What tools help manage polyglot project dependencies?
1,009 words · 6 min read
By Archie Cowan
Senior Prototype Developer for AWS global and strategic customers, former ITHAKA/JSTOR Chief Architect. Generative AI builder. Aspiring to do everything.
I asked my AI assistant to research polyglot build systems. What came back was 47 half-answers and three Wikipedia links.
I wasn't actually picking a build tool for a project. I was curious about the landscape and wanted to create a comprehensive feature matrix comparing all the major players. Isn't this what everyone does on Saturday morning?
Full disclosure: I've really only used Nx extensively. Which is exactly why I needed this systematic research approach - to avoid the "I love my hammer so everything looks like a nail" problem.
But here's what frustrated me about existing comparisons: every article covers different features, uses different criteria, or focuses on just 2-3 tools. What I wanted was one table, all the major tools, evaluated against the same comprehensive set of criteria. What I wanted is a feature matrix.
I needed to understand incremental builds, remote caching, dependency inference, IDE integration, learning curves, and plugin ecosystems across all the tools, with links to actual documentation backing every claim.
So I did what any reasonable person would do in 2026: I turned this into an AI-powered research project.
How I used AI to build the definitive feature matrix
Instead of spending months manually researching each tool, I created a systematic AI-powered research process:
Step 1: Create the draft matrix
Do some market research for me: what tools help manage polyglot project dependencies?
This brought out the headlines but not the structure I wanted. So I then prompted this:
Make me a feature matrix with features as rows and tools as columns
I knew this wouldn't get me a great result in one pass but did give us some scaffolding to work from. I had a table with the right headings on rows and columns. The cells themselves were not good information yet.
Step 2: Define the research framework
I challenged what I saw in the first row of the initial version of the matrix based on what I already knew about these tools, told it I didn't want to see Wikipedia links, and a few other things. Once I was happy with the results, I asked it to create feature matrix research guide that reflected how I reacted to the initial batch of information.
Now, using the feedback/questions I've asked, come up with steps to create good references in a feature matrix like this one that someone could apply to future research.
Step 3: Break down the work systematically
Now that I had my initial table, a good first row, and a research guide, I asked the assistant to create a list of tasks to finish the research for the remaining rows of the table.
Nice. Now, I need a task list to continue to flesh out the feature matrices in my polyglot built article. Include the heading and multi language tasks as completed since we did those here.
Compliment your assistant. It will do a better job.
It created a detailed task breakdown.
Step 4: Research one row at a time
For each task, I had a pretty simple prompt:
I need you to complete the next task inpolyglot-feature-matrix-tasks.mdusingfeature-matrix-research-guide.mdto guide you.
Pretty easy! It was keeping track of what it completed in the task list.
Important tip: start a new session (aka context window) for each task so that the amount of information is consistent for each task.
Step 5: Manual review and validation
As each task completed, I looked at my matrix and the links is selected. If they didn't make sense, I course-corrected with gentle feedback. AI did the legwork but my judgement hopefully improved accuracy and consistency.
You don't want your AI assistant to get defensive so be nice. Practice what you would say to a real person.
This is the kind of workflow I wrote about in Code Your Own Scaffolding First—structure the problem first, then let AI fill in the details. Also, the same point about reviewing everything in code reviews applies here.
