tinyhumansai / openhuman

OpenHuman 
 The fastest, cheapest, most efficient open-source agent harness. Run more than 500 agents on a $10 VPS. 
 A desktop app for people. A Rust library for developers.
 Docs ·
 Rust quickstart ·
 Benchmarks ·
 Discussions ·
 Discord ·
 Reddit ·
 X ·
 @senamakel (creator) 
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> [!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.
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## 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:
 [code] 
 [code] 
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.
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## 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.
 Fast 
 Benchmark results · Cold-start numbers 
 Finishes coding tasks in about 20 seconds, the fastest of seven AI agent tools we tested. Starts up in a tenth of a second. 
 Cheap 
 Cost and token data · How compression works 
 Uses 2.6x fewer tokens than the typical agent tool, and had the lowest total bill in our test. 
 Efficient at scale 
 Fleet measurements · Methodology 
 Uses about 8x less memory and CPU than the typical agent tool. Run more than 500 agents on a $10 server. 
 Built for developers 
 Rust quickstart · Embedding guide · Examples 
 Use it as a Rust library: call an agent like any other function, or run a whole fleet from one small server. 
 Same prompt, Same model, Same reasoning side by side: "Write me a story about agent harnesses in the form of an anime story, write it into an HTML page and open it for me." OpenHuman finished in 20s, using 15k tokens for $0.0054. Hermes took 9min 40s, using 37k tokens for $0.0082. 
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## 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.
 RLM token compression 
 RLM paper · How it works 
 Built on Recursive Language Models . Large tool results get compressed before the AI reads them. For very large ones, the AI gets a handle it can sear