Chlo gives you a visual map of your entire codebase as an interactive dependency graph. Instead of letting AI agents guess what code is relevant, you select exactly what they need and pass it over via MCP. We call it context sniping. There are code metrics for visualizing complexity, coupling, cohesion, and dead code as heatmaps on the graph. Semantic search lets you find code by meaning along with realtime agent observability. Built for engineering teams using Claude Code. Free 14-day trial.
We built Chlo because AI coding agents write code fast but they're terrible at managing it. They forget what's already been written, duplicate functionality instead of reusing it, and codebases explode into something unmanageable. Every new session forces the agent to rediscover what it already knew, wasting time and tokens. And they keep grabbing the wrong context and writing code that doesn't fit the architecture.
So we built context sniping (with hit markers). You visually select the code chunks that matter, group them into context buckets, and pass them to Claude via MCP. You can also set up task plans that auto-snipe relevant code for you.
We also wanted a way to actually see what agents were building across a codebase. Chlo maps your entire repo as an interactive dependency graph with metrics overlays so you can spot complexity hotspots, find dead code, and trace how everything connects.
We just launched the public beta and we're looking for feedback, especially from small engineering teams using AI coding tools heavily. What's working, what's confusing, what's missing. Would love to hear what you think.
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