Inference Providers

IronClaw readily supports multiple LLM providers

IronClaw supports multiple LLM providers out of the box, including NEAR AI , Anthropic, OpenAI, Google Gemini, GitHub Copilot, Ollama, AWS Bedrock, and any OpenAI-compatible endpoint.

Providers can be configured via environment variables or the onboarding wizard. IronClaw’s modular architecture allows seamless integration with new providers by implementing the LLMProvider trait.

Configuring a Provider

To config a new provider, simply run the onboarding wizard:

ironclaw onboard --provider-only

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Provider Overview

Provider	Backend value	Requires API key	Notes
NEAR AI	nearai	OAuth (browser)	Multi-model
Anthropic	anthropic	ANTHROPIC_API_KEY	Claude models
OpenAI	openai	OPENAI_API_KEY	GPT models
Google Gemini	gemini_oauth	OAuth (browser)	Gemini models; function calling
io.net	ionet	IONET_API_KEY	Intelligence API
Mistral	mistral	MISTRAL_API_KEY	Mistral models
Yandex AI Studio	yandex	YANDEX_API_KEY	YandexGPT models
MiniMax	minimax	MINIMAX_API_KEY	MiniMax-M2.7 models
Cloudflare Workers AI	cloudflare	CLOUDFLARE_API_KEY	Access to Workers AI
GitHub Copilot	github_copilot	GITHUB_COPILOT_TOKEN	Multi-models
Ollama	ollama	No	Local inference
AWS Bedrock	bedrock	AWS credentials	Native Converse API
OpenRouter	openai_compatible	LLM_API_KEY	300+ models
Together AI	openai_compatible	LLM_API_KEY	Fast inference
Fireworks AI	openai_compatible	LLM_API_KEY	Fast inference
vLLM / LiteLLM	openai_compatible	Optional	Self-hosted
LM Studio	openai_compatible	No	Local GUI

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NEAR AI

NEARAI_MODEL=claude-3-5-sonnet-20241022
NEARAI_BASE_URL=https://private.near.ai

Popular models: Qwen/Qwen3.5-122B-A10B, black-forest-labs/FLUX.2-klein-4B, zai-org/GLM-5-FP8

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Anthropic (Claude)

LLM_BACKEND=anthropic
ANTHROPIC_API_KEY=sk-ant-...

Popular models: claude-sonnet-4-20250514, claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022

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OpenAI (GPT)

LLM_BACKEND=openai
OPENAI_API_KEY=sk-...

Popular models: gpt-4o, gpt-4o-mini, o3-mini

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Google Gemini (OAuth)

Uses Google OAuth with PKCE (S256) for authentication — no API key required. On first run, a browser opens for Google account login. Credentials (including refresh token) are saved to ~/.gemini/oauth_creds.json with 0600 permissions.

LLM_BACKEND=gemini_oauth
GEMINI_MODEL=gemini-2.5-flash

Supported features

Feature	Status	Notes
Function calling	✅	functionDeclarations / functionCall / functionResponse
generationConfig	✅	temperature, maxOutputTokens passed from request
thinkingConfig	✅	thinkingBudget/thinkingLevel for thinking-capable models (does NOT set includeThoughts)
toolConfig	✅	functionCallingConfig.mode: AUTO/ANY/NONE
SSE streaming	✅	Cloud Code API with streamGenerateContent?alt=sse
Token refresh	✅	Automatic via refresh token

Popular models

Model	ID	Notes
Gemini 3.1 Pro	gemini-3.1-pro-preview	Latest, strongest reasoning
Gemini 3.1 Pro Custom Tools	gemini-3.1-pro-preview-customtools	Enhanced tool use
Gemini 3 Pro	gemini-3-pro-preview	Preview
Gemini 3 Flash	gemini-3-flash-preview	Fast preview with thinking
Gemini 3.1 Flash Lite	gemini-3.1-flash-lite-preview	Preview, lightweight
Gemini 2.5 Pro	gemini-2.5-pro	Stable, strong reasoning
Gemini 2.5 Flash	gemini-2.5-flash	Fast, good quality
Gemini 2.5 Flash Lite	gemini-2.5-flash-lite	Fastest, lightweight

Cloud Code API vs standard API

Models containing -preview (with hyphen) or gemini-3 in the name, as well as any gemini- model with major version >= 2, route through the Cloud Code API (cloudcode-pa.googleapis.com) which supports SSE streaming and project-scoped access. Other models use the standard Generative Language API (generativelanguage.googleapis.com).

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GitHub Copilot

GitHub Copilot exposes chat endpoint at https://api.githubcopilot.com. IronClaw uses that endpoint directly through the built-in github_copilot provider.

LLM_BACKEND=github_copilot
GITHUB_COPILOT_TOKEN=gho_...
GITHUB_COPILOT_MODEL=gpt-4o
# Optional advanced headers if your setup needs them:
# GITHUB_COPILOT_EXTRA_HEADERS=Copilot-Integration-Id:vscode-chat

ironclaw onboard can acquire this token for you using GitHub device login. If you already signed into Copilot through VS Code or a JetBrains IDE, you can also reuse the oauth_token stored in ~/.config/github-copilot/apps.json. If you prefer, LLM_BACKEND=github-copilot also works as an alias.

