# How to Configure Multiple LLM Providers (OpenAI, Anthropic, and Ollama) in Embabel > Learn to configure multiple LLM providers like OpenAI, Anthropic, and Ollama in Embabel easily. Integrate diverse AI models using Spring Boot starters and simple configuration. - Repository: [Embabel/embabel-agent](https://github.com/embabel/embabel-agent) - Tags: how-to-guide - Published: 2026-08-10 --- **Embabel allows you to configure multiple LLM providers simultaneously by adding provider-specific Spring Boot starter dependencies and defining credentials under the `embabel.agent.platform.models.` namespace in your application configuration.** Embabel (from the `embabel/embabel-agent` repository) is a Spring AI-based framework that enables seamless integration of multiple large language model providers within a single application. Unlike single-provider setups, Embabel's architecture supports concurrent initialization of OpenAI, Anthropic, and Ollama through provider-specific autoconfiguration modules and a unified property-based configuration model. ## Understanding Embabel's Multi-Provider Architecture Embabel's core architecture decouples LLM provider integration into **provider-specific starter modules** that autoconfigure distinct `LlmService` beans. Each starter—such as `embabel-agent-starter-openai` or `embabel-agent-starter-anthropic`—registers its own chat model, embedding model, and REST client based on the unified configuration class [`LlmOptionsProperties.kt`](https://github.com/embabel/embabel-agent/blob/main/LlmOptionsProperties.kt). According to the Embabel source code, the configuration hierarchy starts with the base prefix `embabel.agent.platform.models` defined in [`LlmOptionsProperties.kt`](https://github.com/embabel/embabel-agent/blob/main/LlmOptionsProperties.kt) ([`embabel-agent-common/embabel-agent-ai/src/main/kotlin/com/embabel/common/ai/model/LlmOptionsProperties.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-common/embabel-agent-ai/src/main/kotlin/com/embabel/common/ai/model/LlmOptionsProperties.kt)). Provider-specific modules extend this base to support unique parameters like Anthropic's caching headers or Ollama's local endpoint requirements. ## Step 1: Add Provider-Specific Starter Dependencies To enable multiple providers, include the corresponding starter artifacts in your build file. You need the OpenAI starter for OpenAI (and Ollama via compatibility), plus the dedicated Anthropic starter. ```kotlin // build.gradle.kts implementation("com.embabel.agent:emabel-agent-starter-openai:${embabelAgentVersion}") implementation("com.embabel.agent:emabel-agent-starter-anthropic:${embabelAgentVersion}") // Ollama uses the OpenAI-compatible starter (no separate Ollama starter required) implementation("com.embabel.agent:emabel-agent-starter-openai:${emabelAgentVersion}") ``` *If using Maven, add equivalent `` blocks with the same groupId and artifactId values.* ## Step 2: Configure Provider Credentials and Endpoints Define each provider's credentials and model lists under the `embabel.agent.platform.models` hierarchy in [`application.yml`](https://github.com/embabel/embabel-agent/blob/main/application.yml). The **OpenAI** configuration uses the `openai` key, **Anthropic** uses `anthropic`, and **Ollama** uses the `openai-custom` block because it implements the OpenAI-compatible REST schema. ```yaml # OpenAI (default provider) embabel: agent: platform: models: openai: api-key: ${OPENAI_API_KEY} model-list: - gpt-4o - gpt-4-turbo # Anthropic embabel: agent: platform: models: anthropic: api-key: ${ANTHROPIC_API_KEY} model-list: - claude-3-5-sonnet-20240620 - claude-3-opus-20240229 max-attempts: 3 backoff-millis: 500 backoff-multiplier: 2.0 # Ollama (OpenAI-compatible endpoint) embabel: agent: platform: models: openai-custom: api-key: "" base-url: http://localhost:11434/v1 model-list: - llama2 - mistral ``` The [`AnthropicModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/AnthropicModelsConfig.kt) file ([`embabel-agent-anthropic-autoconfigure/src/main/kotlin/com/embabel/agent/config/models/anthropic/AnthropicModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-anthropic-autoconfigure/src/main/kotlin/com/embabel/agent/config/models/anthropic/AnthropicModelsConfig.kt)) handles provider-specific extensions like backoff configuration, while [`OpenAiCustomModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/OpenAiCustomModelsConfig.kt) ([`embabel-agent-autoconfigure/models/embabel-agent-openai-custom-autoconfigure/src/main/kotlin/com/embabel/agent/config/models/openai/custom/OpenAiCustomModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-autoconfigure/models/embabel-agent-openai-custom-autoconfigure/src/main/kotlin/com/embabel/agent/config/models/openai/custom/OpenAiCustomModelsConfig.kt)) enables the generic OpenAI-compatible layer used for Ollama. ## Step 3: Inject and Use Multiple LlmService Beans Once configured, Embabel registers separate `LlmService` beans for each provider. Inject them into your components to route requests to specific backends. ```kotlin import com.embabel.agent.ai.LlmService import org.springframework.beans.factory.annotation.Autowired import org.springframework.stereotype.Component @Component class MultiProviderChat @Autowired constructor( private val openAiChat: LlmService, private val anthropicChat: LlmService, private val ollamaChat: LlmService ) { fun routeToProvider(provider: String, prompt: String): String { return when (provider) { "openai" -> openAiChat.chat(prompt).content "anthropic" -> anthropicChat.chat(prompt).content "ollama" -> ollamaChat.chat(prompt).content else -> throw IllegalArgumentException("Unknown provider") } } } ``` Each injected `LlmService` instance is automatically bound to its respective provider configuration defined in Step 2. ## Step 4: Verify Provider Initialization at Runtime Embabel logs a concise initialization summary at startup, confirming which providers loaded successfully. Look for output similar to: ``` OpenAI: Initialized 1 LLM(s) and 1 embedding(s) Anthropic: Initialized 1 LLM(s) and 0 embedding(s) OpenAI-Custom: