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AgentForge
Developed a local multi-agent software engineering assistant where specialized AI agents collaborate to analyze requirements, design solutions, critique outputs, and produce implementation plans.
OllamaMulti-Agent SystemsTypeScriptLocal InferenceConsensusQwenGPT-OSS
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SYSTEM ARCHITECTURE
Specialized agents collaborate locally with validation, caching, and code-generation workflows.
Problem
Software engineering assistants become more useful when different roles can reason separately instead of forcing one model to do every step.
Architecture
Planner, analyst, architect, critic, and consensus pipeline coordinating local inference and role-based reasoning.
Challenges
- Designed role-based agent separation
- Handled local model execution with Ollama
- Added caching for repeated reasoning steps
- Built consensus reasoning across specialized agents
- Integrated Codex into the workflow where useful
- Balanced response quality with execution latency
Benchmarks
Role-based agents, local inference, caching, consensus reasoning, and Codex integration in a local assistant workflow.
Lessons Learned
- Separating agent roles improves clarity of reasoning
- Local inference is useful when privacy or offline operation matters
- Consensus steps help catch weak outputs before they reach a user
- Modular agent design enables parallel development and testing