Ralph is designed to run without human intervention. Every design decision prioritizes autonomous operation:
- Self-correcting: When validation fails, the agent fixes issues automatically
- Self-documenting: The agent maintains prd.json and AGENTS.md
- Self-organizing: Tasks are prioritized and tracked without manual curation
"It's not if it gets popped, it's when."
Running autonomous agents with full system access is inherently risky. Ralph mitigates this through:
- Docker sandboxing: Default recommendation for autonomous runs
- Resource limits: CPU and memory constraints prevent runaway processes
- Git boundaries: All changes go through commits, nothing is invisible
- Network isolation: Optional network_mode: none for maximum security
Ralph doesn't guess what to build. It reads specifications and implements exactly what's documented:
- Specs define the "what" and acceptance criteria
- AGENTS.md defines the "how" (commands, patterns)
- prd.json tracks progress and priorities
This creates a clear contract between humans (who write specs) and agents (who implement them).
Complex systems aren't built in one pass. Ralph's loop design embraces iteration:
- Each loop picks ONE task, implements it, validates, commits
- Small, atomic commits are easier to review and revert
- Continuous progress is more valuable than perfect plans
Every change Ralph makes is a git commit. This provides:
- Complete audit trail of agent actions
- Easy rollback if something goes wrong
- Integration with existing code review workflows
- No "magic" - everything is visible
- Universal: Bash works on any Unix system without runtime dependencies
- Simple: No version management, dependency hell, or package conflicts
- Transparent: Read the scripts to understand exactly what happens
- Updatable:
git pullto update, no npm/pip update complexities
Different CLIs have different:
- Flag syntax
- Authentication methods
- Model specifications
- Output formats
Adapters abstract these differences while keeping the core loop logic clean.
Prompts and configuration live in the project, not globally, because:
- Tuning: Prompts should be optimized per-project
- Versioning: Track prompt changes in git with the code
- Sharing: Team members get the same prompts
- Reproducibility: Same prompts = same behavior
ralph init asks questions rather than requiring users to edit config files:
- Lower barrier: Start working immediately
- Fewer errors: No typos in configuration keys
- Discoverability: Users learn available options
- Sensible defaults: Most users accept the defaults
Ralph is powerful but not magical. Understand:
- What the prompts tell the agent to do
- What validation commands catch (and don't catch)
- What the agent has access to
"Just implement it" leads to:
- Incomplete features
- Scope creep
- Misaligned expectations
Write specs first. The time invested pays off.
prd.json is the contract. If you:
- Want different priorities → Edit the plan
- Want to skip something → Remove it from the plan
- See wrong estimates → Update the plan
The agent follows the plan. Shape the plan to shape the output.
Ralph isn't meant to "get it right the first time." Expect:
- Multiple passes to complete features
- Learning captured in AGENTS.md
- Plan refinement as implementation reveals complexity
This is normal. Embrace the loop.
Ralph's design leaves room for:
- Multi-agent coordination: Multiple Ralphs working on different features
- Human checkpoints: Pause loops for review at key milestones
- Metrics and observability: Track agent efficiency over time
- Custom validation hooks: Project-specific quality gates
- Cost tracking: Monitor API usage across iterations
These aren't implemented yet, but the architecture supports them.