From 818196223693d5fbe390b68e4021b3851f097e32 Mon Sep 17 00:00:00 2001
From: Paul Kompfner <paul@daily.co>
Date: Mon, 25 Aug 2025 15:03:38 -0400
Subject: [PATCH] Add CHANGELOG entry describing LLM switcher

---
 CHANGELOG.md | 42 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 41 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 47e23c4b1..412cd5754 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,7 +13,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   `LLMContextAggregatorPair`, which will eventually replace `OpenAILLMContext`
   (and the other under-the-hood contexts) and the other context aggregators.
   The new universal `LLMContext` machinery allows a single context to be shared
-  between different LLMs, enabling scenarios like LLM failover.
+  between different LLMs, enabling runtime LLM switching and scenarios like
+  failover.
 
   From the developer's point of view, switching to using the new universal
   context machinery will usually be a matter of going from this:
@@ -36,6 +37,45 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `OpenAILLMService`
   - `GoogleLLMService`
 
+- Added a new `LLMSwitcher` class to enable runtime LLM switching, built atop a
+  new generic `ServiceSwitcher`.
+
+  Switchers take a switching strategy. The first available strategy is
+  `ServiceSwitcherStrategyManual`.
+
+  To switch LLMs at runtime, the LLMs must be sharing one instance of the new
+  universal `LLMContext` (see above bullet).
+
+  ```python
+  # Instantiate your LLM services
+  llm_openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+  llm_google = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+
+  # Instantiate a switcher
+  # (ServiceSwitcherStrategyManual default to OpenAI, as it's first in the list)
+  llm_switcher = LLMSwitcher(
+      llms=[llm_openai, llm_google], strategy_type=ServiceSwitcherStrategyManual
+  )
+
+  # Create your pipeline
+  pipeline = Pipeline(
+    [
+        transport.input(),
+        stt,
+        context_aggregator.user(),
+        llm_switcher,
+        tts,
+        transport.output(),
+        context_aggregator.assistant(),
+    ]
+  )
+  task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+
+  # ...
+  # Whenever is appropriate, switch LLMs!
+  await task.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
+  ```
+
 ### Fixed
 
 - Fixed a `CartesiaTTSService` issue that was causing the application to hang