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LLM Agents with Production-Ready TS

axllm.dev · 1,301 words · saved by 1 readers

DSPy for TypeScript. Working with LLMs is complex—they don't always do what you want. DSPy makes it easier to build amazing things with LLMs. Just define your inputs and outputs (signature) and an efficient prompt is auto-generated and used. Connect together various signatures to build complex systems and workflows using LLMs. Working with LLMs is complex they don’t always do what you want. DSPy makes it easier to build amazing things with LLMs. Just define your inputs and outputs (signature) and an efficient prompt is auto-generated and used. Connect together various signatures to build complex systems and workflows using LLMs And to help you really use this in production we have everything else you need like observability, streaming, support for other modalities (images,audio, etc), error-correction, multi-step function calling, MCP, RAG, etc Efficient type-safe prompts are auto-generated from a simple signature. A prompt signature is made up of a "task description" inputField:type "

Ax Stop prompting. Start programming. A one-line signature declares what goes in and what comes out — Ax turns it into the prompt, the parser, the validators, and the retry loop, and hands back typed data your code can trust. The same contract scales from one typed call to RLM-grade agents on any model — native in TypeScript Python Java C++ Go Rust , and five more languages. Typed, validated outputs Agents on any model — even small local ones DSPy · GEPA · ACE · RLM research inside Native in your language — six supported Get started GitHub GitHub stars npm downloads/week Building with Claude C

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