OpenHands is an open-source platform for AI software-development agents that plan and carry out multi-step engineering work: reading repositories, editing files, running commands and tests, and iterating. Its parts are Agent Canvas, a browser-based control center; a Python SDK and an Agent Server for programmatic and headless use; and a Docker-based sandbox runtime that keeps agent execution isolated from the host. The core is MIT-licensed, while Cloud and Enterprise are the commercial managed and self-hosted offerings.[1][2]
Quick verdict
OpenHands is the open answer to hosted autonomous coding agents: real multi-step engineering work in an isolated Docker sandbox, with any model you choose. That freedom costs operational effort, from Docker and mount scoping to model provisioning and disciplined diff review. It fits engineering teams with test suites and infrastructure they control, while lighter-weight teams should weigh the managed Cloud path. Verify deployment choice against current docs, since the 2026 restructure deprecated older paths.[1][2][3][4]
Capabilities
Autonomous engineering agents OpenHands agents take a task, inspect the codebase, make a plan, edit code, run tests, and iterate on the results. The catalogue describes this as repository-wide planning, editing, and test running, including a headless mode for CI and batch evaluation.[1][2]
Sandboxed Docker runtime Every agent works inside a Docker sandbox container rather than on the host machine, and the documented setup mounts only the project the agent is allowed to change. Because a read-write mount can still modify everything inside it, scoping the mount is the actual safety boundary.[1]
Any model through LiteLLM Model routing goes through LiteLLM, covering hosted providers such as Anthropic, OpenAI, and Google as well as self-hosted endpoints including Ollama, vLLM, LM Studio, and SGLang. Local models are supported but need solid tool use and a context length of at least 22000.[1]
Agent Canvas control center Agent Canvas is the open-source browser client for starting sessions, chatting with agents, reviewing diffs, and approving or rejecting actions. One Canvas instance can connect to local, self-hosted, Cloud, or Enterprise backends.[1]
SDK and Agent Server The Python SDK lets developers build custom agents, and the Agent Server exposes them over REST and WebSocket so agents can run without the browser UI. This is the foundation for programmatic, scheduled, and embedded use.[1]
Review and confirmation gates Canvas shows the agent-proposed diff with test results before anything merges, and confirmation policies range from approving everything to approving nothing or only risky actions. The walkthrough flags the permissive default and recommends risky-only confirmation on important machines.[1]
Integrations and automations Agents can reach the outside world through MCP servers and browser search, and automations connect to tools such as Slack, GitHub, and Linear with scheduled or webhook triggers. That makes recurring repository chores schedulable rather than chat-only.[1]
Restructured open-source project The project was restructured during 2026: the legacy monorepo is preserved as an archived snapshot, active development continues across the Agent Canvas, SDK, Sandbox Server, and Cloud repositories, the CLI is maintained for stability, and the old Local GUI and VS Code extension are deprecated. Each repository carries its own license, so older guides may describe paths that no longer exist.[1]
Who it fits
We would evaluate OpenHands for engineering teams that want autonomous coding agents on infrastructure they control, pairing a self-hosted stack or their own model keys with Docker isolation. The payoff is control over models, data, and execution; the price is operating that stack.[1][2][3]
Teams with real test suites and review discipline fit the tool's gate model: the diff plus test-results review assumes someone actually reads both. Without that habit, autonomous execution becomes unreviewed execution.[1]
Organizations that need access controls, collaboration, usage reporting, and budget management should evaluate the Cloud or Enterprise offerings rather than assembling the open-source pieces and rebuilding those controls.[1]
Boundaries to check
Self-hosting means operating Docker, scoping mounts, provisioning model access, and confirming the Docker socket implications, since mounting the socket hands the agent control of Docker. Local models add their own bar of tool-use strength and context length. This is materially more setup than opening a hosted coding assistant.[1][2]
No single data boundary covers every deployment. A local or self-hosted stack keeps traffic on your infrastructure, while Cloud execution runs on OpenHands infrastructure and mediates GitHub access with tokens it requests for you. Private repositories deserve a deliberate backend choice, not an assumption.[1][2]
Restructure churn is a live hazard: the archived monorepo, deprecated GUI and extension, and per-repository licenses mean tutorials from the old layout can mislead. Confirm guidance against the current documentation before following it.[1]
Reported experience
One independent walkthrough: a DataCamp tutorial in which the author runs OpenHands locally with Docker and a local Devstral model, mounts a target repository, and reviews agent-proposed diffs. Official OpenHands documentation supplies the product-structure and deployment facts.
1 cited community source. Single independent walkthrough plus official documentation; no Reddit or G2 community report with a permitted-reuse quotation was available, so no testimonial is included. The walkthrough author's experience is one person's account on specific versions and models, not a representative sample and not OpenAlternative testing.
