I got tired of rebuilding the same AI plumbing, so I built an open-source pool for local models, APIs and machines

I kept running into the same problem across projects:

\*I already have workstations, VPSes, shared hosting and different model runtimes, but every app somehow ends up needing another hard-wired integration to whatever machine or API happens to provide the model.\*

So I built **ContextBridge.**

The idea is pretty simple: the app describes what it needs instead of deciding where it has to run.

CB filters workers by things like task, provider/model, tags, RAM/VRAM and policy, then ranks compatible candidates using current capacity. route explain shows why something was or wasn’t picked.

A PHP app on shared hosting can, for example, submit work through a relay and have a model running on my PC execute it. The web host itself doesn’t need a GPU, shell access or a ContextBridge process.

Ollama is one possible resource, not the architecture everything is built around. CB can also work with APIs and other runtimes, and can manage a llama.cpp runtime and selected models itself.

There are also durable schedules, deterministic pipelines, MCP and a bounded planner for multi-step work. The planner can propose steps, but it can’t suddenly decide it deserves another provider, more budget, different egress rules or extra retries.

I’ve tried to keep the trust boundaries intentionally boring: explicit worker pairing, scoped credentials, optional E2EE between producer and reserved worker, and no silently replaying ambiguous executions somewhere else.

This is still pre-1.0. I’ve spent a fair amount of time getting rid of happy-path-only assumptions and finding ugly edge cases, but at some point you need other people’s weird setups.

My rubber duck has stopped responding, so go ahead and break assumptions, point at something suspicious or tell me what I’ve overcomplicated. Feedback is very welcome 🦆.

**AI involvement:**  
Yes. I use AI for documentation, review, wording and parts of coding/testing. I treat it as a tool, not an autonomous developer: changes are reviewed against the actual code/contracts and tested in an isolated/mirrored environment before I keep them.

ChatGPT was also used for the logo based on my original concept, and partly for this post, because I’m German and would rather debug a race condition than polish English prose.

\*\*License:\*\* current core is AGPL-3.0-only. Explicitly listed interfaces/examples are Apache-2.0. Previously published v0.6.x releases remain MIT.

IamAngusU/ContextBridge: Connect local models, AI APIs and private machines into one controlled pool for apps and agents. Local-first, self-hosted AI orchestration.