Open-Source Multi-Agent Orchestration: Lessons from AgentForge

We built AgentForge to solve our own problem. Here's what 6 months of production multi-agent deployment taught us. Everyone designs for the happy path. But in multi-agent systems, the failure modes multiply: Agent A succeeds but takes 30s → Agent B times out waiting Agent A returns malformed JSON → Agent B crashes parsing Two agents try to write the same file → Race condition Design your orchestration around "what breaks" first. You need per-agent execution traces. Not just logs — structured traces showing: Input parameters (exact values, not summaries) Output before any post-processing Retry attempts with backoffs Circuit breaker state transitions We built this into AgentForge's execution engine. Every run generates a JSON trace you can replay for debugging. Unbounded conversation history degrades performance. We use a sliding window + summary strategy: Keep last N turns verbatim Summarize older turns into structured context Let agents explicitly "remember" key facts via a memory store Running 5 agents × 4K tokens × GPT-4 gets expensive fast. Our approach: Router agent determines which specialist to invoke (cheaper model) Specialist agents use larger models only when needed Response caching for deterministic queries Result: 60% cost reduction vs. naive implementation. Python 3.11+ Pydantic for schema validation AsyncIO for concurrent agent execution SQLite/Redis for state persistence WebSocket for real-time monitoring UI Open source. No VC pitch. Just code that works. https://github.com/agentforge-cyber/agentforge-mvp Join us: https://discord.gg/Qy6HKHsqP Posted on 2026-06-01 by the AgentForge team.