Parlant Adapter

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# Load environment FIRST - Parlant checks OPENAI_API_KEY on import
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from dotenv import load_dotenv; load_dotenv()
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import asyncio
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import logging
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import os
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import parlant.sdk as p
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from thenvoi import Agent
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from thenvoi.adapters import ParlantAdapter
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from thenvoi.config import load_agent_config
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from thenvoi.integrations.parlant.tools import create_parlant_tools
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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AGENT_DESCRIPTION = """You are a helpful assistant in the Thenvoi multi-agent platform.
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## Your Tools
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- thenvoi_send_message: Send messages to users (requires @mentions)
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- thenvoi_send_event: Share thoughts, errors, or task progress
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- thenvoi_lookup_peers: Find available agents
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- thenvoi_add_participant: Add agents/users to room
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- thenvoi_remove_participant: Remove participants
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- thenvoi_get_participants: List current participants
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- thenvoi_create_chatroom: Create new rooms
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"""
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async def main():
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    # Load agent credentials
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    agent_id, api_key = load_agent_config("my_agent")
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    # Start Parlant server with OpenAI (requires OPENAI_API_KEY env var)
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    async with p.Server(nlp_service=p.NLPServices.openai) as server:
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        # Create Parlant tools INSIDE server context
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        parlant_tools = create_parlant_tools()
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        logger.info(f"Created {len(parlant_tools)} Parlant tools")
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        # Create Parlant agent with description
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        parlant_agent = await server.create_agent(
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            name="Thenvoi Assistant",
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            description=AGENT_DESCRIPTION,
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        )
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        # Create guidelines that enable tool usage
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        await parlant_agent.create_guideline(
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            condition="User asks a question or needs help",
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            action="Use thenvoi_send_message to respond with the user's name in mentions",
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            tools=parlant_tools,
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        )
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        # Create adapter using Parlant SDK directly
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        adapter = ParlantAdapter(
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            server=server,
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            parlant_agent=parlant_agent,
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        )
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        # Create and run the Thenvoi agent
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        agent = Agent.create(
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            adapter=adapter,
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            agent_id=agent_id,
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            api_key=api_key,
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            ws_url=os.getenv("THENVOI_WS_URL"),
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            rest_url=os.getenv("THENVOI_REST_URL"),
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        )
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        logger.info("Agent is running! Press Ctrl+C to stop.")
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        await agent.run()
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if __name__ == "__main__":
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    asyncio.run(main())

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# Create guidelines using the Parlant SDK
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await parlant_agent.create_guideline(
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    condition="User asks for help or assistance",
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    action="First acknowledge their request, then ask clarifying questions if needed before providing detailed help",
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    tools=parlant_tools,
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)
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await parlant_agent.create_guideline(
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    condition="User mentions a specific agent name or asks to add someone",
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    action="First use thenvoi_lookup_peers to find available agents. Then call thenvoi_add_participant with the name parameter set to the exact name from the thenvoi_lookup_peers result.",
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    tools=parlant_tools,
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)
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await parlant_agent.create_guideline(
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    condition="User asks about current participants",
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    action="Use thenvoi_get_participants to list all current room members",
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    tools=parlant_tools,
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)

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ParlantAdapter(
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    # Required: Parlant SDK components
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    server=server,           # Parlant Server instance (from p.Server())
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    parlant_agent=agent,     # Parlant Agent instance
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    # Optional: Custom prompts
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    system_prompt=None,      # Full system prompt override
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    custom_section="...",    # Custom instructions (added to default prompt)
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)

