Archon vs Open SWE
Two of the top projects in Coding agents and assistants, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Archon
YAML workflow engine that runs AI coding agents through fixed dev steps
Open SWE
LangChain coding agent that plans, implements and reviews pull requests
Verdict
Archon ranks higher, 82 to 80. Of the 5 score parts we measure for both, Archon leads on 1, Open SWE on 1 and 3 are even.
- AdoptionArchon leads by 16 points, 70 to 54: a wide gap.
- FreshnessEven at 100.
- MaintenanceOpen SWE leads by 5 points, 100 to 95.
- Easy to runEven at 67.
- Agent-readyEven at 85.
Pick Archon if you are
Developers who want repeatable, scripted AI coding runs across repos
- Workflows are YAML committed to the repo, with repo files overriding bundled defaults
- Each run gets its own git worktree, so parallel runs do not conflict
Pick Open SWE if you are
Engineering teams building an internal coding-agent service
- Covers build, review, investigate and operate flows, with subagents for parallel work
- Durable execution and thread state via LangGraph; sandboxes persist per thread
| What we compare | Archon | Open SWE |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 70, popular | 54, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 100, healthy |
| Easy to run | 67, easy | 67, easy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 23.7k | 11k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | TypeScript | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Archon
Archon lets you define development processes (plan, implement, validate, review, PR) as YAML workflows in `.archon/workflows/`, mixing bash nodes with AI nodes, loops and approval gates. Each run gets its own git worktree. It runs from a CLI, web UI, Slack, Telegram, Discord or GitHub webhooks, using Claude, Codex or Pi, and ships an `sdlc` workflow pack.
Who it is for: Developers who want repeatable, scripted AI coding runs across repos
Strengths
- Workflows are YAML committed to the repo, with repo files overriding bundled defaults
- Each run gets its own git worktree, so parallel runs do not conflict
- Deterministic bash nodes mix with AI nodes, loops and human approval gates
- Ships sdlc pack: triage, investigate, plan, implement, review, validate, PR
Weaknesses
- Claude provider needs Claude Code installed separately; compiled binaries do not bundle it
- Quick-install binary on x64 requires AVX2 CPUs
- Visual workflow builder is labeled experimental and covers a bounded set of nodes
- Sends anonymous telemetry events by default
- no GPU
- Docker + Compose
- Needs Bun, Claude Code, GitHub CLI, SQLite or PostgreSQL
- Models: Claude, Codex, Pi
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
More in Coding agents and assistants
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.