Crawler Summary
🤖 Autonomous multi-agent AI research system — LangChain, CrewAI, RAG, Pinecone — 95% success rate 🤖 AI Research Agent <div align="center"> **Autonomous multi-agent AI system that generates complete research reports from natural language queries — 95% task completion rate** $1 • $1 • $1 • $1 • $1 • $1 </div> --- Overview This system lets you ask a research question in plain English and receive a structured, cited research report — automatically. It uses a swarm of specialized AI agents that collaborate, delegate Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
ai-research-agent is best for crewai, multi-agent workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB REPOS, runtime-metrics, public facts pack
🤖 Autonomous multi-agent AI research system — LangChain, CrewAI, RAG, Pinecone — 95% success rate 🤖 AI Research Agent <div align="center"> **Autonomous multi-agent AI system that generates complete research reports from natural language queries — 95% task completion rate** $1 • $1 • $1 • $1 • $1 • $1 </div> --- Overview This system lets you ask a research question in plain English and receive a structured, cited research report — automatically. It uses a swarm of specialized AI agents that collaborate, delegate
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 15, 2026
Vendor
Kpavan3009
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Unpublished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 4/15/2026.
Setup snapshot
Setup 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
Kpavan3009
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
python
text
Input: "What are the latest advances in transformer-based protein folding models?"
Output: A 5-section research report with web sources, retrieved papers, code snippets,
and a structured summary — generated in ~90 seconds.text
User Query
│
▼
┌─────────────────────────────────────────────────────┐
│ Orchestrator Agent (CrewAI) │
│ - Receives query, breaks into subtasks │
│ - Assigns tasks to specialist agents │
│ - Synthesizes final report │
└──────┬───────────────┬────────────────┬──────────────┘
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
│ Web Search │ │ RAG Agent │ │ Code Execution │
│ Agent │ │ │ │ Agent │
│ │ │ Pinecone │ │ │
│ Grok API │ │ Vector Store │ │ Python REPL │
│ (real-time │ │ (50K docs) │ │ (data analysis, │
│ news/web) │ │ │ │ calculations) │
└──────────────┘ └──────────────┘ └──────────────────┘
│ │ │
└───────────────┴────────────────┘
│
▼
┌─────────────────┐
│ Report Writer │
│ (LangChain │
│ LLM chain) │
└────────┬────────┘
│
▼
Structured Research Report
(Markdown + citations)text
ai-research-agent/ ├── agents/ │ ├── orchestrator.py # CrewAI orchestrator — task planning & delegation │ ├── web_researcher.py # Web search agent using Grok API │ ├── rag_retriever.py # Document retrieval agent using Pinecone │ ├── code_analyst.py # Python REPL execution agent │ └── report_writer.py # Final report synthesis agent ├── tools/ │ ├── web_search.py # Grok API web search tool │ ├── python_repl.py # Sandboxed Python REPL tool │ └── document_loader.py # PDF/URL document loader for RAG ├── rag/ │ ├── embeddings.py # Embedding generation (OpenAI/local) │ ├── vector_store.py # Pinecone index management │ └── retriever.py # Semantic search with re-ranking ├── config/ │ └── settings.py # Configuration and environment variables ├── main.py # Entry point — run a research query ├── requirements.txt └── README.md
bash
git clone https://github.com/Kpavan3009/ai-research-agent.git cd ai-research-agent python -m venv venv source venv/bin/activate pip install -r requirements.txt cp .env.example .env # Fill in: GROK_API_KEY, PINECONE_API_KEY, OPENAI_API_KEY
bash
# Run a research query python main.py --query "What are the latest advances in LLM reasoning?" # With output file python main.py --query "Compare React vs Vue in 2025" --output reports/react_vs_vue.md # Interactive mode python main.py --interactive
markdown
# Research Report: LLM Reasoning Advances (2024-2025) ## Executive Summary ... ## Key Findings 1. Chain-of-Thought prompting improvements... 2. New benchmarks showing... ## Technical Deep Dive ... ## Sources - [Source 1](url) - [Source 2](url)
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB REPOS
Editorial quality
ready
🤖 Autonomous multi-agent AI research system — LangChain, CrewAI, RAG, Pinecone — 95% success rate 🤖 AI Research Agent <div align="center"> **Autonomous multi-agent AI system that generates complete research reports from natural language queries — 95% task completion rate** $1 • $1 • $1 • $1 • $1 • $1 </div> --- Overview This system lets you ask a research question in plain English and receive a structured, cited research report — automatically. It uses a swarm of specialized AI agents that collaborate, delegate
Autonomous multi-agent AI system that generates complete research reports from natural language queries — 95% task completion rate
Overview • Architecture • Agents • Setup • Usage • Examples
</div>This system lets you ask a research question in plain English and receive a structured, cited research report — automatically. It uses a swarm of specialized AI agents that collaborate, delegate tasks, and synthesize information from multiple sources.
