ax-java-ai

A Java coding guide for the dev.axllm:ax package, which lets programs call AI models from different providers. It covers provider clients, model selection, OpenAI-compatible calls, and routing between models or services.

In plain words
What is it for?
Use it when building Java applications that call OpenAI, Gemini, Anthropic, or compatible services, select deployment profiles, return structured results, or add failover and load balancing.
Why use it?
It helps avoid mixing Java APIs with examples from other languages and makes provider-specific settings easier to choose correctly. It also covers expiring credentials and requests that include images.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ax-llm/ax/ax-java-ai
Any agent
npx skills add ax-llm/ax --skill ax-java-ai
Clone the repo
git clone --depth 1 https://github.com/ax-llm/ax

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,736 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.01736
Opus 5 $0.00024 $0.00868
Sonnet 5 $0.00010 $0.00347
Haiku 4.5 $0.00005 $0.00174

Measured today against content hash 56eb9600ff42, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ax-java-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.

Origin

This is a copy

88% identical to ax-cpp-ai — 20 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.

packages/java/skills/ax-java-ai/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AxAI Providers For Java

This skill helps an agent write Java code with the generated Ax package dev.axllm:ax. 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 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: Java.
  • Package: dev.axllm:ax.
  • Package API docs: API.md and axir-api.json.
  • Capability manifest: axir-capabilities.json.
  • Runnable examples: examples/.
  • Real network support: yes.
  • Scripted no-key transport support: yes.
  • Runtime profiles: javascript-quickjs, python-pyodide.

Core Pattern

import dev.axllm.ax.*;

var llm = Ax.ai("openai", java.util.Map.of("apiKey", System.getenv("OPENAI_API_KEY")));

Named Deployment Profiles

  • The first ai / NewAI factory argument selects deployment behavior. The model option selects a model only inside that deployment; never infer request rules from a vendor-looking model ID.
  • openai is the official OpenAI deployment. openai-compatible is the conservative custom-endpoint profile and requires an explicit base URL. Unknown profile names are errors.
  • A Together-hosted DeepSeek model uses the together profile's URL, authentication, reasoning fields, and effort mapping. Native DeepSeek thinking fields apply only to the deepseek profile.
  • 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 none only 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, and json_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_outputs remains the compatibility alias for native JSON Schema only.
  • The exact Vertex google/gemma-4-26b-a4b-it-maas rule prefers json_object, excludes native schema, defaults thinking to max, writes nested enable_thinking, and extracts/replays reasoning_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.

Read the full file on GitHub · 87 lines

Changes

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.

  1. today Changed 56eb9600ff42
  2. 3d ago First seen · 87 lines · 48 tokens per session scan A 7cd8ca953192

Subscribe to this mod's changes

ax-java-ai is a skill published in the GitHub repository ax-llm/ax (2,890 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 1,736 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to ax-cpp-ai, differing in 20 lines, and is treated as a copy.

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