Ask your codebase a question, get the real answer. “What calls this function?” “What breaks if I change it?” Forge walks the actual call graph across C++, Python, and TypeScript — no fuzzy search, no ranked guesses.
Find the patterns your code already has. Forge clusters structural similarities automatically — naming conventions, call shapes, module layouts — so your AI can follow them instead of inventing its own.
Give your AI exactly the context it needs — callers, callees, types, rules, matching patterns — without dumping the whole repo into the prompt. The right context, not all the context.
Generate code that actually fits. Forge assembles the context, your LLM writes the code, then Forge compiles it and checks it against your rules. If it's wrong, Forge retries with the error. The AI doesn't get the last word.
Forge remembers what you've been working on. Recently queried or modified code runs hot. Heat spreads through connections, surfacing related code automatically — so Forge pays attention where you're paying attention.
Use your own Anthropic or OpenAI key. Forge handles the deterministic graph intelligence; your LLM handles generation. Any model punches above its weight when it knows the codebase.
Most AI code tools turn your code into embeddings — high-dimensional vectors that approximate meaning. That works for natural language. Code isn't natural language. Code has exact structure, and Forge uses it.
Your code is parsed into a property graph using language-native parsers. Every function, type, and dependency is a node or edge with exact structural meaning — not an approximation.
“What calls this function?” returns the actual call chain. Impact analysis gives you the exact set of affected functions — not a probability ranking of candidates.
C++ via libclang, Python and TypeScript via tree-sitter. All three feed into a single graph where cross-language relationships are first-class.