PlanckBot

An adaptive tiny-model layer for LLM token optimization.

PlanckBot sits between a host LLM (Claude, GPT-4, any MCP-capable agent) and its tools. It observes every tool call, trains a per-tool LoRA adapter on the observed I/O, and at runtime filters tool output down to the fragments the LLM actually cites — cutting context tokens by up to two orders of magnitude on verbose tools like list_directory, read_file, and search_files.

Website: planckbot.com In-app docs (after planckbot ui): localhost:8080/how-it-works

60-second quickstart

git clone https://github.com/opcastil11/planckbot && cd planckbot
uv venv .venv && source .venv/bin/activate
uv pip install -e ".[dev]"

# One-shot: creates data dir, migrates DB, writes ~/.claude.json
# (with confirmation + backup). Restart Claude Code afterwards.
planckbot init --upstream-path /abs/path/to/a/repo/you/work/on

# Verify: every check should be green.
planckbot doctor

# Open the workbench.
planckbot ui   # → http://localhost:8080

Don't want to wait for real tool calls to populate the dashboard? planckbot demo load inserts a handful of synthetic triples so you can see what a populated workbench looks like. planckbot demo clear undoes it.

What you'll see at each stage

PlanckBot's value grows over time. The dashboard hero speaks to you differently at each stage — here's the progression:

What makes it different

PlanckBot is organized as four adaptation layers:

All four layers share one data unit: a triple (input, output, filtered_output). The filtered_output field is populated automatically by reading Claude Code's conversation logs and matching lines from the tool's output against the assistant's next reply — no manual labeling required.

Architecture at a glance

  host LLM (Claude / GPT-4 / ...)
       │ MCP JSON-RPC
       ▼
  planckbot-fs ──→ upstream MCP server  (Layer B: observe / filter)
       │
       ▼
   SQLite (triples, adapters, tool_versions, cron_jobs, synthesized_tools)
       │
       ├──→ cron daemon (autolabel, detect_gaps, scanner, retrain)
       ├──→ NiceGUI workbench (10 pages: dashboard, tools, training, cron, synth, ...)
       └──→ planckbot-synth MCP server (Layer D: serves synthesized tools)

Everything runs locally. No external services. No GPU required — a trained adapter is ~10 MB and inference is <50 ms on CPU.

Install

Requires Python 3.10+, uv (recommended) or pip, and npx on $PATH for the upstream filesystem MCP.

# 1. clone + install
git clone https://github.com/opcastil11/planckbot
cd planckbot
uv venv .venv && source .venv/bin/activate
uv pip install -e ".[dev]"

# 2. verify the install (must print "195 passed")
python -m pytest -q

# 3. one-shot: create data dir, migrate DB, register MCP servers with
#    Claude Code (prompts before writing ~/.claude.json).
planckbot init --upstream-path /abs/path/to/the/repo/you/want/observed

# 4. background scheduler (systemd user unit, survives reboot).
#    If you're not on systemd, skip and run `planckbot cron daemon` instead.
planckbot systemd install

# 5. launch the workbench
planckbot ui            # http://localhost:8080

After planckbot init, restart Claude Code so it picks up the new MCP servers. Then the filesystem tools appear as mcp__planckbot-fs__* and every call is recorded as a triple in the local SQLite DB.

Manual MCP registration

If you prefer to write ~/.claude.json by hand (e.g. you already have a custom mcpServers block), run planckbot init --print-config to see the exact JSON to paste:

{
  "mcpServers": {
    "planckbot-fs": {
      "command": "/abs/path/.venv/bin/planckbot-mcp",
      "args": [
        "--mode", "observe",
        "--name", "planckbot-fs",
        "--",
        "npx", "-y", "@modelcontextprotocol/server-filesystem",
        "/abs/path/to/your/project"
      ]
    },
    "planckbot-synth": {
      "command": "/abs/path/.venv/bin/planckbot-synth"
    }
  }
}

The self-supervising loop

1. Claude calls a tool via mcp__planckbot-fs__* → proxy records a triple.

2. conversation_scanner cron job reads Claude Code's JSONL log for recent assistant text.

3. autolabel_precise cron job matches each unlabeled triple to the reply immediately following its tool call and writes filtered_output.

4. When enough labeled triples accumulate, the adapter for that tool is retrained.

5. detect_tool_gaps cron job flags repeated N-gram tool sequences as candidates for Layer D synthesis.

