Ax is a TypeScript-first programming framework for building applications with large language models through typed generation, agents, workflows, and optimization tools. It is intended for developers who want one model for LLM programs across TypeScript, Python, Java, C++, Go, Rust, and other runtimes.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ax-llm/ax --skill ax-rust-aigit clone --depth 1 https://github.com/ax-llm/axWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ax-llm/ax/ax-rust-ai)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-rust-ai"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-rust-ai.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.02665 |
| Opus 5 | $0.00023 | $0.01333 |
| Sonnet 5 | $0.00009 | $0.00533 |
| Haiku 4.5 | $0.00005 | $0.00266 |
Grade A, and why
ax-rust-ai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to ax-cpp-ai — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxAI Providers For Rust
This skill helps an agent write Rust code with the generated Ax package axllm. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.
When To Use
- Create provider clients or normalize provider options.
- Choose a named deployment profile separately from the model ID served by that deployment.
- Attach renewable per-request credentials for expiring cloud tokens.
- Resolve structured-output modes from the selected profile and model.
- Choose between model-list routing, ordered failover, and adaptive operational routing.
- Route multimodal requests without flattening native images or files when the selected provider supports them.
- Use scripted transports for deterministic no-key examples.
- Use provider-api examples only when explicit provider credentials are available.
Package Facts
- Language: Rust.
- Package:
axllm. - Package API docs:
API.mdandaxir-api.json. - Capability manifest:
axir-capabilities.json. - Runnable examples:
examples/. - Real network support: yes.
- Scripted no-key transport support: yes.
- Runtime profiles:
javascript-quickjs.
Core Pattern
use axllm::ai;
let llm = ai("openai", options)?;
Named Deployment Profiles
- The first
ai/NewAIfactory argument selects deployment behavior. The model option selects a model only inside that deployment; never infer request rules from a vendor-looking model ID. openaiis the official OpenAI deployment.openai-compatibleis the conservative custom-endpoint profile and requires an explicit base URL. Unknown profile names are errors.- A Together-hosted DeepSeek model uses the
togetherprofile's URL, authentication, reasoning fields, and effort mapping. Native DeepSeekthinkingfields apply only to thedeepseekprofile. - Verified DeepSeek, Grok, Groq, Cerebras, and DeepInfra model rules default an omitted thinking level to logical
max, mapped to the strongest documented deployment effort. - Send
noneonly where the selected deployment and model document reasoning disablement. Unsupported levels fail before network I/O; dynamic Hugging Face Router routes remain conservative. - Structured output is an ordered model-aware capability:
native,function, andjson_object. Exact caller model metadata overrides the first matching profile rule, which overrides the profile default. - An explicit unsupported structured-output mode fails before transport.
structuredOutputs/structured_outputsremains the compatibility alias for native JSON Schema only. - The exact Vertex
google/gemma-4-26b-a4b-it-maasrule prefersjson_object, excludes native schema, defaults thinking tomax, writes nestedenable_thinking, and extracts/replaysreasoning_content. Unknown Vertex models stay conservative. - Use named factories for Azure OpenAI, Cohere, DeepSeek, DeepSeek Responses, Mistral, Reka, Grok, routers, hosted inference, and configurable runtimes. Profile-only branded client constructors were removed.
- Retained client classes are transport/runtime boundaries: OpenAI-compatible Chat Completions, OpenAI Responses, Anthropic Messages, and Gemini GenerateContent. Build ordinary applications through the named factory.
- Provider descriptors and conformance fixtures are generated from the shared profile manifest. Do not add provider-name switches or cross-profile model normalization in a generated package.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +8 lines 3a4b32113127
- yesterday Changed · +18 lines 01eac028f4e1
- 5d ago First seen · 87 lines · 46 tokens per session scan A e0b035b54553
ax-rust-ai is a skill published in the GitHub repository ax-llm/ax (2,893 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 2,665 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to ax-cpp-ai, differing in 22 lines, and is treated as a copy.
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