A deterministic structural model of your codebase for AI coding agents — your real architecture, extracted from source, not guessed.
enola is a local Model Context Protocol (MCP) server. Point it at one or more repositories and it builds a precise graph of your code's architecture — modules, types, routes, dependencies, and how they all connect — straight from your source. It then exposes tools your AI agent can use to read, traverse, query, and reason over that structure. So before your agent writes a line of code, it already knows the shape of the thing it's editing.
1. Install
curl -fsSL https://raw.githubusercontent.com/enola-labs/enola/main/install.sh | sh
export PATH="$HOME/.local/bin:$PATH" # if not already on PATH2. Connect to your agent
Claude Code:
claude mcp add enola enolaCursor (add to mcp.json):
{
"mcpServers": {
"enola": { "command": "enola" }
}
}3. Ask it to map your project
"Generate an architectural snapshot of /path/to/my/project"
Done. Your agent now has a precise structural map of your code. For configuration options, multi-repo setup, and what to ask next, see Quick start below.
Supported languages: Go · JavaScript · TypeScript · Python · Java · Kotlin · Swift · Ruby · C++ · PHP · Vue · Svelte · OpenAPI — with framework awareness (Next.js, Nuxt, SvelteKit, FastAPI, Django, Spring, Rails, Laravel, Symfony, SwiftUI, Jetpack Compose, WordPress, …)
AI coding agents are powerful, but they're non-deterministic. On every task they re-discover your codebase from scratch — grepping, opening files, inferring how things fit together — and they get it subtly wrong often enough to matter. That guessing costs you time (wrong turns, re-prompts) and tokens (re-reading the same files, every session).
enola removes the guessing from the part that should never be guessed: the structure.
It gives your agent a deterministic structural model — a structural architecture graph of your code's real types and relationships, built by parsers and graph algorithms, not by a language model. The structure is extracted from your source, not summarized from it; these are facts, not notes. Run it twice on the same commit and you get the same answer, every run. The agent starts from facts instead of assumptions.
The result is the difference between vibe coding — prompt, hope, fix — and AI-augmented engineering: fewer wrong turns, fewer tokens burned, and work you can reproduce. enola adds determinism where AI lacks it, and your agent spends its intelligence on the actual problem instead of re-learning your repo.
enola is a first step, not a replacement. It runs before your agent explores, so it knows where to look and what connects to what. It doesn't replace grep, file reading, or code search — it makes them precise.
Under the hood, enola models your codebase as a graph of architectural types — which we call kinds — and the relations between them. That's the whole concept: not a magic "knowledge graph," just a deeply technical, structural model of what your code actually contains.
The kinds (the nodes):
- module — a package or directory
- symbol — a function, method, struct, interface, type, class, or constant
- route — an HTTP/API endpoint
- storage — a database table or data store
- dependency — an import relationship
- service — a whole repository (used when you analyze several at once)
The relations (the edges) connect them: declares, imports, calls, implements, depends_on, and more. Because the edges are typed and directed, the graph is queryable, not merely searchable — you compute over it. On top of it, enola builds a small set of tools your agent can call to answer real structural questions with exact answers.
For the full mental model and internals, see ARCHITECTURE.md.
- Anyone pairing with an AI coding agent — Claude Code, Cursor, Copilot, Opencode, or any MCP-compatible tool.
- Teams working across multiple repos — a backend, a web frontend, a mobile app. enola links them into one cross-repo graph so an agent can follow a call from the web client all the way into the service that answers it. And because that's a real graph rather than a fixed set of features, questions you'd otherwise reach for a dedicated tool to answer become plain queries over it — which of the backend's endpoints does no client app call? among them, a cleanup shortlist derived from the same client→server links (verify against callers outside the snapshot — cron jobs, webhooks, third-party consumers — before deleting).
- Anyone about to refactor — and wanting to know the blast radius before touching code.