The definitive polyglot build systems comparison
But enough about methodology. Here's what consulting 200+ references revealed:
Build Systems Feature Matrix
| Feature | Bazel | Pants | Buck | Gradle | Nx | Turborepo | Rush | Lerna |
|---|---|---|---|---|---|---|---|---|
| Multi-Language Support | ✅ Extensive (Starlark rules) | ✅ Excellent (Python backends) | ✅ Extensive (Starlark rules) | ✅ Excellent (JVM plugins) | ✅ Good (community plugins) | ⚠️ JS/TS focused (package.json scripts) | ⚠️ JS/TS only | ⚠️ JS/TS only |
| Supported Languages | Java, C++, Go, Python, Rust, JS, Kotlin, Scala, Objective-C, bash | Python, Java, Go, Scala, Kotlin, Shell, Docker | C++, Python, Rust, Java, Kotlin, Go, Haskell, OCaml, Erlang, Swift | Java, Kotlin, C++, Groovy, Scala, JavaScript | JS, TS + .NET, Go, Rust via plugins | JS, TS (any via package.json) | JS, TS | JS, TS |
| Incremental Builds | ✅ Yes (clean vs incremental) | ✅ Yes (fine-grained caching) | ✅ Yes (optimized actions) | ✅ Yes (up-to-date checks) | ✅ Yes (affected builds) | ✅ Yes (task caching) | ✅ Yes (output preservation) | ✅ Yes (via Nx) |
| Remote Caching | ✅ Yes (HTTP/gRPC servers) | ✅ Yes (REAPI, GitHub Actions) | ✅ Yes (REAPI compatible) | ✅ Yes (Enterprise + Community) | ✅ Yes (Nx Cloud) | ✅ Yes (Vercel + self-hosted) | ✅ Yes (Azure, AWS, local) | ✅ Yes (via Nx Cloud) |
| Distributed Builds | ✅ Yes (remote execution) | ✅ Yes (REAPI compatible) | ✅ Yes (remote execution) | ⚠️ Parallel only (no true distribution) | ✅ Yes (Nx Agents) | ❌ No (parallel only) | ⚠️ Experimental (cobuilds) | ✅ Yes (via Nx) |
| Hermetic Builds | ✅ Yes (strong sandboxing) | ✅ Yes (sandboxed processes) | ⚠️ Partial (remote-first, limited local sandbox) | ⚠️ Partial (isolated projects) | ⚠️ Partial (cache isolation) | ⚠️ Partial (cache isolation) | ❌ No (package manager isolation only) | ❌ No (no build isolation) |
| Dependency Inference | ⚠️ Manual (BUILD files) | ✅ Multi-language (Python, Java, Scala, Go) | ⚠️ Manual (BUCK files) | ⚠️ Manual (build scripts) | ✅ Extensible (graph inference + plugins) | ⚠️ Manual (task config) | ⚠️ Basic (package.json graph) | ⚠️ Basic (via Nx graph) |
| Configuration Complexity | 🔴 Steep learning curve (BUILD files, Starlark DSL) | 🟡 Moderate setup (pants.toml + backends) | 🟡 Structured config (BUCK files per package) | 🟡 Script-based (build.gradle DSL) | 🟢 Minimal config (nx.json + inference) | 🟢 Simple JSON (turbo.json tasks) | 🟡 Enterprise setup (rush.json + policies) | 🟢 Basic config (lerna.json) |
| Learning Curve | 🔴 Steep (30min tutorial, complex concepts) | 🟡 Moderate (6-step setup) | 🟡 Moderate (multi-step tutorial) | 🟡 Moderate (extensive guides, many concepts) | 🟢 Gentle (quick start, good DX) | 🟢 Gentle (simple setup) | 🟡 Moderate (3min demo, enterprise concepts) | 🟢 Gentle (simple init command) |
| IDE Integration | ⚠️ Limited (IntelliJ, VS Code, CLion plugins) | ⚠️ Limited (manual setup required) | ⚠️ Limited (Buck1 IntelliJ plugin only) | ✅ Excellent (native IntelliJ, Eclipse support) | ✅ Excellent (Nx Console for VS Code, JetBrains) | ✅ Good (VS Code LSP, JSON schema) | ✅ Good (standard JS/TS tooling) | ✅ Good (standard JS/TS tooling) |
| Plugin Ecosystem | ✅ Large (extensive Starlark rules) | ⚠️ Growing (Python backends) | ⚠️ Limited (mostly Buck1 legacy) | ✅ Massive (thousands of plugins) | ✅ Rich (official + community plugins) | ⚠️ Growing (limited, focused ecosystem) | ⚠️ Limited (experimental plugins) | ⚠️ Declining (maintenance mode) |
| Performance (Large Repos) | ✅ Excellent (Google-scale, distributed) | ✅ Excellent (fine-grained caching, inference) | ✅ Excellent (2x faster than Buck1, Rust-based) | ✅ Good (up to 100x faster than Maven with cache) | ✅ Excellent (5x faster than DIY, 30-40% faster than alternatives) | ⚠️ Moderate (60-85% build time reduction, JS-focused) | ✅ Good (30min to 30sec with cache) | ⚠️ Poor (30min+ builds, needs Nx) |
| Workspace Management | ✅ Advanced (modules, repos, external deps) | ✅ Advanced (cross-project refactoring, environments) | ✅ Advanced (cells, projects, packages) | ✅ Excellent (multi-project, composite builds) | ✅ Advanced (project graph, generators, migrations) | ✅ Good (package.json workspaces) | ✅ Advanced (subspaces, automatic linking) | ✅ Basic (package management, linking) |
