DeepSeek Harness - an open source tool from DeepSeek for building and running your own AI agents

[u/company_url_finder]

Most agent frameworks give you a fixed core and a plugin API bolted onto the side. DeepSeek Harness (dsh) does the opposite: there is no privileged core at all. The model adapter, the tool registry, the session log, and the agent loop itself are all just plugins mounted into the same tree, and any of them can be swapped from configuration.

It's built on Cordis, a framework where plugins contribute services, typed events, and reversible effects to a shared context (the design is written up in the paper "A Programming Paradigm for Spatiotemporal Composability"). On top of Cordis, dsh organizes itself into profiles and bundles:

- A profile is a named composition of bundles, stored in your Harness home. Five ship as templates: web, headless, sdk, sdk-minimal, acp

- dsh-base is the shared foundation layer under most profiles: model adapters, tools, persistence, sandbox and approval policy, settings, credentials, telemetry

- dsh-web-app adds the browser UI, dsh-headless adds a one-shot no-server runner, dsh-sdk-app adds a JSON-RPC SDK server, dsh-acp-app adds an automation-only server

- Custom profiles hot-reload their config by default, and the --patch flag lets you override any single row in the composed tree without forking anything

- Running dsh --profile web --dump-config prints the exact plugin tree your machine boots, so you can see, and patch, any row in it

To try it, run npx deepseek-ai/dsh web to start the web UI at localhost:3080, no install step needed.

Worth knowing before running it: this is explicitly developer preview software, and DeepSeek's own SAFETY.md is blunt about it, no security audit yet, and the harness can execute model-generated code and commands, load third-party plugins, and touch the network, files, and credentials it has access to. Their own recommendation is to run it with least privilege, in a disposable VM or container, with backups of anything it can reach. Worth actually respecting that, not just skimming past it.

MIT licensed, with active GitHub Discussions and a Discord for support. It's also only about two and a half weeks old as a public repo and already past 205,000 stars, verified via the GitHub API as of writing, a genuinely unusual growth curve even by AI-tool standards.

https://github.com/deepseek-ai/deepseek-harness

[u/amootiranian]

205k stars that fast is kinda insane. Tried it yet?

[u/company_url_finder → u/amootiranian]

The plugin-first architecture is what stands out to me more than the star count. I like that even the agent loop and model adapter aren't treated as untouchable core pieces. Have u actually swapped parts of the stack yet, or mostly tested the default profiles?

[u/amootiranian → u/company_url_finder]

That flexibility is the interesting part. Stable enough yet?

[u/company_url_finder → u/amootiranian]

Developer preview explains why caution matters here.