- name
- research-tool
- description
- Search the web using LLMs via OpenRouter. Use for current web data, API docs, market research, news, fact-checking, or any question that benefits from live internet access and reasoning.
- metadata
- {"openclaw": {"emoji": "🔍", "requires": {"bins": ["research-tool"], "env": ["OPENROUTER_API_KEY"]}, "primaryEnv": "OPENROUTER_API_KEY", "homepage": "https://github.com/aaronn/openclaw-search-tool"}}
OpenClaw Research Tool
Web search for OpenClaw agents, powered by OpenRouter. Ask questions in natural language, get accurate answers with cited sources. Defaults to GPT-5.2 which excels at documentation lookups and citation-heavy research.
Note: Even low-effort queries may take 1 minute or more to complete. High/xhigh reasoning can take 10+ minutes depending on complexity. This is normal — the model is searching the web, reading pages, and synthesizing an answer. Recommended: Run research-tool in a sub-agent so your main session stays responsive: ``sessions_spawn task:"research-tool 'your query here'"`**⚠️ Never set a timeout on exec when running research-tool.** Queries routinely take 1-10+ minutes. UseyieldMsto background it, then poll — but do NOT settimeout` or the process will be killed mid-search.
The :online model suffix gives any model live web access — it searches the web, reads pages, cites URLs, and synthesizes an answer.
Install
cargo install openclaw-search-toolRequires OPENROUTER_API_KEY env var. Get a key at https://openrouter.ai/keys
Quick start
research-tool "What are the x.com API rate limits?"
research-tool "How do I set reasoning effort parameters on OpenRouter?"From an OpenClaw agent
# Best: run in a sub-agent (main session stays responsive)
sessions_spawn task:"research-tool 'your query here'"
# Or via exec — NEVER set timeout, use yieldMs to background:
exec command:"research-tool 'your query'" yieldMs:5000
# then poll the session until completeFlags
--effort, -e (default: low)
Controls how much the model reasons before answering. Higher effort means better analysis but slower and more tokens.
research-tool --effort low "What year was Rust 1.0 released?"
research-tool --effort medium "Explain how OpenRouter routes requests to different model providers"
research-tool --effort high "Compare tradeoffs between Opus 4.6 and gpt-5.3-codex for programming"
research-tool --effort xhigh "Deep analysis of React Server Components vs traditional SSR approaches"| Level | Speed | When to use |
|---|---|---|
low | ~1-3 min | Quick fact lookups, simple questions |
medium | ~2-5 min | Standard research, moderate analysis |
high | ~3-10 min | Deep analysis with careful reasoning |
xhigh | ~5-20+ min | Maximum reasoning, complex multi-source synthesis |
Can also be set via env var RESEARCH_EFFORT.
--model, -m (default: openai/gpt-5.2:online)
Which model to use. Defaults to GPT-5.2 with the :online suffix because it excels at questions where citations and accurate documentation lookups matter. The :online suffix enables live web search and works with any model on OpenRouter.
# Default: GPT-5.2 with web search (great for docs and cited answers)
research-tool "current weather in San Francisco"
# Claude with web search
research-tool -m "anthropic/claude-sonnet-4-20250514:online" "Summarize recent changes to the OpenAI API"
# GPT-5.2 without web search (training data only)
research-tool -m "openai/gpt-5.2" "Explain the React Server Components architecture"
# Any OpenRouter model
research-tool -m "google/gemini-2.5-pro:online" "Compare React vs Svelte in 2026"Can also be set via env var RESEARCH_MODEL.
--system, -s
Override the system prompt to give the model a specific persona or instructions.
research-tool -s "You are a senior infrastructure engineer" "Best practices for zero-downtime Kubernetes deployments"
research-tool -s "You are a Rust systems programmer" "Best async patterns for WebSocket servers"--stdin
Read the query from stdin. Useful for long or multiline queries.
echo "Explain the OpenRouter model routing architecture" | research-tool --stdin
cat detailed-prompt.txt | research-tool --stdin--max-tokens (default: 12800)
Maximum tokens in the response.
--timeout (optional, no default)
No timeout by default — queries run until the model finishes. Set this only if you need a hard upper bound (e.g. --timeout 300).
Output format
- stdout: Response text only (markdown with citations) — pipe-friendly
- stderr: Progress status, reasoning traces, and token usage
🔍 Researching with openai/gpt-5.2:online (effort: high)...
✅ Connected — waiting for response...
[response text on stdout]
📊 Tokens: 4470 prompt + 184 completion = 4654 total | ⏱ 5sStatus indicators
🔍 Researching...— request sent to OpenRouter✅ Connected — waiting for response...— server accepted the request, model is searching/thinking⏳ 15s... ⏳ 30s...— elapsed time ticks (only in interactive terminals, not in agent exec)❌ Connection to OpenRouter failed— couldn't reach OpenRouter (network issue)❌ Connection to OpenRouter lost— connection dropped while waiting. Retry?
Tips for better results
- Write in natural language. "What are the best practices for Rust error handling and when should you use anyhow vs thiserror?" works better than keyword-style queries.
- Provide maximum context. The model starts from zero. Include background, what you already know, and all related sub-questions. Detailed prompts massively outperform vague ones.
- Use effort levels appropriately.
lowfor quick facts,highfor real research,xhighonly for complex multi-source analysis. - Use
-sfor domain expertise. A specific persona produces noticeably better domain-specific answers.
Cost
~$0.01–0.05 per query. Token usage is printed to stderr after each query.