STACKBENCH — Multi-Agent Framework Comparator AI-Powered · Evidence-Driven · Parallel · Developer-Focused
StackBench is a next-generation multi-agent research copilot that analyzes developer frameworks using:
⚙️ Real GitHub Metrics
📘 Clean Wikipedia Summaries with Smart Fallbacks
🧠 LLM-Reasoned Analysis via Groq + Gemini
🤝 Agent-to-Agent Collaboration
📡 Real-Time Logs, Metrics & Parallel Timeline Visualization
It delivers fast, verifiable, and architect-grade recommendations — all inside a modern Streamlit UI.
🚀 Why StackBench? — The Problem
Picking the right technology is a minefield:
❌ Documentation is scattered ❌ GitHub activity is hard to measure manually ❌ Wikipedia info is unreliable or outdated ❌ LLMs hallucinate without verified data ❌ Engineers rarely agree on the same evaluation criteria
💡 StackBench: The Solution
StackBench uses multiple autonomous AI agents—all running in parallel—to perform:
🔍 1. Evidence Collection
GitHub Stats → ⭐ Stars | 🍴 Forks | 🐞 Issues | 👤 Contributors
Wikipedia Summary → Clean, structured, fallback-resistant
🧪 2. Verification
Cross-checks claims
Validates GitHub & Wiki evidence
Produces a confidence score
🧠 3. Architectural Recommendation
Executive summary
Pros & Cons
CTO-level verdict
♊ 4. Gemini Deep-Dive
A “Second Opinion” analysis that identifies blind spots and adds context.
🔥 Core Features (with visual flair) 🧠 Multi-Agent Architecture
📊 Analyst Agent — Collects evidence and generates the technical summary
🔍 Verification Agent — Validates claims, reduces hallucinations
🏛️ Advisor Agent — Crafts the final architectural verdict
🔗 A2A EventBus — Traceable, timestamped agent communication
🧩 Real Integrations
✔ GitHub REST API (stars, forks, issues, contributors) ✔ Wikipedia Summary API (fallback logic + sanitization) ✔ Groq Llama Models for blazing-fast LLM responses ✔ Gemini Models for deeper insights ✔ Simulator Mode when LLM keys are missing
📡 Live Observability Dashboard
Realtime Event Log with color-coded agents
Parallel Execution Timeline — visually shows concurrency
Per-Agent Metrics — API calls, latency, tasks
Confidence Scoring System
Microbenchmark Performance Charts (Altair)
⚙️ Scalable Orchestration
True parallelism with ThreadPoolExecutor
Mission concurrency control
Queue/reject logic for demo hall stability
📦 Downloadable Report
Analyst Output
Verification Notes
Confidence Score
Advisor Recommendation
Evidence + Metrics
JSON Export
🏛️ Architecture Diagram Streamlit UI │ │ Start Mission ▼ ┌────────────────────┐ │ Orchestrator │ └────────────────────┘ │ ├────► Analyst Agent ───┐ │ │ A2A Messages ├────► Verifier Agent ◄─┘ │ └────► Advisor Agent
Agents → EventBus (logs) Metrics → Monitor Report → JSON Output
🧱 Tech Stack Component Technology Frontend/UI Streamlit Agents Python OOP Agents LLM Backend Groq, Gemini, Simulator Evidence APIs GitHub REST, Wikipedia Parallelism ThreadPoolExecutor Observability EventBus + Monitor Charts Altair ⚙️ Setup Instructions 1️⃣ Install dependencies pip install -r requirements.txt
2️⃣ Add Secrets
File: .streamlit/secrets.toml
GROQ_API_KEY="" GITHUB_TOKEN="" GEMINI_API_KEY="" WIKI_API_KEY="" UPTIME_URL="" MAX_CONCURRENT_MISSIONS="3" QUEUE_MISSIONS="true"
3️⃣ Run the app streamlit run app.py
🧪 How to Use StackBench
- Enter a GitHub repo
Example:
pytorch/pytorch
- Select a mission
Adoption Analysis
Code Audit
Migration Plan
- Watch agents run in parallel
Analyst ↔ Verifier show overlapping timeline bars.
- Explore evidence
GitHub stats
Wikipedia summary
Agent logs
- Ask Gemini for deeper assessment
- Download the final JSON report 📄 Example Output (Snapshot) { "target": "streamlit/streamlit", "analyst_summary": "...", "verification_status": "Verified", "confidence_score": 92, "advisor_recommendation": "...", "github_stats": {}, "metrics": {} }
Wikipedia entries can be outdated
GitHub API rate limits apply
Fallback simulator used when LLM keys missing
🧭 Roadmap
Vector-based retrieval
Multi-mission analytics dashboard
Snippet-level code quality scoring
OpenAPI fact verification
🏁 Conclusion
StackBench demonstrates excellence in:
✨ Multi-Agent Collaboration ✨ Evidence-Driven Analysis ✨ Parallel Orchestration ✨ Live Observability & Metrics ✨ Gemini + Groq Hybrid Reasoning ✨ Clean, Modern UI
🧑💻 Author
Varsh Vishwakarma AI • ML • DL • Data Science • Cloud • Full-Stack ML Developer