oh-my-openagent vs Open SWE
Two of the top coding agents and assistants, side by side: score, setup, license, activity and what each review found.
oh-my-openagent
Terminal coding agent that runs parallel sub-agents and keeps git-based memory
Open SWE
LangChain coding agent that plans, implements and reviews pull requests
| What we compare | oh-my-openagent | Open SWE |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 73, popular | 31, known |
| Freshness | 100, active | 100, active |
| Maintenance | 85, healthy | 100, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 70k | 11k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | Python |
| Docker | No | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
oh-my-openagent
OmO ships a single `omo` command, a native binary built on senpi, the project's fork of pi. It takes a prompt and plans, runs and checks the work. Adding `ultrawork` or `mass ulw` makes it fan the job out to many agents, each on a model chosen for that step. Memory is stored as markdown files in a git repository, and `/login` supports Claude, ChatGPT, Kimi and GLM subscriptions.
Who it is for: Developers who want a terminal agent that orchestrates many models
Strengths
- Native binary installs via curl script, bun or npm; no Docker needed
- Logs in with Claude, ChatGPT, Kimi and GLM subscriptions
- Memory is plain markdown in a git repo, so it can be inspected
- `omo setup` migrates keys, MCP servers and skills from the OpenCode edition
Weaknesses
- License is SUL-1.0, not a standard OSI license; check terms before commercial use
- Install is a curl-pipe-to-bash script
- Computer use is documented as experimental
- Hardware needs and supported model list are not stated in the README
- Models: Claude, ChatGPT, Kimi, GLM
Open SWE
LangGraph-based agent that investigates a repository, implements changes in a per-thread Linux sandbox, validates them and opens a pull request, then reviews PRs and watches CI with /baby-sit. Work starts from a dashboard, GitHub issues or PR comments, Slack or Linear. Deploys into your infrastructure with a backend, dashboard, GitHub and Slack apps, for teams building an internal coding-agent service.
Who it is for: Engineering teams building an internal coding-agent service
Strengths
- Covers build, review, investigate and operate flows, with subagents for parallel work
- Durable execution and thread state via LangGraph; sandboxes persist per thread
- Configurable models, reasoning effort, skills, MCP integrations and sandbox providers
- Push approvals gate detected git pushes; PR chat excludes mutation tools
Weaknesses
- Production standalone Agent Server deployments require a license key
- Maintainers are not accepting issues or contributions; breaking changes expected
- LangSmith is the default sandbox and tracing provider; alternatives need configuration
- CLI mode executes commands locally as you, with no sandbox isolation
- no GPU
- Docker + Compose
- Needs LangSmith (default sandbox and tracing), GitHub App, Slack app (optional), model provider credentials
- Models: configurable LLM providers
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