Release/v2.1.0

.fernignore

@@ -10,6 +10,10 @@ src/agora_agent/agentkit/
 # Documentation - managed manually, not generated by Fern
 docs/
 README.md
+reference.md
+
+# Tests - managed manually, not generated by Fern
+tests/
 
 # Compatibility shim and CI/release workflows are managed manually
 compat/

README.md

@@ -20,6 +20,7 @@ pip install agora-agents
 ## Quick Start
 
 Start with the `Agent` builder: create a client with app credentials, choose your ASR, LLM, and TTS providers, then start a session. Omit vendor API keys for supported Agora-managed models, or provide keys when you want BYOK.
+Use `with_interaction_language()` for Agora `asr.language`; provider-specific STT language values remain under `asr.params`.
 
 ```python
 import os
@@ -29,12 +30,9 @@ from agora_agent import (
     Agent,
     Agora,
     Area,
-    DataChannel,
     DeepgramSTT,
-    GenericAvatar,
     MiniMaxTTS,
     OpenAI,
-    XaiGrok,
     expires_in_hours,
 )
 
@@ -56,49 +54,18 @@ def start_conversation() -> str:
         app_certificate=app_certificate,
     )
 
-    agent = Agent(
-        name=f"conversation-{int(time.time())}",
-        instructions=AGENT_PROMPT,
-        greeting=GREETING,
-        failure_message="Please wait a moment.",
-        max_history=50,
-        turn_detection={
-            "config": {
-                "speech_threshold": 0.5,
-                "start_of_speech": {
-                    "mode": "vad",
-                    "vad_config": {
-                        "interrupt_duration_ms": 160,
-                        "prefix_padding_ms": 300,
-                    },
-                },
-                "end_of_speech": {
-                    "mode": "vad",
-                    "vad_config": {
-                        "silence_duration_ms": 480,
-                    },
-                },
-            },
-        },
-        advanced_features={
-            "enable_rtm": True,
-            "enable_tools": True,
-        },
-        parameters={
-            "data_channel": DataChannel.RTM,
-            "enable_error_message": True,
-        },
-    ).with_stt(
+    agent = Agent(name=f"conversation-{int(time.time())}").with_interaction_language("en-US").with_stt(
         DeepgramSTT(
             model="nova-3",
             language="en",
         )
     ).with_llm(
         OpenAI(
             model="gpt-4o-mini",
+            system_messages=[{"role": "system", "content": AGENT_PROMPT}],
             greeting_message=GREETING,
             failure_message="Please wait a moment.",
-            max_history=15,
+            max_history=50,
             params={
                 "max_tokens": 1024,
                 "temperature": 0.7,
@@ -134,10 +101,7 @@ def start_conversation() -> str:
 Use the same `Agent` builder shape, but provide credentials explicitly when you want vendor-managed billing and routing instead of Agora-managed models.
 
 ```python
-agent = Agent(
-    instructions=AGENT_PROMPT,
-    greeting=GREETING,
-).with_stt(
+agent = Agent().with_interaction_language("en-US").with_stt(
     DeepgramSTT(
         api_key=os.environ["DEEPGRAM_API_KEY"],
         model="nova-3",
@@ -146,7 +110,10 @@ agent = Agent(
 ).with_llm(
     OpenAI(
         api_key=os.environ["OPENAI_API_KEY"],
+        base_url="https://api.openai.com/v1/chat/completions",
         model="gpt-4o-mini",
+        system_messages=[{"role": "system", "content": AGENT_PROMPT}],
+        greeting_message=GREETING,
         max_tokens=1024,
         temperature=0.7,
         top_p=0.95,

changelog.md

@@ -4,6 +4,25 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/).
 
