cloud-dependent, no integrity checking |
| [**Supermemory**](https://supermemory.ai) | Fastest retrieval (ms), auto-ingestion from Drive/Notion | Cloud-dependent, auto-writes without review |
| [**claude-mem**](https://github.com/thedotmack/claude-mem) | Purpose-built for Claude Code, ChromaDB vectors | Requires ChromaDB + Express worker, no integrity |
| [**Letta**](https://www.letta.com) | Self-editing memory blocks, sleep-time compute, 74% LoCoMo | Full agent runtime (heavy), not just memory |
| [**Zep**](https://www.getzep.com) | Temporal knowledge graph, bi-temporal model, sub-second at scale | Cloud service, complex architecture |
| [**LangMem**](https://github.com/langchain-ai) | Native LangChain/LangGraph integration | Tied to LangChain ecosystem |
| [**Cognee**](https://www.cognee.ai) | Advanced chunking, web content bridging | Research-oriented, complex setup |
| [**Graphlit**](https://www.graphlit.com) | Multimodal ingestion, semantic search, managed platform | Cloud-only, managed service |
| [**ClawMem**](https://github.com/yoloshii/ClawMem) | Full ML pipeline (cross-encoder + QMD + beam search) | 4.5GB VRAM, 3 GPU processes required |
| [**MemU**](https://github.com/supermemory/memu) | Hierarchical 3-layer memory, multimodal ingestion, LLM-based retrieval | Requires LLM for extraction and retrieval, no hybrid search |
| **MIND-Mem** | Integrity + governance + zero core deps + hybrid search + MIND kernels + 89 MCP tools (incl. MIC/MAP, walkthrough, persona, pipeline-hash) + cross-model consensus audit per release | Lexical recall by default (vector/CE optional) |

### Full Feature Matrix

Compared against every major memory solution for AI agents (as of 2026):

|                 | [Mem0](https://github.com/mem0ai/mem0) | [Supermemory](https://supermemory.ai) | [claude-mem](https://github.com/thedotmack/claude-mem) | [Letta](https://www.letta.com) | [Zep](https://www.getzep.com) | [LangMem](https://github.com/langchain-ai) | [Cognee](https://www.cognee.ai) | [Graphlit](https://www.graphlit.com) | [ClawMem](https://github.com/yoloshii/ClawMem) | [MemU](https://github.com/supermemory/memu) | **MIND-Mem** |
| --------------- | :------------------------------------: | :-----------------------------------: | :-----------------------------------------------------: | :----------------------------: | :---------------------------: | :-----------------------------------------: | :-----------------------------: | :----------------------------------: | :---------------------------------------------: | :------------------------------------------: | :----------: |
| **Recall**      |                                        |                                       |                                                         |                                |                               |                                             |                                 |                                      |                                                 |                                              |              |
| Vector          |                 Cloud                  |                 Cloud                 |                          Chroma                         |              Yes               |              Yes              |                     Yes                     |               Yes               |                 Yes                  |                       Yes                       |                      —                       |  **Optional**  |
| Lexical         |                 Filter                 |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                      BM25                       |                      —                       |   **BM25F**    |
| Graph           |                  Yes                   |                   —                   |                            —                            |               —                |              Yes              |                      —                      |               Yes               |                 Yes                  |                      Beam                       |                      —                       |   **2-hop**    |
| Hybrid + RRF    |                  Part                  |                   —                   |                            —                            |               —                |              Yes              |                      —                      |               Yes               |                 Yes                  |                     **Yes**                     |                      —                       |    **Yes**     |
| Cross-encoder   |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                   qwen3 0.6B                    |                      —                       | **MiniLM 80MB** |
| Intent routing  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                       Yes                       |                      —                       |  **9 types**   |
| Query expansion |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                    QMD 1.7B                     |                      —                       | **RM3 (zero-dep)** |
| **Persistence** |                                        |                                       |                                                         |                                |                               |                                             |                                 |                                      |                                                 |                                              |              |
| Structured      |                  JSON                  |                 JSON                  |                           SQL                           |              Blk               |             Grph              |                     KV                      |              Grph               |                Grph                  |                       SQL                       |                   Markdown                   | **Markdown**   |
| Entities        |                  Yes                   |                  Yes                  |                            —                            |              Yes               |              Yes              |                     Yes                     |               Yes               |                 Yes                  |                        —                        |                     Yes                      |    **Yes**     |
| Temporal        |                   —                    |                   —                   |                            —                            |               —                |              Yes              |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Supersede       |                   —                    |                   —                   |                            —                            |              Yes               |              Yes              |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Append-only     |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| A-MEM metadata  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                       Yes                       |                      —                       |    **Yes**     |
| **Integrity**   |                                        |                                       |                                                         |                                |                               |                                             |                                 |                                      |                                                 |                                              |              |
| Contradictions  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Drift detection |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Validation      |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       | **74+ rules**  |
| Impact graph    |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Coverage        |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Multi-agent     |                   —                    |                   —                   |                            —                            |              Yes               |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       | **ACL-based**  |
| Conflict res.   |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       | **Automatic**  |
| WAL/crash       |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Backup/restore  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Abstention      |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| **Governance**  |                                        |                                       |                                                         |                                |                               |                                             |                                 |                                      |                                                 |                                              |              |
| Auto-capture    |                  Auto                  |                 Auto                  |                          Auto                           |              Self              |              Ext              |                     Ext                     |               Ext               |                 Ing                  |                       Auto                      |                   LLM Ext                    |  **Propose**   |
| Proposal queue  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Rollback        |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| Mode governance |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |  **3 modes**   |
| Audit trail     |                   —                    |                 Part                  |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Full**    |
| **Operations**  |                                        |                                       |                                                         |                                |                               |                                             |                                 |                                      |                                                 |                                              |              |
| Local-only      |                   —                    |                   —                   |                           Yes                           |               —                |               —               |                      —                      |                —                |                  —                   |                       Yes                       |                     Yes                      |    **Yes**     |
| Zero core deps  |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       |    **Yes**     |
| No daemon       |                   —                    |                   —                   |                            —                            |               —                |               —               |                     Yes                     |                —                |                  —                   |                        —                        |                     Yes                      |    **Yes**     |
| GPU required    |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                    **4.5GB**                    |                      No                      |     **No**     |
| Git-friendly    |                   —                    |                   —                   |                            —                            |              Part              |               —               |                      —                      |                —                |                  —                   |                        —                        |                     Yes                      |    **Yes**     |
| MCP server      |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       | **89 tools**   |
| MIND kernels    |                   —                    |                   —                   |                            —                            |               —                |               —               |                      —                      |                —                |                  —                   |                        —                        |                      —                       | **16 source**  |

