For the longest time, I've been trying to figure out a way to "survive" in this new AI age without having to fork over a ton of money just to keep up. I've tried using local models via Ollama, and while they definitely work to a degree, they're (unsurprisingly) not as good as the big model providers.
The local models tend to
- Forget what they're doing
- Struggle to break larger tasks into smaller ones
- Lose focus easily
- Have weaker coding performance
- Drift over longer sessions
So to improve the reliability of fully local, smaller models (and to keep all my data local and in my own network), I created Coyote.
It's a local-first, batteries-included command line tool and runtime for building and running LLM workflows locally. It's model agnostic and supports things like
- Agents and agent delegation
- Roles/personas
- MCP Servers
- RAG
- Custom tools
- Macros
- Workflow Scripting
A lot of the features it supports are specifically designed to compensate for weaknesses in smaller local models. For example:
- Auto continuation to keep pushing models to completion instead of stopping halfway through problems
- Parallel agent delegation so tasks can be split into smaller, focused scopes
- Workflow-based execution ("If this, do that") for building more reliable and repeatable automations
It also supports the major cloud providers if you want them (which definitely helped while testing π), but my long-term goal is simple:
Get as close as possible to Claude Code-style reliability using fully local models.
I'm always open to feedback, questions, or ideas.
![co[yo]de logo: coyote with a hoodie and the aforementioned spelling](https://feddit.org/pictrs/image/2287d9db-d8b8-4193-83d0-2dbe7de66c50.png)