AI Context File Optimizer

Automatically optimize large codebases and docs into clean, low-token context files for LLM agents. Perfect for RAG, agents, and AI workflows—without the mess.

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Inspiration

Large Language Models (LLMs) are incredibly powerful—but context windows are expensive, limited, and chaotic when working with sprawling, unstructured codebases or documentation. As agent-based development and retrieval-augmented generation (RAG) become more common, the need for clean, compact, structured context files is growing fast.

We wanted to solve this problem once and for all—by making context optimization dead simple and developer-friendly.

What it does

AI Context File Optimizer ingests your messy .md, .py, .js, .ipynb, and other project files, and returns optimized, purpose-specific context files:

  • Summarized
  • Deduplicated
  • Structured
  • Token-minimized
  • Task-oriented (e.g., "give this to a planning agent", "give this to an assistant")

It outputs ready-to-drop .context.json files you can feed into RAG systems or agents like AutoGen, OpenDevin, or custom langchain chains.

How we built it

We used Bolt.new to rapidly scaffold the full-stack app with:

  • A file upload UI
  • Bolt-integrated LLM functions to chunk, summarize, classify, and output structured JSON
  • Voice integration (via ElevenLabs) for conversational file walkthroughs
  • Real-time avatars using Tavus (optional) for guidance

We also used:

  • OpenAI GPT-4 for summarization and transformation
  • LangChain for agent routing logic
  • Netlify for deployment
  • RevenueCat (for paywalled advanced usage features)

Challenges we ran into

  • Designing clean JSON context schema that works across use cases
  • Ensuring summaries stayed relevant without hallucination
  • Handling very large files while staying within token limits
  • Integrating multiple LLM pipelines efficiently within Bolt.new

What we learned

  • Prompt engineering is 90% of product value in AI
  • AI UX needs to feel trustworthy and transparent to gain user adoption
  • Bolt.new dramatically reduces the time from idea to execution, especially for LLM tools

What's next

  • Add GitHub repo syncing
  • Auto-agent chaining for documentation flows
  • Export directly into LangChain-ready or OpenDevin context format
  • Public template + leaderboard for optimized context quality

Built With

  • langchain
  • netlify
  • react
  • vite
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