+## [v2.1.0] — 2026-06-02
+
+### Added
+
+- **ASR interaction language** — AgentKit now manages Agora `asr.language` through `interaction_language` / `Agent.with_interaction_language()`, validates it against the supported BCP-47 interaction language list, and sends the default `en-US` when no language is provided.
+- **Provider parameter parity** — ASR, LLM, MLLM, TTS, and avatar wrappers expose typed provider parameters plus passthrough fields where the generated core supports additional properties.
+
+### Changed
+
+- **Generated core refresh** — Regenerated core types from the v2.1 API schema.
+- **Deepgram TTS passthrough** — `DeepgramTTS` now uses `additional_params` for passthrough fields and flattens them into `tts.params`; the removed nested `params.params` shape is no longer documented or emitted.
+- **OpenAI TTS** — Docs and tests now reflect the generated core shape, including `instructions` and `speed` under `tts.params`.
+- **TTS provider docs** — Updated TTS provider reference tables to match implemented wrapper fields and generated core params.
+
+### Fixed
+
+- **Managed-provider validation** — AgentKit validation now distinguishes preset-backed providers from BYOK providers so required provider fields are only required when credentials are caller-supplied.
+- **ASR language separation** — Provider-specific STT language values remain under `asr.params`, while Agora interaction language is emitted separately as `asr.language`.
+
 ## [v2.0.0] — 2026-05-21
 
 ### Added
@@ -52,7 +71,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/).
 
 ### Added
 
-- **`DeepgramTTS`** — New TTS vendor wrapper for Deepgram (Beta). Accepts `api_key`, `model`, `base_url`, `sample_rate`, `params`, and `skip_patterns`.
+- **`DeepgramTTS`** — New TTS vendor wrapper for Deepgram (Beta). Accepts `api_key`, `model`, `base_url`, `sample_rate`, `additional_params`, and `skip_patterns`.
 - **`Agent.with_tools(enabled=True)`** — Dedicated builder method to enable MCP tool invocation (`advanced_features.enable_tools`). Replaces the raw `with_advanced_features(AdvancedFeatures(enable_tools=True))` call.
 - **LLM vendors: `headers` field** — All four LLM vendors (`OpenAI`, `AzureOpenAI`, `Anthropic`, `Gemini`) now accept an optional `headers: Dict[str, str]` parameter. Use this to pass custom HTTP headers to the LLM provider (e.g., tenant identifiers, routing headers).
 - **`AgentSession.think()` / `AsyncAgentSession.think()`** — Send a custom instruction to a running agent through the `agent_management` API.
@@ -107,7 +126,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/).
 
 ### Added
 
-- **`OpenAITTS`** — New optional parameters: `response_format` (str, e.g. `"pcm"`) and `speed` (float).
+- **`OpenAITTS`** — New optional parameters: `instructions` (str) and `speed` (float).
 - **`CartesiaTTS`** — `voice_id` user-facing field is preserved; voice is serialized to the required nested object format automatically.
 - **`RimeTTS`** — New optional parameters: `lang` (str), `sampling_rate` (int, serialized as `samplingRate`), `speed_alpha` (float, serialized as `speedAlpha`).
 - **`OpenAIRealtime`** — New optional parameter: `failure_message` (str).

docs/concepts/agent.md

@@ -12,24 +12,28 @@ The `Agent` class is a fluent builder for configuring AI agent properties. It co
 
 <!-- snippet: executable -->
 ```python
-from agora_agent import Agent
-
-agent = Agent(
-    name='support-assistant',
-    instructions='You are a helpful voice assistant.',
-    greeting='Hello! How can I help you?',
-    failure_message='Sorry, something went wrong.',
-    max_history=20,
+from agora_agent import Agent, OpenAI
+
+agent = Agent(name='support-assistant').with_llm(
+    OpenAI(
+        api_key='your-openai-key',
+        base_url='https://api.openai.com/v1/chat/completions',
+        model='gpt-4o-mini',
+        system_messages=[{'role': 'system', 'content': 'You are a helpful voice assistant.'}],
+        greeting_message='Hello! How can I help you?',
+        failure_message='Sorry, something went wrong.',
+        max_history=20,
+    )
 )
 ```
 
 | Parameter | Type | Required | Description |
 |---|---|---|---|
 | `name` | `str` | No | Agent display name (used as session name if not overridden) |
-| `instructions` | `str` | No | System prompt for the LLM |
-| `greeting` | `str` | No | Message spoken when the agent joins |
-| `failure_message` | `str` | No | Message spoken on error |
-| `max_history` | `int` | No | Maximum conversation history length |
+| `instructions` | `str` | No | Deprecated. Use LLM vendor `system_messages` instead. |
+| `greeting` | `str` | No | Deprecated. Use LLM/MLLM vendor `greeting_message` instead. |
+| `failure_message` | `str` | No | Deprecated. Use LLM/MLLM vendor `failure_message` instead. |
+| `max_history` | `int` | No | Deprecated. Use LLM vendor `max_history` instead. |
 | `turn_detection` | `TurnDetectionConfig` | No | Turn detection settings |
 | `sal` | `SalConfig` | No | SAL (Speech Activity Level) configuration |
 | `advanced_features` | `Dict[str, Any]` | No | Advanced features (e.g., `{'enable_rtm': True}`) |
@@ -57,15 +61,15 @@ Each `with_*` method returns a **new** `Agent` instance — the original is unch
 