### The Gap MIND-Mem Fills

Every tool above does **storage + retrieval**. None of them answer:

- "Do any of my decisions contradict each other?"
- "Which decisions are active but nobody references anymore?"
- "Did I make a decision in chat that was never formalized?"
- "What's the downstream impact if I change this decision?"
- "Is my memory state structurally valid right now?"

**MIND-Mem focuses on memory governance and integrity — the critical layer most memory systems ignore entirely.**

### Why Plain Files Outperform Fancy Retrieval

Letta's August 2025 analysis showed that a plain-file baseline (full conversations stored as files + agent filesystem tools) scored **74.0% on LoCoMo** with gpt-4o-mini — beating Mem0's top graph variant at 68.5%. Key reasons:

- **LLMs excel at tool-based retrieval.** Agents can iteratively query/refine file searches better than single-shot vector retrieval that might miss subtle connections.
- **Benchmarks reward recall + reasoning over storage sophistication.** Strong judge LLMs handle the rest once relevant chunks are loaded.
- **Overhead hurts.** Specialized pipelines introduce failure modes (bad embeddings, chunking errors, stale indexes) that simple file access avoids.
- **For text-heavy agentic use cases, "how well the agent manages context" > "how smart the retrieval index is."**

MIND-Mem's deterministic retrieval pipeline validates these findings: **67.3% on LoCoMo** with zero dependencies, no embeddings, and no vector database — within 1.2pp of Mem0's graph-based approach. The key insight: treating retrieval as a reasoning pipeline (wide candidate pool → deterministic rerank → context packing) closes most of the gap without any ML infrastructure. Unlike plain-file baselines, MIND-Mem adds integrity checking, governance, and agent-agnostic shared memory via MCP that no other system provides.

---

## Recall

### Default: BM25 Hybrid

```bash
python3 -m mind_mem.recall --query "authentication" --workspace .
python3 -m mind_mem.recall --query "auth" --json --limit 5 --workspace .
python3 -m mind_mem.recall --query "deadline" --active-only --workspace .
```

BM25F scoring (k1=1.2, b=0.75) with per-field weighting, bigram phrase matching, overlapping sentence chunking, and query-type-aware parameter tuning. Searches across all structured files.

**BM25F field weighting:** Terms in `Statement` fields score 3x higher than terms in `Context` (0.5x). This naturally prioritizes core content over auxiliary metadata.

**RM3 query expansion:** Pseudo-relevance feedback from top-k initial results. JM-smoothed language model extracts expansion terms, interpolated with the original query at configurable alpha. Falls back to static synonyms for adversarial queries.

**Adversarial abstention:** Deterministic pre-LLM confidence gate. Computes confidence from entity overlap, BM25 score, speaker coverage, evidence density, and negation asymmetry. Below threshold → forces abstention.