Initialized 1 LLM(s) and 0 embedding(s) ``` If a provider fails to initialize (e.g., missing API key or unreachable endpoint), the log line will include the specific failure reason without preventing other providers from starting. ## Customizing Provider-Specific Extensions Beyond basic credential configuration, the **Anthropic** module exposes specialized utilities for cache token tracking via extension functions like `anthropicCacheCreationTokens(usage)`. These provider-specific helpers are located in the respective autoconfigure modules and become available when you import the corresponding starter. For Ollama deployments, extend the `openai-custom` block in [`OpenAiCustomModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/OpenAiCustomModelsConfig.kt) to adjust `base-url` values for self-hosted endpoints or Azure OpenAI compatibility. ## Key Implementation Files - **[`LlmOptionsProperties.kt`](https://github.com/embabel/embabel-agent/blob/main/LlmOptionsProperties.kt)** ([`embabel-agent-common/embabel-agent-ai/src/main/kotlin/com/embabel/common/ai/model/LlmOptionsProperties.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-common/embabel-agent-ai/src/main/kotlin/com/embabel/common/ai/model/LlmOptionsProperties.kt)): Defines the base configuration properties shared across all providers. - **[`OpenAiModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/OpenAiModelsConfig.kt)** ([`embabel-agent-openai-autoconfigure/src/main/kotlin/com/embel/agent/config/models/openai/OpenAiModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-openai-autoconfigure/src/main/kotlin/com/embel/agent/config/models/openai/OpenAiModelsConfig.kt)): Configures the default OpenAI provider including base URL and API key handling. - **[`AnthropicModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/AnthropicModelsConfig.kt)** ([`embabel-agent-anthropic-autoconfigure/src/main/kotlin/com/embel/agent/config/models/anthropic/AnthropicModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-anthropic-autoconfigure/src/main/kotlin/com/embel/agent/config/models/anthropic/AnthropicModelsConfig.kt)): Handles Anthropic-specific settings like model lists and retry backoff strategies. - **[`OpenAiCustomModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/OpenAiCustomModelsConfig.kt)** ([`embabel-agent-autoconfigure/models/embabel-agent-openai-custom-autoconfigure/src/main/kotlin/com/embel/agent/config/models/openai/custom/OpenAiCustomModelsConfig.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-autoconfigure/models/embabel-agent-openai-custom-autoconfigure/src/main/kotlin/com/embel/agent/config/models/openai/custom/OpenAiCustomModelsConfig.kt)): Enables OpenAI-compatible providers like Ollama through generic configuration. - **[`ProviderDetection.kt`](https://github.com/embabel/embabel-agent/blob/main/ProviderDetection.kt)** ([`embabel-agent-common/embel-agent-byok/src/main/kotlin/com/embel/common/byok/ProviderDetection.kt`](https://github.com/embabel/embabel-agent/blob/main/embabel-agent-common/embel-agent-byok/src/main/kotlin/com/embel/common/byok/ProviderDetection.kt)): Supports bring-your-own-key (BYOK) detection logic for dynamic provider identification. - **[`application-test.yml`](https://github.com/embabel/embabel-agent/blob/main/application-test.yml)** ([`embel-agent-api/src/test/resources/application-test.yml`](https://github.com/embabel/embabel-agent/blob/main/embel-agent-api/src/test/resources/application-test.yml)): Reference test configuration demonstrating multi-provider YAML structure. ## Summary Configuring multiple LLM providers in Embabel requires three steps: - **Add starter dependencies** for each provider (`embel-agent-starter-openai`, `embel-agent-starter-anthropic`). - **Define credentials** under `embabel.agent.platform.models.` using environment variables or hardcoded values in [`application.yml`](https://github.com/embabel/embabel-agent/blob/main/application.yml). - **Inject `LlmService` beans** into your Spring components—one per configured provider. Because Ollama implements the OpenAI-compatible REST schema, it integrates through the generic `openai-custom` configuration block without requiring a dedicated starter, simplifying local development alongside cloud providers. ## Frequently Asked Questions ### Can I use Ollama without a dedicated Embabel starter? Yes. Ollama implements an OpenAI-compatible REST API, so you configure it using the `embel-agent-starter-openai` dependency and the `openai-custom` configuration block with `base-url: http://localhost:11434/v1`. This architecture avoids the need for provider-specific code while maintaining full compatibility with Ollama's local model serving. ### How do I secure API keys when configuring multiple providers? Embabel supports Spring Boot's property placeholder syntax. Instead of hardcoding keys, use environment variable references like `api-key: ${OPENAI_API_KEY}` or `api-key: ${ANTHROPIC_API_KEY}` in your [`application.yml`](https://github.com/embabel/embabel-agent/blob/main/application.yml). The [`ProviderDetection.kt`](https://github.com/embabel/embabel-agent/blob/main/ProviderDetection.kt) utility also supports bring-your-own-key (BYOK) patterns for runtime key injection. ### What happens if one provider fails to initialize? Embabel initializes providers independently. If one provider (e.g., Anthropic) fails due to a missing API key or network timeout, the application context still loads successfully for other configured providers. Check the startup logs for specific initialization failure messages under the provider name (e.g., "Anthropic: Failed to initialize"). ### Can I configure multiple instances of the same provider? While the analysis demonstrates single instances per provider type, the `openai-custom` block can be used to configure additional OpenAI-compatible endpoints (such as Azure OpenAI or separate Ollama instances) by specifying different `base-url` values and distinct bean names, though this may require additional `@Qualifier` annotations in your injection points depending on your specific Spring configuration.