Vexp

Cut AI coding costs 58% with pre-indexed codebase context

35 followers

Cut AI coding costs 58% with pre-indexed codebase context

35 followers

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.

This is the 2nd launch from Vexp. View more

Vexp

Launching today

Local-first context engine for AI coding agents

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.

Free Options

Launch tags:Productivity•Developer Tools•Artificial Intelligence

Launch Team / Built With

AppSignal — Get full visibility into app health, errors, and performance

Get full visibility into app health, errors, and performance

Promoted

Maker

📌

Hey PH, I'm Nicola, solo dev behind vexp. I was spending $400+/month on AI coding tokens and noticed something weird: most of those tokens were wasted. Every time I asked Claude Code to fix a bug, it would read 30-40 files that had nothing to do with the task. The answers were worse because of the noise, and I was paying for every irrelevant line. I looked for a solution and found that cloud-based context engines existed, but they required sending my code to their servers. That was a dealbreaker. So I built vexp. What it does: indexes your codebase into a dependency graph using tree-sitter and SQLite, entirely on your machine. When your AI agent needs context, vexp serves a ranked capsule: full source for the functions that matter, skeletonized signatures for everything connected. The agent starts every task already knowing what's relevant. I benchmarked it properly. 100 real GitHub issues from SWE-bench Verified, 4 agents, same model (Claude Opus 4.5), same budget. The setup with vexp hit 73% pass rate at $0.67/task. The most expensive setup without it: 70% at $1.98/task. 8 bugs were solved exclusively by the setup with better context. The benchmark is fully open source: github.com/Vexp-ai/vexp-swe-bench What makes vexp different from Augment Code ($270M+ funded, cloud-based): 100% local. Zero network calls. Your code never leaves your machine. Flat $19/month. No credits, no usage-based surprises. Session memory linked to the code graph. When code changes, old observations are auto-flagged stale. 30 programming languages. Dedicated tree-sitter parser for each. Works with 12 agents out of the box (Claude Code, Cursor, Copilot, Codex, Windsurf, Zed, etc.) Free tier available, no account required. Install the VS Code extension or run npm install -g vexp-cli. Would love your feedback. What's working, what's missing, what would make you actually use this daily.

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6d ago

Congrats on the launch, Nicola!
Any plans to support CI environments, or is vexp strictly meant for local dev workflows?

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5h ago

@ermakovich_sergey At the moment it's designed for developers, but I plan to expand it to include CI in the future

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3h ago

Previous Vexp Launches

VexpCut AI coding costs 58% with pre-indexed codebase context

Launched on March 12th, 2026

Forum Threads

p/vexp

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20d ago

What's the biggest token waste in your AI coding workflow?

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?

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