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). 83 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 — the deterministic AI runtime architecture.

30-Second Demo

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: v4.2.2 — fix a Postgres connection-pool thread leak in the MCP server — Full per-release notes (issues closed, CI run ids, job counts) live in CHANGELOG.md.

Substrate Properties

Table of Contents

• Why MIND-Mem

• Features

• Integrations are the substrate working

• Benchmark Results

• Quick Start

• Health Summary

• Commands

• Architecture

• How It Compares

• Recall

• MIND Kernels

• Auto-Capture

• Multi-Agent Memory

• Governance Modes

• Block Format

• Configuration

• MCP Server

• Security

• Troubleshooting

• Built in MIND lang

• Contributing

• License

Deep-dive docs

• docs/setup.md — install, configure, wire MCP, opt in to MIND native kernels

• docs/usage.md — every surface (MCP tools by category, mm CLI, mind-mem-verify, Python library) with worked examples

• 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 — download + run the star-ga/mind-mem-4b full-FT model locally (transformers, exllamav2, vLLM, llama.cpp, Ollama, MindLLM)

• docs/companion-tools.md — companion tools that complement (not compete with) mind-mem: MindLLM for deterministic + evidence-chained inference, GitNexus for code knowledge-graph

• ROADMAP.md — feature roadmap (genuinely-open items at the top; bulk of v3.2.0→v4.0.0 shipped)

• CHANGELOG.md — release notes for every published version

Related MCP server: umo-memory

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.

Novel Contributions

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

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 configuration 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). Currently INI-format declarative configuration parsed by mind_ffi.py; the MIND-language port that compiles to native .so via the MIND compiler is the forward-looking story — see docs/MIND_CONFIG_VS_MIND_LANG.md for the disambiguation. 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. Note that the canonical IR per RFC 0021 is mic@1 text + mic@3 binary (see 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.

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 (83 tools, 8 resources)

Full Model Context Protocol server with 83 distinct tools and 8 read-only resources (6 static + 2 templated). The server makes 84 mcp.tool(...) registrations, but the consolidated recall dispatcher intentionally shadows the base recall, so the live surface is 83 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). 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

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.

Native MCP integration with 17 AI development tools

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 — any MCP-compatible client connects with one command.

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.

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

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):

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):

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

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)

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 for the current status and methodology.

Performance (Latency & Throughput)

Measured on a 65-block workspace (typical personal workspace) with SQLite FTS5 backend:

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

# 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

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 --output results.json

Hybrid retrieval with any model pair (BM25 + vector + cross-encoder)

python3 benchmarks/locomo_judge.py --hybrid --compress --answerer-model --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:

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):

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:

Selective install:

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

Uninstall:

./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

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

2. Initialize workspace

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 .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

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

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

5. Verify recall + capture

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:

{
  "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+)

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

7. Smoke Test (optional)

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

Architecture

your-workspace/
├── mcp_server.py            # MCP server (FastMCP, 83 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

At a Glance

Full Feature Matrix

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

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

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)

{
  "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)

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)

{
  "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 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). When that integration lands, the build command will look like:

# 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

Performance

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.

FFI Bridge

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

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

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

{
  "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

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

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

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

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:

[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):

{
  "version": "2.8.0",
  "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"
}

MCP Server

MIND-Mem ships with a Model Context Protocol server that exposes memory as resources and tools to any MCP-compatible client.

Pair with 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.

Install

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)

./install.sh --all

Configures all detected clients automatically. See Quick Start.

Manual Setup

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

{
  "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"].

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

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

Direct (stdio / HTTP)

# 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)

Tools (21)

Token Auth (HTTP)

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:

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

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

mind-mem:4b — Purpose-Trained LLM

For best LLM extraction quality, use 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):

# 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:

{
  "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):

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")

Platform Support

Troubleshooting

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.

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. The formal specification is at github.com/star-ga/mind-spec. The agent CLI being built on the same substrate is at github.com/star-ga/mind (RFC 0013, in development). Visit 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 for guidelines.

License

Apache 2.0 — Copyright 2026 STARGA Inc and contributors.