Provider Setup

Providers connect your agents to large language models. Each provider plugin handles API authentication, streaming, tool calling format differences, and thinking/reasoning support so your agent config stays clean.

RivetOS ships with six provider plugins:

Provider	Models	Thinking Support	Notes
Anthropic	Claude Opus, Sonnet, Haiku	✅	Adaptive thinking, prompt caching
xAI	Grok 3, Grok 4	✅	Responses API, conversation caching, live search
Google	Gemini 2.5 Pro, Flash	✅	Thought signatures for function calling
Ollama	Any local model	—	Local inference, no API key needed
OpenAI-compat	vLLM / TGI / llama.cpp llama-server / Groq / Together / Fireworks / LocalAI	✅ (when --reasoning-parser set)	Folds mid-conversation system messages, consumes native reasoning_content
Claude CLI	Anything claude supports	✅	Drives the local claude binary using your subscription OAuth — no API key

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Anthropic (Claude)

1. Get an API key

1. Go to the Anthropic Console
2. Sign up or log in
3. Go to API Keys → Create Key
4. Copy the key (starts with sk-ant-)

Prefer subscription/OAuth auth over an API key? Use the claude-cli provider instead; it drives the local claude binary (Claude Code CLI), which owns the OAuth flow. Run claude login once via the CLI itself; RivetOS does not handle the OAuth handshake. See the claude-cli provider in the Configuration Reference.

2. Configure

Add your key to .env:

ANTHROPIC_API_KEY=sk-ant-...your-key-here

Add to config.yaml:

providers:
  anthropic:
    model: claude-opus-4-7
    max_tokens: 8192

agents:
  myagent:
    provider: anthropic
    default_thinking: medium

Config options

Key	Type	Default	Description
model	string	claude-opus-4-7	Model identifier
max_tokens	number	8192	Maximum output tokens
api_key	string	${ANTHROPIC_API_KEY}	API key. Use env var
context_window	number	—	Override the model’s context-window size (advanced)
max_output_tokens	number	—	Hard cap on output tokens

For subscription/OAuth auth instead of an API key, use the claude-cli provider (it drives the local claude binary and owns the OAuth flow).

Thinking levels

When default_thinking is set on the agent, the provider requests extended thinking with a token budget:

Level	Budget	Best For
off	—	Simple questions, fast responses
low	2,000 tokens	Light reasoning
medium	10,000 tokens	Code review, planning
high	50,000 tokens	Complex architecture, deep analysis

Models

Model	Speed	Intelligence	Context
claude-opus-4-7	Slow	Highest	200K
claude-sonnet-4-6	Fast	High	200K
claude-haiku-4-5-20251001	Fastest	Good	200K

Docs: Anthropic API Reference

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xAI (Grok)

1. Get an API key

1. Go to console.x.ai
2. Sign up or log in
3. Create an API key
4. Copy the key (starts with xai-)

2. Configure

Add your key to .env:

XAI_API_KEY=xai-...your-key-here

Add to config.yaml:

providers:
  xai:
    model: grok-4-1-fast-reasoning

agents:
  grok:
    provider: xai

Config options

Key	Type	Default	Description
model	string	grok-4.20-reasoning	Model identifier
api_key	string	${XAI_API_KEY}	API key
base_url	string	https://api.x.ai/v1	API endpoint
temperature	number	—	Sampling temperature (not used with reasoning models)
store	boolean	true	Server-side conversation storage. When enabled, only new messages are sent each turn
timeout_ms	number	3600000	Request timeout in milliseconds (default: 1 hour for reasoning)

Conversation caching

When store: true (default), xAI stores the conversation server-side. Each turn only sends new messages, reducing token usage and latency. The provider manages previous_response_id automatically.

Models

Model	Type	Notes
grok-4.20-reasoning	Flagship	2M context, fast + agentic, $2.00/$6.00 per M tokens
grok-4-1-fast-reasoning	Fast	10x cheaper ($0.20/$0.50), good for compaction and cheap throughput

Docs: xAI API Documentation

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Google (Gemini)

1. Get an API key

1. Go to Google AI Studio
2. Click Create API Key
3. Select or create a Google Cloud project
4. Copy the key

2. Configure

Add your key to .env:

GOOGLE_API_KEY=AIza...your-key-here

Add to config.yaml:

providers:
  google:
    model: gemini-2.5-pro

agents:
  gemini:
    provider: google
    default_thinking: medium

Config options

Key	Type	Default	Description
model	string	gemini-2.5-pro	Model identifier
api_key	string	${GOOGLE_API_KEY}	API key
max_tokens	number	8192	Maximum output tokens
base_url	string	https://generativelanguage.googleapis.com/v1beta	API endpoint

Thinking levels

Level	Budget
off	0
low	1,024 tokens
medium	8,192 tokens
high	32,768 tokens

Models

Model	Speed	Context	Notes
gemini-2.5-pro	Medium	1M	Best reasoning
gemini-2.5-flash	Fast	1M	Good balance of speed and quality

Docs: Gemini API Documentation

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Ollama (local models)

Ollama runs models locally on your machine. No API key needed, no usage costs, just hardware.