Popular models vary by subscription, but gpt-4o is a safe default. IronClaw keeps model entry manual for this provider because GitHub Copilot model listing may require extra integration headers on some clients. IronClaw automatically injects the standard VS Code identity headers (User-Agent, Editor-Version, Editor-Plugin-Version, Copilot-Integration-Id) and lets you override them with GITHUB_COPILOT_EXTRA_HEADERS.

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Ollama (local)

Install Ollama from ollama.com, pull a model, then:

LLM_BACKEND=ollama
OLLAMA_MODEL=llama3.2
# OLLAMA_BASE_URL=http://localhost:11434   # default

Pull a model first: ollama pull llama3.2

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MiniMax

MiniMax provides high-performance language models with 204,800 token context windows.

LLM_BACKEND=minimax
MINIMAX_API_KEY=...

Available models: MiniMax-M2.7 (default), MiniMax-M2.7-highspeed, MiniMax-M2.5, MiniMax-M2.5-highspeed

To use the China mainland endpoint, set:

MINIMAX_BASE_URL=https://api.minimaxi.com/v1

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AWS Bedrock (requires --features bedrock)

Uses the native AWS Converse API via aws-sdk-bedrockruntime. Supports standard AWS authentication methods: IAM credentials, SSO profiles, and instance roles.

Build prerequisite: The aws-lc-sys crate (transitive dependency via AWS SDK) requires CMake to compile. Install it before building with --features bedrock:

• macOS: brew install cmake
• Ubuntu/Debian: sudo apt install cmake
• Fedora: sudo dnf install cmake

With AWS credentials (IAM, SSO, instance roles)

LLM_BACKEND=bedrock
BEDROCK_MODEL=anthropic.claude-opus-4-6-v1
BEDROCK_REGION=us-east-1
BEDROCK_CROSS_REGION=us
# AWS_PROFILE=my-sso-profile   # optional, for named profiles

The AWS SDK credential chain automatically resolves credentials from environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY), shared credentials file (~/.aws/credentials), SSO profiles, and EC2/ECS instance roles.

Cross-region inference

Set BEDROCK_CROSS_REGION to route requests across AWS regions for capacity:

Prefix	Routing
us	US regions (us-east-1, us-east-2, us-west-2)
eu	European regions
apac	Asia-Pacific regions
global	All commercial AWS regions
(unset)	Single-region only

Popular Bedrock model IDs

Model	ID
Claude Opus 4.6	anthropic.claude-opus-4-6-v1
Claude Sonnet 4.5	anthropic.claude-sonnet-4-5-20250929-v1:0
Claude Haiku 4.5	anthropic.claude-haiku-4-5-20251001-v1:0
Amazon Nova Pro	amazon.nova-pro-v1:0
Llama 4 Maverick	meta.llama4-maverick-17b-instruct-v1:0

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OpenAI-Compatible Endpoints

All providers below use LLM_BACKEND=openai_compatible. Set LLM_BASE_URL to the provider’s OpenAI-compatible endpoint and LLM_API_KEY to your API key.

OpenRouter

OpenRouter routes to 300+ models from a single API key.

LLM_BACKEND=openai_compatible
LLM_BASE_URL=https://openrouter.ai/api/v1
LLM_API_KEY=sk-or-...
LLM_MODEL=anthropic/claude-sonnet-4

Popular OpenRouter model IDs:

Model	ID
Claude Sonnet 4	anthropic/claude-sonnet-4
GPT-4o	openai/gpt-4o
Llama 4 Maverick	meta-llama/llama-4-maverick
Gemini 2.0 Flash	google/gemini-2.0-flash-001
Mistral Small	mistralai/mistral-small-3.1-24b-instruct

Browse all models at openrouter.ai/models.

Together AI

Together AI provides fast inference for open-source models.

LLM_BACKEND=openai_compatible
LLM_BASE_URL=https://api.together.xyz/v1
LLM_API_KEY=...
LLM_MODEL=meta-llama/Llama-3.3-70B-Instruct-Turbo

Popular Together AI model IDs:

Model	ID
Llama 3.3 70B	meta-llama/Llama-3.3-70B-Instruct-Turbo
DeepSeek R1	deepseek-ai/DeepSeek-R1
Qwen 2.5 72B	Qwen/Qwen2.5-72B-Instruct-Turbo

Fireworks AI

Fireworks AI offers fast inference with compound AI system support.

LLM_BACKEND=openai_compatible
LLM_BASE_URL=https://api.fireworks.ai/inference/v1
LLM_API_KEY=fw_...
LLM_MODEL=accounts/fireworks/models/llama4-maverick-instruct-basic

vLLM / LiteLLM (self-hosted)

For self-hosted inference servers:

LLM_BACKEND=openai_compatible
LLM_BASE_URL=http://localhost:8000/v1
LLM_API_KEY=token-abc123        # set to any string if auth is not configured
LLM_MODEL=meta-llama/Llama-3.1-8B-Instruct

LiteLLM proxy (forwards to any backend, including Bedrock, Vertex, Azure):

LLM_BACKEND=openai_compatible
LLM_BASE_URL=http://localhost:4000/v1
LLM_API_KEY=sk-...
LLM_MODEL=gpt-4o                 # as configured in litellm config.yaml

LM Studio (local GUI)

Start LM Studio’s local server, then:

LLM_BACKEND=openai_compatible
LLM_BASE_URL=http://localhost:1234/v1
LLM_MODEL=llama-3.2-3b-instruct-q4_K_M
# LLM_API_KEY is not required for LM Studio