 | Method | Accepts | Purpose |
 |---|---|---|
-| `with_instructions(text)` | `str` | Override the system prompt |
-| `with_greeting(text)` | `str` | Override the greeting message |
+| `with_instructions(text)` | `str` | Deprecated. Use LLM vendor `system_messages` instead. |
+| `with_greeting(text)` | `str` | Deprecated. Use LLM/MLLM vendor `greeting_message` instead. |
 | `with_name(name)` | `str` | Override the agent name |
 | `with_turn_detection(config)` | `TurnDetectionConfig` | Override cascading-flow SOS/EOS detection; use `with_interruption()` for interruption behavior |
 | `with_sal(config)` | `SalConfig` | Set SAL configuration |
 | `with_advanced_features(features)` | `Dict[str, Any]` | Set advanced features |
 | `with_parameters(parameters)` | `SessionParams` | Set session parameters |
-| `with_failure_message(message)` | `str` | Set failure message |
-| `with_max_history(max_history)` | `int` | Set max history length |
+| `with_failure_message(message)` | `str` | Deprecated. Use LLM/MLLM vendor `failure_message` instead. |
+| `with_max_history(max_history)` | `int` | Deprecated. Use LLM vendor `max_history` instead. |
 | `with_geofence(geofence)` | `GeofenceConfig` | Set geofence configuration |
 | `with_labels(labels)` | `Dict[str, str]` | Set custom labels |
 | `with_rtc(rtc)` | `RtcConfig` | Set RTC configuration |
@@ -79,9 +83,14 @@ from agora_agent import Agent
 from agora_agent import OpenAI, ElevenLabsTTS, DeepgramSTT
 