**Stemming:** "queries" matches "query", "deployed" matches "deployment". Simplified Porter stemmer with zero dependencies.

### Hybrid Search (BM25 + Vector + RRF)

```json
{
  "recall": {
    "backend": "hybrid",
    "vector_enabled": true,
    "rrf_k": 60,
    "bm25_weight": 1.0,
    "vector_weight": 1.0
  }
}
```

Thread-parallel BM25 and vector retrieval fused via RRF: `score(doc) = bm25_w / (k + bm25_rank) + vec_w / (k + vec_rank)`. Deduplicates by block ID. Falls back to BM25-only when vector backend is unavailable.

### Graph-Based (2-hop cross-reference boost)

```bash
python3 -m mind_mem.recall --query "database" --graph --workspace .
```

2-hop graph traversal: 1-hop neighbors get 0.3x score boost, 2-hop get 0.1x (tagged `[graph]`). Surfaces structurally connected blocks via `AlignsWith`, `Dependencies`, `Supersedes`, `Sources`, and ConstraintSignature scopes. Auto-enabled for multi-hop queries.

### Vector (pluggable)

```json
{
  "recall": {
    "backend": "vector",
    "vector_enabled": true,
    "vector_model": "all-MiniLM-L6-v2",
    "onnx_backend": true
  }
}
```

Supports ONNX inference (local, no server) or cloud embeddings. Falls back to BM25 automatically if unavailable.

---

## MIND Kernels

MIND-Mem ships **26 `.mind` configuration files** under `mind/` that tune
the scoring pipeline at runtime. These files are **INI-style declarative
configuration** (e.g. `[fusion]` / `rrf_k = 60`), parsed by
`load_kernel_config()` in `src/mind_mem/mind_ffi.py`. They are **not** the
MIND programming language — see
[`docs/MIND_CONFIG_VS_MIND_LANG.md`](docs/MIND_CONFIG_VS_MIND_LANG.md) for
the disambiguation. The Python runtime (in `src/mind_mem/mind_kernels.py`)
implements the actual scoring logic; the `.mind` files only carry
numerical knobs.

### Compilation (forward-looking — not yet wired)

The roadmap moves these numerical hot paths to true MIND-language kernels
that compile to a native shared library via `mindc`
(see [mindlang.dev](https://mindlang.dev)). When that integration lands,
the build command will look like:

```bash
# Once the MIND-language port ships (not currently supported):
mindc mind/*.mind --emit=shared -o lib/libmindmem.so
```

Until then, the Python fallback in `mind_kernels.py` is the authoritative
implementation. `pip install mind-mem` is fully functional without
`mindc`. The 26 config files themselves ship in the wheel under
`<sys.prefix>/share/mind-mem/kernels/` for forward-compatibility tooling.

### Kernel Index

| File               | Functions                                                                            | Purpose                              |
| ------------------ | ------------------------------------------------------------------------------------ | ------------------------------------ |
| `bm25.mind`        | `bm25f_doc`, `bm25f_batch`, `apply_recency`, `apply_graph_boost`                    | BM25F scoring with field boosts      |
| `rrf.mind`         | `rrf_fuse`, `rrf_fuse_three`                                                        | Reciprocal Rank Fusion               |
| `reranker.mind`    | `date_proximity_score`, `category_boost`, `negation_penalty`, `rerank_deterministic` | Deterministic reranking              |
| `rerank.mind`      | `rerank_scores`                                                                      | Score combination pipeline           |
| `abstention.mind`  | `entity_overlap`, `confidence_score`                                                 | Confidence gating                    |
| `ranking.mind`     | `weighted_rank`, `top_k_mask`                                                        | Evidence ranking                     |
| `importance.mind`  | `importance_score`                                                                   | A-MEM importance scoring             |
| `category.mind`    | `category_affinity`, `query_category_relevance`, `category_assign`                   | Category distillation scoring        |
| `prefetch.mind`    | `prefetch_score`, `prefetch_select`                                                  | Signal-based context pre-assembly    |
| `recall.mind`      | `recall_score`                                                                       | Combined recall scoring              |
| `hybrid.mind`      | `hybrid_fuse`                                                                        | BM25 + vector hybrid fusion          |
| `rm3.mind`         | `rm3_weight`                                                                         | RM3 pseudo-relevance feedback        |
| `adversarial.mind` | `adversarial_gate`                                                                   | Adversarial query detection          |
| `temporal.mind`    | `temporal_decay`                                                                     | Time-aware scoring                   |
| `intent.mind`      | `intent_params`                                                                      | Intent classification parameters     |
| `cross_encoder.mind` | `ce_blend`                                                                         | Cross-encoder blending configuration |

