4 compared
Autonomous coding agents compared
End-to-end autonomous coding agents that plan and execute multi-step engineering tasks, compared.
Open in the interactive comparison tool| Field | Devin AI software engineering agent for planning, coding, debugging, and executing development tasks with autonomous workflows. Open dossier | OpenHands AI-driven software development platform with a local GUI, CLI, Software Agent SDK, agent sandboxes, terminal/browser tools, and hosted cloud options. Open dossier | Open SWE Open-source framework for building internal coding agents that accept tasks via Slack, Linear, or GitHub, execute code changes in isolated cloud sandboxes, and open draft pull requests automatically. Open dossier | Goose Open-source, extensible AI agent that goes beyond code suggestions to install, execute, edit, and test with any LLM, available as a desktop app, CLI, and API with 70+ MCP extensions. Open dossier |
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| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy · | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Cognition | OpenHands | LangChain | Agentic AI Foundation |
| Added | 2026-04-27 | 2026-06-03 | 2026-06-05 | 2026-06-05 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | — missing | ✓OpenHands agents can edit files, run terminal commands, browse websites, start servers, and interact with repositories, so each workspace needs a clear permission boundary. The documentation recommends Docker sandboxing for local use; process-based execution is faster but has no container isolation and should be treated as unsafe for sensitive projects. Mounts into the sandbox can be modified by the agent when granted write access, so avoid broad host mounts and review exactly which project files are exposed. Confirmation mode and security analyzers can reduce risk by pausing high-risk actions, but they do not prove that an action is correct, reversible, policy-compliant, or safe to merge. Hosted, cloud, enterprise, and integration workflows add additional access-control, audit, retention, budget, and organization-policy requirements beyond the local open-source project. Benchmark performance, agent planning, context compression, and security analysis are useful signals, but human review is still required before generated changes affect protected branches or production systems. | ✓Each task runs in an isolated cloud Linux sandbox (Modal, Daytona, Runloop, or LangSmith) to prevent production impact. The agent executes shell commands, file operations, web fetches, and HTTP requests inside the sandbox without confirmation prompts — review sandbox provider permissions before deployment. GitHub operations are performed through a GH_TOKEN proxy; scope token permissions to the minimum required repositories. Subagent orchestration can spawn parallel child agents — set appropriate step limits and monitor LangSmith traces to prevent runaway execution. AGENTS.md or CLAUDE.md at the repository root is injected into the system prompt; review this file to control agent behavior and conventions. | ✓Goose installs, executes, edits, and tests code and runs commands locally, so it can change files and system state on your machine. It connects to 70+ MCP extensions; each extension adds capabilities and its own integration risk, so enable only those you trust. Review actions and generated code before allowing changes to important repositories or systems. Because it works across 15+ providers, confirm which provider and model a session uses before sending sensitive context. |
| Privacy notes | — missing | ✓OpenHands may process prompts, issue text, source snippets, diffs, terminal output, browser context, logs, traces, uploaded files, repository metadata, and generated patches. Model providers, local model routes, OpenHands Cloud, enterprise deployments, or connected gateways may receive task context depending on the selected configuration. Local GUI, CLI, SDK, and sandbox workflows can save conversation history, workspace state, logs, screenshots, browser artifacts, and server output on the machine or managed workspace. Cloud and enterprise integrations with GitHub, GitLab, Bitbucket, Slack, Jira, and Linear should be reviewed for repository access, user identity, issue data, retention, and audit visibility. Operators should define retention and redaction rules before sharing OpenHands conversations, trajectories, screenshots, generated patches, or benchmark artifacts outside the project team. | ✓Repository code, Linear issue history, and Slack thread history are sent to the configured model provider API. Sandbox providers (Modal, Daytona, Runloop, LangSmith) process task execution data according to their own privacy policies. LangSmith tracing, when enabled, logs full agent traces including tool inputs and outputs — configure retention and access controls in your LangSmith organization. GitHub OAuth tokens and model API keys should be stored as secrets and never committed to the repository. | ✓Your code and prompts are sent to whichever LLM provider you configure; data handling follows that provider's policies. API keys and provider credentials should be stored securely and never committed to source control. MCP extensions can access local files and external services depending on their scope; review what each extension can reach. |
| Prerequisites | — none listed |
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| Install | — | — | — | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
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