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name: react-inference description: "Configure, validate, and troubleshoot Tongyi DeepResearch ReAct inference workflows, data, tools, and rollout outputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

React Inference

Use this sub-skill when a user needs to set up, inspect, or troubleshoot Tongyi DeepResearch ReAct inference: .env values, JSON/JSONL input data, file references, local vLLM serving, OpenRouter or other OpenAI-compatible adaptation, rollout arguments, ReAct loop behavior, and tool contracts.

Do not use this sub-skill for benchmark judging or metric aggregation; route that to benchmark-evaluation. Do not use it to choose among WebAgent-family projects; route that to webagent-family. Do not run full local inference unless the user has explicitly provided model weights, GPUs, service credentials, and runtime approval.

Operating Procedure

1. Classify the route.
   • Local vLLM route: the repository launcher expects a local model path, eight visible CUDA devices, ports 6001 through 6008, and a .env file.
   • OpenRouter/OpenAI-compatible route: future agents must adapt the ReAct model call in the working copy to the provider base URL, API key, model id, and reasoning-content concatenation convention; do not run the eight-port launcher for this route.
2. Preflight configuration. Use scripts/build_react_env.py to print a safe template, generate an .env, or validate one. Treat MODEL_PATH, DATASET, and OUTPUT_PATH as strict fields. Treat Serper, Jina, summary API, Dashscope, and SandboxFusion credentials as required only when the corresponding tool is enabled or likely to be called.
3. Preflight input data. Use scripts/validate_deepresearch_dataset.py on JSON/JSONL files. If questions contain uploaded-file markers, supply the matching file_corpus directory and verify that all referenced files are present.
4. Explain execution shape before launching. The launcher loads .env, validates MODEL_PATH, starts eight vLLM servers, waits for /v1/models readiness, then runs run_multi_react.py with rollout, split, worker, model, and sampling arguments.
5. Reason from the ReAct transcript. Model messages alternate assistant output with <tool_call>...</tool_call> and user <tool_response>...</tool_response>. A valid final answer must be enclosed in <answer>...</answer>.
6. Troubleshoot by layer. Check configuration, dataset shape, model serving, external service credentials, tool-call JSON, file-corpus placement, token/time limits, and missing <answer> termination in that order.

Bundled References

• references/workflows.md — local vLLM and OpenAI-compatible routes, .env variables, launcher behavior, run_multi_react.py arguments, output layout, and ReAct loop.
• references/data-formats.md — JSON/JSONL input records, file references, file-corpus placement, and rollout output fields.
• references/tool-contracts.md — search, visit, google_scholar, PythonInterpreter, and parse_file contracts and credentials.
• references/troubleshooting.md — symptom-driven checks for common setup, runtime, and ReAct termination failures.

Bundled Scripts

• scripts/build_react_env.py --help — stdlib helper to generate or validate a DeepResearch-style .env without secrets or network calls.
• scripts/validate_deepresearch_dataset.py --help — stdlib helper to validate question/answer JSON or JSONL records and uploaded-file references.

Evidence Basis

This sub-skill distills the repository README, FAQ, environment example, requirements list, ReAct launcher, rollout runner, agent loop, prompt/tool sources, and the provided example JSONL/file-corpus fixtures. It is self-contained; future agents should not need to reopen those source files for routine inference setup questions.