> ## Documentation Index
> Fetch the complete documentation index at: https://launchpad.datalumina.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Providers

> Configure OpenAI, Azure OpenAI, Anthropic, Google, Bedrock, and Ollama via AgentNode

`AgentNode` picks a model backend via the `ModelProvider` enum in `app/launchpad/core/nodes/agent.py`. Switching providers is a one-line change in the node's `get_agent_config()`; credentials come from environment variables loaded by `python-dotenv` at import time.

```python theme={null}
from launchpad.core.nodes.agent import AgentConfig, ModelProvider

class MyNode(AgentNode):
    def get_agent_config(self) -> AgentConfig:
        return AgentConfig(
            model_provider=ModelProvider.OPENAI,
            model_name="gpt-5.4-mini",
            output_type=self.OutputType,
        )
```

<Info>
  All providers are reached through [pydantic-ai](https://ai.pydantic.dev). `AgentConfig.instrument=True` (the default) wires each call into Langfuse when `enable_tracing=True` on the workflow.
</Info>

## Supported providers

### OpenAI — `ModelProvider.OPENAI`

Uses `OpenAIResponsesModel`. Good default for new workflows.

| Env var          | Purpose                  |
| ---------------- | ------------------------ |
| `OPENAI_API_KEY` | Standard OpenAI API key. |

```python theme={null}
AgentConfig(model_provider=ModelProvider.OPENAI, model_name="gpt-5.4-mini")
```

### Azure OpenAI — `ModelProvider.AZURE_OPENAI`

Routes through pydantic-ai's `AzureProvider` with `OpenAIResponsesModel`.

| Env var                    | Purpose                                         |
| -------------------------- | ----------------------------------------------- |
| `AZURE_OPENAI_ENDPOINT`    | Azure resource endpoint.                        |
| `AZURE_OPENAI_API_KEY`     | Resource API key.                               |
| `AZURE_OPENAI_API_VERSION` | API version (defaults to `2025-04-01-preview`). |

Set `model_name` to the Azure deployment name you want to call.

```python theme={null}
AgentConfig(model_provider=ModelProvider.AZURE_OPENAI, model_name="gpt-5-mini")
```

### Anthropic — `ModelProvider.ANTHROPIC`

| Env var             | Purpose            |
| ------------------- | ------------------ |
| `ANTHROPIC_API_KEY` | Anthropic API key. |

```python theme={null}
AgentConfig(model_provider=ModelProvider.ANTHROPIC, model_name="claude-sonnet-4-6")
```

### Google Gemini — `ModelProvider.GOOGLE_GEMINI`

Uses `GoogleModel` with the standard `GoogleProvider` (API-key auth).

| Env var          | Purpose         |
| ---------------- | --------------- |
| `GOOGLE_API_KEY` | Gemini API key. |

```python theme={null}
AgentConfig(model_provider=ModelProvider.GOOGLE_GEMINI, model_name="gemini-2.5-pro")
```

### Google Vertex AI — `ModelProvider.GOOGLE_VERTEX_AI`

Uses a service account to authenticate against Vertex AI.

| Env var                          | Purpose                                       |
| -------------------------------- | --------------------------------------------- |
| `GOOGLE_APPLICATION_CREDENTIALS` | Absolute path to a service account JSON file. |
| `GOOGLE_VERTEX_AI_LOCATION`      | Region (defaults to `europe-west1`).          |

```python theme={null}
AgentConfig(model_provider=ModelProvider.GOOGLE_VERTEX_AI, model_name="gemini-2.5-pro")
```

### AWS Bedrock — `ModelProvider.BEDROCK`

Creates a `boto3` `bedrock-runtime` client and passes it to `BedrockConverseModel`.

| Env var                         | Purpose                       |
| ------------------------------- | ----------------------------- |
| `BEDROCK_AWS_ACCESS_KEY_ID`     | AWS access key.               |
| `BEDROCK_AWS_SECRET_ACCESS_KEY` | AWS secret.                   |
| `BEDROCK_AWS_REGION`            | AWS region hosting the model. |

```python theme={null}
AgentConfig(
    model_provider=ModelProvider.BEDROCK,
    model_name="anthropic.claude-sonnet-4-6-v1:0",
)
```

### Ollama — `ModelProvider.OLLAMA`

Uses `OpenAIChatModel` with the pydantic-ai `OllamaProvider`. Ideal for local development against an `ollama serve` instance.

| Env var           | Purpose                                                                                          |
| ----------------- | ------------------------------------------------------------------------------------------------ |
| `OLLAMA_BASE_URL` | Full base URL, e.g. `http://localhost:11434/v1`. Required; the node raises `KeyError` otherwise. |

```python theme={null}
AgentConfig(model_provider=ModelProvider.OLLAMA, model_name="llama3.2")
```

## Other AgentConfig knobs

`AgentConfig` forwards common pydantic-ai fields so most tuning happens in one place:

* `instructions` — static system prompt (can be augmented with `@self.agent.instructions` for per-run context).
* `output_type` — return a plain `str` or a `BaseModel` subclass for structured output.
* `deps_type` — a Pydantic model containing dependencies exposed via `RunContext` inside tools and instruction callbacks.
* `tools`, `builtin_tools` — pydantic-ai tool definitions.
* `model_settings` — a `ModelSettings` object to override temperature, max tokens, etc.
* `retries`, `output_retries` — retry behavior on model errors and validation failures.
* `instrument` — defaults to `True`; set to `False` to opt a node out of Langfuse instrumentation even when the workflow has tracing enabled.