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 😄), 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.

Repo: https://github.com/Dark-Alex-17/coyote