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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 →

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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:

	DevLens V3 (5 tools)	Graphify	codegraph	semble	bm25
Correctness (agent loop, same run)	0.643	0.619	0.667	0.714	0.667
F1 (agent loop, same run)	0.529	0.526	0.604	0.636	0.581
Correctness (retrieval layer)	0.694	0.662	0.674	0.652	0.664
Response packet	2,230 tokens	15,020 tokens	6,339 tokens	736 tokens	721 tokens
Latency per call	67 ms	2,469 ms	2,736 ms	508 ms	108 ms

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.

Dimension	DevLens	Graphify	GitNexus	Sourcegraph	DeepWiki
Parsing depth	Native semantic parsers (TS compiler, Python ast, go/types, JavaParser, syn), type-resolved	tree-sitter (syntactic, no type info)	tree-sitter + native bindings (no type info)	SCIP/LSIF symbol index + language servers (no semantic parse)	LLM reads source directly (no structured parser)
Edge quality	Type-checked IMPLEMENTS/EXTENDS, framework routes (Next.js, Django, Spring, Gin, axum), ORM data edges (READS_FROM/WRITES_TO)	EXTRACTED/INFERRED/AMBIGUOUS tags, no type or framework awareness	call chains, clusters, route_map, no ORM or data edges	precise symbol cross-references (SCIP), no type-checked inheritance	docs-level relationships (no structured graph)
Per-node AI summaries	Technical + business + security with severity on every node	No (LLM used for docs and concepts)	No (embeddings for semantic query)	Via Cody (hover and inline docs, chat-level)	Auto-generated docs per symbol (no security, no technical/business split)
Security analysis	Per-node severity + blast-radius reach with real exploit descriptions	No	Partial (opt-in PDG/taint)	No (compliance certifications only)	No
Agent / MCP integration	CLI + self-describing MCP server + Web UI	CLI + local skill (no MCP)	CLI + 17-tool MCP + hooks (AGENTS.md)	MCP server (cross-repo search, not per-repo graph queries)	Unknown (no public MCP integration)
Language coverage	TS/JS, Python, Java, Go, Rust with native parsers for each	12 code families + docs/images (shallow syntactic)	Many via tree-sitter (Dart/Kotlin/Swift), shallow syntactic	30+ via language servers (symbol-level, no semantic edges)	Any (LLM reads source, no structured extraction)
License / pricing	AGPL-3.0, free, including commercial use	Apache-2.0	PolyForm Noncommercial (cannot use commercially)	Open-source core, Enterprise paid	Free for public repos, enterprise tiers unlisted

(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.

Tool	Key arguments	What it does
resolve_context	task (required), intent?, focus?, tokenBudget?	The front door. One call returns a task-shaped plain-text packet: an ANSWER line, ranked nodes with one-line meanings, call flow, involved files with matched-line snippets, and an id map — all within a token budget. Intents: pinpoint, reference-list, flow, overview, concept, security-audit, exploratory.
find_symbols	query (required), limit?	Cheap BM25F name lookup: nodeIds plus file:line, no graph, no source.
get_node	node (required), include?	Full detail for one node: technical, business, and security summaries plus callers, callees, and (opt-in) raw source.
impact	target? or from+to commits	Change impact: what breaks if a symbol changes; commit-range diff mode for two analyzed commits.
repo	action	Manage analyzed graphs: list, analyze, freshness, health.

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.

Language	Frameworks / stacks the graph understands	What gets parsed
TypeScript / JavaScript	React, Next.js (app and pages router), Express/Hono/Fastify, React Router, TanStack Router, any Node	components, hooks, state stores, classes, methods, functions, routes
Python	FastAPI, Flask, Django (+DRF), SQLAlchemy / Django ORM, Celery, Pydantic	classes, methods, functions, routes, data models
Java	Spring Boot (controllers, JPA, Spring Data repositories)	classes, methods, interfaces, enums, routes
Go	net/http, Gin, Echo, chi, Fiber, GORM, database/sql	structs, interfaces, methods, functions, routes
Rust	axum, actix-web, rocket, utoipa, Diesel	structs, enums, traits, impl blocks, methods, functions, routes

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

Command	What it does
devlens detect [path] [--deps]	Inspect a repo before analyzing: language, manifest, dependency count, source files
devlens analyze [path] [--summarize] [--force-summarize]	Build the graph and its search index (optionally add AI summaries)
devlens summarize [target]	(Re)generate technical, business, and security summaries
devlens status	Which repos are analyzed, their language and summary coverage
devlens doctor	Environment health check: git, storage, LLM provider, and the four extractor runtimes (go, rust, java, python)
devlens init	First-time setup: configure the LLM provider interactively

Explore and understand

Command	What it does
devlens overview	Big picture: language, framework, stats, central nodes
devlens top-nodes [-l <n>]	Highest-scoring (most central) nodes
devlens find-nodes <name> [-t <type>]	Search by name, type, file, or severity (for example -t ROUTE)
devlens nodes-in-path <path>	All nodes in a file or folder
devlens get-node <id>	Full detail for one node: summaries, callers, callees
devlens node-code <id>	Raw source for a node (expensive, prefer get-node)
devlens architecture	One-call architecture brief: modules, routes, flows, health
devlens get-context <query>	Token-budgeted context packet for an agent

Impact and quality

Command	What it does
devlens blast-radius <id>	What breaks if I change this (upstream dependents)
devlens khop <id>	What does it depend on (downstream)
devlens subgraph <seed>	The cohesive cluster (module) a node belongs to
devlens cycles	Circular dependencies
devlens security [--min-severity ...]	Security findings with severity and explanation
devlens security-brief	Ranked security report with blast-radius reach
devlens diff <from> <to>	Compare two analyzed commits
devlens review-pr <from> <to>	PR review packet: diff, impact, tests, security delta
devlens check-freshness	Is the graph stale versus HEAD?

Manage and integrate

Command	What it does
devlens config	View or set LLM provider config (~/.devlens/config.json)
devlens repos	List analyzed repos
devlens graphs list | delete	Manage stored graphs
devlens reindex [graphId] [commitHash] [--force]	Rebuild the local search index (.search.json) without re-analyzing. No arguments reindexes the latest commit of every graph
devlens serve	Start the backend HTTP API only (used by the MCP server and the Web UI)
devlens mcp	Run the MCP server (see Quick Start)
devlens update [--check]	Update the CLI to the latest published version — package manager auto-detected (npm/pnpm/yarn/bun). --check only checks and reports (pipeable with --json); offline, up-to-date, source-checkout, and non-global installs each get an explicit message

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.