AxAI Providers For Go

Use when writing Go code with github.com/ax-llm/ax/packages/go for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers.

Install

Install only this skill for Go:

npx skills add https://ax-llm.github.io/ax/go/ --skill 'ax-go-ai'

Published skill file: ax-go-ai/SKILL.md.

Source

• Source: packages/go/skills/ax-go-ai/SKILL.md
• Version: 24.0.17

Skill Instructions

This skill helps an agent write Go code with the generated Ax package github.com/ax-llm/ax/packages/go. 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: Go.
• Package: github.com/ax-llm/ax/packages/go.
• 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-goja.

Core Pattern

import ax "github.com/ax-llm/ax/packages/go"

llm := ax.NewAI("openai", map[string]ax.Value{"apiKey": os.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.

Vertex And Prompt Caching

• Configure Gemini or Anthropic Vertex mode with projectId / project_id and region; optionally select a Vertex endpoint with endpointId / endpoint_id.
• Use credentialProvider / credential_provider for expiring Vertex and cloud tokens. It receives profile, operation, method, and URL on every attempt; its headers override static authentication.
• Credential callbacks cover chat, stream, embeddings, Responses, transcription, speech, and retries. Callback errors stop before transport, and completed 401/403 generation responses are not replayed automatically.
• Keep ADC and cloud SDK dependencies host-owned: obtain or refresh the token inside the callback. A required-auth profile accepts either a static key or the callback.
• Core resolves global, us, eu, and regional Vertex hosts. An explicit baseUrl / base_url takes precedence.
• OpenAI GPT-5.6 Chat explicit caching is opt-in through contextCache / context_cache or message/function cache flags. Use promptCacheKey / prompt_cache_key for stable affinity; sessionId / session_id is the fallback.
• Normalized usage separates uncached prompt, cache-read, and cache-creation tokens. get_model_cost / target equivalent uses the shared model catalog, including cache-write pricing and long-context thresholds.
• Start with the OpenAI prompt-caching and Vertex Gemini examples under examples/. Scripted AxAI fixtures verify routing without live credentials.

Routing And Balancing

• Use the multi-service router when a logical model key selects a configured service or concrete model. It combines model lists; it does not learn from outcomes.
• Use ProviderRouter for capability-based selection and optional media degradation. When the selected provider supports images, preserve every native image part with its payload, MIME type, detail level, cache and optimization hints, alt text, and ordering with surrounding text.
• Use the default AxBalancer for deterministic ordered/metric failover with its existing retry policy.
• Opt into AxBalancerAdaptiveStrategy only for operational routing among application-approved equivalent aliases. It learns transient reliability and successful latency, combines them with estimated cost and a deadline, and explores with Thompson sampling.
• Put centralized decision state in an AxBalancerStatsStore. The routing-event callback is best-effort analytics and observability, not a state replication mechanism.
• Shared stores require non-empty, unique, stable route keys. Use slices to isolate workflows, tenants, or traffic classes without putting prompts, responses, raw errors, or sensitive identifiers in keys or events.
• Adaptive balancing does not measure answer quality or semantically choose a model. Only group routes that the application already accepts as substitutes.
• Generated provider streams are incremental and closeable. Retry or failover is allowed only before the first content event; later failures surface without replay, and adaptive latency is recorded at the first chunk.
• Start with examples/adaptive_balancer_no_key for store/reducer syntax, then use the cataloged provider-backed adaptive-balancer example for a complete two-route setup.

Relevant API Surface

• AxAI: axllm.NewAI, axllm.GetSupportedAIModels, axllm.AxCredentialRequest, axllm.AxCredentialProvider, axllm.AxChatStream, axllm.OpenAICompatibleClient, axllm.OpenAIResponsesClient, axllm.GoogleGeminiClient, axllm.AnthropicClient, axllm.AxUsageContext, axllm.AxUsageEvent, axllm.AxUsageObserver, axllm.SetUsageObserver, axllm.AxRuntimeHooks, axllm.AxRateLimitInfo, axllm.AxRateLimiter, axllm.AxTracer, axllm.AxMeter, axllm.AxGlobals, axllm.SetRateLimiter, axllm.SetTracer, axllm.SetMeter, axllm.AxBalancer, axllm.AxBalancerAdaptiveStrategy, axllm.AxBalancerStatsStore, axllm.AxInMemoryBalancerStatsStore, axllm.CreateBalancerRouteStats, axllm.UpdateBalancerRouteStats, axllm.SampleBalancerRouteHealth, axllm.MultiServiceRouter, axllm.ProviderRouter

Guardrails

• Start from package examples for exact native syntax before inventing a new call shape.
• Use provider-api examples only when the user explicitly has provider credentials available.
• Use no-key examples for deterministic local checks and provider request mapping.
• Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
• Do not copy repo-maintainer skills from tools/*/skills/ into user packages.
• When decorating AIClient, forward GetFeatures(model) map[string]Value whenever the wrapped client implements it. AxGen otherwise falls back to permissive capabilities, which can select an unsupported structured-output rung.
• For Vertex OpenAI-compatible MaaS, prefer NewAI("vertex-ai", options) with AxCredentialProviderFunc; do not reintroduce a request-rewriting response-format decorator.