Look, LangChain was great to get the ecosystem started 0 to 1. But let's be honest: taking LangChain to production with 50+ tools is a nightmare of abstraction hell and brittle if/else logic. The AgentExecutor is slow, and relying on it to route complex tools feels like fighting the framework.
Tool discovery shouldn't be an LLM's job, and it shouldn't require a heavy python wrapper. It’s a distributed networking problem.
What we built at US Neural:
We built Mycelium — a decoupled, semantic tool registry. Instead of chaining tools in code, you register them to a local mesh. When a user has a task, the engine routes it semantically (no keyword matching).
The Benchmarks (No BS): We ran this against a synthetic corpus of 100,000 agents to test if local semantic routing is actually viable for production.
Latency: 9.6ms cold discovery (vs 194ms for BM25).
Accuracy: 70.7% family-level intent matching.
Infra: 100% local (all-MiniLM-L6-v2 + ChromaDB).
If you are tired of bloated frameworks and want deterministic, sub-10ms tool routing, check out our methodology.
GitHub & Reproducible Scripts: [https://github.com/udaysaai/mycelium](https://github.com/udaysaai/mycelium)
Visual Dashboard: [https://mycelium-agents.netlify.app/](https://mycelium-agents.netlify.app/)
I’m ready to be roasted. Tell me why this architecture wouldn't scale for your stack.