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Ever wished your AI assistant had coworkers? That's what oh-my-agent does.

Instead of one AI doing everything (and getting confused halfway through), oh-my-agent splits work across specialized agents — frontend, backend, architecture, QA, PM, DB, mobile, infra, debug, design, and more. Each one knows its domain deeply, has its own tools and checklists, and stays in its lane.

Works with all major AI IDEs: Pi, Claude Code, Cursor, Antigravity, Codex CLI, OpenCode, and more.

# macOS / Linux — auto-installs bun, uv & serena if missing
curl -fsSL https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.sh | bash

# Windows (PowerShell) — auto-installs bun, uv & serena if missing
irm https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.ps1 | iex

# Or manual (any OS, requires bun + uv + serena)
bunx oh-my-agent@latest

# All skills, deployed to every detected runtime
# (.claude, .cursor, .codex, .opencode, .github, .agents)
apm install first-fluke/oh-my-agent
# A single skill
apm install first-fluke/oh-my-agent/.agents/skills/oma-frontend

Pick a preset and you're ready:

oh-my-agent keeps .agents/ as the single source of truth and projects it into each runtime's native layout, so every supported tool shares the same skills, workflows, and rules.

_{& more}

Just chat. Describe what you want and oh-my-agent figures out which agents to use.

You: "Build a TODO app with user authentication"
→ PM plans the work
→ Backend builds auth API
→ Frontend builds React UI
→ DB designs schema
→ QA reviews everything
→ Done: coordinated, reviewed code

Or use slash commands for structured workflows:

Auto-detection: You don't even need slash commands — keywords like "architecture", "plan", "review", and "debug" in your message (in 11 languages!) auto-activate the right workflow.

Set model_preset in .agents/oma-config.yaml to choose which AI models each agent uses:

language: en
model_preset: mixed # antigravity | claude | codex | cursor | grok | mixed | qwen
# Optional per-agent overrides
agents:
 backend: { model: openai/gpt-5.5, effort: high }

• oma doctor --profile — prints the per-role resolved model matrix
• Full guide: web/docs/guide/per-agent-models.md

Read why →

• Portable — .agents/ travels with your project, not trapped in one IDE
• Role-based — Agents modeled like a real engineering team, not a pile of prompts
• Token-efficient — Two-layer skill design saves ~75% of tokens
• Quality-first — Charter preflight, quality gates, and review workflows built in:
  • oma verify <agent> — a deterministic check battery per agent type: a shared core (scope violation, charter alignment, hardcoded secrets, TODO scan, declared outputs) plus type-specific checks (TypeScript strict, tests, raw SQL, Flutter analyze, inline styles, ...)
  • session.quota_cap — per-session token / spawn / per-vendor budget caps in oma-config.yaml; orchestrate Step 5 blocks the next spawn when exceeded
  • ralph workflow — independent JUDGE re-verifies every criterion each iteration to catch silent regressions; heavy-test caching for >30s suites
  • Exploration Loop — after 2 retries, orchestrate spawns hypothesis variants in parallel and keeps the highest-scoring result
  • Monorepo auto-routing — detectWorkspace reads pnpm / nx / turbo / lerna and routes each agent to its workspace
• Multi-vendor — Mix Claude, Codex, Cursor, and Qwen per agent type
• Observable — Terminal and web dashboards for real-time monitoring

flowchart TD
 subgraph Workflows["Workflows"]
 direction TB
 W0["/brainstorm"]
 W1["/work"]
 W1b["/ultrawork"]
 W2["/orchestrate"]
 W3["/architecture"]
 W4["/plan"]
 W5["/review"]
 W6["/debug"]
 W7["/deepinit"]
 W8["/design"]
 end
 subgraph Orchestration["Orchestration"]
 direction TB
 PM[oma-pm]
 ORC[oma-orchestrator]
 end
 subgraph Domain["Domain Agents"]
 direction TB
 ARC[oma-architecture]
 FE[oma-frontend]
 BE[oma-backend]
 DB[oma-db]
 MB[oma-mobile]
 DES[oma-design]
 TF[oma-tf-infra]
 end
 subgraph Quality["Quality"]
 direction TB
 QA[oma-qa]
 DBG[oma-debug]
 end
 Workflows --> Orchestration
 Orchestration --> Domain
 Domain --> Quality
 Quality --> SCM([oma-scm])

• Detailed Documentation — Full technical spec and architecture
• Supported Agents — Agent support matrix across IDEs
• Web Docs — Guides, tutorials, and CLI reference

This project is maintained thanks to our generous sponsors.

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See SPONSORS.md for a full list of supporters.

Star History Chart

• Liang, Q., Wang, H., Liang, Z., & Liu, Y. (2026). From skill text to skill structure: The scheduling-structural-logical representation for agent skills (Version 4) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.24026
• Chen, C., Yu, Q., Gu, Y., Huang, Z., Li, H., Liu, H., Liu, S., Liu, J., Peng, D., Wang, J., Yan, Z., Meng, F., Qin, E., Che, C., & Hu, M. (2026). The scaling laws of skills in LLM agent systems (Version 1) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.16508
• Yang, Y., Gong, Z., Huang, W., Yang, Q., Zhou, Z., Huang, Z., Li, Y., Gao, X., Dai, Q., Liu, B., Qiu, K., Yang, Y., Chen, D., Yang, X., & Luo, C. (2026). SkillOpt: Executive strategy for self-evolving agent skills [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.23904
• Huang, Z., Xu, J., Yang, Y., Gong, Z., Yang, Q., Tian, M., Wang, X., Lv, C., Gao, X., Dai, Q., Liu, B., Qiu, K., Yang, X., Chen, D., Zheng, X., & Luo, C. (2026). From raw experience to skill consumption: A systematic study of model-generated agent skills [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.23899

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