Building AI Coding Assistants with OpenHands and Self-Hosted LLMs
Autonomous Coding Agents Are a Different Category
A chat-based coding assistant answers questions and suggests snippets. An autonomous coding agent goes further: it reads a repository, plans a multi-file change, writes the code, runs the test suite, and iterates on failures without a human approving each individual step. OpenHands is the leading open-source project in this category — it exposes a sandboxed execution environment where an LLM can run shell commands, edit files, and browse documentation as part of completing a task.
Why Self-Hosting Matters Here Specifically
Coding agents need broad access to a codebase to be useful, which is precisely the access that makes sending everything to a third-party API uncomfortable for teams working on proprietary code, unreleased features, or anything under an NDA. Running OpenHands against a self-hosted model closes that gap: the agent still gets full repository access, but the code, the prompts, and every intermediate reasoning step stay inside your own infrastructure.
Recommended Architecture
- OpenHands — the agent runtime and sandboxed execution environment
- Ollama or vLLM — local inference for the underlying coding model
- LibreChat — a chat interface for reviewing agent output and giving follow-up instructions
For best results, coding agents need a model specifically capable at code generation and tool use — models in the Qwen Coder, DeepSeek Coder, and Llama families are common choices, all runnable through Ollama or vLLM without any code changes to OpenHands itself.
What This Looks Like Day to Day
A typical task starts with a natural-language instruction — "fix the failing test in the auth module" — and the agent clones the relevant context, proposes a plan, makes the edit, and re-runs the test suite before reporting back. Reviewing the resulting diff still requires a human, but the mechanical work of locating the failure and drafting a fix is handled automatically. Teams running this internally typically scope agents to specific repositories and require review before any merge, treating the agent as a fast first-pass contributor rather than an autonomous committer.
Recommended NexNodo Deployment
The recommended deployment is a Cloud GPU VPS Small:
- 1× H200 GPU
- 15 vCPU
- 256 GB RAM
- 1 TB Storage
- $3.79/hr or $2,767/mo
This is sufficient for a single coding-capable model serving a small engineering team; larger teams running concurrent agent sessions should size up to a Medium GPU tier.
Deploy Faster
OpenHands, LibreChat, and Ollama are available from the NexNodo AI Catalog, or start from the AI Agent Platform Template and add OpenHands as an additional application.