The workflow is simple: generate the snapshot once, then ask. These aren't text lookups — each tool computes over the graph: traverse walks reachability, find_path finds the shortest chain between two points, impact_analysis takes the transitive reverse closure. After the snapshot, your agent has nine tools on top of the graph:
| Tool | The question it answers |
|---|---|
generate_snapshot |
"Snapshot this repo." Build or refresh the graph. Run it first; use append to add more repos. |
explore |
"What's in this module/file/symbol, and what touches it?" A guided tour. |
query_facts |
"List exactly these." Every route, every interface, every external dependency. |
query_insights |
"What did the analysis find?" Fetch the computed findings — unused routes, cycles, god-classes — instead of re-deriving them. |
show_symbol |
"Show me the code." Jump straight to a symbol's source. |
traverse |
"What does X depend on?" / "What depends on X?" Walk the graph. |
find_path |
"How does A reach B?" The call or dependency chain between two points. |
impact_analysis |
"If I change X, what breaks?" The blast radius of a change. |
coverage_report |
"Which cross-repo edges did enola resolve vs. miss?" Tell a genuine leaf service from a coverage gap. |
impact_analysis is the one to know. Before a refactor, it computes the full set of code that transitively depends on what you're about to change — grouped by how many hops away it is, and aware of cross-repo dependencies. Instead of your agent guessing what a change might affect (and missing things), it gets the exact dependent set. That's determinism turned into a concrete payoff: safer changes, planned in the right order, the first time.
See ARCHITECTURE.md for every tool's full parameters.
The examples below ask different models to explain the authentication and authorization flow across three repositories — a web UI client, a backend, and a custom auth provider — using the enola snapshot as context.
Grab a prebuilt binary — no Go toolchain or C compiler required:
curl -fsSL https://raw.githubusercontent.com/enola-labs/enola/main/install.sh | shThis installs enola to ~/.local/bin. If that's not on your PATH, add it:
export PATH="$HOME/.local/bin:$PATH"Binaries are published for Linux, macOS (amd64/arm64), and Windows (amd64). You can also download a specific build from the Releases page, or build from source.
enola needs no config file. Every setting has a built-in default, so out of the box it indexes the current repo with all extractors enabled and writes to .enola/. A config file (mcp-arch.yaml) only overrides those defaults — it never adds capability you'd otherwise lack. When enola can't find one it simply prints warning: …, using defaults and carries on.
The install script installs only the binary, by design — it does not place a config file. Grab the bundled one from the repo whenever you want to customize (tune the ignore globs, pick a subset of extractors, change the output dir, …):
curl -fsSL https://raw.githubusercontent.com/enola-labs/enola/main/mcp-arch.yaml -o mcp-arch.yamlThe examples/ directory has ready-made per-language and multi-repo starting points, and examples/full.yaml documents every option. For the full field reference and defaults, see ARCHITECTURE.md → Configuration.
Claude Code — register enola as an MCP server with one command. This assumes the enola binary is on your PATH (the install script above puts it in ~/.local/bin):
claude mcp add enola enolaThe shape is claude mcp add <name> <command> [args…]: the first enola names the server, the second is the binary. The trailing config path is optional — omit it (as above) to run on built-in defaults, or pass one to override them:
claude mcp add enola enola /path/to/enola/mcp-arch.yamlWhen you do pass a config, its repo: is only the default repository — you can still snapshot any repo by passing repo_path to generate_snapshot. Verify it registered with claude mcp list, then start Claude Code and ask it to generate a snapshot.
Cursor / other MCP clients — add enola to your client's MCP configuration. For example, in Cursor's mcp.json (the config path in args is optional — drop it to use defaults):
{
"mcpServers": {
"enola": {
"command": "enola",
"args": ["/path/to/enola/mcp-arch.yaml"]
}
}
}Open a project and ask your agent to map it:
"Generate an architectural snapshot of /path/to/my/project"
That's it. The snapshot takes milliseconds even on large repos, and your agent now has the tools above plus a ready-to-read summary at .enola/llm_context.md. From here, just ask your questions naturally:
"I just joined this project — based on the snapshot, give me a tour: the main modules, how they relate, and where to start reading."
"I need to add an API endpoint for user preferences. Which packages should I touch, and in what order?"
"Are there cyclic dependencies or layer violations I should know about before refactoring?"
"Where are the architectural risks — god classes with high fan-in, call-graph hotspots, overly complex functions, or modules buried deep in the dependency chain?"
"What would break if I refactor
internal/server? Show me the impact analysis."
Working across several repos? Generate the first, then add the rest with append mode — enola links them into one cross-repo graph:
"Generate a snapshot of /path/to/go-service with append mode"
"If I change the auth service, which other services are impacted?"