### Performance

<details>
<summary>Compiled MIND kernels vs pure Python — 9 core scoring functions (200 iterations, <code>perf_counter</code>)</summary>

&nbsp;

| Function           |     N=100 |   N=1,000 |   N=5,000 |
| ------------------ | --------: | --------: | --------: |
| `rrf_fuse`         | **10.8x** | **69.0x** | **72.5x** |
| `bm25f_batch`      | **13.2x** | **113.8x** | **193.1x** |
| `negation_penalty` |  **3.3x** |  **7.0x** | **18.4x** |
| `date_proximity`   | **10.7x** | **15.3x** | **26.9x** |
| `category_boost`   |  **3.3x** | **19.8x** | **17.7x** |
| `importance_batch` | **22.3x** | **46.2x** | **48.6x** |
| `confidence_score` |  **0.9x** |  **0.8x** |  **0.9x** |
| `top_k_mask`       |  **3.1x** |  **8.1x** | **11.8x** |
| `weighted_rank`    |  **5.1x** | **26.6x** | **121.8x** |
| **Overall**        |           |           | **49.0x** |

> **49x faster** end-to-end at production scale (N=5,000). Individual kernels reach up to **193x** speedup. The compiled library includes 14 runtime protection layers with near-zero overhead.

</details>

### FFI Bridge

The compiled `.so` exposes a C99-compatible ABI. Python calls via `ctypes` through `src/mind_mem/mind_ffi.py`:

```python
from mind_ffi import get_kernel, is_available, is_protected

if is_available():
    kernel = get_kernel()
    scores = kernel.rrf_fuse_py(bm25_ranks, vec_ranks, k=60.0)
    print(f"Protected: {is_protected()}")  # True with the hardened build
```

### Without MIND

If `lib/libmindmem.so` is not present, MIND-Mem uses pure Python implementations. The Python fallback produces identical results (within f32 epsilon). No functionality is lost — MIND is a performance optimization, not a requirement.

---

## Auto-Capture

```
Session end
    ↓
capture.py scans daily log (or --scan-all for batch)
    ↓
Detects decision/task language (26 patterns, 3 confidence levels)
    ↓
Extracts structured metadata (subject, object, tags)
    ↓
Classifies confidence (high/medium/low → P1/P2/P3)
    ↓
Writes to intelligence/SIGNALS.md ONLY
    ↓
User reviews signals
    ↓
/apply promotes to DECISIONS.md or TASKS.md
```

**Batch scanning:** `python3 -m mind_mem.capture . --scan-all` scans the last 7 days of daily logs.

**Safety guarantee:** `capture.py` never writes to `decisions/` or `tasks/` directly. All signals must go through the apply engine.

---

## Multi-Agent Memory

### Namespace Setup

```bash
python3 -m mind_mem.namespaces workspace/ --init coder-1 reviewer-1
```

Creates `shared/` (visible to all) and `agents/coder-1/`, `agents/reviewer-1/` (private) directories with ACL config.

### Access Control

```json
{
  "default_policy": "read",
  "agents": {
    "coder-1": {"namespaces": ["shared", "agents/coder-1"], "write": ["agents/coder-1"], "read": ["shared"]},
    "reviewer-*": {"namespaces": ["shared"], "write": [], "read": ["shared"]},
    "*": {"namespaces": ["shared"], "write": [], "read": ["shared"]}
  }
}
```

### Shared Fact Ledger

High-confidence facts proposed to `shared/intelligence/LEDGER.md` become visible to all agents after review. Append-only with dedup and file locking.

### Conflict Resolution

```bash
python3 -m mind_mem.conflict_resolver workspace/ --analyze
python3 -m mind_mem.conflict_resolver workspace/ --propose
```

Graduated resolution: confidence priority > scope specificity > timestamp priority > manual fallback.

### Transcript Capture

```bash
python3 -m mind_mem.transcript_capture workspace/ --transcript path/to/session.jsonl
python3 -m mind_mem.transcript_capture workspace/ --scan-recent --days 3
```

Scans Claude Code JSONL transcripts for user corrections, convention discoveries, and architectural decisions. 16 patterns with confidence classification.

