vexp gives your AI coding agent only the code that matters. It indexes your codebase into a dependency graph locally (Rust, tree-sitter, SQLite) and serves ranked context via MCP. Instead of reading entire files, the agent gets the relevant functions in full and connected symbols as skeletonized signatures. Works with Claude Code, Cursor, Copilot, and 9 more agents. 30 languages. Session memory with staleness detection. Zero cloud, zero network calls. Your code never leaves your machine.
I've been benchmarking AI coding agents for the past few months and one finding keeps coming up: agents spend ~80% of their context budget on orientation, reading files, grepping, exploring - before they write a single line of code.
On a FastAPI codebase (~800 files), Claude Code averaged 23 tool calls per task just to figure out what's relevant. That's 40K+ tokens burned before any actual work happens.
I've been building a solution to this and we're launching it on Product Hunt tomorrow. But before that, I'm curious:
What's the biggest source of token waste in your workflow? Is it the exploration phase, context window exhaustion mid-task, session restarts, or something else entirely?
vexp is a local-first context engine for AI coding agents. It pre-indexes your codebase into a dependency graph and serves only relevant context via MCP, so Claude Code, Cursor, and other agents stop wasting tokens on blind file exploration. Benchmarked on FastAPI (42 runs): 58% less cost, 63% fewer output tokens, 90% fewer tool calls. Runs 100% locally, no cloud, no account required. Free tier available.
