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Openhands

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NousResearch
openhands

Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

Overview

PublisherNousResearch
Repositoryhermes-agent
Skill nameopenhands
Stars
250.4K
Forks
53.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NousResearch on GitHub. Read the source before you install it.

Installation

Install the Openhands AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/autonomous-ai-agents/openhands
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent
mkdir -p .claude/skills
cp -r /tmp/hermes-agent/optional-skills/autonomous-ai-agents/openhands .claude/skills/openhands
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openhands in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openhands on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openhands is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenHands CLI

Delegate coding tasks to the OpenHands CLI via the terminal tool. OpenHands is model-agnostic: any LiteLLM-supported provider (OpenAI, Anthropic, OpenRouter, DeepSeek, Ollama, vLLM, etc.).

This skill is the headless-mode wrapper for batch / one-shot delegation. The interactive textual UI is not used from Hermes.

When to Use

  • User wants a coding task delegated to OpenHands specifically.
  • User wants a coding agent that can run on a non-Anthropic / non-OpenAI provider (DeepSeek, Qwen, Ollama, vLLM, Nous, etc.) — sibling skills claude-code and codex are tied to one vendor.
  • Multi-step file edits + shell commands inside a workspace.

For Claude-native, prefer claude-code. For OpenAI-native, prefer codex. For Hermes-native subagents, use delegate_task.

Prerequisites

  1. Install upstream (requires Python 3.12+ and uv):

    terminal(command="uv tool install openhands --python 3.12")

    Verify: openhands --version (currently OpenHands CLI 1.16.0 / SDK v1.21.0 at time of writing).

  2. Pick a model and set env vars for --override-with-envs:

    export LLM_MODEL=openrouter/openai/gpt-4o-mini       # or any LiteLLM slug
    export LLM_API_KEY=$OPENROUTER_API_KEY
    export LLM_BASE_URL=https://openrouter.ai/api/v1     # omit for native OpenAI

    LLM_MODEL uses LiteLLM's full slug. When the provider is OpenRouter the slug is doubly-prefixed: openrouter/<vendor>/<model> (e.g. openrouter/anthropic/claude-sonnet-4.5). For native Anthropic: anthropic/claude-sonnet-4-5. For native OpenAI: openai/gpt-4o-mini.

  3. Suppress the startup banner so JSON output isn't preceded by ASCII art:

    export OPENHANDS_SUPPRESS_BANNER=1

How to Run

Always invoke through the terminal tool. Always pass --headless --json --override-with-envs --exit-without-confirmation for automation.

One-shot task

terminal(
  command="OPENHANDS_SUPPRESS_BANNER=1 LLM_MODEL=openrouter/openai/gpt-4o-mini LLM_API_KEY=$OPENROUTER_API_KEY LLM_BASE_URL=https://openrouter.ai/api/v1 openhands --headless --json --override-with-envs --exit-without-confirmation -t 'Add error handling to all API calls in src/'",
  workdir="/path/to/project",
  timeout=600
)

Background for long tasks

terminal(command="<same as above>", workdir="/path/to/project", background=true, notify_on_complete=true)
process(action="poll", session_id="<id>")
process(action="log", session_id="<id>")

Resume a previous conversation

OpenHands prints Conversation ID: <32-hex> and a Hint: openhands --resume <dashed-uuid> line at the end of each run. Use the dashed form to resume:

terminal(
  command="OPENHANDS_SUPPRESS_BANNER=1 LLM_MODEL=... openhands --headless --json --override-with-envs --exit-without-confirmation --resume <dashed-uuid> -t 'Now fix the bug you found'",
  workdir="/path/to/project"
)

Real Flag List

Verified against openhands --help (CLI 1.16.0). Anything not in this table is not a flag — pass it via env var or settings file.

FlagEffect
--headlessNo UI, requires -t or -f. Auto-approves all actions (no --llm-approve in this mode).
--jsonJSONL event stream (requires --headless).
-t TEXTTask prompt.
-f PATHRead task from file.
--resume [ID]Resume conversation. No ID → list recent.
--lastResume most recent (with --resume).
--override-with-envsApply LLM_API_KEY / LLM_BASE_URL / LLM_MODEL env vars. Without this, OpenHands uses ~/.openhands/settings.json and ignores the env.
--exit-without-confirmationDon't show the "are you sure" exit dialog.
--always-approve / --yoloAuto-approve every action (default in --headless).
--llm-approveLLM-based security gate (interactive only — does NOT work in headless).
--version / -vPrint version and exit.

