turbo-agent 0.1.3

An LLM API proxy that improves response quality through concurrent inference, pivot-tournament verification, and context refinement.

Turbo Agent

Turbo Agent is the Claude Code plugin for LLM-as-a-Verifier. It implements an LLM API proxy that improves response quality through concurrent inference, verification, and refinement. It sits between your client (Claude Code, Codex, etc.) and the LLM provider, sending multiple parallel requests and selecting the best response with a Probabilistic Pivot Tournament (PPT) scored by a fine-grained logprob verifier.

Client request
    │
[Context Refinement]   (optional) rewrite/augment the system prompt for clarity
    │
[Concurrent Inference] send N parallel candidates to the backend model
    │
[Verification]         pivot tournament over the candidates, pick the best one
    │
Best response → Client

Verification uses the pivot tournament from the llm-verifier package to pick the best of N candidates.

Install

pip install turbo-agent

Or from source:

pip install -e .

Setup

For turbo agent to work, you need a turbo-agent.yaml. You can copy the reference file in this repo.

turbo-agent.yaml references keys with $VAR_NAME syntax. The recommended way to provide them is a .env file in the project root (next to turbo-agent.yaml) — the proxy loads it automatically on startup. Copy the committed template and fill in your keys:

cp .env.example .env
# then edit .env

# .env
VERTEX_API_KEY=your-vertex-key     # preferred for Gemini 2.5 logprobs (verifier)
# GEMINI_API_KEY=your-gemini-key     # used by gemini/ models (AI Studio)
# OPENAI_API_KEY=...               # only if you route to openai/ models
# ANTHROPIC_API_KEY=...            # only if you route to anthropic/ models

.env is gitignored; .env.example is committed as the template. Keys already exported in your shell environment work too and take nothing extra. The verifier and progress monitor use Gemini logprobs, which are best served by a Vertex AI key (VERTEX_API_KEY + provider: vertex_ai in the config); a plain GEMINI_API_KEY also works for the gemini/ backend models.

Verify your keys are valid:

turbo-agent check

It checks every supported provider (Gemini, Vertex AI, OpenAI, Anthropic) and reports each with ✅ / ❌ / ⚠️ / ⚪️, flagging which keys your config actually uses.

Run

turbo-agent                   # default port 8888
turbo-agent -p 9000           # custom port

Use with Claude Code

ANTHROPIC_BASE_URL=http://localhost:8888 claude

Use with OpenAI-compatible clients

export OPENAI_API_BASE=http://localhost:8888/v1

Configuration

Edit turbo-agent.yaml. API keys can reference environment variables with $VAR_NAME syntax. See the reference turbo-agent.yaml file for reference and usage.

Model prefixes

Prefix	Provider
gemini/	Google Gemini
openai/	OpenAI
anthropic/	Anthropic
(none)	OpenAI-compatible endpoint

API endpoints

Endpoint	Format
POST /v1/messages	Anthropic
POST /v1/chat/completions	OpenAI
GET /v1/models	OpenAI
GET /visualizer	Pipeline visualizer UI
*	Upstream passthrough to api.anthropic.com

Visualizer

A built-in web UI at http://localhost:8888/visualizer shows the pipeline DAG for each request — context refinement, all candidate responses, the pairwise tournament comparisons and scores, and the final selection.

To build the frontend (requires Node.js):

cd frontend
yarn install
yarn build

Publish to PyPI

cd frontend && yarn build && cd ..
pip install build twine
rm -rf dist
python -m build
twine check dist/*
twine upload dist/*