Models and Providers

Choose Connic-managed inference or bring your own provider, then tune model behavior in agent YAML.

Model controls

model: anthropic/claude-sonnet-4-5
fallback_model: connic/glm-5.2
temperature: 0.7
reasoning_effort: high  # supported values depend on the selected model

• model selects the primary model and is required for LLM agents.
• fallback_model is used after the first failed primary request and receives the configured retry_options.attempts. The run is marked so you can filter for it with context.fallback_model_used. Managed and BYOK models can pair in either direction.
• temperature controls output randomness. Lower values are more deterministic.
• reasoning_effort uses model-specific values. Use auto for model-managed behavior; for connic/*, choose an override from the model catalog. Providers can report reasoning-token usage without returning displayable reasoning text. An explicit supported effort requests reasoning text. Captured reasoning appears in run traces when the provider returns it; some models do not expose summaries at any effort.
• reasoning_budget sets a raw token budget on models that accept one. Use reasoning_effort for models with named effort levels.

Supported providers

Select a matching prefix in the agent configuration. Only BYOK providers require credentials in Project Settings.

Custom OpenAI-compatible providers

You can connect any OpenAI-compatible endpoint, including self-hosted models and inference proxies. In Project Settings > LLM Provider, select Add Custom Provider and configure:

• Model prefix: a unique lowercase prefix, such as ollama
• API base URL: the endpoint, such as https://my-llm.example.com/v1
• API key: optional; leave it empty if the endpoint does not require authentication

# Custom provider configured with prefix "ollama"
model: ollama/llama3

# Custom provider configured with prefix "vllm"
model: vllm/mistral-7b

Connic routes requests for that prefix to the configured endpoint. The prefix must not collide with a built-in provider name; connic is reserved.

Example model identifiers

# Using a Connic-managed model
model: connic/glm-5.2

# Using OpenAI with your own key
model: openai/gpt-5.2

# Using Anthropic
model: anthropic/claude-opus-4-7

# Using Google Gemini
model: gemini/gemini-2.5-pro

# Using Azure OpenAI (use your deployment name)
model: azure/my-gpt5-deployment

# Using OpenRouter (provider/model format)
model: openrouter/anthropic/claude-sonnet-4.5

# Using AWS Bedrock
model: bedrock/us.anthropic.claude-opus-4-7-v1:0

# Using Google Vertex AI
model: vertex_ai/gemini-2.5-pro

# Using a custom OpenAI-compatible provider
model: ollama/llama3

Provider documentation

• Connic Model Catalog
• OpenAI Models
• Anthropic Models
• Google Gemini Models
• Azure OpenAI Models
• OpenRouter Models
• AWS Bedrock Models
• Google Vertex AI Models