"Which of my backend's endpoints aren't called by any of the client apps? (Ask via
query_insights(explainer='unused-routes')— cleanup candidates, but check for callers outside these repos first.)"
Regenerate after major changes so the snapshot stays current. Refreshes are fast: enola caches each language's facts and re-parses a language only when one of its files (or a shared config like package.json) actually changed, reusing the rest.
| Language | Detected by |
|---|---|
| Go | go.mod |
| Java | pom.xml (Maven) or .java sources (Spring routes / JPA / Lombok DI / Dubbo SPI aware) |
| JavaScript | tsconfig.json / package.json with TypeScript (parsed by the TypeScript extractor) |
| TypeScript | tsconfig.json / package.json with TypeScript (Next.js & monorepo aware) |
| Vue | package.json with vue dependency (Nuxt / Vue Router / Composition API aware) |
| Svelte | package.json with svelte dependency (SvelteKit routing / $lib alias aware) |
| Python | pyproject.toml, requirements.txt, setup.py, … (FastAPI / Django / SQLAlchemy aware) |
| Kotlin | build.gradle(.kts) with Kotlin/Android (Compose / Hilt / Room aware) |
| Swift | Package.swift, .xcodeproj, .xcworkspace (SwiftUI / UIKit aware) |
| Ruby | Gemfile (Rails / ActiveRecord / Packwerk aware) |
| C++ | .cpp/.hpp/… source or CMakeLists.txt/Makefile + header (header/source method merging, namespaces, templates) |
| PHP | composer.json, WordPress markers, or any .php source (WordPress / Laravel / Symfony route + outbound HTTP-client aware) |
| OpenAPI | any spec with an openapi: / swagger: key |
Framework- and platform-specific detection for each language is described in ARCHITECTURE.md → Supported languages.
Python, Ruby, and PHP are parsed with tree-sitter and contribute call and dependency edges to the graph, so
traverse,find_path, andimpact_analysisreach into them — not just modules and routes.
Prerequisites: Go 1.25+ and a C compiler (for the tree-sitter bindings).
go build -o enola ./cmd/enola # or: go install ./cmd/enolaTo run a one-shot snapshot without starting the MCP server:
enola --generate [config_path] # config_path is optional; defaults to mcp-arch.yaml, falling back to built-in defaults if absentArtifacts are written to the configured output.dir (default .enola/). The config file is optional — see ARCHITECTURE.md → Configuration for the full field reference and defaults.
enola --explain [repo_path] is a one-shot mode that generates a snapshot, computes statistics over the fact graph, and prints a human-readable report to stdout — no MCP server started, no artifacts written to .enola/.
When to use it:
- New contributor getting a first orientation — module count, architecture pattern, hottest packages.
- Pre-refactor sanity check — cycles, layer violations, blast radius of top modules.
- Quick audit without spinning up an AI agent.
# Use the config in the current directory (mcp-arch.yaml)
enola --explain
# Analyze a specific repository path
enola --explain /path/to/repoThe report covers eight sections:
- Overview — path, analysis time, active languages, total fact count
- Architectural kinds — counts of modules, symbols, routes, storage, dependencies, services
- Symbol breakdown — functions, methods, structs, interfaces, and other kinds
- API & data surface — route count broken down by HTTP method, plus storage count
- Dependencies — external, internal, and stdlib import counts
- Architecture — detected pattern with confidence, cyclic dependencies, layer violations, cross-repo edges
- Impact analysis (hotspots) — top modules ranked by fan-in + fan-out coupling, with criticality tier and blast radius
- Code health — per-explainer findings with their top offenders: god classes (high fan-in symbols), call-graph hotspots, deep dependency chains, large public surfaces, and complexity outliers
Every finding carries a confidence score, and it means something exact: 1.0 is a structural fact (a cycle exists; an export ratio measured), while anything below is a flagged heuristic for you to review (a god class is a statistical fan-in outlier, not a rule). The analyses are computed by graph algorithms — Tarjan's SCC for cycles, longest-path for dependency depth, mean+2σ outlier tests for the rest — so the same commit yields the same report.
