inspect-robots-agent 0.1.0

LLM agent policy for Inspect Robots: frontier LLMs (Claude, GPT, anything OpenAI-compatible) drive any registered embodiment through tool calls.

inspect-robots-agent

LLM agent policy for Inspect Robots: frontier LLMs (Claude, GPT, anything behind an OpenAI-compatible API) drive any registered embodiment through tool calls, as a first-class Policy named agent. The same policy runs ad-hoc instructions and scores on registered tasks next to fine-tuned VLAs.

Install

pip install inspect-robots inspect-robots-agent

Quickstart (no hardware)

export ANTHROPIC_API_KEY=sk-ant-...

inspect-robots "pick up the cube" --policy agent \
    -P model=anthropic/claude-fable-5 --embodiment cubepick

Model strings are OpenRouter-style provider/model, resolved from -P model=... or $INSPECT_ROBOTS_MODEL. API keys come from the environment:

1. -P base_url=... (with -P api_key_env=NAME): any OpenAI-compatible endpoint
2. anthropic/* model with ANTHROPIC_API_KEY: the Anthropic compat endpoint
3. openai/* model with OPENAI_API_KEY: OpenAI
4. OPENROUTER_API_KEY: OpenRouter, any model string

How it works

Each LLM tool call becomes one smooth, open-loop action chunk: named partial joint targets are interpolated at the embodiment's control rate (move_joints), displacements are split across steps (move_by), and done/give_up end the trial through the core's policy-stop channel. Every action still passes the CLI's default safety approvers (bounds clamp plus per-step delta limit); the plugin contains no safety-critical code path of its own.

Warning: Guardrails are on by default at the CLI. Never pass --disable-guardrails on real hardware unless you fully trust the policy and the rig.

Configuration knobs (all -P key=value): model, base_url, api_key_env, max_llm_calls, temperature.