How should agentic-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

agentic-research-assistant answer-first brief

Multi-agent AI research assistant using CrewAI with tool-constrained reasoning Agentic Research Assistant using CrewAI A multi-agent AI system that performs autonomous research using tool-constrained reasoning and CrewAI orchestration. --- Overview This project implements a multi-agent AI system that performs autonomous research using the CrewAI framework. The system accepts a user query, retrieves relevant information from the web using a search tool, and generates structured insights through Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

agentic-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 REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

agentic-research-assistant

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Pritighoshh

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Unpublished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 10/9/2026.

View Source

Setup snapshot

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

Pritighoshh

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

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

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

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

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

Agentic Research Assistant using CrewAI

A multi-agent AI system that performs autonomous research using tool-constrained reasoning and CrewAI orchestration.

Overview

This project implements a multi-agent AI system that performs autonomous research using the CrewAI framework.

The system accepts a user query, retrieves relevant information from the web using a search tool, and generates structured insights through coordinated agents.

Why This Project Matters

This project demonstrates how agentic AI systems can automate complex multi-step workflows using reasoning, tool usage, and coordination between specialized agents.

It highlights real-world challenges such as tool control, output consistency, and system reliability.

Features

Multi-agent architecture (Research Agent + Analysis Agent)
Tool-constrained reasoning (single search tool only)
Sequential task execution using CrewAI
Structured output generation (5 bullet insights)
Lightweight feedback loop for improved responses
Error handling and validation

System Architecture

User → Controller → Research Agent → Analysis Agent → Output

Agents

Research Agent

Uses the Serper search tool
Retrieves relevant information
Generates initial insights
Restricted to a single tool call

Analysis Agent

Refines and formats output
Converts insights into clean bullet points
Ensures clarity and structure
Does not use external tools

Tools Used

SerperDevTool

Provides real-time web search results
Returns titles, links, and snippets
Enables up-to-date research capability

Execution Flow

User enters a research query
Controller initializes agents
Research Agent performs web search
Extracts 5 insights
Passes output to Analysis Agent
Analysis Agent refines output
Final structured result is displayed

Feedback Loop (Bonus Feature)

A lightweight feedback loop improves output quality:

If the generated response is too short or lacks detail
The system triggers a second execution with a refined prompt
This simulates a reward-based improvement mechanism inspired by reinforcement learning

Sample Output

Query:
What are the recent trends in electric vehicle adoption in the US?

Output:

EV adoption continues to grow steadily in the US
Market share reached ~10% in recent years
Growth has slowed slightly, indicating a plateau
Hybrid vehicles are gaining traction
Government targets aim for 50% EV sales by 2030

Tech Stack

Python
CrewAI
Serper API
OpenAI / Groq (LLM)
dotenv

Setup Instructions

1. Clone the repository

git clone https://github.com/pritighoshh/agentic-research-assistant.git
cd agentic-research-assistant

2. Install dependencies

pip install -r requirements.txt

3. Create a `.env` file

SERPER_API_KEY=your_api_key
OPENAI_API_KEY=your_api_key

4. Run the project

python main.py

Error Handling

Handles empty input validation
Uses try-catch for runtime errors
Provides graceful error messages

Challenges Faced

Tool overuse leading to inefficiency
API rate limits
Output inconsistency from LLM
Memory tool integration issues

Solutions:

Restricted tool usage
Simplified prompts
Strong output constraints

Limitations

Relies on search snippets instead of full documents
No fact verification layer
Limited session memory
Sequential execution only

Future Improvements

Add fact-checking mechanism
Introduce persistent memory
Support multi-query sessions
Add additional tools (PDF, database, APIs)
Improve source reliability filtering

Key Learnings

Agent specialization improves output quality
Tool constraints improve system stability
Prompt engineering is critical for reliability
Simpler architectures perform better

License

This project is for academic and educational purposes.

Contract & API

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

MissingGITHUB REPOS

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-pritighoshh-agentic-research-assistant/snapshot"

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

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

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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-pritighoshh-agentic-research-assistant/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T01:53:11.016Z"
    }
  },
  "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": "Pritighoshh",
    "href": "https://github.com/pritighoshh/agentic-research-assistant",
    "sourceUrl": "https://github.com/pritighoshh/agentic-research-assistant",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T23:24:37.879Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T23:24:37.879Z",
    "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://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-pritighoshh-agentic-research-assistant/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
  }
]

agentic-research-assistant answer-first brief

Executive Summary

Evidence Ledger

Release & Crawl Timeline

Artifacts Archive

Docs & README

Agentic Research Assistant using CrewAI

Overview

Why This Project Matters

Features

System Architecture

Agents

Research Agent

Analysis Agent

Tools Used

SerperDevTool

Execution Flow

Feedback Loop (Bonus Feature)

Sample Output

Tech Stack

Setup Instructions

1. Clone the repository

2. Install dependencies

3. Create a .env file

4. Run the project

Error Handling

Challenges Faced

Limitations

Future Improvements

Key Learnings

License

Contract & API

Reliability & Benchmarks

Media & Demo

Related Agents

AionUi

activepieces

cherry-studio

CopilotKit

3. Create a `.env` file