session.generate_reply() is ignoring the session chat_ctx and starts the conversation even though the conversation is not a new one but rather resuming.

async def on_enter(self):
    logger.warning(
        "agent entering. chat context is: " + json.dumps(self.session._chat_ctx.to_dict(
            exclude_image=True,
            exclude_audio=True,
            exclude_timestamp=False,
            exclude_function_call=False
        )))

    # Generate initial reply
    await self.session.generate_reply(
        instructions=f"refer to previous messages and if there are previous messages, continue the conversation after saying 'nice to meet you again' then continue the conversation in accordance with the system instruction. if there's no older message, greet and start the conversation. System instruction:" + self.config.instructions,
        allow_interruptions=True
    )

Thanks for reaching out, vnandan. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Yes. because we provide the ability to resume a conversation, so we retrieve the messages from database and add it to the chat_ctx like this:

async def restore_chat_history(session, assistant, messages):
    """Restore previous chat messages"""
    for msg in messages:
        if (msg['role'] not in ['ai', 'user','contact']):
            continue
        session._chat_ctx.add_message(
            role='assistant' if msg['role'] == 'ai' else 'user',
            content=msg['message']
        )

To make sure

session.generate_reply()

uses the restored chat context (

chat_ctx

) for continuing our conversation, I'll walk you through some friendly steps. Think of this like setting up a cozy chat room where we can pick up right where we left off! • Pass chat_ctx During Agent Creation: When you're creating a new agent, just bring along that restored chat context. It's like introducing an old friend to a new conversation:

# Create agent with restored chat context
agent = Agent(
    instructions="Your instructions here",
    chat_ctx=restored_chat_ctx  # Pass the restored context
)

• Update Chat Context Before generate_reply(): Here's a neat trick to make sure we're keeping all our conversation threads intact:

# Copy and modify chat context
chat_ctx = self.chat_ctx.copy()

# Copy context from previous agent if it exists
if userdata.prev_agent:
    items_copy = self._truncate_chat_ctx(
        userdata.prev_agent.chat_ctx.items, keep_function_call=True
    )
    existing_ids = {item.id for item in chat_ctx.items}
    items_copy = [item for item in items_copy if item.id not in existing_ids]
    chat_ctx.items.extend(items_copy)

# Update the chat context
await self.update_chat_ctx(chat_ctx)

# Now generate_reply() will use the updated context
self.session.generate_reply()

• Load Chat History Example: Imagine you're picking up a conversation you had earlier – here's how you can bring that history back to life:

chat_history = [
    {"role": "assistant", "content": "Hello, I am a travel planner. How can I help you?"},
    {"role": "user", "content": "I want to go to Paris this summer."},
    # ... more history
]

chat_ctx = llm.ChatContext.empty()
for item in chat_history:
    chat_ctx.add_message(role=item["role"], content=item["content"])

# Create agent with the loaded context
agent = Agent(
    instructions="You are a helpful travel planner.",
    chat_ctx=chat_ctx,
)

generate_reply()

will automatically use the current chat context set on the agent/session. Just make sure you set things up right, and we'll keep our conversation flowing smoothly – no awkward restarts here! Sources: tests/test_agent_session.py | Workflows | LiveKit Docs | pipeline-llm/simple_content_filter.py