Agent-to-code JIT compiler for Z3-theorem-proving agents
The agent-to-code compiler that optimizes AI for speed 🏎️ and safety 🏁 - a1/examples/z3_reasoning at main · stanford-mast/a1
Agent-to-code JIT compiler for Z3-theorem-proving agents launched on November 13, 2025 with 6 votes, #2,995 of 9,796 launches that month and more than 75% of that year's launches.
What it does
Existing agent frameworks run a static while loop where an LLM dispatches tools at each iteration. This is especially error-prone in domains that have hard logical constraints. The Proof of Thought approach ( https://github.com/DebarghaG/proofofthought ) previously demonstrated that a compound system comprised of an LLM and an SMT solver can work wonders. However, such a system need not be specialized for just SMT solver problems. Agent compilers generalize this approach by compiling an agent to code either ahead-of-time (AOT) or just-in-time, optimizing generated code for each unique agent input. from a1 import Agent , Skill , LLM from pydantic import BaseModel class Answer ( BaseModel ):…from github.com
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