🤖 Multi-Agent LLM Proposal Generator: A Research Prototype

This project simulates a team of autonomous LLM agents collaboratively drafting a cross-functional project proposal. It evaluates how well agents like a Project Manager, Technical Lead, and Business Analyst emulate real-world collaboration.

Built using:

• Mistral-7B Instruct v0.2 (local GGUF) for fast inference via llama-cpp-python
• AutoGen-style roles, with shared memory and log analysis
• Full support for hallucination detection, repetition tracking, and concept growth metrics

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📚 Research Goals

• Can LLM agents emulate realistic cross-functional decision-making?
• Does reasoning improve across multiple rounds of conversation?
• How prone are autonomous agents to hallucination or topic drift?
• What evaluation strategies can benchmark multi-agent collaboration?

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🛠️ Project Overview

Component	Description
conversation_log.txt	Transcript of 5 rounds of agent collaboration
Sarthak_Research_1.ipynb	Main notebook with simulation, visualizations
hallucination_report.csv	Optional export of hallucination flags
llm_topics_by_round.csv	Optional export of LLM-extracted round topics

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💬 Agents

• ProjectManager: Sets timeline, phases, deliverables
• TechnicalLead: Designs architecture, picks tech stack
• BusinessAnalyst: Understands user needs, defines KPIs

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🧠 Evaluation Metrics

Metric	Description
🔁 Repetition	Detects repeated phrases across agent replies
🎯 Role Alignment	Checks if agents stay within their domain keywords
🧠 New Concepts	Tracks keyword and topic diversity across rounds
🚨 Hallucination	Flags off-topic or unverifiable terms
🧩 LLM Topics	Mistral-based topic summarization per round (semantic view)

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📊 Visualizations

• Concept growth per round (line chart)
• New concepts per agent/round (bar chart)
• Repetition and hallucination heatmaps
• LLM-generated topic bar chart (stacked horizontal)

All generated using matplotlib in the main notebook

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📦 How to Run

1. Clone and Load the Model

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

Place your mistral-7b-instruct-v0.2.Q4_K_M.gguf model inside a /models folder.

2. Install Dependencies

pip install llama-cpp-python matplotlib

3. Run Notebook

Open Sarthak_Research_1.ipynb in Jupyter or Colab
Make sure to point to your model path correctly.

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📊 Sample Output

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📄 License

MIT License — feel free to fork and build upon this!

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✍️ Author

Sarthak Chandarana
LLM Systems Researcher
Find me on GitHub or LinkedIn for questions or collaborations!