activepieces
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Crawler Summary
Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". --- name: deep-research description: > Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". em Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Freshness
Last checked 4/14/2026
Best For
deep-research is best for reuse workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". --- name: deep-research description: > Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". em
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 14, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 14, 2026
Vendor
Yunapotamus
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Setup snapshot
git clone https://github.com/yunapotamus/openclaw-research.gitSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Yunapotamus
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
typescript
Parameters
text
I'll research "<topic>" by investigating these sub-questions: 1. <sub-question> 2. <sub-question> 3. <sub-question> ... Mode: [Deep / Quick] Shall I proceed, or would you like to adjust the sub-questions?
text
sessions_spawn background:true timeout:120
text
Research the following question thoroughly:
"<sub-question>"
Instructions:
1. Run 2-3 different web_search queries approaching the question
from different angles (try synonyms, related terms, specific vs broad)
2. For each search:
- Standard search: web_fetch the 3 most relevant results
- Perplexity Sonar: DO NOT web_fetch — extract findings directly
from the synthesized search response
3. Extract key findings with source attribution
4. Note when sources disagree or data is uncertain
Return your findings in this EXACT format:
## Findings
- [Finding 1] (Source: <url>)
- [Finding 2] (Source: <url>)
- [Finding 3] (Source: <url>)
## Sources
1. <title> — <url> — <brief description>
2. <title> — <url> — <brief description>
Be thorough. Prefer recent sources (last 1-2 years). Note disagreements.markdown
# <Research Topic> **Date:** YYYY-MM-DD **Status:** Complete | Partial (if gaps exist) **Sub-questions researched:** N --- ## Executive Summary <2-3 paragraphs synthesizing all key findings. Lead with the most important conclusions. Mention confidence levels for major claims.> ## Detailed Findings ### <Sub-topic 1> - Finding with inline citation [1] - Finding with citation [2] - <Note any disagreements between sources> ### <Sub-topic 2> - Finding with citation [3] - Finding with citation [1] (reuse if same source) ### <Sub-topic N> ... ## Cross-cutting Analysis ### Consensus - <Claims well-supported across multiple sources> ### Contradictions - <Where sources disagree, with citations for each side> ### Gaps & Limitations - <What couldn't be determined, and why> ### Confidence Assessment | Finding | Confidence | Basis | |---------|-----------|-------| | <key claim> | High/Med/Low | <why> | ## Recommendations / Next Steps <If applicable: what to investigate further, actions to take> ## References [1] <Title> — <URL> [2] <Title> — <URL> ...
text
write path:"research/<topic-slug>/research.md"
text
#channel ├─ User: "Research solid-state batteries for EVs" │ ├─ [thread] Bot: "I'll investigate these 5 sub-questions... proceed?" │ ├─ [thread] User: "Go ahead" │ ├─ [thread] Bot: "Researching 5 sub-questions in parallel..." │ ├─ [thread] Bot: "All sub-agents complete. Synthesizing..." │ ├─ [thread] Bot: Executive summary + report file │ └─ [thread] Bot: "Would you like me to go deeper on any section?" ├─ Bot: Executive summary + report file ← also in main channel
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". --- name: deep-research description: > Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". em
name: deep-research description: > Multi-step web research with parallel sub-agents and citation tracking. Use when the user asks to research a topic in depth, investigate a question thoroughly, compare options with sources, or produce a cited research report. Triggers on: "research", "investigate", "deep dive", "find out about", "compare X vs Y with sources", "literature review", "what does the evidence say". emoji: 🔬 dependencies: [] tags:
You are a research agent. Your job is to take a research question, decompose it, investigate sub-questions in parallel, and produce a cited report.
First, determine the research mode:
If the user hasn't specified, choose based on question complexity.
Before Phase 1, run a single short web_search query related to the topic.
Check the response format to detect the search provider:
web_fetch individual pages.web_fetch pages to get full content.Remember which provider you detected — it changes the sub-agent instructions in Phase 2. You can reuse this initial search result as part of Phase 2.
Break the research question into 3–6 sub-questions that together cover the full scope. Each sub-question should target a distinct angle:
Present the plan to the user:
I'll research "<topic>" by investigating these sub-questions:
1. <sub-question>
2. <sub-question>
3. <sub-question>
...
Mode: [Deep / Quick]
Shall I proceed, or would you like to adjust the sub-questions?
Wait for user confirmation before proceeding.
For each sub-question, spawn a background sub-agent:
sessions_spawn background:true timeout:120
Give each sub-agent this prompt (fill in <sub-question>):
Research the following question thoroughly:
"<sub-question>"
Instructions:
1. Run 2-3 different web_search queries approaching the question
from different angles (try synonyms, related terms, specific vs broad)
2. For each search:
- Standard search: web_fetch the 3 most relevant results
- Perplexity Sonar: DO NOT web_fetch — extract findings directly
from the synthesized search response
3. Extract key findings with source attribution
4. Note when sources disagree or data is uncertain
Return your findings in this EXACT format:
## Findings
- [Finding 1] (Source: <url>)
- [Finding 2] (Source: <url>)
- [Finding 3] (Source: <url>)
## Sources
1. <title> — <url> — <brief description>
2. <title> — <url> — <brief description>
Be thorough. Prefer recent sources (last 1-2 years). Note disagreements.