Start the loop with:

# systemd user unit, keeps running across reboots
systemctl --user enable --now planckbot-cron.service

# or ad-hoc
planckbot cron daemon

CLI reference

planckbot                            → launches the UI (back-compat default)
planckbot status [--watch N]         → summary (connection, triples, savings, cost)
planckbot doctor [--json]            → 12-point health check with suggested fixes
planckbot ui                         → launch NiceGUI workbench on :8080

planckbot init                       → first-run setup (data dir, DB, MCP registration)
planckbot uninstall [--purge-data]   → reverses `init`; optionally wipes data/
planckbot systemd install|uninstall  → cron daemon as a systemd user unit

planckbot demo load|clear            → synthetic data for a populated dashboard

planckbot train --tool X --fixture Y.json [--activate]
planckbot label --tool X --recent N [--reference file.txt] [--dry-run]

planckbot bless <ckpt> [--threshold X] [--activate]   → mark adapter safe to serve
planckbot unbless <ckpt>              → revoke + deactivate (emergency revert)

planckbot cron list|add|rm|enable|disable|run|daemon
planckbot synth list|show|activate|deactivate|create|gaps|author

planckbot proxy-demo                  → exercise the intercept path with a trained adapter
planckbot preflight                   → sanity-check the MCP wrapper + upstream

Troubleshooting

The three things that most often go wrong on a first install, and how to diagnose them fast:

The dashboard shows no triples, even after using Claude Code

planckbot doctor

If MCP subprocesses is warn/fail, Claude Code isn't spawning the proxy. Most commonly: Claude Code was opened before planckbot init ran, so it doesn't know about the new mcpServers entry yet. Restart Claude Code fully. If the Connection card on the dashboard says not connected, re-run planckbot init.

Claude uses native Read/LS instead of the MCP tools

This is expected default behavior — Claude Code prefers native tools. Two fixes:

1. Per-session: add to your prompt "use mcp__planckbot-fs__list_directory to ..."

2. Persistent: in your project's CLAUDE.md add "When reading files or listing directories, prefer mcp__planckbot-fs__* over the native Read / LS."

planckbot-fs returns Output validation error

The upstream @modelcontextprotocol/server-filesystem declares outputSchema that Claude Code strictly validates against unstructured output. PlanckBot strips it in the proxy, but the fix requires a fresh proxy subprocess — which means restart Claude Code once more after you update PlanckBot.

When in doubt, planckbot doctor plus the Connection card on the dashboard will tell you which of the 12 things is off.

What works today

• MCP proxy observing every filesystem tool call

• Per-triple auto-labeling from Claude Code JSONL logs

• LoRA adapter training on consumer CPU (~10 min per tool, 16–100 triples)

• Layer C edit_tool with AST whitelist and version tracking

• Layer D pattern detector + synthesize_tool primitive + hot-reload MCP server

• NiceGUI workbench with live dashboard, tool browser, training chart, cron manager, and a /how-it-works onboarding page

• systemd user unit for persistent scheduler

• 195 tests, <10s full run

What's honest work-in-progress

• Preliminary adapters underfit at 16 training triples — expect ~200–500 per tool for net savings.

• Token-savings accounting is currently negative (–55% across 6 historical intervene calls) because the early adapters regressed — expected at this training scale.

• Layer D currently requires a human (or external LLM call) to write the synthesized tool's code body. The framework, AST gate, MCP hot-serve, and gap detector are all live.

Project layout

src/planckbot/
  db/          — SQLite schema + dataclasses (v4)
  ingest/      — TripleSource ABC, manual loader, reference_tracker
  tools/       — ToolRegistry, TriplesStore, meta.edit_tool, versions
  experiments/ — ExperimentManager + metrics
  training/    — LoRA trainer (HF Trainer + PEFT)
  models/      — loader, inference (predict() with confidence), CheckpointManager
  proxy/       — intercept layer + planckbot-mcp stdio server
  synth/       — Layer D: detector, meta.synthesize_tool, planckbot-synth MCP server
  cron/        — CronStore, JobRegistry, Daemon, scanner
  cli.py       — unified planckbot CLI
  ui/          — NiceGUI workbench (10 pages)
scripts/       — train_tool, auto_label, proxy_demo, mcp_preflight
tests/         — 195 tests, ~10s
data/          — SQLite DB + LoRA checkpoints (gitignored except fixtures/)
static/        — branding + UI assets

Dev workflow

python -m pytest         # test suite
planckbot ui             # restart UI after code changes (reload=False)

License

Apache 2.0. See LICENSE.

Acknowledgments

Implementation co-authored with Claude Opus 4.7. Claude is also the first inadvertent supervision source for PlanckBot's adapters.