There is no --model, --max-iterations, --workspace, --sandbox, --sandbox-type flag. Model is LLM_MODEL. Workspace is the workdir you pass to the terminal tool. Sandbox / runtime is the RUNTIME and SANDBOX_VOLUMES env vars.

JSON Event Schema

With --json --headless, OpenHands emits JSONL — one JSON object per line, plus a handful of non-JSON status lines (Initializing agent..., Agent is working, Agent finished, the final summary box, Goodbye!, Conversation ID:, Hint:). Filter for lines starting with {.

Top-level kind field discriminates events:

  • MessageEvent — user / agent text turn. source is user or agent.
  • ActionEvent — agent picked a tool. Read tool_name (file_editor, terminal, finish) and action.kind (FileEditorAction, TerminalAction, FinishAction).
  • ObservationEvent — tool result. observation.is_error is the success flag. source is environment.
  • FinishAction inside an ActionEvent carries the agent's final message in action.message.

The cli prints all stderr from LiteLLM/Authlib first — see Pitfalls. Parse only stdout, line by line, ignoring lines that don't start with {.

Pitfalls

  • LiteLLM warnings on every invocation. The CLI prints bedrock-runtime and sagemaker-runtime warnings to stderr because botocore isn't installed. Plus an Authlib deprecation. These are noise, not failures. Pipe stderr to /dev/null or filter it out before showing the user.
  • Banner spam. Without OPENHANDS_SUPPRESS_BANNER=1, every run starts with a multi-line +--+ ASCII box advertising the SDK. Always export it.
  • --override-with-envs is mandatory for automation. Without it, OpenHands ignores LLM_API_KEY / LLM_BASE_URL / LLM_MODEL and falls back to ~/.openhands/settings.json. On a fresh install this file doesn't exist and the CLI hangs waiting for first-run setup.
  • Model slug is LiteLLM's, not the provider's. openrouter/openai/gpt-4o-mini works; openai/gpt-4o-mini while pointed at OpenRouter does not. anthropic/claude-sonnet-4-5 (hyphen) is native Anthropic; openrouter/anthropic/claude-sonnet-4.5 (dot) is via OpenRouter. Get it wrong → cryptic LiteLLM 400.
  • pip install openhands-ai is the wrong package. That's the legacy V0 SDK. The new CLI is uv tool install openhands --python 3.12. There is no maintained conda package.
  • Resume ID format is fiddly. The CLI ends with Conversation ID: f46573d9cfdb45e492ca189bde40019b (no dashes) and then a Hint: openhands --resume f46573d9-cfdb-45e4-92ca-189bde40019b (with dashes). Use the dashed form.
  • Headless ignores --llm-approve. If you pass it, you get an argparse error. Headless mode hardcodes always-approve.
  • No Windows support upstream. The OpenHands docs require WSL on Windows. This skill is gated [linux, macos] accordingly.
  • ~/.openhands/conversations/<id>/ accumulates. Each run persists a trajectory. Clean it up if running batches.
  • Heavy install (~200 packages). Use uv tool install (isolated venv) to avoid dependency conflicts with the active project.

Verification

terminal(
  command="OPENHANDS_SUPPRESS_BANNER=1 LLM_MODEL=openrouter/openai/gpt-4o-mini LLM_API_KEY=$OPENROUTER_API_KEY LLM_BASE_URL=https://openrouter.ai/api/v1 openhands --headless --json --override-with-envs --exit-without-confirmation -t 'Print the string OPENHANDS_OK to stdout via the terminal tool.'",
  workdir="~/.hermes/cache/scratch",
  timeout=120
)

If the JSONL stream ends with a FinishAction whose action.message mentions OPENHANDS_OK, the install is working.

Related

Frequently asked questions

What does the Openhands AI skill do?

Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

Why use Openhands on TypingMind?

Because you install it once and use it with any model. Openhands is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openhands in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/autonomous-ai-agents/openhands. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Openhands?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openhands?

As many as you like. As long as a model supports skills, you can use Openhands with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openhands AI skill free?

Yes. It is published on GitHub by NousResearch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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