Leanstral: Open-Source foundation for trustworthy vibe-coding

AI agents have proven to be highly capable tools at code generation. Yet, as we push these models to high-stakes domains, ranging from frontier research mathematics to mission-critical software, we encounter a scaling bottleneck: the human review. The time and specialized expertise required to manually verify become the primary impedance of engineering velocity.

We envision a more helpful generation of coding agents to both carry out their tasks and formally prove their implementations against strict specifications. Instead of debugging machine-generated logic, humans dictate what they want. Today, we are taking the first major step toward that vision.

We release Leanstral, the first open-source code agent designed for Lean 4. Lean4 is a proof assistant capable of expressing complex mathematical objects such as perfectoid spaces and software specifications like properties of Rust fragments . Unlike existing proving systems that act as wrappers around large generalist models or focus on single math problems, Leanstral is designed to be highly efficient (with 6B active parameters) and trained for operating in realistic formal repositories.

Open and accessible: We release Leanstral weights under an Apache 2.0 license, in an agent mode within Mistral vibe, and through a free API endpoint. We will also release a tech report detailing our training approach, and a new evaluation suite FLTEval, to move evaluations beyond their focus on competition math.

Efficient and mighty: We use a highly sparse architecture for Leanstral, and optimise it for proof engineering tasks. Leveraging parallel inference with Lean as a perfect verifier, Leanstral is both performant and cost-efficient against existing closed-source competitors.

Own the Weights: Download the Apache 2.0 licensed model and run it on your own metal.

When breaking changes hit a new Lean release, migrating code can be a massive headache. We fed Leanstral a real-world question from the Proof Assistants Stack Exchange about a script that mysteriously stopped compiling in Lean 4.29.0-rc6 (which we did not train with due to its recency). The culprit was a rewrite ( rw ) tactic that suddenly failed to match patterns involving a simple type alias, initially written as def T2 := List Bool .

Upgradable via MCP: Leanstral supports arbitrary MCPs through vibe, and was specifically trained to achieve maximal performance with the frequently used lean-lsp-mcp.

To reflect usefulness in realistic proof engineering scenarios, we benchmark Leanstral for completing all formal proofs and correctly defining new mathematical concepts in each PR to the FLT project, instead of isolated mathematical problems. We compare Leanstral against leading coding agents (Claude Opus 4.6, Sonnet 4.6, Haiku 4.5) and open-source models (Qwen3.5 397B-A17B, Kimi-K2.5 1T-A32B, GLM5 744B-A40B).

Leanstral-120B-A6B demonstrates a significant efficiency advantage over its much larger open-source peers. While models like GLM5-744B-A40B and Kimi-K2.5-1T-32B struggle to scale, capping their FLTEval scores at approximately 16.6 and 20.1 respectively, Leanstral outperforms them both with just a single pass.