Pacific Slate lets you host your own multi-agent AI system

Running a dedicated server or repurposing an old laptop just to keep LLM agents synced is a huge pain, which is exactly why I put together Pacific Slate. It is a self-hosted, model-agnostic framework that lets you orchestrate multiple agents without being locked into a specific provider or forced into a rigid, cloud-based subscription model.

The core philosophy here is flexibility. Most AI orchestration tools try to force you into a "one size fits all" workflow, but the reality is that the best agent configuration for a coding task is completely different from what you'd need for research or content generation. Pacific Slate is designed to be configured individually. I've kept the architecture open so you can tweak the logic, swap out the underlying models, and define how agents interact based on your specific needs.

For those looking for a practical tutorial on getting this moving, the deployment focuses on minimalism. Since it is model-agnostic, you can hook it up to local runners like Ollama or connect it to external APIs via standard endpoints. The goal is to create a persistent environment where your agents maintain state and context without requiring you to leave a browser tab open or a high-powered machine humming in the corner of your room 24/7.

If you are building your own AI workflow, here is the general logic of how the system handles agent coordination:

{
  "agent_config": {
    "id": "research_agent_01",
    "model": "claude-3-5-sonnet",
    "role": "Information Synthesis",
    "capabilities": ["web_search", "document_parsing"],
    "memory_persistence": true
  },
  "orchestrator": {
    "routing_logic": "dynamic",
    "handoff_threshold": 0.85
  }
}

The "model-agnostic" part is the real winner here. You can have a heavy-duty model handling the complex reasoning and a faster, smaller local model managing the routine routing or formatting. This hybrid approach keeps latency low and costs down while maintaining the quality of the output.

I've structured the project to be easy to unpack and replicate. Instead of a rigid product, think of it as a blueprint for a personalized LLM agent ecosystem. You can start from scratch by defining your first agent and then gradually scale the complexity of the interactions. Whether you are doing a deep dive into autonomous agents or just want a more private way to manage your AI tools, this setup removes the friction of hardware dependency. It is essentially about taking back control of your data and your compute.