> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.boundaryml.com/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.boundaryml.com/_mcp/server. # Client Registry If you need to modify the model / parameters for an LLM client at runtime, you can modify the `ClientRegistry` for any specified function. **Quick Override**: If you just need to change which client a function uses, you can use the simpler [`client` option](/ref/baml_client/client-option) instead of creating a full `ClientRegistry`: ```python result = await b.ExtractResume("...", baml_options={"client": "GPT4"}) ``` ```python import os from baml_py import ClientRegistry async def run(): cr = ClientRegistry() # Creates a new client cr.add_llm_client(name='MyAmazingClient', provider='openai', options={ "model": "gpt-5-mini", "temperature": 0.7, "api_key": os.environ.get('OPENAI_API_KEY') }) # Creates a client using the OpenAI Responses API cr.add_llm_client(name='MyResponsesClient', provider='openai-responses', options={ "model": "gpt-4.1", "api_key": os.environ.get('OPENAI_API_KEY') }) # Sets MyAmazingClient as the client cr.set_primary('MyAmazingClient') # ExtractResume will now use MyAmazingClient as the calling client res = await b.ExtractResume("...", { "client_registry": cr }) ``` ```typescript import { ClientRegistry } from '@boundaryml/baml' async function run() { const cr = new ClientRegistry() // Creates a new client cr.addLlmClient('MyAmazingClient', 'openai', { model: "gpt-5-mini", temperature: 0.7, api_key: process.env.OPENAI_API_KEY }) // Creates a client using the OpenAI Responses API cr.addLlmClient('MyResponsesClient', 'openai-responses', { model: "gpt-4.1", api_key: process.env.OPENAI_API_KEY }) // Sets MyAmazingClient as the client cr.setPrimary('MyAmazingClient') // ExtractResume will now use MyAmazingClient as the calling client const res = await b.ExtractResume("...", { clientRegistry: cr }) } ``` ```ruby require_relative "baml_client/client" def run cr = Baml::ClientRegistry.new # Creates a new client cr.add_llm_client( 'MyAmazingClient', 'openai', { model: 'gpt-5-mini', temperature: 0.7, api_key: ENV['OPENAI_API_KEY'] } ) # Creates a client using the OpenAI Responses API cr.add_llm_client( 'MyResponsesClient', 'openai-responses', { model: 'gpt-4.1', api_key: ENV['OPENAI_API_KEY'] } ) # Sets MyAmazingClient as the client cr.set_primary('MyAmazingClient') # ExtractResume will now use MyAmazingClient as the calling client res = Baml.Client.extract_resume(input: '...', baml_options: { client_registry: cr }) end # Call the asynchronous function run ``` ```go package main import ( "context" "fmt" "os" "github.com/boundaryml/baml" ) func main() { ctx := context.Background() // Create a client registry cr := baml.NewClientRegistry() // Creates a new client err := cr.AddLLMClient("MyAmazingClient", "openai", map[string]interface{}{ "model": "gpt-5-mini", "temperature": 0.7, "api_key": os.Getenv("OPENAI_API_KEY"), }) if err != nil { panic(fmt.Sprintf("Failed to add client: %v", err)) } // Creates a client using the OpenAI Responses API err = cr.AddLLMClient("MyResponsesClient", "openai-responses", map[string]interface{}{ "model": "gpt-4.1", "api_key": os.Getenv("OPENAI_API_KEY"), }) if err != nil { panic(fmt.Sprintf("Failed to add responses client: %v", err)) } // Sets MyAmazingClient as the client cr.SetPrimary("MyAmazingClient") // ExtractResume will now use MyAmazingClient as the calling client res, err := baml.ExtractResume(ctx, "...", b.WithClientRegistry(cr)) if err != nil { panic(fmt.Sprintf("Failed to extract resume: %v", err)) } fmt.Printf("Result: %+v\n", res) } ``` ```rust use baml::ClientRegistry; use myproject::baml_client::sync_client::B; use std::collections::HashMap; fn main() { let mut registry = ClientRegistry::new(); // Creates a new client let mut options = HashMap::new(); options.insert("model".to_string(), serde_json::json!("gpt-5-mini")); options.insert("temperature".to_string(), serde_json::json!