Providers

Provider Routing

Hivemind uses a provider router to map model names to the correct LLM provider. The agent and planner call generate(model_name, prompt); the router selects the provider and the provider performs the API call.

Model selection: Configure worker and planner in [models] (or legacy worker_model / planner_model) in config or environment (e.g. HIVEMIND_WORKER_MODEL, HIVEMIND_PLANNER_MODEL). The router infers the vendor from the model name (e.g. gpt-* → OpenAI, claude-* → Anthropic, gemini-* → Google).

Automatic model routing

You can set planner = "auto" and worker = "auto" in [models]. The router then selects a model based on task type:

• Planner — Prefers higher-quality models for planning and DAG generation.
• Worker — Balances cost, latency, and quality for task execution.

This avoids hard-coding model names while keeping runs predictable. You still need at least one provider configured: set API keys via environment variables or the credential store (hivemind credentials set ... or hivemind credentials migrate). See Configuration.

Model Spec Format

You can think of model selection as provider + model:

Spec	Provider	Example model
openai:model	OpenAI	openai:gpt-4o, openai:gpt-4o-mini
anthropic:model	Anthropic	anthropic:claude-sonnet, anthropic:claude-3-haiku-20240307
azure:model	Azure OpenAI	azure:gpt-4o (deployment name)
gemini:model	Google	gemini:gemini-2.5-pro, gemini:gemini-1.5-flash
github:model	GitHub Models (Copilot)	github:gpt-4o, github:claude-3.5-sonnet, github:phi-3

In practice, the code uses the model name (e.g. gpt-4o, claude-3-haiku-20240307, gemini-1.5-flash). The router decides the provider from the name prefix:

• gpt*, o1*, o3*, o4* → OpenAI (or Azure OpenAI when Azure env is set)
• claude* → Anthropic (or Azure Anthropic when Azure Anthropic env is set)
• gemini* → Gemini

So you configure models like:

• gpt-4o — OpenAI or Azure OpenAI depending on env
• claude-sonnet — Anthropic or Azure Anthropic depending on env
• gemini-2.5-pro — Google Gemini

Examples:

• openai:gpt-4o → use OpenAI’s gpt-4o
• anthropic:claude-sonnet → use Anthropic’s Claude Sonnet
• azure:gpt-4o → use Azure OpenAI deployment named gpt-4o
• gemini:gemini-2.5-pro → use Google’s Gemini 2.5 Pro

(If your codebase uses a literal provider:model string, the router can be extended to parse it; otherwise you set the model name and the router infers the provider.)

GitHub Models (Copilot)

Hivemind can use GitHub Models (Copilot API) as a provider. Set GITHUB_TOKEN in the environment (or in config) and use the github:model spec:

• Examples: github:gpt-4o, github:claude-3.5-sonnet, github:phi-3
• The router maps these to the GitHub Models API; model names follow the same conventions as other providers.
• Useful when you want to run agents via GitHub’s model endpoints without separate OpenAI/Anthropic keys.

Azure Deployment Support

• Azure OpenAI: Set AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, and optionally AZURE_OPENAI_DEPLOYMENT_NAME (and AZURE_OPENAI_API_VERSION if needed). When these are set, GPT-style model names are served by Azure OpenAI; the deployment name typically matches the model name (e.g. gpt-4o, gpt-5-mini).
• Azure Anthropic (e.g. Azure AI Foundry): Set AZURE_ANTHROPIC_ENDPOINT (or AZURE_ANTHROPIC_API_KEY) and AZURE_ANTHROPIC_DEPLOYMENT_NAME. When set, Claude-style model names use Azure Anthropic.

Where to set credentials:

• Credential store (recommended): hivemind credentials set azure api_key, etc. Keys are stored in the OS keychain and injected into the environment when config is resolved. Use hivemind credentials export azure to print env-style lines.
• Environment variables: Set AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, etc., in your shell or .env.
• TOML (not recommended for secrets): You can still put non-secret values (e.g. endpoint, deployment) in [providers.azure] in ~/.config/hivemind/config.toml or project TOML; they are applied to the environment when not already set. Prefer the credential store or env for API keys.

See Configuration and CLI.