DeepSeek Harness (DSH) vs Pi Agent: Everything you need to know

DeepSeek released its own agent harness this August. It crossed 66k stars in about a day (yes, sir!) and went over 200k by early September. That is already pretty crazy for something that is still a developer preview. But while researching this post, I found something much more interesting. DeepSeek Harness actually uses Pi's model layer to connect to models outside DeepSeek. Yep. The new agent runtime from DeepSeek uses Pi under the hood for part of its model support. And Tianyi Cui , who leads the Harness project, has publicly said Pi is a favourite daily driver for many people at DeepSeek. Lol. That makes this comparison way more fun because they agree on quite a lot. Both think the harness matters, both are open source, both let you change a lot of the runtime, and both try to avoid locking you into one model. But they take the idea in very different directions. Pi gives you a tiny coding agent with four tools and lets you build from there. DeepSeek Harness gives you a full agent runtime where even the agent loop itself can be swapped. This is going to be an interesting one. Stick to it! TL;DR Category Pi DeepSeek Harness Winner Real tool use in our eval 21/30 passed 20/30 passed Pi Cost per shared success $0.031 $0.028 DeepSeek Harness Median time per task 362.9s 252.1s DeepSeek Harness Avg runtime tokens in our run 924,990 88,562, with a catch DeepSeek Harness Simplicity Four main tools and a tiny prompt Much larger runtime with 53 built in tools Pi Deep runtime control Powerful TypeScript extensions around a small core Even the agent loop can be replaced DeepSeek Harness Model support 25+ providers and very easy switching Broad support, partly through Pi's model layer Pi Local models Ollama, vLLM, and custom providers are well supported Possible, but needs more setup Pi Sandbox None in the core Three built in sandbox modes DeepSeek Harness Session inspection Very readable session tree Full append only trajectory and replay DeepSeek Harness Daily use Very smal