Example:
Input: "What are the latest advances in transformer-based protein folding models?"
Output: A 5-section research report with web sources, retrieved papers, code snippets,
and a structured summary — generated in ~90 seconds.
Key metrics:
User Query
│
▼
┌─────────────────────────────────────────────────────┐
│ Orchestrator Agent (CrewAI) │
│ - Receives query, breaks into subtasks │
│ - Assigns tasks to specialist agents │
│ - Synthesizes final report │
└──────┬───────────────┬────────────────┬──────────────┘
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
│ Web Search │ │ RAG Agent │ │ Code Execution │
│ Agent │ │ │ │ Agent │
│ │ │ Pinecone │ │ │
│ Grok API │ │ Vector Store │ │ Python REPL │
│ (real-time │ │ (50K docs) │ │ (data analysis, │
│ news/web) │ │ │ │ calculations) │
└──────────────┘ └──────────────┘ └──────────────────┘
│ │ │
└───────────────┴────────────────┘
│
▼
┌─────────────────┐
│ Report Writer │
│ (LangChain │
│ LLM chain) │
└────────┬────────┘
│
▼
Structured Research Report
(Markdown + citations)
| Agent | Role | Tools | |-------|------|-------| | Orchestrator | Receives query, plans subtasks, delegates, synthesizes | CrewAI delegation | | Web Researcher | Finds current information, news, and sources | Grok API web search | | RAG Retriever | Semantic search over document knowledge base | Pinecone + embeddings | | Code Analyst | Runs Python for calculations, data analysis, plotting | Python REPL sandbox | | Report Writer | Compiles findings into structured markdown report | LangChain LLM chain |
ai-research-agent/
├── agents/
│ ├── orchestrator.py # CrewAI orchestrator — task planning & delegation
│ ├── web_researcher.py # Web search agent using Grok API
│ ├── rag_retriever.py # Document retrieval agent using Pinecone
│ ├── code_analyst.py # Python REPL execution agent
│ └── report_writer.py # Final report synthesis agent
├── tools/
│ ├── web_search.py # Grok API web search tool
│ ├── python_repl.py # Sandboxed Python REPL tool
│ └── document_loader.py # PDF/URL document loader for RAG
├── rag/
│ ├── embeddings.py # Embedding generation (OpenAI/local)
│ ├── vector_store.py # Pinecone index management
│ └── retriever.py # Semantic search with re-ranking
├── config/
│ └── settings.py # Configuration and environment variables
├── main.py # Entry point — run a research query
├── requirements.txt
└── README.md
git clone https://github.com/Kpavan3009/ai-research-agent.git
cd ai-research-agent
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# Fill in: GROK_API_KEY, PINECONE_API_KEY, OPENAI_API_KEY
# Run a research query
python main.py --query "What are the latest advances in LLM reasoning?"
# With output file
python main.py --query "Compare React vs Vue in 2025" --output reports/react_vs_vue.md
# Interactive mode
python main.py --interactive
The agent generates structured reports like:
# Research Report: LLM Reasoning Advances (2024-2025)
## Executive Summary
...
## Key Findings
1. Chain-of-Thought prompting improvements...
2. New benchmarks showing...
## Technical Deep Dive
...
## Sources
- [Source 1](url)
- [Source 2](url)
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://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/snapshot"
curl -s "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/contract"
curl -s "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/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.
Rank
70
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
Traction
No public download signal
Freshness
Updated 2d ago
Rank
70
AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
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!
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
The Frontend for Agents & Generative UI. React + Angular
Traction
No public download signal
Freshness
Updated 23d ago
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://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_REPOS",
"generatedAt": "2026-04-17T04:05:47.946Z"
}
},
"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": "crewai",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "multi-agent",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}Facts JSON
[
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Kpavan3009",
"href": "https://github.com/Kpavan3009/ai-research-agent",
"sourceUrl": "https://github.com/Kpavan3009/ai-research-agent",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:34.782Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:34.782Z",
"isPublic": true
},
{
"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": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-kpavan3009-ai-research-agent/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
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