> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/gym/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/gym/_mcp/server. # Anthropic Messages > Every Gym model server exposes POST /v1/messages so Anthropic-native harnesses like Claude Code can run against any backend Every NeMo Gym model server speaks the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) in addition to Responses and Chat Completions. That means Anthropic-native harnesses — notably the [Claude Code](https://code.claude.com/docs/en/overview) CLI — can target **any** Gym model backend (vLLM, OpenAI, Inference Providers, and so on) without a separate Anthropic proxy. ## How it works `SimpleResponsesAPIModel` registers `POST /v1/messages` on every model server by default. The handler maps the inbound Anthropic Messages request to Gym's native Responses schema, calls that server's own `responses()` implementation (whatever upstream the server is configured for), and maps the result back to an Anthropic Messages response. When the client sets `stream: true` (Claude Code always does), the complete response is re-emitted as a synthesized Anthropic SSE event stream. ```mermaid flowchart LR CLI["Claude Code CLI"] -->|"POST /v1/messages"| MS["Gym model server"] MS -->|"Messages → Responses"| R["responses()"] R -->|"upstream call"| BE["vLLM / OpenAI / provider / …"] BE --> R R -->|"Responses → Messages"| MS MS --> CLI ``` You do not configure a separate "Claude model" server. Point Claude Code at any existing Gym model server URL; the `/v1/messages` dialect is already there. ## Wire Claude Code to a Gym model server The built-in [`claude_code_agent`](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/claude_code_agent) runs `claude -p` as a subprocess. Set its `model_server` ref to the Gym model server you want to use. That ref takes precedence over `anthropic_base_url`: the agent resolves `ANTHROPIC_BASE_URL` to the model server, and the CLI appends `/v1/messages`. The showcase config [`reasoning_gym_claude_code_agent_model_server.yaml`](https://github.com/NVIDIA-NeMo/Gym/blob/main/resources_servers/reasoning_gym/configs/reasoning_gym_claude_code_agent_model_server.yaml) already wires `model_server` to `policy_model`. Compose it with any model server: ```bash gym env start \ --resources-server reasoning_gym/reasoning_gym_claude_code_agent_model_server \ --model-type vllm_model ``` This path needs only the model server's credentials (`policy_base_url`, `policy_api_key`, `policy_model_name` in `env.yaml` or as `+` overrides) — no `anthropic_*` variables. ```bash gym eval run --no-serve \ --agent reasoning_gym_claude_code_agent_model_server \ --input resources_servers/reasoning_gym/data/example.jsonl \ --output results/claude_code_via_model_server_rollout.jsonl \ --limit 1 ``` ### Choose the right model type | Model type | Upstream API | When to use | | -------------------- | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | | `vllm_model` | OpenAI-compatible **chat** (`/chat/completions`) | vLLM, NVIDIA API, most hosted chat providers | | `openai_model` | OpenAI **Responses** (`/responses`) | OpenAI / Azure Responses endpoints only — chat-only hosts return 404 | | `inference_provider` | Provider chat Completions | Fireworks, Together.ai, OpenRouter, and other [Inference Providers](/model-server/inference-providers) | ### Config snippet To wire Claude Code yourself, set `model_server` on the agent and leave `anthropic_base_url` null: ```yaml my_claude_agent: responses_api_agents: claude_code_agent: entrypoint: app.py resources_server: type: resources_servers name: my_verifier model_server: type: responses_api_models name: policy_model model: ${policy_model_name} anthropic_api_key: EMPTY anthropic_base_url: null concurrency: 32 max_turns: 30 ``` With `model_server` set, model calls go through Gym and can be recorded by [model-call capture](/model-server/model-call-capture). Direct Anthropic or `anthropic_base_url` runs bypass Gym capture. ## Call Anthropic (or another Messages endpoint) directly If you want Claude Code to hit Anthropic's API — or any other host that already speaks `/v1/messages` — omit `model_server` and set the Anthropic credentials instead: ```yaml # env.yaml anthropic_api_key: sk-ant-... anthropic_model_name: claude-sonnet-4-6 anthropic_base_url: null # null = real Anthropic API ``` For a local vLLM or Ollama endpoint that already serves Messages: ```yaml anthropic_api_key: EMPTY anthropic_model_name: Qwen/Qwen3-4B-Instruct-2507 anthropic_base_url: http://localhost:8000 ``` `anthropic_base_url` must **not** include `/v1`. Claude Code appends `/v1/messages` itself. ```bash gym env start --resources-server reasoning_gym/reasoning_gym_claude_code_agent gym eval run --no-serve \ --agent reasoning_gym_claude_code_agent \ --input resources_servers/reasoning_gym/data/example.jsonl \ --output results/claude_code_rollout.jsonl \ --limit 1 ``` ## Smoke-test `/v1/messages` Launch a model server, take its URL from the `gym env start` log (`'url': 'http://127.0.0.1:'`), then: ```bash gym env start --model-type vllm_model \ +policy_base_url=https://integrate.api.nvidia.com/v1 \ '+policy_api_key=${oc.env:NVIDIA_API_KEY}' \ +policy_model_name=meta/llama-3.1-8b-instruct # 1. Proxy speaks Anthropic Messages (add "stream": true for the SSE path): curl $URL/v1/messages -H 'content-type: application/json' \ -d '{"model":"x","max_tokens":64,"messages":[{"role":"user","content":"2+2?"}]}' # 2. Real Claude Code CLI against the same server: ANTHROPIC_BASE_URL=$URL ANTHROPIC_AUTH_TOKEN=local \ claude -p --output-format stream-json --max-turns 2 \ --model meta/llama-3.1-8b-instruct -- "What is 2+2?" ``` ## Related * Agent runtime options (`bare`, MCP, skills, thinking): [`claude_code_agent` README](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/claude_code_agent) * Skills evaluation pattern: [Agent Skills](/agent-server/agent-skills) * MCP tools from a Resources Server: [MCP Resources Server](/environment-tutorials/mcp-resources-server) * Capture model HTTP evidence: [Model-call capture](/model-server/model-call-capture)