9 Best OpenHands Alternatives for AI Coding in 2026

1. OpenCode

OpenCode is an open-source AI coding agent available as a terminal-based interface, a desktop app, or an IDE extension.

It is the closest match to OpenHands' model-agnostic design, and OpenHands Agent Canvas can run it as an ACP agent, as the swap section below shows.

• License - MIT, with about 213k GitHub stars; v1.19.0 is the latest GitHub release, and the docs now offer OpenCode v2 as well.
• Models - 75+ providers through the AI SDK and Models.dev, plus local models through Ollama or LM Studio.
• Unattended runs - opencode run for scripts, opencode serve for a headless HTTP server with a JS/TS SDK, and opencode github run for GitHub Actions.
• Permissions - each tool is set to allow, ask, or deny in the config.
• Pricing - free with your own provider; OpenCode Zen and the Go subscription are optional paid model access.
• Watch for - the docs describe no execution sandbox, so actions run on the host unless you run OpenCode in a container.

How it compares with a managed agent is covered in OpenCode vs Claude Code.

2. Cline

Cline is an AI coding agent that lives in your editor and your terminal, and every action requires your explicit approval.

• Runs in - VS Code, Cursor, JetBrains, Windsurf, VSCodium, and Antigravity, plus a CLI, a desktop app for macOS and Windows (Windows in beta), and an SDK.
• License - Apache 2.0, with about 70k GitHub stars; the JetBrains plugin is not open source.
• Models - OpenRouter, Cline's own provider, local models through Ollama or LM Studio, and any OpenAI-compatible API.
• Unattended runs - a headless CLI with JSON output and cron-scheduled agents that run independently of any terminal session.
• Pricing - free for individual developers, who pay for inference on a usage basis; credits, ClinePass, and an Enterprise plan are optional.
• Watch for - the CLI's --auto-approve flag defaults to true, and commands run directly in your terminal rather than in a sandbox.

3. Goose

Goose is a general-purpose AI agent that runs on your machine, not just for code, and ships as a native desktop app, a full CLI, and an API you can embed.

Block's goose was one of the founding contributions when the Linux Foundation formed the Agentic AI Foundation, alongside the Model Context Protocol and AGENTS.md.

Foundation stewardship makes Goose the pick for teams that want vendor-neutral governance.

• License - Apache 2.0, with about 55k GitHub stars; the repository moved from block/goose to aaif-goose/goose.
• Models - 15+ providers, existing Claude, ChatGPT, or Gemini subscriptions through ACP, and local models through Ollama or LM Studio.
• Unattended runs - goose run for headless tasks, goose schedule for recurring recipes, and goose serve to run an ACP server.
• Pricing - free; you pay your model provider.
• Watch for - Autonomous mode is the default, and isolation needs the Container Use extension or a Docker setup.

4. Codex CLI

Codex CLI is a coding agent from OpenAI that runs locally on your computer.

Its agent approvals and security defaults include no network access and write permissions limited to the active workspace, enforced with Seatbelt policies on macOS and bubblewrap plus seccomp on Linux.

Isolation without Docker is its main edge over OpenHands.

• Windows - a native Windows sandbox is supported as well.
• License - Apache-2.0, with about 129k GitHub stars; release 0.162.1 shipped on October 9, 2026.
• Models - OpenAI models, custom providers, or local models through Ollama or LM Studio with --oss.
• Unattended runs - codex exec for scripts and CI, and the openai/codex-action GitHub Action.
• Pricing - included with ChatGPT plans, or billed at API pricing with an API key, which drops the cloud features.
• Watch for - inside a Docker container, the Linux sandbox can fail when the container blocks namespaces, setuid bwrap, or seccomp.

Sandbox modes and CI setup are covered in the Codex CLI guide.

5. Kilo Code

Kilo Code is an AI coding agent for VS Code, JetBrains, and the CLI that is open source with open pricing, and the Kilo CLI is a fork of OpenCode.

Its model catalog is the widest on this list.

• License - MIT, including the JetBrains plugin, with about 28k GitHub stars.
• Models - 500+ models through the Kilo AI Gateway at the provider's rate, 30+ providers with your own keys, and local models through Ollama or LM Studio.
• Unattended runs - kilo run --auto for CI, kilo serve for a headless server, and kilo acp for ACP clients.
• Pricing - free and open source with model usage billed separately; Kilo Pass, Teams, and Enterprise are optional.
• Roo Code lineage - Kilo started as a Roo fork in 2025 and the two codebases share git history, according to its Roo to Kilo migration guide.
• Roo Code shutdown - the Roo Code repository was archived by its owner on May 15, 2026, so Roo users need a replacement.
• Watch for - the OS-level sandbox is off by default, does not restrict file reads, and is not available on Windows.

