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. Use

yieldMs

to background it, then poll — but do NOT set

timeout

or the process will be killed mid-search.

The

:online

Install

cargo install openclaw-search-tool

Requires

OPENROUTER_API_KEY

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 complete

Flags

--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"

low

medium

high

xhigh

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

:online

# 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 | ⏱ 5s

Status 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.

  low

  for quick facts,

  high

  for real research,

  xhigh

  only for complex multi-source analysis.
• Use

  -s

  for 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.