AgentIndex icon
AgentIndex
CALM logo
CALM
★ 17
vs
enola logo
enola
★ 277

CALM vs enola

CALM: CALM (Coding Agent Liveness Map) provides AI coding agents with a live, graph-verified map of the codebase, enabling them to understand dependencies and structural relationships before making changes. This ensures safer and more accurate code edits, preventing common pitfalls like breaking call sites or critical components due to lack of visibility.; enola: Enola is a local Model Context Protocol (MCP) server that generates compact architectural snapshots of code repositories, providing a structured overview of modules, symbols, and dependencies before AI agents read individual files. This upfront context helps AI coding agents like Claude Code, Cursor, or Copilot understand the codebase structure, making their exploration and code generation more effective.

01

TL;DR

CALM logo
CALMCode Assistant · ★ 17

Empowering AI agents to safely refactor and edit code directly, by providing a deep understanding of code dependencies and impact.

Read CALM full review →
enola logo
enolaCode Assistant · ★ 277

Onboarding new developers: Quickly get a structural tour of a codebase for new team members.

Read enola full review →
02

Side-by-Side Comparison

Field
CALM logoCALM
enola logoenola
Category
Code Assistant
Code Assistant
Stars
★ 17
★ 277
License
MIT
Apache-2.0
Install Command
curl -fsSL https://raw.githubusercontent.com/Eilodon/CALM/main/scripts/install.sh | sh && calm setup --project-root .
go install ./cmd/enola
Updated
2d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Code Intelligence, AI Agent, Call Graph
AI Agent Tooling, Codebase Analysis, Architectural Snapshot
03

Features

CALM logoCALM
01Real-time, Graph-Verified Codebase Map: Provides full call graphs for 13 languages out of the box (24 languages parsed in total), showing actual dependencies rather than similarity guesses.
02Hard Safety Gates on Write Path: Enforces hash-verified and syntax-checked edits, with hub/high-risk symbols requiring explicit confirmation and review of callers before changes are committed.
03Confidence-Graded Call Edges with Compiler Verification: Call edges are confidence-graded (textual → inferred → resolved → formal), with SCIP/LSP overlays upgrading best-guess edges to compiler-verified ground truth for 12 languages.
04Automated Codebase Health Reporting: Generates fitness reports based on metrics like hub concentration, dead code rate, complexity, and architectural boundary violations, queryable mid-session or enforceable in CI.
05Local-First and Privacy-Preserving Operation: Runs entirely on your machine with no code leaving for indexing or editing, using an MIT license and vendored embedding models for offline use.
enola logoenola
01Generates compact architectural snapshots of repositories for AI agents.
02Supports multiple programming languages including Go, Kotlin, Python, TypeScript, Swift, Ruby, and OpenAPI.
03Integrates with MCP-compatible AI coding agents (e.g., Claude Code, Cursor) to provide upfront context.
04Enables cross-repository analysis by building a combined fact store and linking dependencies between services.
05Provides tools for querying facts, exploring code, traversing dependency graphs, and performing impact analysis.
04

Use Cases

CALM logoCALM
↳Empowering AI agents to safely refactor and edit code directly, by providing a deep understanding of code dependencies and impact.
↳Ensuring code quality and architecture compliance in CI/CD pipelines, by automatically flagging dead code, high-complexity functions, and boundary violations.
↳Onboarding developers to new codebases or areas by offering an interactive, live map of the project's structure and hotspots.
↳Facilitating secure and validated code modifications within various IDEs and AI client environments, preventing unintended breakages.
enola logoenola
↳Onboarding new developers: Quickly get a structural tour of a codebase for new team members.
↳Refactoring planning: Understand transitive dependencies and impact of changes before code modifications.
↳Debugging complex systems: Trace call chains and understand how different modules or services interact.
↳Multi-repo development: Analyze and query dependencies across multiple microservices or monorepos.
↳AI agent enhancement: Provide AI coding agents with deep architectural context to improve code understanding and generation.
FAQ

FAQ

What is the difference between CALM and enola?
Both CALM and enola are in the Code Assistant category. CALM has 17 stars, while enola has 277 stars.
Which is better, CALM or enola?
The best choice depends on your use case. Choose CALM if Empowering AI agents to safely refactor and edit code directly, by providing a deep understanding of code dependencies and impact., and enola if Onboarding new developers: Quickly get a structural tour of a codebase for new team members..
Is CALM free or open source?
Yes, CALM is open source on GitHub (MIT).
Is enola free or open source?
Yes, enola is open source on GitHub (Apache-2.0).
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