v0.10.0 — first public release

A local-first, config-driven router across OpenAI-compatible backends you own. TangleBrain
routes each task to a backend you've configured — a local model by default, with optional
authenticated CLIs and your own paid API keys as overflow — and keeps the whole roster in one
plain, editable YAML file. Adding or removing a backend is a config edit, not a code change.

Highlights

• Local-first routing — ships pointing at a free local model server; nothing leaves your
  machine unless you configure a backend that does.
• Config-driven roster — every routable backend is one entry in a plain YAML list,
  auto-discovered from $TANGLEBRAIN_ROSTER → ~/.config/tanglebrain/roster.yaml → the packaged
  example, so a git pull never clobbers your config.
• Frontier-first orchestration — drive authenticated CLI backends as orchestrators with
  rotation and failover across them for resilience.
• Local sub-task delegation — an orchestrator can offload bulk sub-tasks to the local backend
  over an MCP tool, then review the results.
• Optional classifier gate — a cheap local pre-filter can send trivial requests straight to the
  local backend (off by default, fails safe).
• Measurement — every routed task is logged with an estimated cloud-equivalent "spend avoided,"
  rolled up by tanglebrain --stats.
• Knob GUI — a localhost panel (tanglebrain-gui) to view the roster, pricing, and rollup, edit
  a focused set of config knobs, and run a prompt.
• Gated paid-API tier — bring-your-own-key overflow, off by default behind two independent
  switches.

Getting started

Requires Python ≥ 3.10.

make venv
.venv/bin/tanglebrain --local "Write a haiku about local inference."

See the README to add orchestrator backends, ARCHITECTURE.md for
how the pieces fit, and DISCLAIMER.md for the opt-in / bring-your-own-key posture.

For contributors

CI runs the hermetic test suite on every push and PR. See CONTRIBUTING.md —
adding a backend is usually a config edit, a great first contribution.

MIT licensed.