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Introduction

dev-agent provides semantic code search and context bundling to AI assistants like Cursor and Claude Code via MCP.

We built this for ourselves. When exploring large codebases, we found AI tools spending too much time grepping through files and reading entire files to find relevant code. dev-agent gives them a faster path: search by meaning, get code snippets, bundle context.

What it does

  1. Indexes your codebase locally with embeddings (all-MiniLM-L6-v2)
  2. Returns code snippets β€” not just file paths, reducing input tokens by 99%
  3. Bundles context β€” dev_plan assembles issue + code + commits in one call
  4. Integrates with GitHub to search issues and PRs semantically

Measured impact

We benchmarked dev-agent against baseline Claude Code across different task types:

Task TypeCost SavingsTime SavingsWhy
Debugging42%37%Semantic search beats grep chains
Exploration44%19%Find code by meaning
Implementation29%22%Context bundling via dev_plan

Key insight: Savings scale with task complexity. Simple lookups show no improvement; complex debugging shows 42% cost reduction.

Trade-off: Faster but sometimes less thorough. Baseline Claude provides more diagnostic shell commands.

Why it saves money

What dev-agent doesManual equivalentImpact
Returns code snippets in searchRead entire files99% fewer input tokens
dev_plan bundles issue + code + commits5-10 separate tool calls29% cost reduction
Semantic search finds relevant codegrep chains + filtering42% cost reduction

Key Features

FeatureDescription
Context Bundlingdev_plan replaces 5-10 tool calls with one
Code SnippetsSearch returns code, not just file paths
Semantic SearchFind code by meaning, not keywords
Git HistorySemantic search over commits
100% LocalYour code never leaves your machine

Architecture

dev-agent is a monorepo:

  • @lytics/dev-agent-core β€” Scanning, vector storage, GitHub integration
  • @lytics/dev-agent-cli β€” Command-line interface
  • @lytics/dev-agent-mcp β€” MCP server with 9 tool adapters
  • @lytics/dev-agent-subagents β€” Planner, explorer agents

Next Steps

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