 agent = (
-    Agent(name='my-agent', instructions='You are a helpful assistant.')
-    .with_llm(OpenAI(api_key='your-openai-key', model='gpt-4o-mini'))
-    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id'))
+    Agent(name='my-agent')
+    .with_llm(OpenAI(
+        api_key='your-openai-key',
+        base_url='https://api.openai.com/v1/chat/completions',
+        model='gpt-4o-mini',
+        system_messages=[{'role': 'system', 'content': 'You are a helpful assistant.'}],
+    ))
+    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id', base_url='wss://api.elevenlabs.io/v1'))
     .with_stt(DeepgramSTT(api_key='your-deepgram-key', language='en-US'))
 )
 ```
@@ -97,9 +106,14 @@ from agora_agent import Agent, Agora, Area, OpenAI, ElevenLabsTTS, DeepgramSTT
 client = Agora(area=Area.US, app_id='your-app-id', app_certificate='your-app-certificate')
 
 base = (
-    Agent(instructions='You are a helpful assistant.')
-    .with_llm(OpenAI(api_key='your-openai-key', model='gpt-4o-mini'))
-    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id'))
+    Agent()
+    .with_llm(OpenAI(
+        api_key='your-openai-key',
+        base_url='https://api.openai.com/v1/chat/completions',
+        model='gpt-4o-mini',
+        system_messages=[{'role': 'system', 'content': 'You are a helpful assistant.'}],
+    ))
+    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id', base_url='wss://api.elevenlabs.io/v1'))
     .with_stt(DeepgramSTT(api_key='your-deepgram-key', language='en-US'))
 )
 

docs/concepts/session.md

@@ -40,9 +40,14 @@ from agora_agent import Agent, Agora, Area, OpenAI, ElevenLabsTTS, DeepgramSTT
 client = Agora(area=Area.US, app_id='your-app-id', app_certificate='your-app-certificate')
 
 agent = (
-    Agent(name='my-agent', instructions='You are helpful.')
-    .with_llm(OpenAI(api_key='your-openai-key', model='gpt-4o-mini'))
-    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id'))
+    Agent(name='my-agent')
+    .with_llm(OpenAI(
+        api_key='your-openai-key',
+        base_url='https://api.openai.com/v1/chat/completions',
+        model='gpt-4o-mini',
+        system_messages=[{'role': 'system', 'content': 'You are helpful.'}],
+    ))
+    .with_tts(ElevenLabsTTS(key='your-elevenlabs-key', model_id='eleven_flash_v2_5', voice_id='your-voice-id', base_url='wss://api.elevenlabs.io/v1'))
     .with_stt(DeepgramSTT(api_key='your-deepgram-key', language='en-US'))
 )
 

docs/concepts/vendors.md

@@ -21,21 +21,21 @@ Used with `agent.with_llm()` for the cascading flow (ASR → LLM → TTS).
 
 | Class | Provider | Required Parameters |
 |---|---|---|
-| `OpenAI` | OpenAI | `api_key` |
-| `AzureOpenAI` | Azure OpenAI | `api_key`, `endpoint`, `deployment_name` |
-| `Anthropic` | Anthropic | `api_key` |
-| `Gemini` | Google Gemini | `api_key` |
-| `Groq` | Groq | `api_key` |
-| `VertexAILLM` | Google Vertex AI | `api_key`, `project_id`, `location` |
-| `AmazonBedrock` | Amazon Bedrock | `api_key`, `url`, `model` |
-| `Dify` | Dify | `api_key`, `url` |
+| `OpenAI` | OpenAI | `model` for Agora-managed models; `api_key`, `base_url`, `model` for BYOK |
+| `AzureOpenAI` | Azure OpenAI | `api_key`, `model`, `endpoint`, `deployment_name` |
+| `Anthropic` | Anthropic | `api_key`, `model`, `url`, `headers`, `max_tokens` |
+| `Gemini` | Google Gemini | `api_key`, `model` |
+| `Groq` | Groq | `api_key`, `model`, `base_url` |
+| `VertexAILLM` | Google Vertex AI | `api_key`, `model`, `project_id`, `location` |
+| `AmazonBedrock` | Amazon Bedrock | `access_key`, `secret_key`, `region`, `model` |
+| `Dify` | Dify | `api_key`, `url`, `model` |
 | `CustomLLM` | OpenAI-compatible LLM | `api_key`, `base_url`, `model` |
 
 <!-- snippet: executable -->
 ```python
 from agora_agent import OpenAI
 
-llm = OpenAI(api_key='your-openai-key', model='gpt-4o-mini')
+llm = OpenAI(api_key='your-openai-key', base_url='https://api.openai.com/v1/chat/completions', model='gpt-4o-mini')
 ```
 
 ## TTS Vendors
@@ -44,17 +44,17 @@ Used with `agent.with_tts()`. Each TTS vendor produces audio at a specific sampl
 
 | Class | Provider | Required Parameters | Sample Rate |
 |---|---|---|---|
-| `ElevenLabsTTS` | ElevenLabs | `key`, `model_id`, `voice_id` | 16000, 22050, 24000, or 44100 Hz |
+| `ElevenLabsTTS` | ElevenLabs | `key`, `model_id`, `voice_id`, `base_url` | 16000, 22050, 24000, or 44100 Hz |
 | `MicrosoftTTS` | Microsoft Azure | `key`, `region`, `voice_name` | 8000, 16000, 24000, or 48000 Hz |
-| `OpenAITTS` | OpenAI | `key`, `voice` | 24000 Hz (fixed) |
-| `CartesiaTTS` | Cartesia | `key`, `voice_id` | 8000–48000 Hz |
+| `OpenAITTS` | OpenAI | `voice` for Agora-managed `tts-1`; `api_key`, `model`, `base_url`, `voice` for BYOK | 24000 Hz (fixed) |
+| `CartesiaTTS` | Cartesia | `api_key`, `voice_id`, `model_id` | 8000–48000 Hz |
 | `GoogleTTS` | Google Cloud | `key`, `voice_name` | — |
-| `AmazonTTS` | Amazon Polly | `access_key`, `secret_key`, `region`, `voice_id` | — |
-| `HumeAITTS` | Hume AI | `key` | — |
-| `RimeTTS` | Rime | `key`, `speaker` | — |
-| `FishAudioTTS` | Fish Audio | `key`, `reference_id` | — |
+| `AmazonTTS` | Amazon Polly | `access_key`, `secret_key`, `region`, `voice_id`, `engine` | — |
+| `HumeAITTS` | Hume AI | `key`, `voice_id`, `provider` | — |
+| `RimeTTS` | Rime | `key`, `speaker`, `model_id` | — |
+| `FishAudioTTS` | Fish Audio | `key`, `reference_id`, `backend` | — |
 | `GroqTTS` | Groq | `key` | — |
-| `MiniMaxTTS` | MiniMax | `key` | — |
+| `MiniMaxTTS` | MiniMax | `model` for supported Agora-managed models; `key`, `group_id`, `model`, `voice_id`, `url` for BYOK | — |
 | `DeepgramTTS` | Deepgram | `api_key`, `model` | Configurable |
 | `SarvamTTS` | Sarvam | `api_key` | — |
 
@@ -66,6 +66,7 @@ tts = ElevenLabsTTS(
     key='your-elevenlabs-key',
     model_id='eleven_flash_v2_5',
     voice_id='your-voice-id',
+    base_url='wss://api.elevenlabs.io/v1',
     sample_rate=24000,
 )
 ```
@@ -74,15 +75,17 @@ tts = ElevenLabsTTS(
 
