vedangvatsa123

vedangvatsa123 / vedang-swarm-prediction

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Swarm Intelligence Prediction Engine - Multi-agent AI debate for calibrated forecasting

49
1
100% credibility
Found Mar 17, 2026 at 49 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Vedang is a prediction tool that creates diverse AI agents to analyze user data, debate a question over multiple rounds, and produce a consensus forecast with visualizations and reports.

How It Works

1
🌐 Discover Vedang

You find a tool that lets groups of smart agents debate your data to make better predictions than one alone.

2
❓ Ask your question

Type in what outcome you want to predict and add your documents, text, or web link as the basis for discussion.

3
⚙️ Choose depth

Pick a quick run for fast insights or a deeper one for more agents and thorough arguing.

4
🚀 Launch the swarm

Connect your AI helper with a private passcode and press start to unleash the agents on your question.

5
👀 Watch the debate

Follow along as agents read your info, post opinions, challenge each other, and shift views when convinced.

📊 See the prediction

Get a full report on the consensus view, interaction map, who changed minds, and chat to dig deeper.

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Star Growth

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AI-Generated Review

What is vedang-swarm-prediction?

Vedang-swarm-prediction is a Python web app (Flask backend, Vue frontend) that turns documents and a prediction question into a calibrated forecast via multi-agent AI debate. Upload PDFs, Markdown, or text, state your goal like "Will this merger succeed?", and it simulates 10-100 agents arguing over rounds, tracking stance shifts and producing a Markdown report with consensus view. You get a live graph UI showing agent interactions, full debate transcript, and chat for follow-ups—perfect for swarm intelligence definition in action, like ants debating outcomes.

Why is it gaining traction?

Unlike single-LLM predictors, this delivers transparent swarm intelligence principles: watch github swarm agents form clusters, flip positions, and reach calibrated predictions through visible debate. Depth presets (quick 1-min runs to max 15-min sims) balance cost and insight, with BYOK support for OpenAI, Anthropic, Groq, and more via simple env vars. The D3-powered swarm UI and pipeline endpoint make it a github swarm simulator devs can fork and deploy fast, no Docker Swarm GitHub Actions needed.

Who should use this?

Strategic analysts parsing earnings reports or policy docs for outcome forecasts. AI researchers testing swarm intelligence from natural to artificial systems in calibrated debate setups. Product managers evaluating market risks from whitepapers, needing agent breakdowns over black-box answers.

Verdict

Grab it for prototyping multi-agent forecasting—solid docs, demo at veda.ng/swarm-prediction, and easy local run make the 49 stars and 1.0% credibility score forgivable for an early project. Scale with your own LLM credits, but add tests before prod.

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