### Backup & Restore

```bash
python3 -m mind_mem.backup_restore backup workspace/ --output backup.tar.gz
python3 -m mind_mem.backup_restore export workspace/ --output export.jsonl
python3 -m mind_mem.backup_restore restore workspace/ --input backup.tar.gz
python3 -m mind_mem.backup_restore wal-replay workspace/
```

---

## Governance Modes

| Mode          | What it does                                             | When to use                                               |
| ------------- | -------------------------------------------------------- | --------------------------------------------------------- |
| `detect_only` | Scan + validate + report only                            | **Start here.** First week after install.                 |
| `propose`     | Report + generate fix proposals in `proposed/`           | After a clean observation week with zero critical issues. |
| `enforce`     | Bounded auto-supersede + self-healing within constraints | Production mode. Requires explicit opt-in.                |

**Recommended rollout:**
1. Install → run in `detect_only` for 7 days
2. Review scan logs → if clean, switch to `propose`
3. Triage proposals for 2-3 weeks → if confident, enable `enforce`

---

## Block Format

All structured data uses a simple, parseable markdown format:

```markdown
[D-20260213-001]
Date: 2026-02-13
Status: active
Statement: Use PostgreSQL for the user database
Tags: database, infrastructure
Rationale: Better JSON support than MySQL for our use case
ConstraintSignatures:
- id: CS-db-engine
  domain: infrastructure
  subject: database
  predicate: engine
  object: postgresql
  modality: must
  priority: 9
  scope: {projects: [PRJ-myapp]}
  evidence: Benchmarked JSON performance
  axis:
    key: database.engine
  relation: standalone
  enforcement: structural
```

Blocks are parsed by `block_parser.py` — a zero-dependency markdown parser that extracts `[ID]` headers and `Key: Value` fields into structured dicts.

---

## Configuration

All settings in `mind-mem.json` (created by `init_workspace.py`):

```json
{
  "version": "4.4.0",
  "workspace_path": ".",
  "auto_capture": true,
  "auto_recall": true,
  "governance_mode": "detect_only",
  "recall": {
    "backend": "bm25",
    "rrf_k": 60,
    "bm25_weight": 1.0,
    "vector_weight": 1.0,
    "vector_model": "all-MiniLM-L6-v2",
    "vector_enabled": false,
    "onnx_backend": false
  },
  "proposal_budget": {
    "per_run": 3,
    "per_day": 6,
    "backlog_limit": 30
  },
  "compaction": {
    "archive_days": 90,
    "snapshot_days": 30,
    "log_days": 180,
    "signal_days": 60
  },
  "scan_schedule": "daily"
}
```

| Key                             | Default              | Description                                                  |
| ------------------------------- | -------------------- | ------------------------------------------------------------ |
| `version`                       | `"2.8.0"`          | Config file version                                          |
| `auto_capture`                  | `true`               | Run capture engine on session end                            |
| `auto_recall`                   | `true`               | Show recall context on session start                         |
| `governance_mode`               | `"detect_only"`      | Governance mode (`detect_only`, `propose`, `enforce`)        |
| `recall.backend`                | `"scan"`             | `"scan"` (BM25), `"hybrid"` (BM25+Vector+RRF), or `"vector"` |
| `recall.rrf_k`                  | `60`                 | RRF fusion parameter k                                       |
| `recall.bm25_weight`            | `1.0`                | BM25 weight in RRF fusion                                    |
| `recall.vector_weight`          | `1.0`                | Vector weight in RRF fusion                                  |
| `recall.vector_model`           | `"all-MiniLM-L6-v2"` | Embedding model for vector search                            |
| `recall.vector_enabled`         | `false`              | Enable vector search backend                                 |
| `recall.onnx_backend`           | `false`              | Use ONNX for local embeddings (no server needed)             |
| `proposal_budget.per_run`       | `3`                  | Max proposals generated per scan                             |
| `proposal_budget.per_day`       | `6`                  | Max proposals per day                                        |
| `proposal_budget.backlog_limit` | `30`                 | Max pending proposals before pausing                         |
| `compaction.archive_days`       | `90`                 | Archive completed blocks older than N days                   |
| `compaction.snapshot_days`      | `30`                 | Remove apply snapshots older than N days                     |
| `compaction.log_days`           | `180`                | Archive daily logs older than N days                         |
| `compaction.signal_days`        | `60`                 | Remove resolved/rejected signals older than N days           |
| `scan_schedule`                 | `"daily"`            | `"daily"` or `"manual"`                                      |

---

## MCP Server

MIND-Mem ships with a [Model Context Protocol](https://modelcontextprotocol.io/) server that exposes memory as resources and tools to any MCP-compatible client.

> **Pair with [mind-nerve](https://pypi.org/project/mind-nerve/) for token-cheap routing.** When your agent host loads many skills/tools/MCP servers, mind-nerve sits in front and returns only the top-K relevant to each request — typically a 95%+ reduction in skill-listing tokens. Apache-2.0 wheel, `pip install mind-nerve`. See [`star-ga/mind-nerve`](https://github.com/star-ga/mind-nerve).

