Xpersona

Crawler Summary

Multi-Agent-Research-Assistant answer-first brief

πŸ”¬ Production-grade multi-agent research assistant powered by LangGraph + CrewAI. Three specialized AI agents (SearchAgent, SummarizerAgent, FactCheckerAgent) work in a coordinated pipeline to deliver fact-checked research reports in seconds. Stack: Python Β· FastAPI Β· LangChain Β· LangGraph Β· CrewAI Β· GPT-4o Β· Tavily Β· LangSmith Β· Redis Β· n8n 🌐 Live Demo πŸ‘‰ $1 πŸ“Š Project Stats 🧠 Multi-Agent Research Assistant A production-grade, LLM-powered research pipeline that orchestrates specialized AI agents for **web search**, **summarization**, and **fact-checking** β€” reducing manual research time by **60%**. --- πŸ“ Architecture Overview --- πŸ—‚οΈ Project Structure --- ⚑ Quick Start 1. Clone & Install 2. Configure Environment 3. Run with Docker 4. Run Locally Visi Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 5/31/2026.

Freshness

Last checked 5/31/2026

Best For

Multi-Agent-Research-Assistant 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 OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 66/100

Multi-Agent-Research-Assistant

πŸ”¬ Production-grade multi-agent research assistant powered by LangGraph + CrewAI. Three specialized AI agents (SearchAgent, SummarizerAgent, FactCheckerAgent) work in a coordinated pipeline to deliver fact-checked research reports in seconds. Stack: Python Β· FastAPI Β· LangChain Β· LangGraph Β· CrewAI Β· GPT-4o Β· Tavily Β· LangSmith Β· Redis Β· n8n 🌐 Live Demo πŸ‘‰ $1 πŸ“Š Project Stats 🧠 Multi-Agent Research Assistant A production-grade, LLM-powered research pipeline that orchestrates specialized AI agents for **web search**, **summarization**, and **fact-checking** β€” reducing manual research time by **60%**. --- πŸ“ Architecture Overview --- πŸ—‚οΈ Project Structure --- ⚑ Quick Start 1. Clone & Install 2. Configure Environment 3. Run with Docker 4. Run Locally Visi

OpenClawself-declared

Public facts

4

Change events

0

Artifacts

0

Freshness

May 31, 2026

Verifiededitorial-contentNo verified compatibility signals1 GitHub stars

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 5/31/2026.

1 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 31, 2026

Vendor

Palpriyanshu94

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 5/31/2026.

View Source

Setup snapshot

git clone https://github.com/palpriyanshu94/Multi-Agent-Research-Assistant.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    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.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Palpriyanshu94

profilemedium
Observed May 31, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed May 31, 2026Source linkProvenance
Adoption (1)

Adoption signal

1 GitHub stars

profilemedium
Observed May 31, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

python

Executable Examples

text

User Query
    β”‚
    β–Ό
FastAPI Gateway  ──────────────────────────────────────────────────┐
    β”‚                                                               β”‚
    β–Ό                                                               β”‚
LangGraph Orchestrator                                         LangSmith
    β”‚                                                           (Tracing)
    β”œβ”€β”€β–Ί SearchAgent (Web Search via Tavily/SerpAPI)                β”‚
    β”‚         β”‚                                                     β”‚
    β”œβ”€β”€β–Ί SummarizerAgent (OpenAI GPT-4o)                            β”‚
    β”‚         β”‚                                                     β”‚
    └──► FactCheckerAgent (Cross-reference + Confidence Score)      β”‚
              β”‚                                                      β”‚
              β–Ό                                                      β”‚
         CrewAI Crew (Parallel Coordination) β—„β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
              β”‚
              β–Ό
         Research Report  ──►  n8n Webhook  ──►  Downstream Systems

text

multi-agent-research-assistant/
β”œβ”€β”€ agents/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ base_agent.py          # Abstract base class for all agents
β”‚   β”œβ”€β”€ search_agent.py        # Web search with Tavily/SerpAPI
β”‚   β”œβ”€β”€ summarizer_agent.py    # GPT-4o powered summarization
β”‚   └── fact_checker_agent.py  # Cross-reference & confidence scoring
β”œβ”€β”€ api/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ main.py                # FastAPI app entrypoint
β”‚   β”œβ”€β”€ routes/
β”‚   β”‚   β”œβ”€β”€ research.py        # /research endpoints
β”‚   β”‚   └── health.py          # /health endpoints
β”‚   β”œβ”€β”€ models.py              # Pydantic request/response models
β”‚   └── middleware.py          # Auth, CORS, rate limiting
β”œβ”€β”€ workflows/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ graph.py               # LangGraph state machine
β”‚   β”œβ”€β”€ crew.py                # CrewAI crew & task definitions
β”‚   └── pipeline.py            # End-to-end orchestration
β”œβ”€β”€ utils/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ tracing.py             # LangSmith integration
β”‚   β”œβ”€β”€ cache.py               # Redis caching layer
β”‚   └── logger.py              # Structured logging
β”œβ”€β”€ n8n/
β”‚   └── workflow.json          # n8n automation workflow export
β”œβ”€β”€ langsmith/
β”‚   └── config.py              # LangSmith tracing configuration
β”œβ”€β”€ frontend/
β”‚   └── static/
β”‚       β”œβ”€β”€ index.html         # Research dashboard UI
β”‚       β”œβ”€β”€ css/style.css
β”‚       └── js/app.js
β”œβ”€β”€ tests/
β”‚   β”œβ”€β”€ test_agents.py
β”‚   β”œβ”€β”€ test_api.py
β”‚   └── test_workflows.py
β”œβ”€β”€ docs/
β”‚   └── API.md                 # API reference documentation
β”œβ”€β”€ .env.example
β”œβ”€β”€ requirements.txt
β”œβ”€β”€ docker-compose.yml
└── README.md

