diff --git a/CLAUDE.md b/CLAUDE.md index b73f24a..f53fc9d 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -81,4 +81,5 @@ Both APIs are fully supported with sync/async variants and streaming support. - **Core**: `pydantic>=2`, `httpx>=0.27`, `twilio>=9.8.3` - **Server** (optional): `fastapi`, `uvicorn`, `python-multipart` — install with `pip install tac[server]` -- **Dev**: `pytest`, `ruff`, `mypy`, `openai`, `openai-agents` +- **Dev**: `pytest`, `ruff`, `mypy` +- **Examples**: `openai`, `openai-agents`, `langchain-core`, `langchain-openai`, `boto3`, `strands-agents`, `python-dotenv` diff --git a/README.md b/README.md index bebe009..7770738 100644 --- a/README.md +++ b/README.md @@ -169,7 +169,7 @@ For detailed architecture and advanced usage, see [CLAUDE.md](https://github.com **Examples & Guides:** - **[Getting Started Guide](https://github.com/twilio/twilio-agent-connect-python/tree/main/getting_started/)** - Setup wizard, examples, and comprehensive documentation -- **[Partner SDK Examples](https://github.com/twilio/twilio-agent-connect-python/tree/main/getting_started/examples/partners/)** - Integration examples for OpenAI, AWS Bedrock Agent, AWS Bedrock AgentCore, and AWS Strands +- **[Partner SDK Examples](https://github.com/twilio/twilio-agent-connect-python/tree/main/getting_started/examples/partners/)** - Integration examples for OpenAI (Chat Completions, Responses API, Agents SDK), LangChain, AWS Bedrock Agent, AWS Bedrock AgentCore, and AWS Strands - **[ConversationRelay-Only Mode](https://github.com/twilio/twilio-agent-connect-python/blob/main/getting_started/examples/features/relay_only.py)** - Get started with voice using just ConversationRelay - More examples coming soon diff --git a/getting_started/README.md b/getting_started/README.md index 3ce3b24..f150f6e 100644 --- a/getting_started/README.md +++ b/getting_started/README.md @@ -47,6 +47,8 @@ Learn the core pattern for manually extracting and injecting TAC memory into **a Production-ready examples integrating TAC with partner SDKs: - **`openai_chat_completions.py`**: OpenAI Chat Completions API with automatic memory injection via `with_tac_memory()` - **`openai_responses_api.py`**: OpenAI Responses API with automatic memory injection +- **`openai_agents.py`**: OpenAI Agents SDK integration +- **`langchain.py`**: LangChain integration - **`aws_bedrock_agent.py`**: AWS Bedrock Agent integration - **`aws_bedrock_agentcore.py`**: AWS Bedrock AgentCore integration - **`aws_strands.py`**: AWS Strands agents integration @@ -85,6 +87,8 @@ walks up from the script's directory, so it'll find uv run getting_started/examples/overview.py uv run getting_started/examples/partners/openai_chat_completions.py uv run getting_started/examples/partners/openai_responses_api.py +uv run getting_started/examples/partners/openai_agents.py +uv run getting_started/examples/partners/langchain.py uv run getting_started/examples/partners/aws_bedrock_agent.py uv run getting_started/examples/partners/aws_bedrock_agentcore.py uv run getting_started/examples/partners/aws_strands.py diff --git a/getting_started/examples/.env.example b/getting_started/examples/.env.example index f432cc7..cc8f79b 100644 --- a/getting_started/examples/.env.example +++ b/getting_started/examples/.env.example @@ -37,7 +37,7 @@ TWILIO_WHATSAPP_NUMBER=whatsapp:+1xxxxxxxxxx # Partner Examples - OpenAI # ============================================================================ -# Required for partners/openai_*.py examples +# Required for partners/openai_*.py and partners/langchain.py examples OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # ============================================================================ diff --git a/getting_started/examples/partners/langchain.py b/getting_started/examples/partners/langchain.py new file mode 100644 index 0000000..dc23494 --- /dev/null +++ b/getting_started/examples/partners/langchain.py @@ -0,0 +1,130 @@ +""" +Example: Using LangChain with TAC + +Demonstrates how to connect Twilio Agent Connect with LangChain +for voice and SMS channels. + +Prerequisites: + pip install langchain-core langchain-openai + +Environment Variables: + OPENAI_API_KEY - Your OpenAI API key +""" + +from dotenv import load_dotenv +from langchain_core.messages import AIMessage, HumanMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_openai import ChatOpenAI + +from tac import TAC, TACConfig +from tac.adapters import MemoryPromptBuilder +from tac.channels.sms import SMSChannel, SMSChannelConfig +from tac.channels.voice import VoiceChannel, VoiceChannelConfig +from tac.core.logging import get_logger +from tac.models.session import ConversationSession +from tac.models.tac import TACMemoryResponse +from tac.server import TACFastAPIServer + +load_dotenv() + +logger = get_logger(__name__) + +# Initialize TAC with configuration from environment variables +tac = TAC(config=TACConfig.from_env()) + +# Create channel handlers for Voice and SMS +voice_channel = VoiceChannel(tac, config=VoiceChannelConfig(memory_mode="once")) +sms_channel = SMSChannel(tac, config=SMSChannelConfig(memory_mode="always")) + +# Create LangChain LLM +llm = ChatOpenAI(model="gpt-5.4-mini", temperature=0) + +# Create prompt template with conversation history support +prompt_template = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a customer service agent speaking with a user over voice or SMS. " + "Keep responses short and conversational — a sentence or two. " + "Do not use markdown, asterisks, bullets, or emojis; your words will be " + "spoken aloud or sent as plain text.", + ), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{user_message}"), + ] +) + +# Create chain using LCEL (pipe operator) +chain = prompt_template | llm + +# Store conversation history per conversation +conversation_history: dict[str, list[HumanMessage | AIMessage]] = {} + + +async def handle_message_ready( + user_message: str, + context: ConversationSession, + memory_response: TACMemoryResponse | None, +) -> str: + """ + Callback invoked when a message is ready to be processed. + + This example uses LangChain with LCEL to process the message + and maintains conversation history per conversation. + + Args: + user_message: The customer's message text + context: Session data (conversation_id, channel, profile, etc.) + memory_response: Optional retrieved memories (observations, summaries, communications) + + Returns: + Response string to send to the channel + """ + conv_id = context.conversation_id + + try: + # Initialize conversation history for new conversations + if conv_id not in conversation_history: + conversation_history[conv_id] = [] + + # Compose user message with memory context (user message first, then memory) + prompt = MemoryPromptBuilder.compose( + system_prompt=user_message, + memory_response=memory_response, + context=context, + ) + + # Invoke the chain with conversation history and composed prompt + response = await chain.ainvoke( + { + "chat_history": conversation_history[conv_id], + "user_message": prompt, + } + ) + + # Extract text content from the response + llm_response = response.content if hasattr(response, "content") else str(response) + + # Update conversation history + conversation_history[conv_id].append(HumanMessage(content=user_message)) + conversation_history[conv_id].append(AIMessage(content=llm_response)) + + return llm_response + + except Exception as e: + logger.error("Error processing message", conversation_id=conv_id, error=str(e)) + return "Sorry, I encountered an error processing your message." + + +# Register the message handler callback +tac.on_message_ready(handle_message_ready) + +if __name__ == "__main__": + # TACFastAPIServer creates a FastAPI app with all required endpoints: + # - /twiml: Voice call webhook (returns TwiML with ConversationRelay) + # - /ws: WebSocket endpoint for Voice channel + # - /webhook: Conversation webhook for all channels + server = TACFastAPIServer( + tac=tac, voice_channel=voice_channel, messaging_channels=[sms_channel] + ) + server.start() diff --git a/getting_started/examples/partners/openai_agents.py b/getting_started/examples/partners/openai_agents.py new file mode 100644 index 0000000..d7993bc --- /dev/null +++ b/getting_started/examples/partners/openai_agents.py @@ -0,0 +1,102 @@ +""" +Example: Using OpenAI Agents SDK with TAC + +Demonstrates how to connect Twilio Agent Connect with the OpenAI Agents SDK +for voice and SMS channels. + +Prerequisites: + pip install openai-agents + +Environment Variables: + OPENAI_API_KEY - Your OpenAI API key (optional, uses default client if not set) +""" + +from agents import Agent, Runner +from dotenv import load_dotenv + +from tac import TAC, TACConfig +from tac.adapters import MemoryPromptBuilder +from tac.channels.sms import SMSChannel, SMSChannelConfig +from tac.channels.voice import VoiceChannel, VoiceChannelConfig +from tac.core.logging import get_logger +from tac.models.session import ConversationSession +from tac.models.tac import TACMemoryResponse +from tac.server import TACFastAPIServer + +load_dotenv() + +logger = get_logger(__name__) + +# Initialize TAC with configuration from environment variables +tac = TAC(config=TACConfig.from_env()) + +# Create channel handlers for Voice and SMS +voice_channel = VoiceChannel(tac, config=VoiceChannelConfig(memory_mode="once")) +sms_channel = SMSChannel(tac, config=SMSChannelConfig(memory_mode="always")) + +# Create OpenAI Agent with instructions +agent = Agent( + name="Customer Service Agent", + model="gpt-5.4-mini", + instructions=( + "You are a customer service agent speaking with a user over voice or SMS. " + "Keep responses short and conversational — a sentence or two. " + "Do not use markdown, asterisks, bullets, or emojis; your words will be " + "spoken aloud or sent as plain text." + ), +) + + +async def handle_message_ready( + user_message: str, + context: ConversationSession, + memory_response: TACMemoryResponse | None, +) -> str: + """ + Callback invoked when a message is ready to be processed. + + This example uses the OpenAI Agents SDK to run a stateless agent. + + Args: + user_message: The customer's message text + context: Session data (conversation_id, channel, profile, etc.) + memory_response: Optional retrieved memories (observations, summaries, communications) + + Returns: + Response string to send to the channel + """ + conv_id = context.conversation_id + + try: + # Compose user message with memory context (user message first, then memory) + prompt = MemoryPromptBuilder.compose( + system_prompt=user_message, + memory_response=memory_response, + context=context, + ) + + # Run the agent with the composed prompt + result = await Runner.run(agent, prompt) + + # Extract the agent's response + llm_response = result.final_output or "No response from agent." + + return llm_response + + except Exception as e: + logger.error("Error processing message", conversation_id=conv_id, error=str(e)) + return "Sorry, I encountered an error processing your message." + + +# Register the message handler callback +tac.on_message_ready(handle_message_ready) + +if __name__ == "__main__": + # TACFastAPIServer creates a FastAPI app with all required endpoints: + # - /twiml: Voice call webhook (returns TwiML with ConversationRelay) + # - /ws: WebSocket endpoint for Voice channel + # - /webhook: Conversation webhook for all channels + server = TACFastAPIServer( + tac=tac, voice_channel=voice_channel, messaging_channels=[sms_channel] + ) + server.start() diff --git a/pyproject.toml b/pyproject.toml index 47d3185..56c762d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,6 +53,9 @@ examples = [ "boto3>=1.34.0", 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