by rajudandigam
250+ real-world TypeScript AI projects: workflows, agents, and multi-agent systems with production-ready architecture, not chatbot demos.
# Add to your Claude Code skills
git clone https://github.com/rajudandigam/Ultimate-TypeScript-Real-World-AI-ProjectsGuides for using ai agents skills like Ultimate-TypeScript-Real-World-AI-Projects.
Last scanned: 6/20/2026
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}Ultimate-TypeScript-Real-World-AI-Projects is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by rajudandigam. 250+ real-world TypeScript AI projects: workflows, agents, and multi-agent systems with production-ready architecture, not chatbot demos. It has 120 GitHub stars.
Yes. Ultimate-TypeScript-Real-World-AI-Projects passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/rajudandigam/Ultimate-TypeScript-Real-World-AI-Projects" and add it to your Claude Code skills directory (see the Installation section above).
Ultimate-TypeScript-Real-World-AI-Projects is primarily written in TypeScript. It is open-source under rajudandigam on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Ultimate-TypeScript-Real-World-AI-Projects against similar tools.
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263 blueprint-grade workflows, agents, and multi-agent systems — TypeScript-first. The Project catalog is documentation you can build from; only selected paths also have runnable reference code under reference-implementations/.
Most AI repos stop at call an API and hope it works. Plenty of content teaches SDK calls; far less shows how to ship systems that hold up in production. This repo goes further: real system design — not chatbot demos.
If you are trying to level up:
This repository has two layers:
projects/, each with README.md and architecture.md.packages/, runnable code under reference-implementations/, the benchmarks/ harness, and CI quality gates (.github/workflows/production-reference-ci.yml).Most projects are blueprints only (documentation you implement in your own codebase). Selected catalog ideas become runnable reference implementations that share @repo/* packages and tests — not a promise that all 263 rows ship as code in this repo.
flowchart TD
A[README Project Catalog] --> B[Blueprint Projects]
A --> C[Learning Docs]
A --> D[Top Projects]
B --> B1[projects/domain/project/README.md]
B --> B2[projects/domain/project/architecture.md]
C --> C1[Learning Path]
C --> C2[Production Patterns]
C --> C3[Contribution Guides]
D --> E[Reference Implementations]
E --> E1[AI Cost Monitoring Engine]
E --> E2[Future: Multi-Agent Incident Response]
E --> E3[Future: Enterprise RAG Platform]
E --> F[Shared Packages]
F --> F1["@repo/core"]
F --> F2["@repo/governance"]
F --> F3["@repo/evals"]
E --> G[Benchmarks]
G --> G1[Methodology]
G --> G2[Benchmark Suites]
G --> G3[Local Results]
E --> H[CI / Quality Gates]
This repository remains catalog-first. The Project catalog (263 blueprint rows) is the main learning resource: every entry points at projects/<domain>/<slug>/ with README.md (problem, stack, evaluation, failure modes) and architecture.md (diagrams and engineering depth). Nothing here replaces that contract with a second registry.
Alongside the catalog, an additive production reference architecture layer helps you go from “what to build” to “how it can look in TypeScript” without pretending every blueprint is shipping code:
reference-implementations/ that mirror specific catalog paths. They demonstrate ingestion, rollups, governance hooks, and similar patterns you can adapt; they are not a completeness matrix for all 263 ideas.packages/ (@repo/core, @repo/governance, @repo/evals) for runtime and governance primitives. These are building blocks and documentation-backed patterns — not compliance certifications (see each package README).benchmarks/ with explicit methodology (benchmarks/methodology.md): local timings, environment metadata, and no implied SLAs or competitive “wins” without controlled reproduction.Roadmap and boundaries: docs/PRODUCTION_REFERENCE_ARCHITECTURE_PLAN.md.
First flagship (runnable): reference-implementations/ai-cost-monitoring-engine — companion to the blueprint projects/devtools/ai-cost-monitoring-engine.
263 project ideas, each with README.md + architecture.md under projects/. The catalog tables below are the authoritative registry.
Selected runnable systems that exercise shared packages and show production-shaped wiring (observability seams, governance integration, honest limitations). They link back to catalog paths for context; they do not renumber or replace catalog rows.
Reusable TypeScript modules (@repo/core, @repo/governance, @repo/evals) shared by reference apps and future flagships — narrow APIs, strict typing, tests where code exists. Prefer extending these over copy-pasting patterns into every new implementation.
GitHub Actions: .github/workflows/production-reference-ci.yml runs on push and pull_request to main: install pnpm, install dependencies, pnpm run typecheck, pnpm test, and a benchmark smoke (@repo/benchmarks cost-monitoring suite with mock-only data — no API keys, no external LLM calls).
Run tests locally (repository root):
pnpm install
pnpm run typecheck
pnpm test
Run benchmarks locally
benchmarks/) — cost monitoring suite (writes timestamped artifacts under benchmarks/results/ unless gitignored):cd benchmarks
pnpm suite:cost-monitoring
# optional: faster smoke
BENCHMARK_ITERATIONS=5 COST_MONITORING_EVENT_SCALE=20 pnpm suite:cost-monitoring
cd reference-implementations/ai-cost-monitoring-engine
pnpm benchmark
Interpret timings using benchmarks/methodology.md (local reference only).
After building and reviewing real AI products, one wall kept showing up: “We have prompts… now what?” Teams stall at prompt + API call while the product still needs:
This collection bridges that gap with practical blueprints you can actually build: problems, architecture, TypeScript-friendly stacks, evaluation, failure modes, and scaling — systems thinking applied to AI.
| Category | Count |
|---|---|
| Total projects | 263 |
| Workflow / workflow-first | 77 |
| Agent | 144 |
| Agentic UI (AG-UI) | 10 |
| Multi-agent | 32 |
| Level 5 (production-grade target) | 43 |
Summary counts match the Project catalog below. Each catalog entry is a blueprint: problem, architecture, TypeScript-oriented stack, evaluation, failure modes, and scaling — not a vague idea list.
This repo is catalog-driven (one registry in the README project catalog).
projects/ and pick a domain folder (for example projects/devtools/, projects/healthcare/, projects/fintech/, projects/enterprise-ai/, projects/security/, projects/travel/, and many more). Each project slug folder contains README.md and architecture.md.by-system-type/ or by-complexity/ trees; the catalog is the filter.Authoring rules for new catalog entries are described in CONTRIBUTING.md.
| Area | Examples |
|---|---|
| DevTools & engineering | projects/devtools/pr-risk-analyzer, projects/devtools/ai-code-review-agent, projects/devtools-ai/schema-migration-safety-agent, projects/devtools/ai-debugging-assistant |
| Infra & platforms | projects/devops/multi-agent-incident-response-system, projects/devtools/ai-cost-monitoring-engine, projects/ai-infra/mcp-tool-registry-system, projects/energy-ai/grid-load-balancing-multi-agent-system |
| Enterprise & knowledge | [`projects/enterprise-ai/organiza |