awesome-llm-apps/advanced_ai_agents/multi_agent_apps/devpulse_ai/README.md

🧠 DevPulseAI - Multi-Agent Signal Intelligence Pipeline

A reference implementation demonstrating a multi-agent system for aggregating, analyzing, and synthesizing technical signals from multiple developer-focused sources.

Features

• Multi-Source Signal Collection - Aggregates data from GitHub, ArXiv, HackerNews, Medium, and HuggingFace
• LLM-Powered Analysis - Four specialized agents working in concert
• Structured Intelligence Output - Prioritized digest with actionable recommendations

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Signal Intelligence Pipeline                  │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐         │
│  │ GitHub │ │ ArXiv  │ │  HN    │ │ Medium │ │   HF   │ ← Data  │
│  └───┬────┘ └───┬────┘ └───┬────┘ └───┬────┘ └───┬────┘         │
│      └──────────┴──────────┼──────────┴──────────┘               │
│       │             │             │                              │
│       └─────────────┼─────────────┘                              │
│                     ▼                                            │
│           ┌─────────────────┐                                    │
│           │ Signal Collector│   ← Agent 1: Ingestion            │
│           └────────┬────────┘                                    │
│                    ▼                                             │
│           ┌─────────────────┐                                    │
│           │ Relevance Agent │   ← Agent 2: Scoring (0-100)      │
│           └────────┬────────┘                                    │
│                    ▼                                             │
│           ┌─────────────────┐                                    │
│           │   Risk Agent    │   ← Agent 3: Security Assessment  │
│           └────────┬────────┘                                    │
│                    ▼                                             │
│           ┌─────────────────┐                                    │
│           │ Synthesis Agent │   ← Agent 4: Final Digest         │
│           └────────┬────────┘                                    │
│                    ▼                                             │
│           ┌─────────────────┐                                    │
│           │ Intelligence    │   ← Prioritized Output            │
│           │ Digest          │                                    │
│           └─────────────────┘                                    │
└─────────────────────────────────────────────────────────────────┘

Agent Responsibilities

Agent	Role	Output
SignalCollectorAgent	Aggregates & normalizes signals	Unified signal list
RelevanceAgent	Scores developer relevance (0-100)	Score + reasoning
RiskAgent	Identifies security/breaking changes	Risk level + concerns
SynthesisAgent	Produces final intelligence digest	Prioritized recommendations

How to Get Started

1. Clone the repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd advanced_ai_agents/multi_agent_apps/devpulse_ai

1. Install dependencies

pip install -r requirements.txt

1. Set your Gemini API key (optional for live mode)

export GOOGLE_API_KEY=your_api_key

1. Run the verification script (no API key needed)

python verify.py

1. Run the full pipeline (requires API key for LLM agents)

python main.py

Streamlit Demo

A modern, interactive dashboard is included to visualize the multi-agent pipeline:

1. Launch the app:

streamlit run streamlit_app.py

1. Configure sources and signal counts in the sidebar.
2. Provide a Gemini API key (optional) to use full LLM intelligence.
3. View real-time progress as agents collaborate.

Note

: The default configuration is optimized for fast demo runs.

Verification Script

The verify.py script tests the entire pipeline using mock data only - no network calls or API keys required:

python verify.py

Expected output:

[OK] DevPulseAI reference pipeline executed successfully

Optional: n8n Automation

An n8n workflow is included for those who want to automate the pipeline:

• Location: workflows/signal-intelligence-pipeline.json
• Import: n8n → Settings → Import from File
• Requires: n8n instance + configured credentials

This is entirely optional - the Python implementation works standalone.

Directory Structure

devpulse_ai/
├── adapters/
│   ├── github.py       # GitHub trending repos
│   ├── arxiv.py        # AI/ML research papers
│   ├── hackernews.py   # Tech news stories
│   ├── medium.py       # Tech blog RSS feeds
│   └── huggingface.py  # HuggingFace models
├── agents/
│   ├── __init__.py
│   ├── signal_collector.py
│   ├── relevance_agent.py
│   ├── risk_agent.py
│   └── synthesis_agent.py
├── workflows/
│   └── signal-intelligence-pipeline.json
├── main.py             # Full pipeline demo (CLI)
├── streamlit_app.py    # Interactive dashboard (UI)
├── verify.py           # Mock data verification
├── requirements.txt
└── README.md

How It Works

1. Signal Collection: Adapters fetch data from GitHub, ArXiv, HackerNews, Medium, and HuggingFace
2. Normalization: SignalCollectorAgent unifies signals to a common schema
3. Relevance Scoring: RelevanceAgent rates each signal 0-100 for developer relevance
4. Risk Assessment: RiskAgent flags security issues and breaking changes
5. Synthesis: SynthesisAgent produces a prioritized intelligence digest

Built With

• Agno - Multi-agent framework
• Google Gemini 1.5 Flash - LLM backbone
• httpx - Async HTTP client