Getting Started with Forge

Why Forge?

AI coding assistants like Claude Code, Cursor, and Codex are incredibly powerful, but their accuracy is directly limited by the context they receive. Feeding an entire repository into a Large Language Model (LLM) is inefficient, expensive, and leads to context window bloating. Hallucinations happen because the model is overwhelmed by noise.

Forge is a repository-aware context optimization runtime. It bridges the gap between your local codebase and remote AI models. By indexing your repository, mapping dependencies, and performing AI context engineering, Forge ensures your assistant reads only the definitions and paths critical to the active task.

Core Concepts

• Repository Context: Instead of raw text dumps, Forge extracts symbols and dependencies, then ranks files by semantic relevance to your prompt.
• AI Context Engineering: Dynamic filtering based on semantic imports, symbol references, and abstract syntax tree (AST) parsing.
• Token Optimization: Compression and pruning of code block declarations to save API costs and lower latency.
• MCP Server: Standardized data exchange using the Model Context Protocol (MCP).

Developer Productivity Gains

By delivering high-fidelity, pruned context, Forge users report faster response times from AI agents, tokens saved on large projects, and significantly higher code correctness without hallucination loops.