olympus-ai

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⚡ Why Olympus?

AI coding assistants are powerful — but out of the box, they produce prototypes, not production software. The code works, but there is no structured workflow, no learning between sessions, no persistence when tasks get complex, and no coordination between specialized capabilities. You end up doing the orchestration yourself.

Olympus adds a quality layer on top of Claude Code. It brings structured development workflows, a pantheon of specialized agents, a self-learning system that carries context across sessions, and persistence mechanisms that keep Claude working until your tasks are actually done.

The result is less "AI that answers questions" and more "AI development team that ships software."

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Quick Start

Get started in under 60 seconds:

# Install globally
npm install -g olympus-ai

# Initialize Olympus
olympus-ai install

# Start Claude Code
claude

# Try it out
/olympus implement a REST API for user management

That's it. Olympus is now active and learning from your interactions.

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What is Olympus?

Olympus is a multi-agent orchestration system for Claude Code. It turns a single Claude session into a coordinated team of specialized agents — each optimized for a different kind of work.

Key capabilities: 20+ specialized agents (Oracle, Prometheus, Olympian, Librarian, and more), smart model routing that auto-selects Haiku/Sonnet/Opus based on task complexity, an AI-DLC structured development workflow, a self-learning system that persists context across sessions, continuation enforcement so tasks never stall, 20 slash commands, and support for any tech stack — Python, .NET, Go, Rust, Java, or anything else.

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🔧 How It Works

Olympus operates as a multi-agent orchestration system with smart model routing and a continuous learning loop:

graph TD
    A[User Request] --> B[Orchestrator]
    B --> C{Task Analysis}
    C -->|Simple| D[Haiku Agent]
    C -->|Standard| E[Sonnet Agent]
    C -->|Complex| F[Opus Agent]
    D --> G[Result]
    E --> G
    F --> G
    G --> H{Incomplete todos?}
    H -->|Yes| B
    H -->|No| I[Done]
    D -.-> J[Learning System]
    E -.-> J
    F -.-> J
    J --> K[(Feedback Storage)]
    K -.->|Next session| B

Session Flow:

1. User Request → Arrives with learned context already injected from previous sessions
2. Orchestrator → Analyzes task complexity and delegates to specialized agents
3. Model Routing → Selects the appropriate model tier: Haiku (simple), Sonnet (standard), or Opus (complex)
4. Agents Execute → Specialized subagents complete their tasks and return results
5. Learning System → Hooks passively capture corrections, preferences, and discoveries during execution
6. Persistence → If incomplete todos remain, the orchestrator continues working
7. Result → User sees the completed work

Cross-Session Learning:

The learning system operates across session boundaries:

• During Session: Hooks passively capture feedback from user corrections ("No, use async/await"), preferences ("Always use TypeScript"), and agent discoveries (gotchas, workarounds)
• Storage: Writes to ~/.claude/olympus/learning/ (global) and .olympus/learning/ (project-specific)
• Next Session Start: SessionStart hook reads stored learnings and injects relevant context into the initial prompt
• Context Injected:
  • User preferences (verbosity, autonomy, explicit rules)
  • Recurring corrections (mistakes to avoid)
  • Project conventions (tech stack, patterns)
  • Recent discoveries (technical insights about your codebase)

Key Insight: Context injection happens at the beginning of each session (via SessionStart hook), not in the result. Every new conversation starts with Claude already aware of your preferences and past learnings.