@xinchen03minta
The context quality layer for AI agents — memory that checks itself: lifecycle governance, calibrated confidence, and staged claim gates. Local-first, MCP 19 tools, DeepSeek Harness plugin.
- Stars
- 4
- Language
- Python
- Created
- May 31, 2026
- Updated
- Aug 23, 2026
Introduction
The context quality layer for AI agents.
Your AI remembers. Minta tells you when it remembers wrong — and what it's allowed to claim.
⭐ New (2026-08): open-core v2 — memory engine + research compliance engine + expert domain pack, now with DeepSeek Harness integration (verified).
Why Minta
Every memory system stores more. Minta's job is to make sure what the agent knows is still true — and that it doesn't claim what it hasn't done.
| What others do | What Minta does |
|---|---|
| "Here are your relevant memories" | "2 of these conflict. 1 is stale. Here's the truth." |
| Store everything forever | Detect what expired, flag it, decide with you |
| Treat all memories equally | Type-specific decay: preferences last longer than project state |
| Hope the LLM figures it out | Lifecycle scan + health score + staged gates (no over-claims) |
Contents · Why Minta · Quick Start · Features · Open-Core · Benchmarks · DeepSeek Harness · Roadmap
Product UI
The full Minta workspace (Personal Context Layer, V8.3 engine UI). The layers you see — research cockpit, expert infer, memory health — map to the engine tiers below; the open-core dist ships the memory hub UI, and the rest activate through the same API.
 |  | |
| Context Hub — "Stop re-onboarding your AI" | Context Draw — 3D knowledge graph + card recall | |
 |  | |
| Context Health — lifecycle dashboard (decay/conflict at a glance) | Inbox — confirm/discard corrections, counter-example review | |
 |  | |
| Skills Library — 50 registered workflows | Research Workspace — projects, evidence, run packages |
Three layers, one engine:
L1 Memory governance → stale / conflict / redundant / fragile, found not stored
L2 Expert knowledge → rules promoted from your corrections, domain-typed
L3 Claim gates → the agent cannot claim a stage it never did (math-model
/ research workflows) — with calibrated confidence
Quick Start
60 seconds. Local-first, no cloud, no API subscription for the open core.
git clone https://github.com/xinchen03/minta.git
cd minta
python -m pip install -r server/requirements.txt
python minta_cli.py start # API :8772 · Autopilot :18730 · MCP :18721
Or Docker: docker compose up -d. Then connect your agent:
# any MCP-capable editor/agent — Claude Code / Codex / Cursor / dsh
python minta_cli.py connect claude
# DeepSeek Harness: dsh plugin --profile web add @xxinchen/dsh-plugin (or connect via MCP → docs/dsh-integration.md)
The web UI opens at http://127.0.0.1:8772 — memory health dashboard, 3D knowledge graph, inbox review, expert panels.
Configuration & Keys (first run)
cp .env.example .env # then edit secrets
python -c "import secrets; print('MINTA_API_KEY=minta_'+secrets.token_hex(32))" # generate a secure key
| Variable | Default | What it does |
|---|---|---|
MINTA_DATABASE_URL | sqlite:///./minta.db | Zero-config SQLite; switch to MySQL in one line |
MINTA_JWT_SECRET | (must set) | Session signing secret — generate, don't copy |
MINTA_API_KEY | auto-generated on first run | Programmatic access + MCP (connect your editor → python minta_cli.py connect claude) |
Full variable reference, SMTP, CORS, feature flags → docs/configuration.md.
Agent integration per editor → docs/mcp-integration.md.
Features
| Layer | Feature | What you get |
|---|---|---|
| Memory | Hybrid retrieval (vector + BM25 + entities + FTS) | Picks the right memory, not the most |
| Memory | Lifecycle engine (decay/conflict/redundancy/fragmentation) | Quality checks run on schedule, not on faith |
| Correction loop | Inbox + counter-example capture (hooks: SessionStart → UserPromptSubmit → PostToolUse → Stop) | What you correct becomes a rule — after your confirm |
| Expert domains | Multi-domain rules (ankle/knee/c-spine injury, ISO9001, PRISMA…) + CUMCM staged workflow | Domain-typed reasoning with trust metrics |
| Research | Manuscript inventory + compliance rule evaluator | "Does this draft meet the venue checklist?" — before submission |
| Metacognition | Conformal confidence (calibrated, data-locked) | The agent says what it knows with a coverage guarantee |
| Delivery | Dist web UI + MCP (19 tools, stdio + HTTP) + DSH plugin verified | Three entry points, one memory |
Open-Core: Open Code, Locked Assets
| In this repo (Apache-2.0, free) | Via API key / Enterprise license |
|---|---|
| Memory engine — full, runnable | Managed engine + monitoring |
| Quality-kernel algorithms (conformal, rule promotion, DGM, compiler) | Full precision: auto-calibration, private domains |
| Research compliance engine + domain pack (CUMCM stages) | Sports-medicine / clinical packs |
| Web dist · MCP · DSH integration · 12 guides | Data flywheel: calibration sets, weights, rule bases |
The hosted tiers above are roadmap features — the open core is always a complete, runnable memory system.