bash

git clone https://github.com/yourname/multi-agent-research-assistant.git
cd multi-agent-research-assistant
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

bash

cp .env.example .env
# Edit .env with your API keys

bash

docker-compose up --build

bash

uvicorn api.main:app --reload --port 8000

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

πŸ”¬ Production-grade multi-agent research assistant powered by LangGraph + CrewAI. Three specialized AI agents (SearchAgent, SummarizerAgent, FactCheckerAgent) work in a coordinated pipeline to deliver fact-checked research reports in seconds. Stack: Python Β· FastAPI Β· LangChain Β· LangGraph Β· CrewAI Β· GPT-4o Β· Tavily Β· LangSmith Β· Redis Β· n8n 🌐 Live Demo πŸ‘‰ $1 πŸ“Š Project Stats 🧠 Multi-Agent Research Assistant A production-grade, LLM-powered research pipeline that orchestrates specialized AI agents for **web search**, **summarization**, and **fact-checking** β€” reducing manual research time by **60%**. --- πŸ“ Architecture Overview --- πŸ—‚οΈ Project Structure --- ⚑ Quick Start 1. Clone & Install 2. Configure Environment 3. Run with Docker 4. Run Locally Visi

Full README

🌐 Live Demo

πŸ‘‰ Try it live

πŸ“Š Project Stats

Tests Python FastAPI Docker Railway

🧠 Multi-Agent Research Assistant

A production-grade, LLM-powered research pipeline that orchestrates specialized AI agents for web search, summarization, and fact-checking β€” reducing manual research time by 60%.

πŸ“ Architecture Overview

User Query
    β”‚
    β–Ό
FastAPI Gateway  ──────────────────────────────────────────────────┐
    β”‚                                                               β”‚
    β–Ό                                                               β”‚
LangGraph Orchestrator                                         LangSmith
    β”‚                                                           (Tracing)
    β”œβ”€β”€β–Ί SearchAgent (Web Search via Tavily/SerpAPI)                β”‚
    β”‚         β”‚                                                     β”‚
    β”œβ”€β”€β–Ί SummarizerAgent (OpenAI GPT-4o)                            β”‚
    β”‚         β”‚                                                     β”‚
    └──► FactCheckerAgent (Cross-reference + Confidence Score)      β”‚
              β”‚                                                      β”‚
              β–Ό                                                      β”‚
         CrewAI Crew (Parallel Coordination) β—„β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
              β”‚
              β–Ό
         Research Report  ──►  n8n Webhook  ──►  Downstream Systems

πŸ—‚οΈ Project Structure

multi-agent-research-assistant/
β”œβ”€β”€ agents/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ base_agent.py          # Abstract base class for all agents
β”‚   β”œβ”€β”€ search_agent.py        # Web search with Tavily/SerpAPI
β”‚   β”œβ”€β”€ summarizer_agent.py    # GPT-4o powered summarization
β”‚   └── fact_checker_agent.py  # Cross-reference & confidence scoring
β”œβ”€β”€ api/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ main.py                # FastAPI app entrypoint
β”‚   β”œβ”€β”€ routes/
β”‚   β”‚   β”œβ”€β”€ research.py        # /research endpoints
β”‚   β”‚   └── health.py          # /health endpoints
β”‚   β”œβ”€β”€ models.py              # Pydantic request/response models
β”‚   └── middleware.py          # Auth, CORS, rate limiting
β”œβ”€β”€ workflows/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ graph.py               # LangGraph state machine
β”‚   β”œβ”€β”€ crew.py                # CrewAI crew & task definitions
β”‚   └── pipeline.py            # End-to-end orchestration
β”œβ”€β”€ utils/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ tracing.py             # LangSmith integration
β”‚   β”œβ”€β”€ cache.py               # Redis caching layer
β”‚   └── logger.py              # Structured logging
β”œβ”€β”€ n8n/
β”‚   └── workflow.json          # n8n automation workflow export
β”œβ”€β”€ langsmith/
β”‚   └── config.py              # LangSmith tracing configuration
β”œβ”€β”€ frontend/
β”‚   └── static/
β”‚       β”œβ”€β”€ index.html         # Research dashboard UI
β”‚       β”œβ”€β”€ css/style.css
β”‚       └── js/app.js
β”œβ”€β”€ tests/
β”‚   β”œβ”€β”€ test_agents.py
β”‚   β”œβ”€β”€ test_api.py
β”‚   └── test_workflows.py
β”œβ”€β”€ docs/
β”‚   └── API.md                 # API reference documentation
β”œβ”€β”€ .env.example
β”œβ”€β”€ requirements.txt
β”œβ”€β”€ docker-compose.yml
└── README.md

