Open-Source AgentJIT Compiles AI Agent Workflows from 30s LLM Chains to 0.1ms Python

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A developer has open-sourced AgentJIT, a just-in-time compiler designed to eliminate latency and cost inefficiencies in AI agent workflows. The tool works by tracing an agent's tool-call sequence on its first run and compiling the resulting execution path into a deterministic Python AST pipeline for all subsequent runs. This approach reduces multi-step LLM chain execution times from up to 45 seconds down to under 0.1 milliseconds, with no token costs on compiled runs. The compiler automatically inserts runtime input guards that trigger a fallback to the original LLM agent if unexpected inputs are detected, preventing crashes or regressions. AgentJIT is available as a self-contained Python library with no mandatory external dependencies and can be applied via simple decorators.

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