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

Pacific Slate, a new open-source project, enables users to host their own multi-agent AI system with a model-agnostic architecture that supports local runners like Ollama and external APIs. The system allows for dynamic agent coordination, persistent memory, and hybrid model usage to balance latency and cost, giving users control over their data and compute.

Pacific Slate lets you host your own multi-agent AI system 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. Is this the end of the "escape the sandbox" fear for LLMs? 7h ago /en/news/5687/ DeepSeek R1 can actually detect when it's being tested and 8h ago /en/news/5680/ ByteDance is reportedly training a 10-trillion parameter model 1d ago /en/news/5541/ DeepSeek R1 is basically the AI equivalent of a breakout artist 2d ago /en/news/5391/ AI Model Evolution: Impact on Governance and Control 3d ago /en/news/5345/ DeepSeek Pricing Shift: Is the Low-Cost Era Ending? 3d ago /en/news/5267/ Next Spending $1. → /en/news/5712/ a guide to making money with AI https://tanyan888.com/ , with plenty of directly applicable cases.