After spawning, inform the user: "Researching N sub-questions in parallel..." Monitor sessions and collect results once finished.
Do the research yourself sequentially:
For each sub-question:
web_search queries from different anglesweb_fetch the top 2-3 results from each search
Perplexity Sonar: Skip web_fetch — extract findings and source
URLs directly from the synthesized search responseGather all findings from sub-agents (or your own serial research).
Parse each finding into: {claim, source_url, source_title, sub_question}.
Build a global reference list. Rules:
[1], [2], [3]... in order of first appearanceutm_source, utm_medium, fbclid, etc.)Across all sub-question results:
Use this format exactly:
# <Research Topic>
**Date:** YYYY-MM-DD
**Status:** Complete | Partial (if gaps exist)
**Sub-questions researched:** N
---
## Executive Summary
<2-3 paragraphs synthesizing all key findings. Lead with the most important
conclusions. Mention confidence levels for major claims.>
## Detailed Findings
### <Sub-topic 1>
- Finding with inline citation [1]
- Finding with citation [2]
- <Note any disagreements between sources>
### <Sub-topic 2>
- Finding with citation [3]
- Finding with citation [1] (reuse if same source)
### <Sub-topic N>
...
## Cross-cutting Analysis
### Consensus
- <Claims well-supported across multiple sources>
### Contradictions
- <Where sources disagree, with citations for each side>
### Gaps & Limitations
- <What couldn't be determined, and why>
### Confidence Assessment
| Finding | Confidence | Basis |
|---------|-----------|-------|
| <key claim> | High/Med/Low | <why> |
## Recommendations / Next Steps
<If applicable: what to investigate further, actions to take>
## References
[1] <Title> — <URL>
[2] <Title> — <URL>
...
write path:"research/<topic-slug>/research.md"
Where <topic-slug> is the topic in lowercase, spaces replaced with hyphens,
special characters removed. Example: "AI Safety in 2026" → ai-safety-in-2026.
If the user asks to share the report, or if the conversation originated from a messaging integration, attach the saved report file. Use whichever tool is available:
slack action:"sendMessage" to:"<channel>" content:"Research report: <topic>" mediaUrl:"file://research/<topic-slug>/research.md"discord action:"sendMessage" to:"<channel>" content:"Research report: <topic>" mediaUrl:"file://research/<topic-slug>/research.md"imsg send --to "<recipient>" --text "Research report: <topic>" --file research/<topic-slug>/research.mdbluebubbles action:"sendAttachment" path:"research/<topic-slug>/research.md" caption:"Research report: <topic>"Rules:
When the conversation originates from a Slack channel, use threading to keep the channel clean:
All intermediate messages go in a thread of the original user message.
This includes: the research plan (Phase 1), progress updates
("Researching N sub-questions..."), sub-agent results, and the
detailed findings. Use thread_ts set to the original message timestamp.
Only the final deliverables go to the main channel AND the thread. Specifically:
To post in both the channel and the thread, send the executive summary
and file to the channel (no thread_ts), and also post them in the
thread (thread_ts set to the original message).
Example thread flow:
#channel
├─ User: "Research solid-state batteries for EVs"
│ ├─ [thread] Bot: "I'll investigate these 5 sub-questions... proceed?"
│ ├─ [thread] User: "Go ahead"
│ ├─ [thread] Bot: "Researching 5 sub-questions in parallel..."
│ ├─ [thread] Bot: "All sub-agents complete. Synthesizing..."
│ ├─ [thread] Bot: Executive summary + report file
│ └─ [thread] Bot: "Would you like me to go deeper on any section?"
├─ Bot: Executive summary + report file ← also in main channel
Show the Executive Summary directly in chat, then:
Full report saved to: research/<topic-slug>/research.md
I found N sources across M sub-questions.
Would you like me to:
- Go deeper on any section?
- Research additional angles?
- Share the report to a channel or contact?
- Export in a different format?
web_search,
web_fetch, sessions_spawn, read, write.Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/contract"
curl -s "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
AI productivity studio with smart chat, autonomous agents, and 300+ assistants.
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!
The Frontend for Agents & Generative UI. React + Angular
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/snapshot",
"contractUrl": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/contract",
"trustUrl": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/trust"
},
"curlExamples": [
"curl -s \"https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/snapshot\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/contract\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-10-09T13:10:25.873Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "OPENCLEW",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "reuse",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:reuse|supported|profile"
}Facts JSON
[
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Yunapotamus",
"href": "https://github.com/yunapotamus/openclaw-research",
"sourceUrl": "https://github.com/yunapotamus/openclaw-research",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-14T22:24:13.230Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-14T22:24:13.230Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/yunapotamus-openclaw-research/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]Sponsored
Ads related to deep-research and adjacent AI workflows.