Key Facts
- ✓ A new client enables Ollama's local models to function within a Claude Code-style workflow, allowing for offline AI coding assistance.
- ✓ The project was initiated after Ollama released support for an Anthropic-compatible API on January 16, 2026.
- ✓ The client automatically detects and switches to local Ollama models when an internet connection is unavailable, maintaining a seamless user experience.
- ✓ In initial testing, the qwen3-coder:30b model has proven to be the most effective local model for this workflow.
- ✓ Another model, glm-4.7-flash, was tested but is not yet usable due to difficulties in following tool-calling instructions.
- ✓ The project has been discussed on Hacker News, part of the Y Combinator community, where it has received 6 points and 2 comments.
Local AI for Code
A new development in the AI coding space has emerged, enabling developers to use Ollama's local models within a Claude Code style workflow. This client allows for local inference, providing a solution for coding assistance without requiring an active internet connection.
The project was not initially planned as a major standalone release. Instead, it was born from curiosity following a specific update to the Ollama platform, which opened the door for this integration.
The Catalyst for Change
The project became possible due to a significant update from Ollama on January 16. The platform added support for an Anthropic-compatible API, which sparked the idea to test the limits of this new capability.
The developer decided to experiment by plugging local Ollama models directly into a Claude Code-style workflow. The primary goal was to see if the entire process could function from start to finish using local resources.
Here is the release note from Ollama that made this possible.
"Here is the release note from Ollama that made this possible."
— Project Developer
How It Works
The technical implementation of the client is designed to be straightforward and seamless for the end-user. The system first detects which local models are currently available within the user's Ollama installation.
When an internet connection is not available, the client automatically switches to using Ollama-backed local models instead of remote ones. From the user's perspective, the experience mirrors the standard Claude Code flow, but the underlying inference is handled locally.
- Detects available local Ollama models
- Automatically switches to local models offline
- Maintains the familiar Claude Code user experience
Model Performance
Testing has identified a current leader among the local models for this specific workflow. The qwen3-coder:30b model has demonstrated the best performance so far, proving to be the most effective for the client's requirements.
Another recently released model, glm-4.7-flash, was also evaluated. However, it currently struggles with reliably following tool-calling instructions, which makes it unsuitable for this workflow at this time.
Community Reaction
The project has garnered attention within the developer community, particularly on platforms like Y Combinator's Hacker News. The discussion thread for this release has attracted engagement from users interested in local AI and coding tools.
As of the latest update, the post has received 6 points and generated 2 comments, indicating initial interest in the potential of local model integration for development workflows.
Looking Ahead
This development represents a practical step toward more accessible and private AI-assisted coding. By leveraging local inference, developers can maintain productivity and privacy without relying on constant cloud connectivity.
The project highlights the growing flexibility of the Ollama ecosystem and its ability to integrate with established workflows. Future improvements will likely focus on expanding model compatibility and refining the user experience as more models become capable of handling complex tool-calling instructions.