### Install

```bash
pipx install "mind-mem[mcp]"   # preferred — isolated venv with mind-mem-mcp on PATH
# or
pip install --user "mind-mem[mcp]"
```

The `[mcp]` extra pulls `fastmcp>=3.2.0` (the version line declared in
pyproject.toml) and registers the `mind-mem-mcp` console script.

### Automatic Setup (Recommended)

```bash
./install.sh --all
```

Configures all detected clients automatically. See [Quick Start](#quick-start).

### Manual Setup

For Claude Code, Claude Desktop, Cursor, Windsurf, and Gemini CLI, add to the respective JSON config under `mcpServers`:

```json
{
  "mcpServers": {
    "mind-mem": {
      "command": "mind-mem-mcp",
      "args": [],
      "env": {"MIND_MEM_WORKSPACE": "/path/to/your/workspace"}
    }
  }
}
```

`mind-mem-mcp` is the console script registered by `pipx install
"MIND-Mem[mcp]"` (or `pip install --user "MIND-Mem[mcp]"`). If you're running
out of a source checkout instead, replace `"command": "mind-mem-mcp"` with
`"command": "python3", "args": ["/path/to/mind-mem/mcp_server.py"]`.

| Client              | Config File                                   |
| ------------------- | --------------------------------------------- |
| **Claude Code CLI** | `~/.claude/mcp.json`                          |
| **Claude Desktop**  | `~/.config/Claude/claude_desktop_config.json` |
| **Gemini CLI**      | `~/.gemini/settings.json`                     |
| **Cursor**          | `~/.cursor/mcp.json`                          |
| **Windsurf**        | `~/.codeium/windsurf/mcp_config.json`         |

For **Codex CLI** (TOML format), add to `~/.codex/config.toml`:

```toml
[mcp_servers.mind-mem]
command = "mind-mem-mcp"
args = []

[mcp_servers.mind-mem.env]
MIND_MEM_WORKSPACE = "/path/to/your/workspace"
```

For **Zed**, add to `~/.config/zed/settings.json` under `context_servers`:

```json
{
  "context_servers": {
    "mind-mem": {
      "command": {
        "path": "mind-mem-mcp",
        "args": [],
        "env": {"MIND_MEM_WORKSPACE": "/path/to/your/workspace"}
      }
    }
  }
}
```

### Direct (stdio / HTTP)

```bash
# stdio transport (default)
MIND_MEM_WORKSPACE=/path/to/workspace mind-mem-mcp

# HTTP transport (multi-client / remote) — requires MIND_MEM_TOKEN per v3.7.0 fail-closed contract
MIND_MEM_WORKSPACE=/path/to/workspace MIND_MEM_TOKEN=$(openssl rand -hex 32) \
  mind-mem-mcp --transport http --host 127.0.0.1 --port 8765
```

### Resources (read-only)

| URI                          | Description                                   |
| ---------------------------- | --------------------------------------------- |
| `mind-mem://decisions`       | Active decisions                              |
| `mind-mem://tasks`           | All tasks                                     |
| `mind-mem://entities/{type}` | Entities (projects, people, tools, incidents) |
| `mind-mem://signals`         | Auto-captured signals pending review          |
| `mind-mem://contradictions`  | Detected contradictions                       |
| `mind-mem://health`          | Workspace health summary                      |
| `mind-mem://recall/{query}`  | BM25 recall search results                    |
| `mind-mem://ledger`          | Shared fact ledger (multi-agent)              |

### Tools (21)

| Tool                  | Description                                                    |
| --------------------- | -------------------------------------------------------------- |
| `recall`              | Search memory with BM25 (query, limit, active_only)            |
| `propose_update`      | Propose a decision/task — writes to SIGNALS.md only            |
| `approve_apply`       | Apply a staged proposal (dry_run=True by default)              |
| `rollback_proposal`   | Rollback an applied proposal by receipt timestamp              |
| `scan`                | Run integrity scan (contradictions, drift, signals)            |
| `list_contradictions` | List contradictions with auto-resolution analysis              |
| `hybrid_search`       | Hybrid BM25+Vector search with RRF fusion                      |
| `find_similar`        | Find blocks similar to a given block                           |
| `intent_classify`     | Classify query intent (9 types with parameter recommendations) |
| `index_stats`         | Index statistics, MIND kernel availability, block counts       |
| `retrieval_diagnostics` | Pipeline rejection rates, intent histogram, hard negatives     |
| `reindex`             | Rebuild FTS5 index (optionally including vectors)              |
| `memory_evolution`    | View/trigger A-MEM metadata evolution for a block              |
| `list_mind_kernels`   | List available MIND kernel configurations                      |
| `get_mind_kernel`     | Read a specific MIND kernel configuration as JSON              |
| `category_summary`    | Category summaries relevant to a given topic                   |
| `prefetch`            | Pre-assemble context from recent conversation signals          |
| `delete_memory_item`  | Delete a memory block by ID (admin-scope)                      |
| `export_memory`       | Export workspace as JSONL (user-scope)                         |
| `calibration_feedback` | Submit quality feedback for a retrieved block (thumbs up/down) |
| `calibration_stats`   | View per-block and global calibration statistics               |

