I Built a Lock-Free Agent Runtime in C++17 — Here's Why Python Frameworks Are 2500x Slower

TL;DR: I replaced Python's LLM orchestration layer with C++17 lock-free data structures. The result: 25,000 sessions/sec vs LangChain's ~10-50. Here's what I learned about why the gap exists, how lock-free programming works, and why it matters for the future of AI infrastructure. rahugur / forge-lock-free Forge — Lock-Free Agent Orchestration Runtime A high-performance C++17 agent runtime that orchestrates LLM-powered workflows using lock-free concurrency primitives. Built to demonstrate that agent orchestration doesn't have to be slow — Forge handles 25,000+ sessions/sec where Python frameworks like LangChain manage ~50. Why This Exists Every major AI agent framework today — LangChain, CrewAI, AutoGen — is written in Python. Python is great for prototyping, but it has a fundamental problem for production agent workloads: the Global Interpreter Lock (GIL) . The GIL means only one thread can execute Python bytecode at a time, even on a 64-core server. When you're orchestrating hundreds