AI and Monorepos

AI and monorepos elevate each other: monorepos provide unified context for powerful agentic workflows, while AI helps navigate and automate monorepo complexity.

AI and Monorepos Elevate Each Other

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Your AI Assistant Might Get Lost

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The Missing Map

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AI Agents and CI

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# AI and Monorepos Elevate Each Other

Monorepos Make AI More Powerful

Monorepos provide AI assistants with a unified workspace view, enabling better dependency tracing and cross-module understanding.

But it's not just about context. Having all related projects in the same place enables powerful agentic AI workflows that are nearly impossible across distributed repositories. AI agents can perform atomic cross-project changes as single pull requests with full testing and review.

Use AI to Make Monorepos Easier

Monorepos come with complexity costs that make people hesitant to adopt them. AI helps offset this complexity, making monorepo adoption much easier.

AI supporting and enhancing monorepo development workflows

Agentic Refactoring: AI can perform maintenance refactorings like removing dead code, updating deprecated patterns, and cleaning up tech debt that otherwise gets left behind and accumulates over time.
Codebase Exploration: AI can instantly map dependencies and guide you to the right files when integrating frontend components with backend APIs across different projects.
Impact Analysis: AI can analyze a shared library update to predict which teams will be affected and involve the right code owners to discuss and plan out changes.
Monorepo Tooling Maintenance: AI can automate tooling modernization across projects, updating configurations and handling dependency conflicts when upgrading build tools, linters, or testing frameworks.

# Your AI Assistant Might Get Lost

Turns out, just having all your code in one place doesn't automatically make AI more effective

It's Like Only Navigating with StreetView

LLMs rely entirely on provided context. Monorepos have all code in one place, but raw code access isn't enough. It's analogous to navigating a city using only street view. You can see individual files (streets) clearly, but without an aerial view of the architecture (city layout), it's unlikely to pick optimal routes.

AI assistant only seeing individual files without understanding the broader architectural context

Getting Lost in the Middle

Research has shown that it's not about providing as much context as possible, but it's more about providing the right context.

Context Degradation

Recent research on 'context rot' reveals that even though modern LLMs have million-token context windows, their performance actually degrades with longer inputs. Simply feeding more code doesn't improve understanding. It can make it worse.

Missing Semantic Understanding

Just relying on lexical matching (finding specific code patterns) is not enough. Having a higher-level understanding of the architecture and dependencies is essential to provide effective results.

The 'Lost in the Middle' Problem

AI models struggle most with information buried in the middle of long contexts. In a large monorepo, the most relevant context might be scattered across dozens of files, making it effectively invisible to file-level analysis.

# The Missing Map

Moving from file-level scanning to semantic workspace understanding

The solution to context rot and file-level limitations isn't to provide less context, it's to provide the right context at the right level of abstraction. Instead of overwhelming AI with thousands of individual files, smart monorepo tooling elevates understanding from file-level to architectural-level, giving AI the "map view" it needs to navigate complex codebases effectively.

Traditional AI assistant overwhelmed by scattered individual files, unable to understand project relationships and workspace structure

Basic AI Assistant

Monorepo-Aware AI

Teaching AI the Higher-Level Picture

Project Graph & Workspace Structure: Provides AI with the missing "map view" of workspace structure and relationships, enabling higher-level understanding vs just file-level "street view". This architectural overview directly addresses the "lost in the middle" problem by surfacing relevant connections upfront.
Workspace & Project Metadata: Access to project-level information including dependencies with other projects, external dependencies, technology stacks, and ownership details. While this information is technically extractable via file-level search, structured access prevents context rot by avoiding the need to parse and correlate scattered configuration files across large monorepos.
Task Intelligence & Monorepo Features: Understanding of available tasks, task dependencies, and monorepo-specific features like caching input/outputs, enabling AI to provide optimization suggestions and intelligent workflow recommendations across the entire workspace.

How This Works

AI assistant seamlessly integrating with monorepo tooling through structured protocols, accessing workspace intelligence and architectural insights

Model Context Protocol (MCP): Protocol for structured workspace access and targeted information retrieval.
AI Configuration & Rules Files: Configuration files that instruct AI how to use monorepo tooling effectively.
Agent Experience (Ax): CLI tools with discoverable commands and structured output for autonomous AI exploration.