### Token Auth (HTTP)

```bash
MIND_MEM_TOKEN=your-secret mind-mem-mcp --transport http --port 8765
```

**As of v3.7.0, HTTP authentication fails CLOSED.** If neither
`MIND_MEM_TOKEN` nor `MIND_MEM_ADMIN_TOKEN` is set, the server refuses
to start. For local development you can opt back into the legacy
behaviour, but only on a loopback bind:

```bash
MIND_MEM_ALLOW_UNAUTHENTICATED_LOCALHOST=1 \
  mind-mem-mcp --transport http --host 127.0.0.1 --port 8765 \
               --allow-unauthenticated-localhost
```

The flag is a no-op if the bind host isn't `127.0.0.1` / `::1` /
`localhost` — the server still refuses to start. Production
deployments should always set a token.

### Safety Guarantees

- **`propose_update` never writes to DECISIONS.md or TASKS.md.** All proposals go to SIGNALS.md.
- **`approve_apply` defaults to dry_run=True.** Creates a snapshot before applying for rollback.
- **All resources are read-only.** No MCP client can mutate source of truth through resources.
- **Namespace-aware.** Multi-agent workspaces scope resources by agent ACL.

---

## Security

### Threat Model

| What we protect       | How                                                                  |
| --------------------- | -------------------------------------------------------------------- |
| Memory integrity      | 74+ structural checks, ConstraintSignature validation                |
| Accidental overwrites | Proposal-based mutations only (never direct writes)                  |
| Rollback safety       | Snapshot before every apply, atomic `os.replace()`                   |
| Symlink attacks       | Symlink detection in restore paths                                   |
| Path traversal        | All paths resolved via `os.path.realpath()`, workspace-relative only |

| What we do NOT protect against | Why                                                            |
| ------------------------------ | -------------------------------------------------------------- |
| Malicious local user           | Single-user CLI tool — filesystem access = data access         |
| Network attacks                | No network calls, no listening ports, no telemetry             |
| Encrypted storage              | Files are plaintext Markdown — use disk encryption if needed   |

### No Network Calls

MIND-Mem makes **zero network calls** from its core. No telemetry, no phoning home, no cloud dependencies. Optional features (vector embeddings, cross-encoder) may download models on first use.

---

## Requirements

- **Python 3.10+**
- **No external packages** — stdlib only for core functionality

### Optional Dependencies

| Package                      | Purpose                 | Install                               |
| ---------------------------- | ----------------------- | ------------------------------------- |
| `fastmcp`                    | MCP server              | `pip install mind-mem[mcp]`           |
| `onnxruntime` + `tokenizers` | Local vector embeddings | `pip install mind-mem[embeddings]`    |
| `sentence-transformers`      | Cross-encoder reranking | `pip install mind-mem[cross-encoder]` |
| `ollama`                     | LLM extraction (local)  | `pip install ollama`                  |

### mind-mem:4b — Purpose-Trained LLM

For best LLM extraction quality, use **[mind-mem:4b](https://huggingface.co/star-ga/mind-mem-4b)** — a full fine-tune of Qwen3.5-4B on MIND-Mem's 8 extraction tasks (entity extraction, fact extraction, observation compression, contradiction detection, governance analysis, intent classification, axis-aware retrieval, LLM reranking). Empirical on RTX 3080 (Q4_K_M, 2.6GB VRAM): **104 tok/s generation, 1585 tok/s prefill**.

**Ollama (recommended):**
```bash
# Download the GGUF from HuggingFace
wget https://huggingface.co/star-ga/mind-mem-4b/resolve/main/mind-mem-4b-Q4_K_M.gguf

# Create Ollama model
cat > Modelfile << 'EOF'
FROM ./mind-mem-4b-Q4_K_M.gguf
SYSTEM "You are mind-mem, a governance-aware memory extraction assistant."
PARAMETER temperature 0.1
PARAMETER num_ctx 8192
PARAMETER num_predict 1024
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
EOF
ollama create mind-mem:4b -f Modelfile
```

Then set in `mind-mem.json`:
```json
{
  "extraction": {
    "enabled": true,
    "model": "mind-mem:4b",
    "backend": "ollama"
  }
}
```

Empirical on RTX 3080 (Q4_K_M, 2.6GB VRAM): **104 tok/s generation, 1585 tok/s prefill**.

