plan.md

Project Plan: AI-Powered Iterative Report Writer using LangGraph

Overview

This plan outlines the development steps, timelines, and responsibilities for building the AI-powered report writing and feedback system using LangGraph. The system will iterate over reports to enhance quality based on user-defined iteration counts.

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Phase 1: Project Setup

Tasks

• Create GitHub repository and define branch strategy
• Initialize LangGraph project (e.g., langgraph-template)
• Install LangChain, LangGraph, dotenv, and any LLM SDK
• Set up .env with API keys (OpenAI or Claude)
• Scaffold folder structure (e.g., /nodes, /state, /cli, /tests)
• Set up basic CLI runner script (main.ts or main.py)

Deliverables

• Repository ready for development
• Development environment with LangGraph dependency

Timeline

Duration: 1-2 hours

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Phase 2: LangGraph Architecture Design

Tasks

• Define ReportState schema in code (track prompt, iterations, versions, etc.)
• Write transition function definitions for looping logic
• Design node flow (sketch flow diagram)
• Plan node input/output contracts (e.g., context.input, context.state)
• Map iteration flow: Write → Feedback → Rewrite → Check → Loop

Deliverables

• Graph definition file
• Validated state schema and transitions

Timeline

Duration: 1 - 2 hours

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Phase 3: Node Development

Tasks

• Implement Prompt Input Node: captures topic + n iterations
• Build Report Generator Node: generates v1 from topic
• Create Feedback Node: uses prompt to critique current version
• Create Rewrite Node: uses feedback to regenerate report
• Implement Iteration Controller Node: tracks current iteration, triggers stop/continue
• Add Export Node: collects final report + all intermediate versions

Deliverables

• Functional LangGraph nodes
• Fully integrated report-processing graph

Timeline

Duration: 1 - 2 hours

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Phase 4: Testing and Debugging

Tasks

• Write Jest (TS) or Pytest (Python) unit tests for each node
• Simulate full iteration with test topic and validate outputs
• Inject edge cases (empty prompt, too many iterations) for robustness
• Test graph determinism and state propagation

Deliverables

• Passing test suite
• Debugged LangGraph flow

Timeline

Duration: 1- 2 hours

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Phase 5: Interface Layer

Tasks

• Build CLI input prompt for topic and iteration number
• Display each iteration step (report + feedback)
• Add flag to include/exclude all iterations in output
• Optional: Build simple web UI using React or Streamlit

Deliverables

• Working CLI or UI demo
• User input/output workflow

Timeline

Duration: 1 - 2 hours

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Phase 6: Finalization and Deployment

Tasks

• Write README.md with setup, usage, examples
• Support output to Markdown and JSON files
• Add command to download final report with metadata
• Create Dockerfile for local + cloud deployments
• Deploy to a cloud function (Render, Vercel, Fly.io)

Deliverables

• Production-ready app
• Complete documentation

Timeline

Duration: 1 day

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Optional: Future Enhancements

• Feedback fine-tuning with user preferences
• Pluggable LLMs with cost and speed optimization
• Role-based critique (grammarist, educator, analyst)
• Integration with cloud-based editors

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Total Estimated Time: ~ 2 to 3 days

Team Roles (Assuming Solo Developer or Small Team)

• Dev Lead / Engineer: Full-stack and LangGraph logic
• Tester: Automated and manual workflow checks
• User Advocate: Feedback on interface and usability