Readme

README.md

@@ -3,7 +3,7 @@
 [![fern shield](https://img.shields.io/badge/%F0%9F%8C%BF-Built%20with%20Fern-brightgreen)](https://buildwithfern.com?utm_source=github&utm_medium=github&utm_campaign=readme&utm_source=https%3A%2F%2Fgithub.com%2FAgoraIO-Conversational-AI%2Fagent-server-sdk-python)
 [![pypi](https://img.shields.io/pypi/v/agora-agent-server-sdk)](https://pypi.python.org/pypi/agora-agent-server-sdk)
 
-The Agora Conversational AI SDK provides convenient access to the Agora Conversational AI APIs, enabling you to build voice-powered AI agents with support for both cascading flows (ASR -> LLM -> TTS) and multimodal flows (MLLM) for real-time audio processing.
+The Agora Agent Server SDK for Python lets you build real-time voice agents on Agora Conversational AI with a high-level `Agent` / `AgentSession` API and a generated low-level REST client.
 
 ## Requirements
 
@@ -17,55 +17,170 @@ pip install agora-agent-server-sdk
 
 ## Quick Start
 
-Minimal builder-based example using supported preset-backed models with no vendor API keys:
+The recommended onboarding path is a server-side builder flow: define the agent once, configure preset-backed providers in the builder, and let AgentKit infer the reseller `preset` values when the session starts.
 
 ```python
+import os
+import time
+
 from agora_agent import Agora, Area
-from agora_agent.agentkit import Agent, DeepgramSTT, OpenAI, OpenAITTS
+from agora_agent.agentkit import (
+    Agent,
+    DataChannel,
+    DeepgramSTT,
+    MiniMaxTTS,
+    OpenAI,
+    expires_in_hours,
+)
+
+AGENT_PROMPT = (
+    "You are a concise, technically credible voice assistant. "
+    "Keep replies short unless the user asks for detail."
+)
+
+GREETING = "Hi there! I am your Agora voice assistant. How can I help?"
+
+
+def start_conversation() -> str:
+    app_id = os.environ["AGORA_APP_ID"]
+    app_certificate = os.environ["AGORA_APP_CERTIFICATE"]
 
-def main() -> None:
     client = Agora(
         area=Area.US,
-        app_id="your-app-id",
-        app_certificate="your-app-certificate",
+        app_id=app_id,
+        app_certificate=app_certificate,
     )
 
     agent = Agent(
-        instructions="You are a concise voice assistant.",
-        greeting="Hello! How can I help you today?",
+        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(
-        DeepgramSTT(model="nova-3")
+        DeepgramSTT(
+            model="nova-3",
+            language="en",
+        )
     ).with_llm(
-        OpenAI(model="gpt-5-mini")
+        OpenAI(
+            model="gpt-4o-mini",
+            greeting_message=GREETING,
+            failure_message="Please wait a moment.",
+            max_history=15,
+            params={
+                "max_tokens": 1024,
+                "temperature": 0.7,
+                "top_p": 0.95,
+            },
+        )
     ).with_tts(
-        OpenAITTS(voice="alloy")
+        MiniMaxTTS(
+            model="speech_2_6_turbo",
+            voice_id="English_captivating_female1",
+        )
     )
 
     session = agent.create_session(
         client,
-        channel="support-room-123",
-        agent_uid="1",
-        remote_uids=["100"],
+        channel=f"demo-channel-{int(time.time())}",
+        agent_uid="123456",
+        remote_uids=["*"],
+        idle_timeout=30,
+        expires_in=expires_in_hours(1),
+        debug=False,
     )
 
-    agent_id = session.start()
-    print(agent_id)
-
-
-if __name__ == "__main__":
-    main()
+    return session.start()
 ```
 
 ### Why no token or vendor key in the example?
 
-The SDK-managed path is the recommended path. `Agora` generates the required ConvoAI REST auth and RTC join tokens automatically, and AgentKit infers the matching supported presets from the vendor configs when you omit vendor API keys.
+`Agora` generates the required ConvoAI REST auth and RTC join tokens automatically when you provide `app_id` and `app_certificate`. AgentKit then inspects the builder-provided vendor configs and infers the matching supported `preset` values for reseller-backed models, so you do not pass vendor API keys in this flow.
+
+### BYOK version of the same builder flow
+
+Use the same `Agent` builder shape, but provide credentials explicitly when you want vendor-managed billing and routing instead of Agora-managed presets.
+
+```python
+agent = Agent(
+    instructions=AGENT_PROMPT,
+    greeting=GREETING,
+).with_stt(
+    DeepgramSTT(
+        api_key=os.environ["DEEPGRAM_API_KEY"],
+        model="nova-3",
+        language="en",
+    )
+).with_llm(
+    OpenAI(
+        api_key=os.environ["OPENAI_API_KEY"],
+        model="gpt-4o-mini",
+        max_tokens=1024,
+        temperature=0.7,
+        top_p=0.95,
+    )
+).with_tts(
+    MiniMaxTTS(
+        key=os.environ["MINIMAX_API_KEY"],
+        group_id=os.environ["MINIMAX_GROUP_ID"],
+        model="speech_2_6_turbo",
+        voice_id="English_captivating_female1",
+        url="wss://api-uw.minimax.io/ws/v1/t2a_v2",
+    )
+)
+```
 
 ## BYOK
 
 If you want to bring your own vendor credentials instead of using Agora-managed presets, use the BYOK guide:
 
 - [BYOK Guide](./docs/guides/byok.md)
 
+## MLLM (Realtime / Multimodal)
+
+Use `with_mllm()` for OpenAI Realtime or Gemini Live. No STT, LLM, or TTS vendor is needed when MLLM mode is enabled.
+
+```python
+from agora_agent.agentkit import Agent, OpenAIRealtime
+
+agent = Agent(name="realtime-assistant").with_mllm(
+    OpenAIRealtime(
+        api_key=os.environ["OPENAI_API_KEY"],
+        model="gpt-4o-realtime-preview",
+        greeting_message="Hello! Ready to chat.",
+    )
+)
+```
+
+See the [MLLM Flow guide](./docs/guides/mllm-flow.md) for full examples with Gemini Live and Vertex AI.
+
 ## Documentation
 
 - [Overview](./docs/index.md)