**Full fine-tune (transformers, no adapter):**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("star-ga/mind-mem-4b", device_map="auto", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("star-ga/mind-mem-4b")
```

| Resource | Link |
|----------|------|
| Model (GGUF + bf16 safetensors) | [star-ga/mind-mem-4b](https://huggingface.co/star-ga/mind-mem-4b) |
| Base model | Qwen/Qwen3.5-4B |
| Training | Full fine-tune on Runpod H200 SXM (141 GB HBM3e), v3.12.0 corpus (4,392 examples), bf16, paged-AdamW-8bit, batch 2 × accum 16, max_length 2048, LR 1.5e-5 cosine + 3% warmup |
| Eval (v3.12.0-fullft, shipped in v3.12.1) | **95/95 = 100%** across ten categories — tool_call (20/20), block_schema (10/10), workflow (5/5), v39_new_tools (13/13), v39_transform_hash (3/3), v39_transport_guard (4/4), v311_new_tools (10/10), v311_explain_field (10/10), v312_quality_gate_strict_mode (10/10), v312_lineage_staleness (10/10). Two probes intentionally softened — see HF model card "Known model errors" section. |

### Platform Support

| Platform               | Status      | Notes                                   |
| ---------------------- | ----------- | --------------------------------------- |
| Linux                  | Full        | Primary target                          |
| macOS                  | Full        | POSIX-compliant shell scripts           |
| Windows (WSL/Git Bash) | Full        | Use WSL2 or Git Bash for shell hooks    |
| Windows (native)       | Python only | Use `validate_py.py`; hooks require WSL |

---

## Troubleshooting

| Problem                                     | Solution                                                                                                          |
| ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `validate.sh` says "No mind-mem.json found" | Run in a workspace, not the repo root. Run `init_workspace.py` first.                                             |
| `recall` returns no results                 | Workspace is empty. Add decisions/tasks first.                                                                    |
| `capture` says "no daily log"               | No `memory/YYYY-MM-DD.md` for today. Write something first.                                                       |
| `intel_scan` finds 0 contradictions         | Good — no conflicting decisions.                                                                                  |
| Tests fail on Windows                       | Use `validate_py.py` instead of `validate.sh`. Hooks require WSL.                                                 |
| MIND kernel not loading                     | Expected — the `.mind` files are INI configs, not yet MIND-language source. Pure-Python scoring (in `mind_kernels.py`) is the authoritative path. See [`docs/MIND_CONFIG_VS_MIND_LANG.md`](docs/MIND_CONFIG_VS_MIND_LANG.md). |

### FAQ

**No results from recall?**
Check that the workspace path is correct and points to an initialized workspace
containing decisions, tasks, or entities. If the FTS5 index is stale or missing,
run the `reindex` MCP tool to rebuild it.

**MCP connection failed?**
Verify that `fastmcp` is installed (`pip install fastmcp`). Check the transport
configuration in your client's MCP config (stdio vs HTTP). Ensure the
`MIND_MEM_WORKSPACE` environment variable points to a valid workspace directory.

**MIND kernels not loading?**
Run `bash src/mind_mem/build.sh` to compile the MIND source files (requires `mindc`).
If the MIND compiler is not available, MIND-Mem automatically uses the pure Python
fallback with identical results.

**Index corrupt?**
Run the `reindex` MCP tool, or from the command line:
`python3 -m mind_mem.sqlite_index --rebuild --workspace /path/to/workspace`.
This drops and recreates the FTS5 index from all workspace files.

---

## Specification

For the formal grammar, invariant rules, state machine, and atomicity guarantees, see **[SPEC.md](SPEC.md)**.

---

## Built in MIND lang

mind-mem's scoring kernels live in the `mind/` directory of this repo. The BM25F field-weighting, RRF fusion, reranking, negation penalty, date proximity, category boost, importance decay, entity overlap, confidence gating, and top-k selection are all written in MIND source and compiled to native shared libraries via the MIND compiler. The pure Python fallback mirrors them exactly — same results, no compilation required.

The MIND language compiler is at [github.com/star-ga/mind](https://github.com/star-ga/mind). The formal specification is at [github.com/star-ga/mind-spec](https://github.com/star-ga/mind-spec). The agent CLI being built on the same substrate is at [github.com/star-ga/mind](https://github.com/star-ga/mind) (RFC 0013, in development). Visit [mindlang.dev](https://mindlang.dev) to see the substrate that makes byte-identical replay possible.

For many developers, `pip install mind-mem` is their first encounter with a MIND-native system. The scoring kernels in `mind/` are readable MIND source — the language is approachable, and the compiler produces output that is byte-identical on every architecture mind-mem CI targets.

---

## Contributing

Contributions welcome. Please open an issue first to discuss what you'd like to change.

See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

---

## License

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