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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 seven other code graph and retrieval tools — Graphify, codegraph, serena, semble, codebase-memory, and others. One headless coding agent, one model, one real TypeScript repository, 133 real questions written from actual pull requests and symbols, two runs each: 2,394 agent runs in total.

* serena's symbol tool returned no results on every question, so its tiny packet means it found nothing, not that it is efficient. The low response-payload figures for codebase-memory, semble, and BM25-style tools come from line lists rather than structured answers.

DevLens is the only tool ranked first on correctness, F1, recall, and precision — and it wins with the fastest calls and one of the smallest packets. Head-to-head against Graphify on identical questions: packet 6.7× smaller on all 54 of 54 comparable questions, ~37× faster per call, and a paired 15 wins to 9 losses.

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, all nine tools, five languages: 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. Every graph tool takes a graphId (returned by list_analyzed_repos / analyze) plus the arguments below.

Plus repo-level tools: architecture_brief (one-call architecture report), security_brief (ranked security report), review_pr (from/to commits — PR review packet), onboarding_tour (modules, routes, flows, glossary), analyze_changes (from/to commits — commit diff impact), analyze (analyze a repo path), get_repo_overview, and get_context (raw keyword/seed context query) — 24 tools in total.

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

Explore and understand

Impact and quality

Manage and integrate

Hands-on examples

devlens analyze . --summarize             # graph + summaries + search index
devlens find-nodes -t ROUTE               # every route in the app
devlens blast-radius "src/auth/login.ts::login"
devlens reindex                          # index everything up front

Full reference: src/cli/README.md for every command with options and examples.

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How to summarize

Summaries are per-node AI descriptions that make querying much richer. Run them during analysis, or any time later:

devlens analyze . --summarize                  # during analysis (recommended)
devlens summarize .                            # current directory's repo
devlens summarize <graphId>                    # a stored graph
devlens summarize . --force-summarize          # re-generate even if summarized
devlens summarize . --provider openai --provider-name deepseek --model deepseek-v4-flash

When a repo is re-summarized, only nodes without summaries are processed, so tokens are not spent twice. Configure your provider once with devlens init.

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Configuration

Config lives in ~/.devlens/config.json, set via devlens init or devlens config. Models are discovered dynamically from each provider's /models endpoint, so there are no hardcoded model lists — pick whatever fits your budget and quality bar.

devlens config --set                            # interactive setup
devlens config --provider openai --provider-name deepseek --model deepseek-v4-flash --api-key <key>
devlens config --active openai:deepseek         # switch saved provider
devlens doctor                                  # health check

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What DevLens understands

A graph is per repo and per language. Node types include FILE, FUNCTION, METHOD, CLASS, INTERFACE, STRUCT, ENUM, TRAIT, IMPL_BLOCK, ROUTE, COMPONENT, HOOK, STATE_STORE, UTILITY, TEST, STORY, and THIRD_PARTY (language dependent). Every node carries an importance score plus its functional, technical, and security summaries once summarized.

Edge types (the connections the graph draws): CALLS, IMPORTS, READS_FROM, WRITES_TO, PROP_PASS, EMITS, LISTENS, WRAPPED_BY, GUARDS, HANDLES, TESTS, USES, NEXTJS_API_CALL, NAVIGATES_TO, IMPLEMENTS (class to interface, trait, or ABC), EXTENDS (class to base class). EXPORTS, THROWS, MODULE, and PACKAGE are reserved for future languages.

Router awareness: routes are real graph nodes, for Next.js (app and pages), React Router, TanStack Router, wouter, Express, Fastify, Hono, Koa, Django URLconf and DRF, Flask blueprints, @RestController (Spring), Gin, Echo, chi, plain HTTP handlers, axum, actix, and rocket.

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Search index

Each analyzed commit gets a derived BM25F search index next to the graph, at ~/.devlens/graphs/<graphId>/commits/<commitHash>.search.json.

• Every devlens analyze and devlens summarize rebuilds the index for the commit it produced, so search is ready immediately.
• Graphs built by older DevLens versions have no index. They are indexed lazily on the first search, or you can index everything up front with devlens reindex.
• The index is derived data: deleting it is safe, it rebuilds on demand.

devlens reindex                                  # latest commit of every graph
devlens reindex <graphId>                        # every commit of one graph
devlens reindex <graphId> <commitHash> --force   # one commit, rebuilt even if present

The same operations are available over HTTP: POST /api/reindex and POST /api/reindex/:graphId?commitHash=...&force=1.

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Screenshots

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Why it's fast and cheaper

A node summary is roughly 50 tokens. The file it describes is roughly 2,000. Querying summaries and graph slices costs a fraction of reading files, so humans get answers faster and AI agents spend dramatically fewer tokens on the same task.

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Who is this for

• Developers and teams: onboard in hours not weeks, review PRs with impact context, catch circular dependencies and god-files, keep living documentation.
• Engineering leaders: a bird's-eye architecture view, spot debt before it becomes a crisis.
• AI-augmented developers: stop letting your agent burn tokens re-reading files; it queries the graph instead.

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Repository layout

devlensOSS/
├── src/
│   ├── cli/                  # `devlens` CLI (commander program + commands)
│   ├── core/                 # Shared query core (CLI + MCP, they never drift)
│   ├── mcp/                  # MCP server (stdio + HTTP) and packet renderer
│   ├── search/               # Local BM25F search stack (index, query, cache)
│   └── server/               # HTTP API for the Web UI + Next.js proxy
├── frontend/                 # Next.js graph visualizer (Cytoscape)
├── plugins/devlens/          # Deprecated agent skill source (old installs)
├── packages/skill-installer/ # @devlensio/skill (deprecated)
├── bin/ + npm/<platform>/    # Launcher and prebuilt binary packages
├── scripts/                  # Release tooling + start.mjs (bun start build-or-skip)
└── server.json               # MCP registry manifest

The analysis engine (native parsers and graph build) ships as the separate devlensio package.

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DevLens Cloud

A hosted version is in development: shareable graphs, team workspaces, live per-commit documentation with node summaries, detailed PR analysis (summary, impact, security), commit diffs showing added/modified/deleted nodes, an AI-native chat over your codebase, and no local setup.

Join the waitlist →

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License

AGPL-3.0. Part of the devlensio family of tools.