TricorderKit

The local-first Agentic Knowledge OS — turn intentions into traceable, auditable, reusable workflows.
CLI-first · self-improving · measured · runs on your own machine.

Quick start • Guardrails • Architecture • Measured results • What's inside • FAQ

Why TricorderKit?

Most agent setups are a pile of prompts and scripts that nobody can audit, reproduce, or improve. TricorderKit treats an agent like an operating system for knowledge work: every intention becomes a workflow that is traceable, testable, and reusable — and the system measures and improves itself over time.

Honesty first: every number below comes from the selftests and offline benchmarks in this repo. No inflated metrics, no fake stars.

🛡️ Governance & guardrails

Handing an agent real autonomy over your second brain is only safe if it can't leak your secrets, act on a malicious web page, publish private notes, or run away on cost. TricorderKit ships a numbered, versioned rule-set enforced by deterministic gates — not prose an agent can ignore:

• Untrusted tool output is data, never instructions — an embedded "do X" is surfaced, not executed (anti prompt-injection)
• Secret scanning on every commit (gitleaks) — secrets live in a vault, never in the repo
• Public / private routing — a boundary gate blocks private terms & personal paths before any public push
• Irreversibility gates — explicit confirmation before push / send / delete; pre-push shows exactly what ships
• Cost & loop circuit-breaker · zero-loss memory (boot cache, session logs, immediate backup)
• The Rule of Two — never combine untrusted input + sensitive access + external write unattended

→ Full model: docs/09_GOVERNANCE_GUARDRAILS.md

🚀 Quick start

git clone https://github.com/GeekFamilyCorp/TricorderKit.git
cd TricorderKit

# 1. Health check — what's installed, what's missing
python cli/tk.py doctor

# 2. Bring up the optional local stack (RAG + workflows + observability)
docker compose --profile graph up -d        # Neo4j + Qdrant
#   ... profiles: graph | workflows | observability (start only what you need)

# 3. Try it
python cli/tk.py status
python cli/tk.py research "<topic>"          # autonomous research pipeline

No GPU required. The heavy components are opt-in (Docker profiles) so a fresh clone boots light.

🧭 Architecture

flowchart TD
    U([User intention]) --> MB[MainBrain v1.5<br/>router + guards + budget]
    MB --> SK[Skills & Plugins<br/>13 plugins]
    MB --> MEM[Memory<br/>boot · temporal SQLite · daily logs]
    SK --> RAG[Hybrid RAG · graphify<br/>Qdrant + BM25 + RRF + reranker]
    SK --> RES[Deep research<br/>web · APIs · sources]
    RAG --> KB[(Local Knowledge Base<br/>Neo4j + Qdrant)]
    RES --> KB
    MB --> SI[Self-Improving loop<br/>learning-engine · god-mode radar]
    SI -. proposals only .-> SK
    MB --> EV[eval-lab<br/>RAGAS · dedup · retrieval]
    SK --> GATE{{Quality gates<br/>public-boundary · docs-sync}}
    GATE --> GIT[(Git · CI)]
    LLM[Local LLM · Ollama / LiteLLM gateway] --- MB
    LLM --- RAG

Everything is local-first: the agent (Claude or a local model via the Ollama/LiteLLM gateway), the knowledge base (Neo4j + Qdrant), the memory (SQLite), and the workflow engine (Temporal) all run on your machine.

📊 Measured results

Real numbers from the offline benchmarks shipped under experiments/ (each has a --selftest). Reproduce with python experiments/<name>/<script>.py --selftest.

Plus 634 tests in CI and a god-mode innovation radar that scans the state of the art weekly and proposes improvements (human-validated, never auto-adopted).

🧩 What's inside

plugins/ — 13 plugins, e.g.: deep-research-core (autonomous research), graphify (local-first hybrid RAG), learning-engine (self-improvement), token-optimizer (model routing + budget), eval-lab (quality evaluators), workflow-engine (Temporal), security-audit-cli, memory-boot, and more.

skills/ — composable skills incl. god-mode (innovation radar), code-corrector (web fix/hardening), agent-config-audit (audit the agent's own MCP/hooks/permissions/secrets), doc-to-skill, dev-protocol, subtitle-fix.

experiments/ — isolated, offline-runnable PoCs (RAGAS, temporal memory, dedup, GraphRAG, OpenEvolve). Promoted only on decision.

cli/tk.py — one CLI: status · doctor · skill · workflow · vault · research · project · security · mcp · rapport.

See STATUS.md for the per-plugin dashboard and ROADMAP.md for what's next.

❓ FAQ

No. TricorderKit is local-first and runs against a local LLM (Ollama via a LiteLLM gateway with retry + local fallback). Cloud models are optional.

The public engine is generic. It's a CLI-first agentic OS for knowledge work; the knowledge base, sources, and skills are yours to define.

The learning loop only produces proposals (drafts). Promotion requires green tests and human review. Quality gates (public-boundary + docs-sync) run on every push and in CI.

After the Star Trek tricorder — a tool that scans, analyzes, and synthesizes information on demand.

License

MIT — see LICENSE. Contributions and stars welcome. ⭐