# AI Agents and CI

CI is already a challenge in monorepos as teams scale. Now, with AI agents autonomously submitting PRs just like developers, this pressure intensifies even further. While existing tooling like remote caching and distributed execution can alleviate performance bottlenecks, there's still the human cost of attending to breaking PRs—reviews, fixes, and workflow management.

AI Agents

Growing Teams

Better Tools

Queue increasing

CI Bottleneck

App Release

Autonomous AI Agents Managing the PR Pipeline

This is where AI agents become most valuable, not just contributing code, but managing the CI pipeline. Autonomous AI agents can handle the tedious work of reviewing PRs and dealing with failed PRs to get them through to the main branch faster.

AI-Assisted Code Reviews: AI-powered tools can help with first pass reviewing by automatically analyzing large diffs and flagging important parts that capture the developer reviewer's attention. This provides another pair of eyes that can help lead to faster reviews.
Auto-fixing Common Issues: Sometimes linting and formatting issues slip through the cracks. While CI checks catch these before merging, fixing them requires switching branches and submitting a patch. This is a tedious distraction from real work. An autonomous AI agent with sufficient codebase context can handle these routine fixes automatically, removing the hassle of attending to failed PRs.
And many more...: There are more potential use cases where the reasoning capabilities of AI agents can address some of the challenges you might face in CI in large monorepos.

AI Support in Monorepo Tools

Current AI assistant integration capabilities across monorepo tools.

Model Context Protocol (MCP) Support

Native support for Model Context Protocol (MCP) servers that enable AI assistants to access structured workspace information and execute tool-specific operations.

community implementation Bazel: Community-maintained MCP servers available (nacgarg/bazel-mcp-server and aaomidi/bazel-mcp) providing build, test, and dependency analysis capabilities.
community implementation Gradle: Community MCP server (IlyaGulya/gradle-mcp-server) with project information, task execution, and test running capabilities.
not supported Lage: No MCP server implementation available.
not supported Lerna: No MCP server implementation available.
natively supported moon: Official MCP server via CLI command (moon mcp) with comprehensive project and task management, workspace synchronization, and VCS integration.
natively supported Nx: Official MCP server (npx nx-mcp) with the most comprehensive AI integration including documentation access, code generation, IDE integration, and cloud analytics.
not supported Pants: No MCP server implementation available.
natively supported Rush: Official MCP server (@rushstack/mcp-server) with workspace analysis, project migration, conflict resolution, and command validation capabilities.
not supported Turborepo: No MCP server implementation available.

Workspace Analysis for AI

The ability to provide AI assistants with structured workspace understanding including project relationships, dependency graphs, and overall architecture insights.

community implementation Bazel: Community MCP servers provide target querying, dependency analysis, and build structure understanding.
community implementation Gradle: Community MCP server offers project structure analysis and build environment insights.
not supported Lage: No AI workspace analysis capabilities available.
not supported Lerna: No AI workspace analysis capabilities available.
natively supported moon: Comprehensive workspace analysis including project relationships, task dependencies, and VCS integration for change detection.
natively supported Nx: Advanced workspace analysis with project graph visualization, filtering capabilities, and real-time workspace monitoring for AI assistants.
not supported Pants: No AI workspace analysis capabilities available.
natively supported Rush: Detailed workspace topology analysis with project dependency mapping and structured data optimized for AI consumption.
not supported Turborepo: No AI workspace analysis capabilities available.

Project Data & Metadata Access

Access to structured project-level information including configurations, dependencies, technology stacks, and ownership details for AI analysis and recommendations.

community implementation Bazel: Community implementations provide target information, dependency relationships, and source file associations.
community implementation Gradle: Community MCP server provides project details, build environment information, and task configurations.
not supported Lage: No AI project data access capabilities available.
not supported Lerna: No AI project data access capabilities available.
natively supported moon: Detailed project metadata including configurations, task definitions, and dependency relationships with optional recursive dependency fetching.
natively supported Nx: Comprehensive project details including complete configurations, external dependencies, fuzzy project matching, and generator schemas for project creation.
not supported Pants: No AI project data access capabilities available.
natively supported Rush: Complete project metadata in structured JSON format including package.json content, folder paths, subspace information, and Rush-specific metadata.
not supported Turborepo: No AI project data access capabilities available.