6. mini-SWE-agent

mini-SWE-agent is the minimal agent from the Princeton and Stanford team behind SWE-bench and SWE-agent, and its README says it scores above 74% on SWE-bench Verified.

The SWE-agent FAQ says the original SWE-agent has been superseded by mini-SWE-agent and is now in maintenance-only mode.

It suits teams that used OpenHands for research or model evaluation rather than daily feature work.

• Size - about 100 lines of Python for the agent class, with bash as its only tool.
• Environments - local, Docker or Podman, Singularity or Apptainer, and bubblewrap, with a batch mode for SWE-bench runs.
• License - MIT, with about 8.4k GitHub stars; v2.4.6 shipped on July 23, 2026.
• Models - any model through LiteLLM, OpenRouter, or Portkey.
• Watch for - every action runs independently, so the agent cannot change directories or keep environment variables between steps.

7. Aider

Aider is AI pair programming in your terminal, editing code in your local git repository.

Its git integration commits every file edit with a descriptive commit message, so each agent change can be reviewed or undone like any other commit.

• Scripting - a single --message run applies one change and exits, and --yes-always answers every confirmation.
• License - Apache 2.0, with about 50k GitHub stars.
• Models - almost any LLM, including local models through Ollama, though the docs warn that less capable models may not work well.
• Pricing - free; you pay your model provider.
• Watch for - the last tagged GitHub release, v0.86.0, dates from August 2025, the docs describe no sandbox or MCP support, and Aider skips pre-commit hooks by default.

8. Gemini CLI

Gemini CLI is an open-source AI agent that brings the power of Gemini directly into your terminal.

Google's Antigravity CLI transition stopped Gemini CLI from serving Google AI Pro and Ultra subscribers and free Gemini Code Assist for individuals users on June 18, 2026, while paid Gemini API keys keep working.

The cutoff is why Gemini CLI ranks lower than its feature set suggests.

• Who can still use it - teams with paid Gemini API keys or Gemini Code Assist Standard or Enterprise.
• License - Apache-2.0, with about 107k GitHub stars; v0.63.0 shipped on October 6, 2026.
• Models - Gemini only.
• Unattended runs - headless mode with -p and JSON or stream-JSON output, plus the run-gemini-cli GitHub Action.
• Sandbox - off by default, with macOS Seatbelt, Docker or Podman, a native Windows sandbox, and gVisor on Linux as options; YOLO approval mode turns it on.

9. Devin

Devin is Cognition's autonomous AI software engineer that can write, run, and test code.

It is the managed pick on this list, for teams that want to stop running agent infrastructure and accept a commercial product in place of open source.

• Surfaces - Devin Cloud, Devin Desktop (the new name for Windsurf since June 2, 2026), Devin CLI, and Slack or Teams.
• Self-hosting - Devin Outposts runs command execution, file edits, and repository access on machines you operate, while inference and planning stay in Devin's cloud.
• Models - Devin CLI supports models from Anthropic, OpenAI, Google, and Cognition, plus open models such as DeepSeek, Kimi, and GLM.
• Pricing - Free, Pro, Max, and Teams self-serve plans with included usage, and Agent Compute Units for Enterprise.

Step 1: Write One Spec

Save the task as task.txt and pass the same file to every agent. The disabled-button rule and the exact confirmation text are easy to miss, which is what makes it a useful check:

In the current directory, build a single-page web app in one file named
index.html (plain HTML, CSS and JavaScript, no external dependencies).

It is a newsletter signup form titled "TestMu Weekly" with an email input
labeled "Email address", a select labeled "Plan" with the options
"Weekly digest" and "Daily briefing", a checkbox labeled
"I agree to the terms", and a button labeled "Subscribe".

1. The Subscribe button stays disabled until the terms checkbox is checked.
2. If the email is invalid, show "Please enter a valid email address."
   below the email input and do not submit.
3. If the email is valid, hide the form and show exactly:
   "Thanks! <email> is subscribed to the <plan>."

Do not ask me any questions. When index.html is complete, stop.

