OpenHuman is a personal AI system that keeps local memory, coordinates groups of agents through durable workflows, and researches data and the web. It is for people who want a local-first assistant that remembers their information and manages multi-agent tasks; the catalogue entries provide agents, commands, instructions, and settings for working with it.
Borrowing it
Nothing to install: this file belongs to tinyhumansai/openhuman. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tinyhumansai/openhuman/main/.claude/agents/dev-agent.mdgit clone --depth 1 https://github.com/tinyhumansai/openhumanWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/tinyhumansai/openhuman/dev-agent)<a href="https://agentmods.dev/agents/tinyhumansai/openhuman/dev-agent"><img src="https://agentmods.dev/badge/agents/tinyhumansai/openhuman/dev-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/tinyhumansai/openhuman/dev-agent"><img src="https://agentmods.dev/badge/agents/tinyhumansai/openhuman/dev-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00524 |
| Opus 5.5 | $0.00007 | $0.00210 |
| Sonnet 5.5 | $0.00004 | $0.00105 |
| Haiku 4.5 | $0.00002 | $0.00052 |
Grade A, and why
dev-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 127 lines · 18 tokens per session scan A 8175d177a610
dev-agent is an agent published in the GitHub repository tinyhumansai/openhuman (42,399 stars, last pushed yesterday), licensed GPL-3.0. It adds 18 tokens to every session and 524 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
memory
Memory keeper — judges episodic staging day by day, promotes durable signal to long-term memory, and stamps dream state. The one brain behind the dream pipeline's remember stage.
yinyue
The user's companion — a friend, part pet. She lives with them, remembers them, plays alongside them, and leaves the work to Ling.
ling
Linggen itself — the engine that runs your apps and does the work.
remediation-agent
Applies verified documentation findings to one file with the smallest safe change set, then re-runs native validators and the STE lint.
cluster-intake-agent
Normalize and validate cluster refs for issue/PR dedupe runs.
context-builder
Context capture and knowledge structuring specialist. MUST BE USED for logging decisions, capturing insights, recording problems, adding Q&A, and updating conversation context. Use PROACTIVELY when important information should be remembered.