1. Install Ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# macOS
brew install ollama

# Or download from https://ollama.com/download

2. Pull a model

ollama pull qwen2.5:32b

Browse available models at ollama.com/library.

3. Configure

No .env needed; Ollama runs locally without authentication.

providers:
  ollama:
    model: qwen2.5:32b
    base_url: http://localhost:11434

agents:
  local:
    provider: ollama
    local: true    # Extended context (tokens are free)

Config options

Key	Type	Default	Description
model	string	llama3.1	Model name (must be pulled via ollama pull)
base_url	string	http://localhost:11434	Ollama API endpoint
temperature	number	0.7	Sampling temperature
top_p	number	0.9	Nucleus sampling threshold
num_ctx	number	model default	Context window size in tokens
keep_alive	string	30m	How long to keep model loaded in memory

Tips

• Set local: true on the agent; this includes extended workspace context (CAPABILITIES.md, daily notes) since tokens are free with local inference.
• num_ctx is critical for tool-using agents. Most models default to 2048-4096 tokens, which isn’t enough. Set 8192 or higher.
• keep_alive controls how long the model stays in VRAM after the last request. Set to 0 to unload immediately, or 24h to keep it warm.
• Remote Ollama: If Ollama runs on a different machine, change base_url to point at it (e.g., http://192.0.2.50:11434).

Docs: Ollama API Documentation

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vLLM (local / self-hosted)

Dedicated provider for a vLLM server. Exposes the full vLLM surface: sampling extensions (top_k, min_p, repetition_penalty, min_tokens), mm_processor_kwargs / chat_template_kwargs, the extra_body escape hatch, video_url content blocks, and reasoning_content parsing.

Start a server: vllm serve <model> --port 8000 [--reasoning-parser ...] [--enable-auto-tool-choice].

providers:
  vllm:
    base_url: http://localhost:8000      # trailing /v1 optional
    model: default                       # 'default' auto-discovers from /v1/models
    top_k: 40
    min_p: 0.05
    # api_key: ${VLLM_API_KEY}           # only if you started vLLM with --api-key

agents:
  local:
    provider: vllm
    local: true                          # extended context — tokens are free

Leave model: default and the provider auto-selects the served model (and adopts its context window) from /v1/models. For native <think> reasoning, start vLLM with a --reasoning-parser; the AI SDK reasoning surface consumes reasoning_content.

Config options

Key	Type	Default	Description
base_url	string	required	vLLM server URL (/v1 optional).
model	string	default	Served model id; default auto-discovers.
api_key	string	${VLLM_API_KEY}	Bearer token (only if vLLM was started with --api-key).
max_tokens	number	4096	Maximum output tokens.
temperature / top_p	number	0.7 / 0.95	Standard sampling.
top_k / min_p	number	—	vLLM sampling extensions.
presence_penalty / frequency_penalty / seed / stop	—	—	Standard OpenAI knobs.
repetition_penalty / min_tokens	number	—	vLLM extensions.
mm_processor_kwargs / chat_template_kwargs / extra_body	object	—	vLLM passthroughs.
default_tool_choice	string	auto	auto, none, or required.
verify_model_on_init	boolean	false	Probe /v1/models at boot.
context_window / max_output_tokens	number	—	Runtime budgeting overrides.

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llama.cpp llama-server (local)

Dedicated provider for llama.cpp’s llama-server. Deliberately lean: the standard OpenAI sampling knobs plus llama.cpp’s top_k / min_p and a generic extra_body escape hatch (grammar, n_probs, …). It carries none of the vLLM-only machinery; use the vllm provider for that.

Start a server: llama-server -m <model.gguf> --port 8080 [--reasoning-format deepseek].

providers:
  llama-server:
    base_url: http://localhost:8080
    model: default
    top_k: 40
    min_p: 0.05

agents:
  local:
    provider: llama-server
    local: true

For native <think> reasoning, start llama-server with --reasoning-format deepseek so it emits reasoning_content. Set LLAMA_SERVER_API_KEY only if you started the server with --api-key.

Config options

Key	Type	Default	Description
base_url	string	required	llama-server URL (/v1 optional).
model	string	default	Served model id; default auto-discovers.
api_key	string	${LLAMA_SERVER_API_KEY}	Bearer token (only if started with --api-key).
max_tokens	number	4096	Maximum output tokens.
temperature / top_p	number	0.7 / 0.95	Standard sampling.
top_k / min_p	number	—	llama.cpp sampling extensions.
presence_penalty / frequency_penalty / seed / stop	—	—	Standard OpenAI knobs.
extra_body	object	—	Escape hatch (grammar, n_probs, …).
default_tool_choice	string	auto	auto, none, or required.
verify_model_on_init	boolean	false	Probe /v1/models at boot.
context_window / max_output_tokens	number	—	Runtime budgeting overrides.

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Checking provider health

# Run provider connectivity checks
npx rivetos doctor

# Smoke test — send a test message to each provider
npx rivetos test

# Check which providers are loaded
npx rivetos status

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Next steps

• Channel Setup: Connect your agents to Discord, Telegram, voice
• Configuration Reference: full option tables for all config sections
• Plugin Development: Build your own provider plugin