⚑ Quick Start

1. Clone & Install

git clone https://github.com/yourname/multi-agent-research-assistant.git
cd multi-agent-research-assistant
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Configure Environment

cp .env.example .env
# Edit .env with your API keys

3. Run with Docker

docker-compose up --build

4. Run Locally

uvicorn api.main:app --reload --port 8000

Visit: http://localhost:8000 β€” Dashboard UI
Docs: http://localhost:8000/docs β€” Swagger UI

πŸ”‘ Environment Variables

| Variable | Description | Required | |---|---|---| | OPENAI_API_KEY | OpenAI API key (GPT-4o) | βœ… | | TAVILY_API_KEY | Tavily Search API key | βœ… | | LANGCHAIN_API_KEY | LangSmith tracing key | βœ… | | LANGCHAIN_PROJECT | LangSmith project name | βœ… | | REDIS_URL | Redis connection URL | Optional | | N8N_WEBHOOK_URL | n8n workflow trigger URL | Optional | | SECRET_KEY | API authentication secret | βœ… |

πŸ€– Agents

SearchAgent

  • Uses Tavily Search API for real-time web search
  • Returns ranked, deduplicated sources with relevance scores
  • Configurable depth: basic | advanced

SummarizerAgent

  • Powered by GPT-4o with structured prompting
  • Produces concise summaries with key entities extracted
  • Supports multi-document synthesis

FactCheckerAgent

  • Cross-references claims across multiple sources
  • Outputs a confidence score (0–1) per claim
  • Flags contradictions and uncertain information

πŸ“Š LangSmith Tracing

Every research pipeline run is traced in LangSmith:

  • Full agent execution tree
  • Token usage per agent
  • Latency breakdown
  • Intermediate outputs for debugging

Configure in .env:

LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=lsv2_...
LANGCHAIN_PROJECT=research-assistant

πŸ”„ n8n Automation

Import n8n/workflow.json into your n8n instance to enable:

  • Webhook trigger β†’ start research pipeline
  • Data transformation β†’ normalize results
  • Report generation β†’ format and deliver output
  • Integrations β†’ Slack, Email, Google Docs, Notion

πŸ§ͺ Testing

pytest tests/ -v
pytest tests/ --cov=. --cov-report=html

πŸ“ˆ Performance

| Metric | Result | |---|---| | Research time reduction | 60% | | Pipeline setup time reduction | 50% | | Development error reduction | 45% | | Queries handled in testing | 1,000+ | | Avg. response time | ~8–15s per query |

πŸ“„ License

MIT License β€” see LICENSE

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

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

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_OPENCLEW@x1pay/langchain

Rank

65

LangChain/LangGraph tools for AI agent x402 payments on X1

Traction

No public download signal

Freshness

Updated 4mo ago

OPENCLAW
GITHUB_OPENCLEW@langchain/langgraph-swarm

Rank

65

An implementation of a multi-agent swarm using LangGraph

Traction

No public download signal

Freshness

Updated 4mo ago

OPENCLAW
GITHUB_OPENCLEWoceanbus-langchain

Rank

65

LangChain tools for OceanBus β€” give your LangChain and CrewAI agents a global identity, encrypted messaging, and Yellow Pages service discovery with a single import.

Traction

No public download signal

Freshness

Updated 4mo ago

OPENCLAW
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

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/crewai-palpriyanshu94-multi-agent-research-assistant/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-08T22:21:09.187Z"
    }
  },
  "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",
    "label": "Vendor",
    "value": "Palpriyanshu94",
    "category": "vendor",
    "href": "https://github.com/palpriyanshu94/Multi-Agent-Research-Assistant",
    "sourceUrl": "https://github.com/palpriyanshu94/Multi-Agent-Research-Assistant",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:37.271Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:37.271Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "traction",
    "label": "Adoption signal",
    "value": "1 GitHub stars",
    "category": "adoption",
    "href": "https://github.com/palpriyanshu94/Multi-Agent-Research-Assistant",
    "sourceUrl": "https://github.com/palpriyanshu94/Multi-Agent-Research-Assistant",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:37.271Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-palpriyanshu94-multi-agent-research-assistant/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[]

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Ads related to Multi-Agent-Research-Assistant and adjacent AI workflows.