 Used with `agent.with_stt()`.
 
+Use `agent.with_interaction_language()` for Agora `asr.language`; it defaults to `en-US`. STT vendor `language` options are serialized under `asr.params` using each provider's own format.
+
 | Class | Provider | Required Parameters |
 |---|---|---|
 | `SpeechmaticsSTT` | Speechmatics | `api_key`, `language` |
-| `DeepgramSTT` | Deepgram | — (all optional) |
-| `MicrosoftSTT` | Microsoft Azure | `key`, `region` |
+| `DeepgramSTT` | Deepgram | `model` for Agora-managed `nova-2`/`nova-3`; `api_key` for BYOK |
+| `MicrosoftSTT` | Microsoft Azure | `key`, `region`, `language` |
 | `OpenAISTT` | OpenAI | `api_key` |
-| `GoogleSTT` | Google Cloud | `api_key` |
-| `AmazonSTT` | Amazon Transcribe | `access_key`, `secret_key`, `region` |
-| `AssemblyAISTT` | AssemblyAI | `api_key` |
+| `GoogleSTT` | Google Cloud | `project_id`, `location`, `adc_credentials_string`, `language` |
+| `AmazonSTT` | Amazon Transcribe | `access_key`, `secret_key`, `region`, `language` |
+| `AssemblyAISTT` | AssemblyAI | `api_key`, `language` |
 | `AresSTT` | Ares | — (all optional) |
 | `SarvamSTT` | Sarvam | `api_key`, `language` |
 

docs/getting-started/authentication.md

@@ -20,9 +20,12 @@ client = Agora(
 )
 
 agent = (
-    Agent(instructions="Be concise.")
+    Agent()
     .with_stt(DeepgramSTT(model="nova-3"))
-    .with_llm(OpenAI(model="gpt-4o-mini"))
+    .with_llm(OpenAI(
+        model="gpt-4o-mini",
+        system_messages=[{"role": "system", "content": "Be concise."}],
+    ))
     .with_tts(MiniMaxTTS(model="speech_2_6_turbo", voice_id="English_captivating_female1"))
 )
 

docs/getting-started/quick-start.md

@@ -27,14 +27,14 @@ def main() -> None:
     )
 
     agent = (
-        Agent(
-            name="support-assistant",
-            instructions="You are a concise support voice assistant.",
-            greeting="Hello! How can I help you today?",
-            max_history=10,
-        )
+        Agent(name="support-assistant")
         .with_stt(DeepgramSTT(model="nova-3", language="en"))
-        .with_llm(OpenAI(model="gpt-4o-mini"))
+        .with_llm(OpenAI(
+            model="gpt-4o-mini",
+            system_messages=[{"role": "system", "content": "You are a concise support voice assistant."}],
+            greeting_message="Hello! How can I help you today?",
+            max_history=10,
+        ))
         .with_tts(MiniMaxTTS(model="speech_2_6_turbo", voice_id="English_captivating_female1"))
     )