Here's the actual report for Apache Airflow — a large polyglot codebase (Python, Java, TypeScript, and OpenAPI specs) analyzed in a single pass, 112,792 facts in ~3.5s (extraction parses files in parallel across cores; timing measured on a 16-core machine):
════════════════════════════════════════════════════════════
Repository explanation: apache/airflow
════════════════════════════════════════════════════════════
Overview
Generated: 2026-06-24T11:28:08Z
Analysis time: 3.522345875s
Languages: java, openapi, python, typescript
Total facts: 112792
Architectural kinds
module 2492
symbol 64192
route 6359
storage 64
dependency 39685
Symbol breakdown
method 41127
function 12371
class 8621
type 1393
variable 636
interface 36
enum 6
constant 2
API & data surface
routes 6359
(unspecified) 6182
GET 105
POST 28
PATCH 22
DELETE 16
PUT 6
storage 64
Dependencies
internal 17810
stdlib 12430
external 9445
Architecture
Pattern: (none detected)
cyclic dependencies 26
layer violations 0
Impact analysis (hotspots)
coupled modules 840
high criticality 486
medium criticality 354
Top hotspots (by coupling):
module fan-in fan-out crit blast radius
airflow-core/src/airflow/models 1396 315 high 56732
devel-common/src/tests_common/t… 1450 85 high 35780
providers/common/compat/src/air… 1214 1 high 51068
airflow-core/src/airflow/utils 1029 69 high 62870
airflow-core/src/airflow 871 39 high 67265
providers/common/compat/tests/u… 690 0 high 61945
providers/google/src/airflow/pr… 323 284 high 14305
providers/amazon/tests/system/a… 1 510 high 177
Code health
god classes (high fan-in) 298
airflow-core/src/airflow/ui/openapi-gen/req… 153 dependents
airflow-ctl/tests/airflow_ctl/api/test_oper… 79 dependents
providers/cncf/kubernetes/tests/unit/cncf/k… 71 dependents
airflow-core/tests/unit/ti_deps/deps/test_t… 49 dependents
providers/hashicorp/tests/unit/hashicorp/ho… 45 dependents
call-graph hotspots 133
airflow-core/src/airflow/ui/openapi-gen/req… fan-in 153 / out 12
providers/cncf/kubernetes/tests/unit/cncf/k… fan-in 71 / out 6
providers/openlineage/tests/unit/openlineag… fan-in 3 / out 21
providers/edge3/src/airflow/providers/edge3… fan-in 3 / out 3
providers/google/src/airflow/providers/goog… fan-in 10 / out 18
deep dependency chains 10
airflow-core/tests/unit/api_fastapi/core_ap… depth 57
airflow-core/tests/unit/api_fastapi/core_ap… depth 56
airflow-core/tests/unit/assets depth 56
airflow-core/tests/unit/jobs depth 56
providers/fab/tests/unit/fab/plugins depth 56
large public surfaces 20
airflow-core/src/airflow/ui/openapi-gen/req… 911/911 (100%)
airflow-core/src/airflow/ui/openapi-gen/que… 864/864 (100%)
task-sdk/src/airflow/sdk/execution_time/com… 119/129 (92%)
providers/edge3/src/airflow/providers/edge3… 3/3 (100%)
task-sdk/src/airflow/sdk/definitions/mapped… 100/111 (90%)
complexity outliers 15
airflow-core/src/airflow/jobs/scheduler_job… complexity 69
task-sdk/src/airflow/sdk/execution_time/sup… complexity 61
airflow-core/src/airflow/ui/src/pages/DagsL… complexity 54
airflow-core/src/airflow/ui/src/hooks.useDa… complexity 53
dev/breeze/src/airflow_breeze/commands/ci_c… complexity 52
No artifacts are written; .enola/ is not touched. For a persistent snapshot with agent-readable output, use --generate or the MCP server.
For interactive per-module blast-radius queries with configurable depth, see the impact_analysis tool reference in ARCHITECTURE.md → The tools.
- ARCHITECTURE.md — the concept, the fact model, the pipeline, and the full tool reference.
- examples/ — ready-made per-language and multi-repo configs.
Apache License 2.0 — see LICENSE.
enola bundles third-party components under their own licenses; see NOTICE. Swift parsing uses the tree-sitter-swift grammar by Alex Pinkus (MIT), vendored under internal/extractors/swiftextractor/grammar/.