1. Introduction & Context

Why multi-agent AI systems are important.

Accounting me AI ka role (automation, orchestration, reliability).

2. Project Overview

Short intro (already likha hua hai tumne).

Roles of agents (Researcher, Planner, Summarizer).

3. System Design & Architecture

Step-by-step flow (user → orchestrator → agents → tools → DB → final output).

Insert system diagram image (architecture flow).

4. Agents in Detail

Researcher → what exactly it does.

Planner → how it breaks tasks.

Summarizer → how it ensures clarity.
(paragraphs me likho, sirf bullets nahi)

5. Tools & Integrations

FileReader, Calculator, LocalLLM, DB adapters (JSON, SQLite, Postgres stub).

Offline-first design philosophy.

6. Setup Instructions

Requirements, installation steps, curl test.

Add screenshots (terminal run, sample JSON output).

7. Sample Input/Output

Already likha hai, but make it formatted JSON code block.

8. Best Practices & Notes

Security, modularity, reproducibility, no external APIs.

How this aligns with Module 2 requirements (≥3 agents, ≥3 tools, orchestration, DB adapters).

9. Future Work

Expand with more specialized agents.

Add evaluation metrics.

UI via Streamlit/Gradio.

10. Conclusion

Multi-agent approach shows flexibility + scalability.

Offline + modular design is unique.

License:

This project is released under the MIT License.
It is intended for research and educational use, showcasing a modular and production-ready multi-agent system architecture.