How should agentic-rag-system 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-rag-system answer-first brief

Production-ready Agentic RAG system with multi-LLM orchestration, semantic PDF retrieval, vector search (Qdrant), and autonomous web fallback using CrewAI + Streamlit. Agentic RAG System with Multi-LLM Orchestration An advanced, production-ready Retrieval-Augmented Generation (RAG) application built using **CrewAI** and **Streamlit**. The system intelligently parses, chunks, and searches through uploaded PDF documents using semantic analysis, falling back to live web search whenever document context is insufficient. Live Application 🔗 **$1** --- Key Technical Features - **Multi-Mo Capability contract not published. No trust telemetry is available yet. Last updated 5/26/2026.

Freshness

Last checked 5/26/2026

Best For

agentic-rag-system 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

Claim this agent

Agent DossierGITHUB REPOSSafety: 66/100

agentic-rag-system

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

May 26, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 5/26/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 26, 2026

Vendor

Preethiragu

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Unpublished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 5/26/2026.

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

Preethiragu

profilemedium

Observed May 26, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 26, 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 REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

├── assets/                  # UI assets and logos
├── src/
│   └── agentic_rag/        # Core agent and tool logic
├── thumbnail/               # Media and documentation graphics
├── .gitignore               # Excludes environment files and system caches
├── README.md                # Main repository documentation
├── app_deep_seek.py         # App version configured for DeepSeek-R1
├── app_llama3.2.py          # Main deployment file utilizing Llama 3.2
└── requirements.txt         # Tailored, clean production dependencies

bash

python --version

bash

git clone https://github.com/your-username/your-repo-name.git

bash

cd your-repo-name

bash

python -m venv .venv.venv\Scripts\activate

bash

python3 -m venv .venvsource .venv/bin/activate

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 RAG System with Multi-LLM Orchestration

An advanced, production-ready Retrieval-Augmented Generation (RAG) application built using CrewAI and Streamlit. The system intelligently parses, chunks, and searches through uploaded PDF documents using semantic analysis, falling back to live web search whenever document context is insufficient.

Live Application

🔗 https://agentic-rag-system-3gr6lrvk7qdrjmrihjmiks.streamlit.app

Key Technical Features

Multi-Model Orchestration: Supports local and cloud deployment architectures utilizing Llama 3.2 and DeepSeek-R1 processors via Groq.
Agentic Workflow Architecture: Powered by CrewAI agents configured with explicit search roles, goals, and backstories to maximize response accuracy.
Advanced Semantic Chunking: Replaced standard token splitting with advanced semantic splitters (Chonkie & Microsoft's MarkItDown) to preserve structural meaning during text ingestion.
Cloud Vector Indexing: Connects seamlessly to a high-performance Qdrant Cloud vector database cluster for fast, dense document retrieval.
Autonomous Web Fallback: Integrated with a Firecrawl tool agent to scour live web indexes if the uploaded document lacks direct answers.

Technology Stack

Frontend / UI: Streamlit Framework
Agentic Framework: CrewAI, LangChain
Inference Engines: Groq API (Llama 3.2 / DeepSeek-R1)
Vector Storage: Qdrant Cloud DB
Document Processing: MarkItDown, Chonkie Parsing Engine

System Workflow

User uploads a PDF or enters a query
Document is chunked using semantic chunking (Chonkie + MarkItDown)
Embeddings are generated and stored in Qdrant vector DB
CrewAI agents retrieve relevant chunks
LLM (LLaMA 3.2 / DeepSeek-R1) generates response
If context is insufficient → Firecrawl web search is triggered

System Architecture

The following diagram represents the end-to-end workflow of the Agentic RAG system:

Repository Structure

├── assets/                  # UI assets and logos
├── src/
│   └── agentic_rag/        # Core agent and tool logic
├── thumbnail/               # Media and documentation graphics
├── .gitignore               # Excludes environment files and system caches
├── README.md                # Main repository documentation
├── app_deep_seek.py         # App version configured for DeepSeek-R1
├── app_llama3.2.py          # Main deployment file utilizing Llama 3.2
└── requirements.txt         # Tailored, clean production dependencies

Installation and Setup Guide

Follow these steps to set up and run the project locally.

Prerequisites

Make sure you have the following installed:

Python 3.10 or 3.11
Git
pip (Python package manager)

Check if Python is installed:

python --version

Local Setup Steps

Clone the Repository

git clone https://github.com/your-username/your-repo-name.git

cd your-repo-name

Create Virtual Environment windows:

python -m venv .venv.venv\Scripts\activate

Mac / Linux:

python3 -m venv .venvsource .venv/bin/activate

Install Dependencies
```
pip install -r requirements.txt
```
Configure Environment Variables Create a .env file in the root directory:
```
touch .env
```
Add the following: GROQ_API_KEY="your-groq-api-key" QDRANT_URL="your-qdrant-url" QDRANT_API_KEY="your-qdrant-api-key" FIRECRAWL_API_KEY="your-firecrawl-api-key"

Run the Application

streamlit run app_llama3.2.py

Alternative Model (optional):

streamlit run app_deep_seek.py

📸 Screenshots

Home Page

Upload Page

Chat Interface

Future Improvements

Add voice-based query interface
Improve memory-based chat history
Add authentication system
Support more document formats

Why This Project

This project demonstrates real-world AI system design including:

Retrieval-Augmented Generation (RAG)
Multi-agent AI systems
Vector database integration
LLM orchestration
Fallback reasoning systems

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-preethiragu-agentic-rag-system/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/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-preethiragu-agentic-rag-system/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-08T22:19:00.632Z"
    }
  },
  "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": "Preethiragu",
    "category": "vendor",
    "href": "https://github.com/PreethiRagu/agentic-rag-system",
    "sourceUrl": "https://github.com/PreethiRagu/agentic-rag-system",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-26T06:45:28.503Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-26T06:45:28.503Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-preethiragu-agentic-rag-system/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[]