Daedalus: An Autonomous Multi-Agent Software Development System Enabling Production-Grade Apps with Free Models

Daedalus is an autonomous software development system based on LangGraph. Leveraging three core technologies—hash-anchored editing, confidence-gated loop, and structured debugging—it can build complete MERN applications even with free low-inference models.

Most AI programming tools on the market today are stuck at the level of "code completion" or "snippet generation". They do speed up developers' coding, but cannot independently build a complete application. Daedalus changes this landscape—it is an autonomous multi-agent software development system that can independently complete the entire workflow from a natural language description: requirement analysis, architecture design, code writing, code review, debugging and fixing, and deployment verification.

What's more striking is that Daedalus only uses free OpenRouter models during development, yet achieves performance beyond standard AI programming pipelines through three research-validated reliability technologies.

Daedalus's core competitiveness comes from three research-validated technologies, each addressing key pain points in autonomous AI development.

The most common failure mode in autonomous AI development is stale line edit corruption. When an AI agent tries to modify a file, if the target line has been changed by another agent after the agent read the file, line number-based editing will cause code corruption. Daedalus borrows the method from oh-my-openagent, attaching a content hash tag to each line of code. Editing operations are only executed if the hash matches; otherwise, they are rejected. This fundamentally eliminates code corruption caused by stale line edits.

A common problem with traditional AI pipelines is premature progression—agents move to the next stage without fully completing the current task. Daedalus introduces the "Ralph Loop" mechanism: after each stage is completed, the agent needs to self-score against a structured checklist, and can only proceed to the next stage if it reaches an 85% confidence level. This self-assessment mechanism prevents cascading errors caused by premature progression.

Daedalus adopts the two-stage code review process from the Superpowers methodology:

• Stage 1: Specification Compliance Review—Check if the implementation meets the requirement specifications
• Stage 2: Code Quality Review—Check code style and best practices

This layered review ensures that implementation errors are caught before focusing on code style.

The core of Daedalus's architecture is a 30-node LangGraph state machine that supports conditional routing and retry loops. The system includes the following agent roles:

• PM Agent: Analyzes requirements and asks 3-8 clarification questions if requirements are ambiguous
• Architect Agent: Generates system architecture design
• Developer Agent: Writes code implementations
• Reviewer Agent: Performs two-stage code reviews
• Debugger Agent: Handles errors and exceptions

Each agent has a specially configured model:

• Mistral for orchestration and coordination
• DeepSeek Coder for code generation
• LLaMA for reasoning tasks

This multi-model routing strategy ensures each task is handled by the most suitable model, rather than using one model for everything.

Daedalus automatically generates AGENTS.md context files for each folder, recording key information and constraints in that directory. This hierarchical context management significantly reduces the hallucination problem of agents.