In the independent walkthrough, the author runs OpenHands locally with Docker and a local Devstral model, mounts only the target repository, and reviews agent-proposed diffs with test results before merging. The author advises running the project's own test suite rather than trusting the agent's report, and checking that the agent did not edit tests to make them pass. This is the author's account, not OpenAlternative testing.[1]
The same walkthrough positions OpenHands against Claude Code, which it calls more polished but less controllable, and against Copilot's cloud agents, which it describes as the closest comparison but GitHub-only with curated models. Its verdict favors OpenHands where control matters: readable source, self-chosen hardware, and any model including local ones.[1]
How it compares
OpenHands is positioned as the open answer to hosted autonomous coding products such as Devin, and the catalogue records it as an alternative to GitHub Copilot. Its differentiator is deployment freedom: the same agent approach runs local, self-hosted, or managed, where the hosted incumbents run only on their clouds.[1][2]
Choose on the control-convenience axis. Teams that prize auditability and model freedom accept the Docker-and-keys operating burden; teams that prize zero setup should compare the managed Cloud path or a hosted incumbent and price the model and hosting spend honestly either way.[1][2]
Questions worth asking
Is OpenHands a no-code tool for non-technical users?
No. OpenHands assumes an engineering workflow: a Docker runtime, a code repository, API keys or a local model server, and someone able to review diffs and test results. A non-technical user who wants finished software without that setup would be better served by a managed builder or by asking an engineer to run OpenHands for them.[1][2]
What does OpenHands cost per month?
The open-source core is MIT-licensed and free to self-host, so there is no fixed monthly price. The real spend is model inference through your own provider keys plus hosting for a self-managed deployment; the managed Cloud service bills for usage with reporting and budget management instead. Low or bursty use can favor hosted inference, while steady heavy use can favor self-hosted models.[1][2][3]
Can I self-host OpenHands?
Is OpenHands tied to one model provider?
No. Model routing goes through LiteLLM, so hosted models from Anthropic, OpenAI, and Google work alongside local options such as Ollama, vLLM, LM Studio, and SGLang. Model choice affects results: the walkthrough notes local models can struggle with tool use and need a context length of at least 22000.[1]
Does using OpenHands keep all my code local?
Only if you run the local or self-hosted stack, where code and model traffic stay on infrastructure you control. The managed Cloud service runs execution on OpenHands infrastructure and requests GitHub tokens on your behalf, so teams with strict data boundaries should compare the local, self-hosted, Cloud, and Enterprise options before connecting private repositories.[1][2]
Sources and editorial review
Source research. Reviewed on October 9, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.
We have not run or tested this product.
One independent walkthrough: a DataCamp tutorial in which the author runs OpenHands locally with Docker and a local Devstral model, mounts a target repository, and reviews agent-proposed diffs. Official OpenHands documentation supplies the product-structure and deployment facts. Single independent walkthrough plus official documentation; no Reddit or G2 community report with a permitted-reuse quotation was available, so no testimonial is included. The walkthrough author's experience is one person's account on specific versions and models, not a representative sample and not OpenAlternative testing.
- OpenHands documentation: introduction
official · Observed October 8, 2026
Official docs landing: platform restructure (Agent Canvas, SDK, Agent Server, Sandbox Server); Cloud managed service and Enterprise licensed deployment; legacy monorepo archived snapshot; per-repository licenses; Canvas connects to any backend.
- OpenHands documentation: getting started
official · Observed October 8, 2026
Official setup guide: Docker Compose all-in-one and Canvas Desktop run paths; agent sandbox container; mount-only-what-matters guidance; Docker socket control caveat; CLI maintained; Local GUI deprecated.
- OpenHands: Devin alternatives compared
official · Observed October 8, 2026
Vendor comparison article: MIT core free to self-host with hosted and Enterprise paths; real spend is model inference plus hosting; hosted inference can suit low or bursty use; positions OpenHands against Devin on control and cost.
- OpenHands: how to self-host AI models
official · Observed October 8, 2026
Vendor self-hosting guide for AI models: hosted versus self-hosted inference trade-offs; cost, control, and latency considerations for running models on own infrastructure.
- DataCamp tutorial: set up and use the open-source AI coding agent
community · Observed October 8, 2026
Independent walkthrough: local Docker plus Ollama Devstral setup; LiteLLM routing across hosted and local providers with 22000 minimum context; task loop of inspect, plan, edit, test, iterate; MCP and browser tools; Slack/GitHub/Linear automations; diff and test-results review advice including checking test edits; confirmation policies; Claude Code and Copilot comparison on control versus polish.
- GitHub: All-Hands-AI/OpenHands repository
official · Observed October 8, 2026
Catalogue-recorded repository for the published openhands entity: portal snapshot records MIT license, Python/FastAPI/React/TypeScript/Docker stack, and 85,799 stars at observation. Repository metrics are dated observations, not ratings; the docs restructure means current development spans several repositories.