Ax
DSPy-style framework for building LLM programs and agents in TypeScript.
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Use cases
About Ax
Ax is listed in our frameworks category and is aimed at builders. DSPy-style framework for building LLM programs and agents in TypeScript. Pricing, as the maker states it: Open source. Maturity: Active. We last checked the official link on 2026-10-16.
What is Ax?
Ax is a DSPy-style framework for building LLM programs and agents, TypeScript-first and published as @ax-llm/ax.
The same signatures, provider mappings, agents, flows, runtime contracts and optimizers are also compiled into generated Python, Java, C++, Go and Rust libraries, giving one programming model across languages.
Sources: the official README and our directory listing, checked 2026-10-18. Official site: github.com/ax-llm/ax.
Who is Ax for?
TypeScript developers (and polyglot teams) who like DSPy's signature-and-optimizer approach.
Ax at a glance
- Open source: Yes (Apache License 2.0)
- GitHub: ax-llm/ax · 2,968 stars (as of Oct 21, 2026)
Key features
- DSPy-style signatures and optimizers
- TypeScript-first
- Generated libraries for Python, Java, C++, Go and Rust
- Agents and flows
Ax FAQ
What is Ax?
Ax is a DSPy-style framework for building LLM programs and agents, TypeScript-first and published as @ax-llm/ax.
Who is Ax for?
TypeScript developers (and polyglot teams) who like DSPy's signature-and-optimizer approach.
Is Ax free?
Ax's pricing, as the maker states it: Open source. Open-source tools are free to use, though you may still pay for model API usage or a hosted plan. Check the official site for current plans.
Is Ax open source?
Yes. Ax is released under the Apache License 2.0, according to its GitHub repository.
What are alternatives to Ax?
Similar tools include Haystack, Trigger.dev, PocketFlow, LazyLLM, agentUniverse and EvoAgentX.
Ax ratings and reviews
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Ax alternatives
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agentUniverse
Multi-agent framework with reusable collaboration patterns for building LLM agent applications.
EvoAgentX
Framework for building, evaluating and automatically evolving LLM agents and agentic workflows.
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