We build AI agents
that work.

You tell us what to automate. We deliver a production agent on your infrastructure — no data leaves your network. Open source stack, no lock-in.

For operations teams at companies that need AI running reliably, not chatbots that break.

Discuss your use case

From problem to production agent

1. You describe the workflow

What do you need automated? Operational monitoring, compliance reporting, document processing, research pipelines, internal tooling — if it can be defined, it can be an agent.

2. We build and deploy

We deliver a working prototype fast, then iterate with your team. Runs 24/7, handles failures gracefully, learns from feedback.

3. You own everything

Open source infrastructure. Your data, your hosting, your code. No vendor lock-in. Walk away any time with everything you need to run it yourself.

Proof, not promises

llm-news

Our first agent monitors 67 sources daily and delivers personalized AI industry briefings. Running in production now.

news.llm-works.ai

Purpose-built infrastructure

appinfra — Foundation

Production-grade Python infrastructure. Configuration, logging, scheduling, and service management.

llm-infer — Inference

Multi-backend inference server. Native, vLLM, and Ollama with unified client interface.

llm-kelt — Fine-tuning

Context management, feedback collection, RAG retrieval, and LoRA fine-tuning pipeline.

llm-saia — Structured AI

Typed verbs for LLM interactions — verify, critique, extract, synthesize. Predictable, testable outputs.

llm-gent — Agents

Agent framework with trait-based architecture and learning capabilities.

250k
lines of code
6,500+
tests
70%
coverage
6
packages

Full stack on GitHub

Built by an engineer

18 years of building high-performance systems. PhD in computer science (signal processing, 3D graphics). Real-time and mission-critical platform engineering across multiple industries — designed from scratch, operated at scale.

LLM Works exists because AI agents should be infrastructure — reliable, tested, production-grade.

Let's discuss your use case

Describe your workflow. We'll tell you how we'd build it and what it takes.

Schedule a technical assessment