(0.7)); registry.add_llm_client("MyAmazingClient", "openai", options); // Sets MyAmazingClient as the client registry.set_primary_client("MyAmazingClient"); // ExtractResume will now use MyAmazingClient as the calling client let res = B.ExtractResume .with_client_registry(®istry) .call("...") .unwrap(); println!("Result: {:?}", res); } ``` The API supports passing client registry as a field on `__baml_options__` in the request body. Example request body: ```json { "resume": "Vaibhav Gupta", "__baml_options__": { "client_registry": { "clients": [ { "name": "OpenAI", "provider": "openai", "retry_policy": null, "options": { "model": "gpt-5-mini", "api_key": "sk-..." } }, { "name": "OpenAIResponses", "provider": "openai-responses", "retry_policy": null, "options": { "model": "gpt-4.1", "api_key": "sk-..." } } ], "primary": "OpenAI" } } } ``` ```sh curl -X POST http://localhost:2024/call/ExtractResume \ -H 'Content-Type: application/json' -d @body.json ``` ### The `set_primary` Method The `set_primary` method can be called with either one of the clients added to the `ClientRegistry` at runtime using `add_llm_client` or a client defined in BAML files. `set_primary` simply selects a client from all the available clients in the `ClientRegistry`, but it does not mean there will be a "secondary" client used anywhere. You can, however, define a fallback client and then use `ClientRegistry` to set that client as the calling client for BAML functions. Here's a simple example: ```baml example.baml function ExtractResume(input: string) -> Resume { client "openai/gpt-5-mini" // Uses GPT-5 Mini by default prompt #" Extract from this content: {{ resume }} {{ ctx.output_format }} "# } // This client uses GPT-5 first and if it fails it will use Opus 4 as a fallback. client GptOpusFallback { provider fallback options { strategy ["openai/gpt-5", "anthropic/claude-opus-4-1-20250805"] } } ``` Then, in your code, you can use the `ClientRegistry` to set the `GptOpusFallback` at runtime, which will try to use GPT 5 and if it fails Opus 4: ```python cr = ClientRegistry() cr.set_primary("GptOpusFallback") res = await b.ExtractResume("...", { "client_registry": cr }) ``` ```typescript const cr = new ClientRegistry() cr.setPrimary("GptOpusFallback") const res = await b.ExtractResume("...", { clientRegistry: cr }) ``` ```ruby cr = Baml::ClientRegistry.new cr.set_primary("GptOpusFallback") res = Baml.Client.extract_resume(input: "...", baml_options: { client_registry: cr }) ``` ```go cr := baml.NewClientRegistry() cr.SetPrimary("GptOpusFallback") res, err := b.ExtractResume(ctx, "...", b.WithClientRegistry(cr)) ``` ```rust use baml::ClientRegistry; use myproject::baml_client::sync_client::B; let mut registry = ClientRegistry::new(); registry.set_primary_client("GptOpusFallback"); let res = B.ExtractResume.with_client_registry(®istry).call("...").unwrap(); ``` Now the calling client will be `GptOpusFallback`, **nothing else**. ## ClientRegistry Interface Note: `ClientRegistry` is imported from `baml_py` in Python and `@boundaryml/baml` in TypeScript, not `baml_client`. As we mature `ClientRegistry`, we will add a more type-safe and ergonomic interface directly in `baml_client`. See [Github issue #766](https://github.com/BoundaryML/baml/issues/766). Methods use `snake_case` in Python and `camelCase` in TypeScript. ### add\_llm\_client / addLlmClient A function to add an LLM client to the registry. The name of the client. Using the exact same name as a client also defined in .baml files overwrites the existing client whenever the ClientRegistry is used. This configures which provider to use. The provider is responsible for handling the actual API calls to the LLM service. The provider is a required field. The configuration modifies the URL request BAML runtime makes. | Provider Name | Docs | Notes | | ------------------ | ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `anthropic` | [Anthropic](/ref/llm-client-providers/anthropic) | Supports [/v1/messages](https://docs.anthropic.com/en/api/messages) endpoint | | `aws-bedrock` | [AWS Bedrock](/ref/llm-client-providers/aws-bedrock) | Supports [Converse](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html) and [ConverseStream](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html) endpoint | | `google-ai` | [Google AI](/ref/llm-client-providers/google-ai-gemini) | Supports Google AI's [generateContent](https://ai.google.dev/api/generate-content) and [streamGenerateContent](https://ai.google.dev/api/generate-content#method:-models.streamgeneratecontent) endpoints | | `vertex-ai` | [Vertex AI](/ref/llm-client-providers/google-vertex) | Supports Vertex's [generateContent](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent) and [streamGenerateContent](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/streamGenerateContent) endpoints | | `openai` | [OpenAI](/ref/llm-client-providers/open-ai) | Supports [/chat/completions](https://platform.openai.com/docs/api-reference/chat) endpoint | | `openai-responses` | [OpenAI Responses API](/ref/llm-client-providers/open-ai-responses-api) | Supports OpenAI's most advanced [/responses](https://platform.openai.com/docs/api-reference/responses) endpoint | | `azure-openai` | [Azure OpenAI](/ref/llm-client-providers/open-ai-from-azure) | Supports Azure's [/chat/completions](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions) endpoint | | `openai-generic` | [OpenAI (generic)](/ref/llm-client-providers/openai-generic) | Any other provider that supports OpenAI's `/chat/completions` endpoint | A non-exhaustive list of providers you can use with `openai-generic`: | Inference Provider | Docs | | ------------------ | ---------------------------------------------------------------- | | Azure AI Foundry | [Azure AI Foundry](/ref/llm-client-providers/azure-ai-foundary) | | Groq | [Groq](/ref/llm-client-providers/groq) | | Hugging Face | [Hugging Face](/ref/llm-client-providers/huggingface) | | Keywords AI | [Keywords AI](/ref/llm-client-providers/keywordsai) | | Litellm | [Litellm](/ref/llm-client-providers/litellm) | | LM Studio | [LM Studio](/ref/llm-client-providers/lmstudio) | | Ollama | [Ollama](/ref/llm-client-providers/ollama) | | OpenRouter | [OpenRouter](/ref/llm-client-providers/openrouter) | | Vercel AI Gateway | [Vercel AI Gateway](/ref/llm-client-providers/vercel-ai-gateway) | | TogetherAI | [TogetherAI](/ref/llm-client-providers/together) | | Unify AI | [Unify AI](/ref/llm-client-providers/unify) | | vLLM | [vLLM](/ref/llm-client-providers/vllm) | We also have some special providers that allow composing clients together: | Provider Name | Docs | Notes | | ------------- | ----------------------------------------------------- | -------------------------------------------- | | `fallback` | [Fallback](/ref/llm-client-strategies/fallback) | Used to chain models conditional on failures | | `round-robin` | [Round Robin](/ref/llm-client-strategies/round-robin) | Used to load balance | These vary per provider. Please see provider specific documentation for more information. Generally they are pass through options to the POST request made to the LLM. The name of a retry policy that is already defined in a .baml file. See [Retry Policies](/ref/llm-client-strategies/retry-policy). ### set\_primary / setPrimary This sets the client for the function to use. (i.e. replaces the `client` property in a function) The name "primary" does not imply that there will be a "secondary" or fallback client used anywhere. It simply means that you choose one client from all the available clients in your `ClientRegistry`. See [The `set_primary` Method](#the-set_primary-method) section for more details. The name of the client to use. This can be a new client that was added with `add_llm_client` or an existing client that is already in a .baml file.