DeepSeek Harness v0.1 addresses the core frustration developers face when building AI agents: most frameworks lock the agent loop, tool registry, and session store into a fixed codebase, forcing teams to work around limited hooks or fork the project to change behavior. This rigidity slows experimentation, complicates compliance audits, and makes it hard to swap models or tools without touching the core harness.
The solution is a plugin‑first architecture built on the Cordis kernel. Every capability—models, tools, skills, sessions, sandboxes, storage, control loops, scheduling, and even the UI—lives behind a plugin boundary. Teams can select, swap, or extend any component through a simple configuration file without recompiling or modifying the harness source. This turns the harness into a reusable kit for assembling custom agent runtimes rather than a single purpose coding assistant.
Four preset runtime modes let teams start quickly: Standard mode provides a full coding agent; Code mode exposes tools via a TypeScript SDK for multi‑step orchestration; Minimal mode strips everything down to a persistent bash session and a file editor for clean model benchmarking; Creator mode adds runtime inspection and preset‑authoring tools for rapid prototyping. Each mode loads a different default plugin set, so the same kernel can serve production agents, evaluation benchmarks, or experimental workspaces.
Traceability is built in: every prompt, reasoning step, tool call, result, subagent decision, and context injection is written to an append‑only session log. Resume, fork, search, and replay all operate on the same event stream, giving teams full observability and the ability to debug or audit agent behavior without extra instrumentation.
Model routing is also a plugin. Adding a DeepSeek, Anthropic, OpenAI, Bedrock, Vertex, Azure, Codex, or any OpenAI‑compatible endpoint requires only an API key or credential reference stored locally; the harness picks up the new provider on the next request without a restart.
Because the project is MIT‑licensed and self‑hosted, regulated enterprises can pilot it on‑premises, AI‑native startups can iterate quickly, and developer‑experience teams can internalize the harness as a reusable platform. The developer preview (v0.1) is ready for experimentation and feedback, providing a practical path from rigid agents to flexible, observable, and extensible AI workflows.
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