Benchmarks
| Detection | Metric | Score | Mem0 | Hindsight |
|---|---|---|---|---|
| Conflict | F₁ | 0.81 (held-out, 5 unseen domains) | N/A | N/A |
| Staleness | UFA | 0.86 (12 fact-pair templates) | N/A | N/A |
| Redundancy | Compression RR | 0.67 (25 clusters) | N/A | N/A |
| Fragmentation | MCR | 0.746 (15 fragment sets) | N/A | N/A |
| Retrieval (LoCoMo) | Recall@20 | 97.1% | — | — |
Research first
Minta started as the memory layer of a research workflow — literature notes, manuscript checklists, journal compliance, verdict-gated claim tracking. See runtime/compliance/ and docs/interaction-guide.md.
Companion execution skills (Apache-2.0, separate repo): nature-skills — reading, figures, citations, polishing.
DeepSeek Harness
Verified integration (2026-08): connect Minta as an MCP server in DSH in 2 minutes — see docs/dsh-integration.md for the exact cordis.patch.yml insert. The open-core plugin bundle is published on npm (@xxinchen/dsh-plugin).
Building & contributing
python scripts/build_open_release.py # sync publish lineage (A-level only)
python -m pytest tests/ # server test suite
We welcome good-first-issue PRs: entity_linker English patterns, richer demo scenarios. More in CONTRIBUTING.md.
Guides
Interaction Guide · Startup Order · DSH Integration · Configuration · User Guide · MCP Integration
Data & Privacy
- Local-first: database, vectors and logs stay on your machine. No telemetry by default.
- Data export / delete:
GET /api/user/export-data·DELETE /api/user/delete-data(authenticated). - Secrets: generated on first run into
.minta_api_key(never committed); privileged APIs are off by default unless explicitly configured. - See
SECURITY.mdfor disclosure policy.
Vision: Where This Is Going
Memory is the easy part; truth is the product. The agent era already has plenty of
"remember more" systems. The bottleneck is the opposite — AIs confidently serve stale,
contradicted, or unearned claims. Minta's answer is a context quality layer:
the memory knows its own health (stale / conflict / redundant / fragile), the expert
layer knows its own limits (calibrated coverage), and the claim gates know what was
actually done. The long thesis:
- Personal: every AI assistant, every session starts from a context hub that already understands you — stop re-onboarding your AI.
- Team / enterprise: memory, expertise, and compliance checks shared across a research group or a clinical unit — with audit trails and governance reports.
- Vertical: sports-medicine, clinical-triage, and manufacturing expert packs layered on the same engine, tuned by their users' corrections (data flywheel).
Roadmap
- 2026 Q4 — hosted API (full precision, monitoring), sports-medicine domain pack, npm plugin v1 release
- 2027 Q1 — enterprise private deployment + governance audit reports; SME (structure-mapping) engine public
- 2027 — multi-agent shared memory workspaces (team context layers)
Community & Contact
- 🐛 GitHub Issues — bugs, feature requests (we respond fast)
- 💬 GitHub Discussions — questions, RFCs, show-your-work
- 📮 Research contact — papers, collaboration & governance consulting: open an issue tagged
researchor DM via Discussions. are the publishable signs of this repo's claims; HackerNews/DSH plugin discussions welcome at every release.
Star Us
🔭 If Minta saved you an hour, give it a ★. One click, three seconds — and it tells the next contributor, integrator, and journal reviewer that this experiment deserves their attention.
References & Lineage
Where the ideas come from (and how Minta differs):
| Work | What Minta took | What Minta differs in |
|---|---|---|
| Mem0 / MemOS | Memory store + hybrid retrieval | They store; Minta verifies quality (decay, conflict, redundancy, fragmentation) |
| Vovk (2005), conformal prediction | Distribution-free coverage guarantee | Used as the metacognitive gate, not just an estimator |
| JEPA (LeCun) | Predict in latent space, not raw space | Domain rules > JEPA — predictions only fire when history exists |
| Ebbinghaus-inspired decay (MemoryBank et al.) | Time-aware forgetting | Type-specific half-lives: preferences > project state |
| Paperclip doc-maintenance | Audit-driven maintenance | Same discipline, now for AI memory, not files |
License
Apache-2.0. Upstream bundled resources retain their own licenses — see skills/ notes if added later.