[!NOTE]
🎉 Within one week of launch, OpenHuman became the number one trending repository on GitHub for nine days in a row.
Early beta. OpenHuman is under active development, so expect rough edges.
Install
The easiest way is to download the desktop app from tinyhumans.ai/openhuman or the latest release. There is a .dmg for macOS, an .msi or .exe for Windows, and a .deb or .AppImage for Linux.
Prefer the terminal? The install script picks the right package for your system, checks its checksum and installs it:
# macOS and Linux
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
# Windows (PowerShell)
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
To preview what the macOS and Linux script would do, end the command with bash -s -- --dry-run. Other options and troubleshooting are in INSTALL.md.
Why OpenHuman?
Most agent harnesses run one heavy process per agent and resend a big prompt on every call. OpenHuman does the same work with far less. It is the only feature-rich open-source harness built for large fleets of agents: 500 of them fit on a $10 server.
Major innovations
Most agent harnesses are a simple loop: send everything to the model, wait, repeat. That works for one agent, but it gets slow and expensive quickly, and it falls apart when you run hundreds.
OpenHuman rethinks the parts that cost the most: how much text the AI has to read, how it finds the right tool, how features load, and how much machine the whole thing needs. The six ideas below are where the speed and savings above come from. Each card links to the docs if you want the details.
Benchmarks
We ran seven harnesses on the same SWE-bench tasks, with the same model, API key and container, and metered every call. The setup and results are public in openhuman-benchmarks, so anyone can rerun them. The latest run is swe-x86-1, with ten tasks.
OpenHuman finished its tasks in less than half the median time, using 2.6x fewer tokens and an eighth of the memory and CPU. It made 114 model calls where the others made 134 to 212, and its whole run cost $0.05 where the others cost $0.08 to $0.35.
It wins by doing less work per step. The core is compiled Rust in a single process, not Node or Python, so it uses little memory and almost no CPU between model calls. It sends the smallest prompt of the seven, 4.6k tokens with its 20 tools included, so each call is cheaper and comes back faster (1.65 s median, also the fastest). It also needs fewer calls to reach an answer, which is where most of the savings come from.
| Per solved task | OpenHuman (vs. median) | Median of the other six | Best of the other six |
|---|
| Wall time (p50) | 19.8 s (2.3x faster) | 46.5 s | 28.4 s (OpenCode) |
| Tokens | 167k (2.6x fewer) | 435k | 370k (Codex) |
| Cost | $0.0077 (-36%) | $0.012 | $0.0077 (OpenCode, a tie) |
| Peak memory | 68 MB (7.7x less) | 523 MB | 123 MB (Codex) |
| CPU time | 1.3 s (8x less) | 10.4 s | 2.9 s (DeepSeek Harness) |
| System prompt | 4.6k tokens (-40%) | 7.7k | 6.2k (DeepSeek Harness) |
For users
If you use Claude Code, Codex, OpenClaw or Hermes, you already know the ideas: an agent loop with tools, MCP, skills, BYOK models and memory. OpenHuman has all of them, in a desktop app, a terminal app or a headless server.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