Task Execution & Intelligence

The ability for AI assistants to understand task definitions, execute tasks, monitor progress, and provide intelligent workflow recommendations across the workspace.

community implementation Bazel: Community implementations support build and test execution with dependency management and target resolution.
community implementation Gradle: Community MCP server provides task execution with detailed test result reporting and hierarchical output management.
not supported Lage: No AI task execution capabilities available.
not supported Lerna: No AI task execution capabilities available.
natively supported moon: Task execution with dependency management, workspace synchronization, and project-specific task coordination.
natively supported Nx: Advanced task execution with real-time monitoring, IDE integration, generator UI access, and comprehensive task dependency analysis for AI optimization suggestions.
not supported Pants: No AI task execution capabilities available.
natively supported Rush: Task execution with command validation, conflict resolution capabilities, and automated lockfile management for streamlined AI-driven operations.
not supported Turborepo: No AI task execution capabilities available.

Extended AI Capabilities

Advanced AI integration features that go beyond core workspace, project, and task capabilities, offering specialized functionality for enhanced developer productivity.

community implementation Bazel: Deep dependency graph analysis with reverse dependency tracking, precise source file association, and intelligent target resolution for complex build scenarios across multiple community implementations.
community implementation Gradle: Sophisticated test execution with hierarchical result reporting, intelligent output filtering to reduce noise, and comprehensive build environment analysis for AI-powered optimization recommendations.
natively supported moon: Advanced VCS integration for intelligent change detection, automated workspace synchronization capabilities, and dependency management with recursive relationship analysis across the entire project ecosystem.
natively supported Nx: Documentation access with contextual search, code generation with generator schemas, IDE integration for real-time assistance, cloud analytics with performance insights, and comprehensive AI-powered CI/CD features including automated PR fixes and conversational CI analytics.
natively supported Rush: Automated conflict resolution for lockfiles, intelligent project migration between locations and subspaces, command validation to prevent execution errors, and comprehensive workspace topology analysis optimized for large-scale enterprise monorepos.

Overview

Bazel (by Google)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

Dependency Analysis

VCS Integration

Gradle (by Gradle, Inc)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

Test Execution

Build Environment Analysis

Lage (by Microsoft)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

No extended capabilities available

Lerna (maintained by Nx team)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

No extended capabilities available

moon (by moonrepo)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

VCS Integration

Workspace Synchronization

Dependency Management

Nx (by Nrwl)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

Documentation Access

Code Generation

IDE Integration

Cloud Analytics

Performance Insights

AI-Powered CI/CD

Pants (by Pants Build)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

No extended capabilities available

Rush (by Microsoft)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

Documentation Access

Conflict Resolution

Project Migration

Command Validation

Turborepo (by Vercel)

AI Integration Level

Overall Support

MCP Implementation

MCP Server

AI Config Files

Available

Core Capabilities

Workspace Analysis

Project Management

Task Execution

Extended Capabilities

No extended capabilities available

Tool	AI Integration Level	MCP Server	AI Config Files	Extended Capabilities
Bazel by Google	Basic	Community	None	Dependency Analysis VCS Integration
Gradle by Gradle, Inc	Basic	Community	None	Test Execution Build Environment Analysis
Lage by Microsoft	None	None	None	None
Lerna maintained by Nx team	None	None	None	None
moon by moonrepo	Comprehensive	Official	None	VCS Integration Workspace Synchronization Dependency Management
Nx by Nrwl	Comprehensive	Official	Yes	Documentation Access Code Generation IDE Integration Cloud Analytics Performance Insights AI-Powered CI/CD
Pants by Pants Build	None	None	None	None
Rush by Microsoft	Comprehensive	Official	None	Documentation Access Conflict Resolution Project Migration Command Validation
Turborepo by Vercel	None	None	None	None

Legend

Available/Supported

Basic/Community

Not Available

AI Integration Levels

Comprehensive: Full-featured AI integration with official support

Basic: Limited AI features or community-driven solutions

None: No AI assistant integration available

# Resources

Here is a curated list of resources to explore how AI and monorepos work together.

AI & Monorepo Videos

Watch presentations and demos about AI integration in monorepo environments.

We Just Shipped the Monorepo MCP for Copilot!

AI & Monorepo Articles

Here is a curated list of articles about AI and monorepo integration.

AI Development Tools

Essential tools and protocols for AI-enhanced monorepo development.