How should Research_Assistant be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

Research_Assistant answer-first brief

Multi-Agent AI Research Assistant — CrewAI + LangGraph + FAISS + Flask --- title: Research Assistant emoji: 🔬 colorFrom: gray colorTo: blue sdk: docker pinned: false --- 🔬 AI Research Assistant **Multi-Agent RAG System** — Research any paper topic and get an AI-generated report with an interactive visualization demo. --- 🚀 Live Demo 🔗 **$1** --- 🧠 What It Does Enter any research paper topic → 3 AI agents collaborate to research, write, and review a full report → an interactive visu Capability contract not published. No trust telemetry is available yet. Last updated 5/31/2026.

Freshness

Last checked 5/31/2026

Best For

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

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGitHubSafety: 66/100

Research_Assistant

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

May 31, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 5/31/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 31, 2026

Vendor

Zerminewajid

Artifacts

Benchmarks

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Unpublished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. Last updated 5/31/2026.

View Source

Setup snapshot

git clone https://github.com/zerminewajid/Research_Assistant.git

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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

Zerminewajid

profilemedium

Observed May 31, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 31, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

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

Examples

Snippets

Languages

python

Executable Examples

text

User Input (Topic)
        ↓
   Flask REST API  (/research)
        ↓
   LangGraph State Machine
   ┌────────────────────────────────┐
   │  Researcher Agent  (CrewAI)    │  ← searches FAISS vector store
   │          ↓                     │
   │  Writer Agent      (CrewAI)    │  ← structures the report
   │          ↓                     │
   │  Critic Agent      (CrewAI)    │  ← reviews + finalizes
   └────────────────────────────────┘
        ↓
   /detect-demo endpoint
        ↓
   LLM decides if interactive demo is appropriate
        ↓
   JSON response → Browser renders report + demo

bash

# 1. Clone
git clone https://github.com/zerminewajid/Research_Assistant.git
cd Research_Assistant

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate   # Windows
# source venv/bin/activate  # Mac/Linux

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add API key
cp .env.example .env
# Add your Groq key: https://console.groq.com/keys

# 5. Run
python app.py
# Visit http://localhost:5000

text

Research_Assistant/
├── app.py                  # Flask server + API endpoints
├── pipeline/
│   ├── agents.py           # CrewAI agent definitions
│   ├── graph.py            # LangGraph state machine
│   └── state.py            # Pipeline state schema
├── rag/
│   └── vectorstore.py      # FAISS vector store
├── templates/
│   └── index.html          # Frontend (UI + interactive demos)
├── requirements.txt
└── .env.example

Docs & README

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

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Full README

title: Research Assistant emoji: 🔬 colorFrom: gray colorTo: blue sdk: docker pinned: false

🔬 AI Research Assistant

Multi-Agent RAG System — Research any paper topic and get an AI-generated report with an interactive visualization demo.

🚀 Live Demo

🔗 Try it here →

🧠 What It Does

Enter any research paper topic → 3 AI agents collaborate to research, write, and review a full report → an interactive visualization demo is generated if the topic matches a known AI/ML domain.

Works for any domain — AI, economics, biology, social science, etc. If no demo is available for a topic, nothing is shown.

🏗️ Architecture

User Input (Topic)
        ↓
   Flask REST API  (/research)
        ↓
   LangGraph State Machine
   ┌────────────────────────────────┐
   │  Researcher Agent  (CrewAI)    │  ← searches FAISS vector store
   │          ↓                     │
   │  Writer Agent      (CrewAI)    │  ← structures the report
   │          ↓                     │
   │  Critic Agent      (CrewAI)    │  ← reviews + finalizes
   └────────────────────────────────┘
        ↓
   /detect-demo endpoint
        ↓
   LLM decides if interactive demo is appropriate
        ↓
   JSON response → Browser renders report + demo

⚙️ Tech Stack

| Layer | Technology | |-------|-----------| | Web Server | Flask 3.0 | | Agent Framework | CrewAI 1.11 | | Pipeline Orchestration | LangGraph 0.2 | | Vector Database | FAISS (local, CPU) | | Embeddings | HuggingFace all-MiniLM-L6-v2 | | LLM | Groq llama-3.1-8b-instant (free) | | Frontend | Vanilla HTML/CSS/JS + Canvas API |

🎮 Interactive Demos

The app uses the LLM itself to detect whether an interactive demo is appropriate for the topic — no hardcoded keywords. Supported visualizations:

| Topic | Demo | |-------|------| | Transformers / Attention | Self-attention token visualizer | | CNN / Computer Vision | Convolutional feature map drawer | | LSTM / GRU / RNN | Gate mechanism slider | | Federated Learning | FedAvg round simulator | | RAG / Vector Search | Semantic similarity ranker | | MLOps / Deployment | Pipeline stage tracker | | Any other domain | No demo (null output) |

🛠️ Local Setup

# 1. Clone
git clone https://github.com/zerminewajid/Research_Assistant.git
cd Research_Assistant

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate   # Windows
# source venv/bin/activate  # Mac/Linux

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add API key
cp .env.example .env
# Add your Groq key: https://console.groq.com/keys

# 5. Run
python app.py
# Visit http://localhost:5000

📁 Project Structure

Research_Assistant/
├── app.py                  # Flask server + API endpoints
├── pipeline/
│   ├── agents.py           # CrewAI agent definitions
│   ├── graph.py            # LangGraph state machine
│   └── state.py            # Pipeline state schema
├── rag/
│   └── vectorstore.py      # FAISS vector store
├── templates/
│   └── index.html          # Frontend (UI + interactive demos)
├── requirements.txt
└── .env.example

🔌 API Endpoints

| Method | Endpoint | Description | |--------|----------|-------------| | GET | / | Serves the web UI | | POST | /research | Runs the 3-agent pipeline | | POST | /detect-demo | LLM decides if demo is appropriate |

👩‍💻 Author

Zermine Wajid BS Artificial Intelligence — GIK Institute of Engineering Sciences and Technology (GIKI)

⭐ If you found this useful, give it a star!

Contract & API

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

MissingGITHUB OPENCLEW

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

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-zerminewajid-research-assistant/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-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

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

An implementation of a multi-agent swarm using LangGraph

Traction

No public download signal

Freshness

Updated 4mo ago

OPENCLAW

GITHUB_OPENCLEW@langchain/langgraph-supervisor

Rank

LangGraph Multi-Agent Supervisor

Traction

No public download signal

Freshness

Updated 4mo ago

OPENCLAW

GITHUB_OPENCLEWoceanbus-langchain

Rank

LangChain tools for OceanBus — 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

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-zerminewajid-research-assistant/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-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": "2026-10-08T22:20:15.603Z"
    }
  },
  "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": "Zerminewajid",
    "category": "vendor",
    "href": "https://github.com/zerminewajid/Research_Assistant",
    "sourceUrl": "https://github.com/zerminewajid/Research_Assistant",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:45.856Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:45.856Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-zerminewajid-research-assistant/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[]