DevLens

DevLens

Codebase Intelligence for AI agents and Developers.

Analyze your repo once. Your AI agent then queries a precomputed code graph through MCP — ranked files, call flow, impact, and security — instead of re-reading your codebase file by file. Five self-describing MCP tools, plain-text packets measured at a third of the tokens of reading the code, and the smallest response in its benchmark field.

[Join the DevLens Cloud Waitlist →](https://devlens.io)

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Click the image to watch the demo

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Why DevLens?

• Your agent stops burning tokens. One MCP call returns a task-shaped packet — the files and symbols that matter, ranked, within a token budget. No grep loops, no re-reading files.
• It wins the benchmark. First of nine tools on correctness, F1, recall, and precision — the only tool that leads every quality column.
• It is fast and small. 67 ms per call, packets 6.7× smaller than the closest graph competitor, and it never exceeds the token budget you set.
• Developers get it too. Interactive graph visualization, blast radius before you change a symbol, PR review packets, and per-node security analysis.

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Benchmarks

We benchmarked DevLens MCP against the other code graph and retrieval tools — Graphify, codegraph, semble, bm25 — on one headless coding agent, one model, one real TypeScript repository, and 133 real questions written from actual pull requests and symbols:

Head-to-head against Graphify — the most direct competitor (tree-sitter graph, MCP server) — DevLens wins on every axis: correctness in the agent loop (0.643 vs 0.619, same run) and in the retrieval layer (0.694 vs 0.662, paired 15 wins to 9 losses on identical questions), a 6.7× smaller packet (2,230 vs 15,020 tokens, smaller on all 54 of 54 comparable questions), and ~37× faster per call (67 ms vs 2,469 ms). Graphify's graph is also 64% larger with 3.6× the edges (16,999 nodes / 71,694 edges vs 10,346 / 19,847) for the lower quality.

The pattern behind the numbers: syntactic tools return big blobs or bare line lists. DevLens returns a task-shaped packet from a type-resolved graph — the files that matter, ranked, within your token budget, in milliseconds.

Full methodology, per-type tables, and the nine-tool round: docs/PUBLIC-BENCHMARKS.md

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How DevLens compares

DevLens is the only tool in this space that combines native semantic parsing, per-node AI summaries with per-node security analysis, and framework-aware data edges — and the only option you can use commercially under AGPL.

*(Feature comparison from public sources, Aug 2026.)*

Why teams choose DevLens: semantic edges that syntactic tools cannot produce, per-node security analysis no other open-source tool provides, benchmark-winning retrieval quality, and a single MCP front door — free for commercial use.

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Quick Start

Step 1 — Install the CLI

npm install -g @devlensio/cli

Or install the standalone binary (no Node.js needed):

# Linux / macOS

curl -fsSL https://raw.githubusercontent.com/devlensio/devlensOSS/main/scripts/install.sh | sh

Windows (PowerShell)

irm https://raw.githubusercontent.com/devlensio/devlensOSS/main/scripts/install.ps1 | iex

Step 2 — Configure a model (optional)

Only needed if you want AI summaries. Structure-only analysis works offline with no API key.

devlens init

This walks you through picking a provider, API type, key, endpoint, and model interactively — press ESC at any step to go back a stage. See Configuration for recommended models.

Step 3 — Analyze your repo

cd your-project

devlens analyze . --summarize

This builds the graph, generates summaries, and creates the search index — once. After this, everything below is instant.

Step 4 — Connect your AI agent

Start the MCP server:

devlens mcp

Then register it in your agent. Pick your tool:

Claude Code / Claude Desktop

claude mcp add devlens -- devlens mcp

Cursor — add to .cursor/mcp.json:

{

"mcpServers": { "devlens": { "command": "devlens", "args": ["mcp"] } } }

Codex — add to ~/.codex/config.toml:

[mcp_servers.devlens]

command = "devlens" args = ["mcp"]

Hermes — add to config.yaml:

mcp:

servers: devlens: command: devlens args: [mcp]

Any other MCP client — the server speaks standard MCP over stdio. Point your client at the command devlens mcp. For an HTTP transport instead:

devlens mcp http -p 7000

The server self-describes its full tool list through tools/list, so your agent discovers everything on its own. That's it — ask your agent "how does auth work in this repo?" and watch it query the graph instead of reading files.

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What your agent can ask

The server self-describes every tool's parameters through tools/list — your agent fills them in automatically. Five tools, no `graphId` anywhere — the repo is resolved from your working folder.

Every response carries a provenance block (commitHash, analyzedAt, hasGit), so your agent always knows which snapshot answered the question.

Full reference: docs/mcp-reference.md

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Supported languages

DevLens parses five language families with native parsers (no AI, no regex, no tree-sitter) and understands their frameworks. Summaries are never generated silently: you confirm before tokens are spent, and structure-only analysis needs no provider at all.

Each repo is analyzed with its language's own parser (Python ast, JavaParser, Go go/ast plus go/types, Rust syn, TS compiler API), so edges are real: type-checked interfaces (IMPLEMENTS), framework routes (HANDLES), and ORM data layers (READS_FROM, WRITES_TO).

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For developers: the Web UI

*For when you want to see your codebase laid out as an interactive graph.*

Open the Web UI, paste your repo path, and explore a force-directed canvas. Click any node to see its summaries, callers, callees, and security flags. Search, filter, and diff commits across versions. The Project panel shows search index readiness with a one-click reindex action.

bun start   # from the source repo: API + Web UI on one port (http://localhost:3000)

The Web UI runs from the source tree, it is not bundled into the installed CLI binary.

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CLI

*For scripts, CI, and fast answers without leaving the terminal. Every command supports --json for piping into scripts, -v/--verbose for diagnostics, and --quiet for minimal output.*

Analyze and summarize

| devlens analyze [path] [--summarize] [--force-summarize] | Build the graph and its search index (optionally add AI sum