| Task Orchestration | ✅ Advanced (DAG execution, parallel tasks) | ✅ Advanced (rule graph, async execution) | ✅ Advanced (dependency graph, action nodes) | ✅ Advanced (task graph, parallel execution) | ✅ Advanced (task graph from project graph) | ✅ Good (pipeline config, DAG) | ✅ Good (dependency-aware builds) | ⚠️ Basic (delegates to Nx) |
| Versioning/Publishing | ❌ No native support (requires external tools) | ✅ Python packages (PyPI via Twine) | ❌ No native support (build-focused only) | ✅ Excellent (Maven, Ivy, custom repos) | ✅ Comprehensive (Nx Release with changelogs) | ⚠️ External tools (Changesets recommended) | ✅ Advanced (change files, policies) | ✅ Full featured (fixed/independent) |
| Best For | Large-scale enterprises (multi-language, complex builds) | Python-heavy projects (fine-grained dependency management) | Mobile development (Android/iOS optimization) | JVM ecosystem (Java, Kotlin, Scala projects) | JS/TS monorepos (advanced task orchestration) | Simple JS setups (high-performance caching) | Publishing-focused (many NPM packages) | Small JS repos (legacy projects) |
| Maintained By | Community | Meta | Gradle Inc. | Nrwl/Nx team | Vercel | Microsoft | Nx team |
What the research suggests
I hope this matrix helps you make your own informed decisions. Based on the patterns that emerged from consulting 200+ references, here are some observations that might be useful:
For JavaScript/TypeScript projects: Nx and Turborepo both show strong performance and developer experience, with Nx offering more polyglot capabilities if you need them.
For JVM ecosystems: Gradle's massive plugin ecosystem (50,000+ plugins) and mature tooling make it the established choice.
For Python-heavy teams: Pants' automatic dependency inference could save significant time compared to manual configuration approaches.
For true polyglot at scale: Bazel and Buck2 handle the most languages natively, though they require more investment in learning.
For simple setups: Turborepo and Lerna offer minimal configuration overhead if you don't need advanced features.
The matrix above provides the detailed feature comparisons - hopefully it helps you evaluate what matters most for your specific situation.
Nx is currently my choice for most projects using Typescript and Python on AWS that need to get started quickly and scale.
The AI research methodology
This matrix represents consulting 200+ references, but only a couple hours of my actual time. The rest was AI doing systematic searches, link verification, and feature validation.
Treating AI like a research assistant, not an oracle, changes everything. I provided the framework, search strategies, and quality standards. The AI executed the searches and compiled the results. I reviewed and course-corrected everything.
AI excels at systematic execution but needs human judgment for accuracy and context.
This is the future of technical research: human judgment directing AI execution. AI handles the systematic searches and link checking. Humans provide the context, standards, and critical validation.
I can now research complex technical topics by consulting 200+ references in just a couple hours, while maintaining higher accuracy than pure AI-generated content.
Want to try this approach? Use these steps as a starting point for your own systematic research projects.
I used this same methodology to research and prioritize features for the AWS Serverless AI Gateway sample. The time savings are addictive once you get the process down.
- Ask for high-level market research.
- Make a feature matrix with the information.
- Work with the assistant closely on the first refinement task.
- Ask it to create a style/quality guide reference that it can refer to on each task.
- Ask it to create tasks to complete the work.
- Review the output of each task and course correct as needed.
The next time you need comprehensive research on any technical topic, you'll have a systematic approach that scales your curiosity without sacrificing your weekend.
© 2026 Archie Cowan