TricorderKit

<div align="center"> <img src="assets/banner.png" alt="TricorderKit — the local-first Agentic Knowledge OS" width="820" /> # TricorderKit **The local-first Agentic Knowledge OS** — turn intentions into traceable, auditable, reusable workflows. CLI-first · self-improving · measured · runs on your own machine. [](CHANGELOG.md) [](.planning/STATE.md) [](STATUS.md) [](docker-compose.yml) [](LICENSE) [Quick start](#-quick-start) • [Architecture](#-architecture) • [Measured results](#-measured-results) • [What's inside](#-whats-inside) • [FAQ](#-faq) </div> --- ## 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. | | Ad-hoc agent setup | **TricorderKit** | |---|---|---| | **Where it runs** | Cloud, your data leaves the machine | **Local-first** — Ollama, Neo4j, Qdrant on your box | | **Claims** | "It works on my prompt" | **Measured** — offline benchmarks + 634 tests | | **Quality** | Hope | **Gates** — public-boundary + docs-sync, pre-push & CI | | **Evolution** | Manual prompt-tweaking | **Self-improving loop** — proposals, gated by tests + human review | | **Reproducibility** | "Works on my laptop" | Versioned plugins, runbooks, deterministic selftests | > Honesty first: every number below comes from the **selftests and offline benchmarks in this repo**. No inflated metrics, no fake stars. --- ## 🚀 Quick start ```bash 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 ```mermaid 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/`](experiments/) (each has a `--selftest`). Reproduce with `python experiments/<name>/<script>.py --selftest`. | Capability | Benchmark | Result | |---|---|---| | **Embedding-blocking dedup** | vs. exhaustive fuzzy, equal quality | **F1 1.0 at −91 % comparisons** | | **Temporal memory** (bi-temporal, SQLite) | "what was true at time T" | **100 % accuracy, −95 % tokens** vs full-context | | **GraphRAG** | multi-hop relational questions, equal budget | **100 % coverage vs 50–67 %** flat RAG | | **Evaluator-driven tuning** (OpenEvolve-style) | auto-tune dedup thresholds | **F1 0.909 → 1.0**, GPU-free, local LLM | | **RAG evaluation** (RAGAS) | faithfulness / relevancy / context | objective scoring, LLM-as-judge optional | 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](STATUS.md) for the per-plugin dashboard and [ROADMAP.md](ROADMAP.md) for what's next. --- ## ❓ FAQ <details><summary><b>Do I need a GPU or a cloud API?</b></summary> No. TricorderKit is local-first and runs against a local LLM (Ollama via a LiteLLM gateway with retry + local fallback). Cloud models are optional. </details> <details><summary><b>Is it tied to a specific domain?</b></summary> 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. </details> <details><summary><b>How does "self-improving" stay safe?</b></summary> 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. </details> <details><summary><b>Why "TricorderKit"?</b></summary> After the Star Trek tricorder — a tool that scans, analyzes, and synthesizes information on demand. </details> --- ## License MIT — see [LICENSE](LICENSE). Contributions and stars welcome. ⭐ <div align="center"> *TricorderKit v1.1.0 — GeekFamilyCorp — 2026* </div>