Why SecTrust

Most multi-agent frameworks give you coordination and hope for the best. SecTrust gives you an operating base where AI colleagues hold real jobs — and every consequential step has a human checkpoint:

• A minimal, reliable contract. AI coworkers on the same machine exchange task metadata through one local SQLite file (WAL mode) — zero ops, transactional, crash-safe. Need to go cross-machine? HTTP API and an optional Redis-backed queue are already there.
• Humans approve by default. An ApprovalCenter gates dangerous actions (pending → approved/rejected/expired, expires to deny, never to allow), with WeCom / Feishu push out of the box.
• Costs are routed, not burned. A built-in cost router grades every request simple / medium / complex — a one-liner runs as a single task, not a 7-node DAG. Estimated LLM calls are attached to every task for attribution.
• Three collaboration modes. Pipeline (planner + DAG), iterative refinement (quality gate sends work back with feedback until it passes), and voting (fan the same task out to N workers, score the proposals with the same quality rules, recommend a winner — a human confirms; ranking alone never takes effect).

Quickstart

Zero dependencies (pure Python stdlib). Python ≥ 3.10, no pip install needed:

git clone https://github.com/nanshandongli/SecTrust.git
cd SecTrust

python demo.py                                        # ① core contract self-test (real cross-process concurrency)
python -m unittest discover -s tests -p "test_*.py"   # ② full suite: 482 tests
python task_center_web.py                             # ③ open http://127.0.0.1:8090 — live task dashboard

Wire it into any MCP host (Claude Desktop, WorkBuddy, …):

{
  "mcpServers": {
    "sectrust": {
      "command": "python",
      "args": ["/absolute/path/to/SecTrust/mcp_server.py"]
    }
  }
}

36 tools go live immediately: task register/claim, DAG orchestration (dag_*), quality rework loops, proposal voting (ballot_*), compliance scanning, long-term memory, approval queries.

What's inside

Honest status

What's proven: single-machine multi-process orchestration end-to-end; 482 tests; benchmark-aligned scanner (2 TP / 0 FP on gitleaks vectors); stress-tested write throughput (~685 TPS at 8 processes, CAS claiming with zero duplicates); MCP tools verified from a clean install.

What's not proven yet: cross-machine deployments (implemented, untested under real network latency); production traffic; PyPI/MCP Registry listing (materials ready); real WeCom/Feishu credentials. The Chinese docs (README.zh-CN.md, PRODUCT_SUMMARY.md) keep a running "honesty ledger" of exactly this kind.

Docs

• DESIGN.md — runtime extension architecture (channels / skills / models), trust boundaries, key management
• README.zh-CN.md — full Chinese documentation (deployment, WAL ops, workflows)
• PRODUCT_SUMMARY.md — feature inventory, market position, roadmap (P0/P1/P2)
• SECURITY.md — security narrative; SELF_AUDIT_REPORT.md — reproducible self-audit
• RELEASING.md — PyPI / MCP Registry publishing steps
• CHANGELOG.md — version history

License

AGPL-3.0-only. In short: use it, study it, modify it, run your AI colleagues with it — if you offer SecTrust itself as a hosted service, the network-use clause requires you to share your modified source. Commercial dual-licensing may be considered; open an issue to discuss.