Step 2: Run Both Agents Headless

OpenCode is one npm package. Set edit and bash to allow in its permission config so the run never stops for a prompt, then call opencode run with the model and the spec:

npm install -g opencode-ai
export GOOGLE_GENERATIVE_AI_API_KEY=<your key>
opencode run -m google/gemini-3.1-pro-preview "$(cat task.txt)"

For OpenHands, the documented headless command is openhands --headless -t, and the CLI docs run it under WSL on Windows.

The OpenHands Software Agent SDK is the engine behind the OpenHands CLI and OpenHands Cloud, and its agents can use the local machine as their workspace, so the same agent also runs from a short Python script:

# pip install openhands-sdk openhands-tools
import os
from openhands.sdk import LLM, Agent, Conversation, Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.task_tracker import TaskTrackerTool
from openhands.tools.terminal import TerminalTool

llm = LLM(model="gemini/gemini-3.1-pro-preview", api_key=os.getenv("LLM_API_KEY"))
agent = Agent(llm=llm, tools=[
    Tool(name=TerminalTool.name),
    Tool(name=FileEditorTool.name),
    Tool(name=TaskTrackerTool.name),
])
conversation = Conversation(agent=agent, workspace=os.getcwd())
conversation.send_message(open("task.txt").read())
conversation.run()

Both agents finished on the first attempt, wrote a single index.html, and exited cleanly. OpenCode took 46 seconds; OpenHands took 66 seconds and ended its run by reporting that every feature was implemented as specified.

Step 3: Grade Both Builds With Kane CLI

An agent's closing summary is a claim about its own work. Kane CLI checks the rendered page instead: it drives a real Chrome browser from a plain-English objective, streams NDJSON in agent mode, and exits with 0 for pass, 1 for a failed assertion, 2 for an environment error, and 3 for a timeout. Serve each build locally and run the identical objective against both:

kane-cli run "go to http://127.0.0.1:8731/openhands-app/index.html, assert the Subscribe button is disabled, check the 'I agree to the terms' checkbox, type 'not-an-email' in the Email address field, click Subscribe, assert the page shows 'Please enter a valid email address.', clear the Email address field and type 'qa@testmu.ai', select 'Daily briefing' in the Plan dropdown, click Subscribe, assert the page shows 'Thanks! qa@testmu.ai is subscribed to the Daily briefing.', store the confirmation message as 'confirmation'" --agent --headless --timeout 400

The last line of the stream, the run_end event, carries the verdict and the stored value for the OpenHands build:

{
  "type": "run_end",
  "status": "passed",
  "one_liner": "subscribed qa@testmu.ai to the Daily briefing on the subscription page",
  "reason": "Objective completed",
  "final_state": {
    "confirmation": "Thanks! qa@testmu.ai is subscribed to the Daily briefing.",
    "invalid_email_validation_message": "Please enter a valid email address."
  }
}

On a fully specified task this small, the two agents tied, and the same browser check confirmed both. What separates them for a migration is everything around the code: OpenCode installs as one package with no platform service to run, while OpenHands brings sandboxes, automations, and Agent Canvas that a swap inside the platform keeps. Run the same steps on a real ticket from your backlog, where specs are vaguer and the gap between agents widens.

Step 4: Keep Kane CLI in the Agent's Loop

A one-off check proves one build. Whichever agent you keep, give it Kane CLI as a tool, so every later change that touches the UI gets the same real-browser check before anyone reviews the pull request:

• One skill install - npx @testmuai/kane-cli-skill installs the Kane CLI skill for Claude Code, Codex CLI, and Gemini CLI in one step, the same three agents OpenHands Cloud runs over ACP.
• An AGENTS.md rule - OpenHands and OpenCode both read AGENTS.md, so a short rule there gives them the same habit (example below).
• Evidence for the reviewer - each run seals an evidence pack with per-step screenshots, a HAR network log, and console output, and saves the test case to TestMu AI Test Manager with a share link for the pull request.
• Saved tests - a passing run can be kept as a _test.md file that replays in seconds on later changes.
• A CI gate - the same command runs headless in GitHub Actions, GitLab CI, Jenkins, or Bitbucket Pipelines, where exit code 1 stops the merge.

## Browser checks
After any change to a page a user sees:
1. Serve the app locally.
2. Run: kane-cli run "<the user journey and what must be true>" --agent --headless --timeout 300
3. Read the last line of output (the run_end event).
   - Exit code 0: the check passed. Include the share link in your summary.
   - Exit code 1: an assertion failed. Fix the code and run the check again.
   - Exit code 2 or 3: an environment problem or timeout. Report it; do not mark the task done.