# Extending Server Configuration
## Add an LLM client and target
Define the upstream once under `llm_clients`, then reference it from targets.
```toml
[llm_clients.provider]
format = "openai_chat"
base_url = "https://example.com/v1"
api_key_env = "PROVIDER_API_KEY"
max_retries = 2
[targets.model]
id = "provider/model"
llm_client = "provider"
extra_body = { chat_template_kwargs = { enable_thinking = false } }
```
`extra_body` is target-specific. It shallow-merges top-level provider options into
the outbound request, while explicit request fields win on conflicts.
The `chat_template_kwargs.enable_thinking` example is a provider/model-specific
vLLM option. It is not a portable Switchyard reasoning switch. Use it on a judge
target only when the upstream supports it and would otherwise return the verdict
outside normal assistant `content`.
To support another wire format, add its `ClientFormat` variant and explicit construction match in
`src/config.rs`. Add a client type only when a second implementation exists.
## Add an algorithm
1. Implement and export the algorithm from `libsy`.
2. Add its TOML fields as an `AlgorithmConfig` variant in `src/config.rs`.
3. Construct it in the `build_algorithm` match, resolving target names with `resolve_targets`.
4. Add a parsing test and an end-to-end server test when the algorithm makes LLM calls.