THE BEST AGENT UPGRADE MIGHT NOT BE A NEW MODEL

it might be one of these 10 repos:

1. deepseek-harness

Build the agent around plugins instead of hard-wiring everything

Swap tools, interfaces or behavior without rebuilding the whole thing

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

2. teamai-cli

Keep the same skills, rules and project docs across Claude Code, Codex and other coding tools

Change something once instead of maintaining a different setup for every agent

https://github.com/Tencent/teamai-cli…

3. headroom

Cut down the junk before it reaches the context window

Logs, JSON, tool output and large files get compressed instead of eating tokens turn after turn

https://github.com/headroomlabs-ai/headroom…

4. ECC

Inspect what’s happening around the model, not just the model itself

Routing, context, hooks and checks can all be tuned instead of accepting the default setup

https://github.com/affaan-m/ECC

5. hermes-agent

Give an agent something closer to memory between sessions

It can reuse previous work, build skills and search old conversations instead of starting cold every time

https://github.com/NousResearch/hermes-agent…

6. ruflo

Split a bigger job across multiple agents instead of asking one context window to do everything

Good for workflows where research, coding, review and testing can happen separately

https://github.com/ruvnet/ruflo

7. aidlc-workflows

Give coding agents an actual development process

Requirements -> design -> implementation -> verification instead of one giant “build this” prompt

https://github.com/awslabs/aidlc-workflows…

8. agent-browser

Let the agent use the product it just built

Open pages, click buttons, fill forms and test flows in a real browser instead of stopping at “the code compiles”

https://github.com/vercel-labs/agent-browser…

9. rtk

Clean up terminal output before the agent has to read it

Tests, git output, grep and build logs get condensed so useful context survives longer

https://github.com/rtk-ai/rtk

10. okf-agent-memory

Keep project decisions and knowledge around after the session ends

The next agent can search what happened before without loading the entire history again

https://github.com/okf-memory/okf-agent-memory…

the model still matters

but before chasing the next release, I’d fix context, memory, tools and verification first

deepseek-ai/deepseek-harness

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

DeepSeek Harness

English | 中文

DeepSeek Harness (dsh) is an open-source agent harness developed by DeepSeek AI.

It is built on an everything-is-a-plugin architecture and powered by Cordis, whose design is described in A Programming Paradigm for Spatiotemporal Composability.

Documentation: https://deepseek-harness.github.io/deepseek-harness/

Developer preview

DeepSeek Harness is in developer preview and iterating rapidly. THERE WILL BE COMPATIBILITY-BREAKING CHANGES.

Review the safety notice before running the project.

Run

Run from npm

Install Node.js, then run:

npx @deepseek-ai/dsh web

The command starts the Web UI at http://127.0.0.1:3080 by default and opens it in the default browser for a local launch. An SSH launch only prints the host URL because the SSH client or editor owns the local forwarded address. Pass --no-open to run the server without opening a browser. See Web UI guide.

Run from source

To run from a repository checkout:

git clone https://github.com/deepseek-ai/deepseek-harness.git
cd deepseek-harness
pnpm install
pnpm run build
pnpm dsh web

pnpm run build prepares the repository artifacts. pnpm dsh web uses those built artifacts without rebuilding.

Community and support

• Submit feedback or bug reports through GitHub Discussions.
• Add the dsh-plugin topic to your plugin repository for discoverability.
• Join DeepSeek Harness Discord community.

Contributing

See CONTRIBUTING.md.

Development

Start with the development guide and architecture documentation.

For agents, follow AGENTS.md.

Citation

@misc{deepseek-harness2026,
  title={DeepSeek Harness: Everything is a Plugin},
  author={DeepSeek-AI},
  year={2026},
  publisher={GitHub},
  howpublished={\url{https://github.com/deepseek-ai/deepseek-harness}},
}

License

MIT

Third-party dependencies and their licenses are disclosed in THIRD_PARTY_NOTICES.md.