AIAgentFlow：本地优先的多智能体软件工程工作流编排器

AIAgentFlow is a local-first CLI tool that orchestrates a collaborative pipeline of six specialized AI agents (Architect, Coder, Reviewer, Tester, Fixer, Judge) to automate the full software engineering process from task planning to code delivery. Its core principle is "Bring your own API keys. Your code stays on your machine"—balancing cloud LLM power with data privacy and security.

As LLM capabilities advance, integrating AI into software development faces challenges: most solutions rely on cloud services (posing data privacy risks) or lack full-cycle coverage. AIAgentFlow addresses this as a local-first tool. Its key philosophy ensures users use their own API keys and code remains local, combining cloud model strength with privacy protection.

AIAgentFlow's design centers on a 6-stage agent pipeline:

1. Architect: Analyzes requirements, creates implementation plans, tech stack constraints, and development roadmap.
2. Coder: Writes production-grade code per the plan, supporting multiple languages/frameworks.
3. Reviewer: Checks code for bugs, security issues, performance, and style consistency.
4. Tester: Generates unit/integration tests (for frameworks like Jest/Pytest) and executes them.
5. Fixer: Addresses issues found by Reviewer/Tester to form a self-improving loop.
6. Judge: Final quality gate—approves code only if it meets preset thresholds before version control submission.

Multi-Provider Support: AIAgentFlow works with various LLM providers (Anthropic, OpenAI, Groq, Google Gemini, Ollama) with default models and use cases (e.g., Anthropic for complex reasoning, Ollama for privacy-sensitive scenarios). Users can mix providers to balance performance and cost. Workflow Modes: Three presets:

• Fast: For prototypes/quick iterations (fewer approval steps).
• Balanced: For daily development (speed-quality balance).
• Strict: For critical code (full quality gates + manual approval). It also supports batch processing from PRD docs to automate large project tasks.

Context Awareness: Inject domain knowledge via:

1. Auto-loading from .aiagentflow/context/ (API specs, security rules, architecture docs).
2. --context parameter for single-run reference docs.
3. Customizable prompts per agent to adjust behavior. Local-First Advantages: Code stays local (no third-party uploads), user-managed API keys (no中间人 risks), offline use (with Ollama), and Git native integration (auto branches/commits).

AIAgentFlow applies to:

• New feature development: End-to-end automation from requirements to deliverable code.
• Code refactoring: Safe refactoring of legacy code with function equivalence checks.
• Test completion: Auto-generate missing tests for existing codebases.
• Document sync: Ensure code aligns with PRD/API specifications.

AIAgentFlow integrates AI into software engineering via multi-agent collaboration—enhancing workflows rather than replacing developers. Its local-first design offers a trusted choice for privacy-focused teams. It aims to become a key tool in local AI-assisted development, supporting secure and efficient automation.