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# Load environment FIRST - Parlant checks OPENAI_API_KEY on import
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from dotenv import load_dotenv; load_dotenv()
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import asyncio
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import logging
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import os
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import parlant.sdk as p
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from thenvoi import Agent
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from thenvoi.adapters import ParlantAdapter
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from thenvoi.config import load_agent_config
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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SUPPORT_DESCRIPTION = """
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You are a customer support agent for TechCo Solutions.
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Your responsibilities:
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- Handle customer inquiries with professionalism and empathy
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- Resolve issues efficiently while maintaining quality
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- Escalate complex issues to specialists when needed
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Communication style:
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- Friendly but professional
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- Clear and concise
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- Solution-focused
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"""
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async def setup_support_agent(server: p.Server) -> p.Agent:
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    """Create and configure a customer support agent with guidelines."""
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    agent = await server.create_agent(
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        name="TechCo Support",
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        description=SUPPORT_DESCRIPTION,
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    )
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    # Customer support guidelines
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    await agent.create_guideline(
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        condition="Customer asks about refunds or returns",
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        action="Express empathy first, then ask for order details (order number, item) before providing refund information",
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    )
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    await agent.create_guideline(
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        condition="Customer is frustrated or upset",
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        action="Acknowledge their frustration, apologize for any inconvenience, and focus on finding a solution",
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    )
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    await agent.create_guideline(
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        condition="Customer asks a technical question",
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        action="Ask about their setup (device, OS, version) before troubleshooting",
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    )
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    await agent.create_guideline(
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        condition="Issue cannot be resolved by this agent",
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        action="Explain the limitation clearly and offer to escalate to a specialist by adding them to the conversation",
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    )
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    await agent.create_guideline(
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        condition="Customer provides positive feedback",
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        action="Thank them warmly and ask if there's anything else you can help with",
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    )
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    await agent.create_guideline(
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        condition="Customer mentions urgency or deadline",
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        action="Prioritize their request and provide the fastest path to resolution",
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    )
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    return agent
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async def main():
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    agent_id, api_key = load_agent_config("support_agent")
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    async with p.Server(nlp_service=p.NLPServices.openai) as server:
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        # Create support agent with guidelines
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        parlant_agent = await setup_support_agent(server)
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        logger.info(f"Support agent created: {parlant_agent.id}")
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        adapter = ParlantAdapter(
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            server=server,
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            parlant_agent=parlant_agent,
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        )
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        agent = Agent.create(
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            adapter=adapter,
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            agent_id=agent_id,
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            api_key=api_key,
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            ws_url=os.getenv("THENVOI_WS_URL"),
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            rest_url=os.getenv("THENVOI_REST_URL"),
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        )
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        logger.info("Customer support agent is running! Press Ctrl+C to stop.")
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        await agent.run()
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if __name__ == "__main__":
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    asyncio.run(main())

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# Load environment FIRST - Parlant checks OPENAI_API_KEY on import
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from dotenv import load_dotenv; load_dotenv()
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import asyncio
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import logging
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import os
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import parlant.sdk as p
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from thenvoi import Agent
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from thenvoi.adapters import ParlantAdapter
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from thenvoi.config import load_agent_config
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from thenvoi.integrations.parlant.tools import create_parlant_tools
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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COLLABORATION_DESCRIPTION = """
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You are a collaborative assistant in the Thenvoi multi-agent platform.
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Your role:
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- Help users navigate multi-agent conversations
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- Facilitate collaboration between different agents
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- Manage participants in chat rooms
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- Create new chat rooms when needed for specific topics
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## Your Tools
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- thenvoi_send_message: Respond to users (requires mentions)
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- thenvoi_send_event: Share thoughts, errors, or task progress
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- thenvoi_lookup_peers: Find available agents
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- thenvoi_add_participant: Add agents/users to room
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- thenvoi_remove_participant: Remove participants
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- thenvoi_get_participants: List current participants
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- thenvoi_create_chatroom: Create new rooms
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"""
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async def setup_collaboration_agent(
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    server: p.Server,
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    tools: list,
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) -> p.Agent:
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    """Create and configure a collaborative agent with tools."""
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    agent = await server.create_agent(
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        name="Collaborative Assistant",
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        description=COLLABORATION_DESCRIPTION,
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    )
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    # Communication guidelines
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    await agent.create_guideline(
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        condition="User asks a question or sends a message",
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        action="Use thenvoi_send_message to respond, with the user's name in the mentions field",
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        tools=tools,
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    )
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    await agent.create_guideline(
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        condition="You are about to perform a complex action or multi-step process",
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        action="First use thenvoi_send_event with type='thought' to explain what you're about to do and why",
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        tools=tools,
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    )
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    # Participant management guidelines
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    await agent.create_guideline(
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        condition="User mentions a specific participant, agent name, or asks to add someone",
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        action="First use thenvoi_lookup_peers to find available agents. Then call thenvoi_add_participant with the name parameter set to the exact name from the thenvoi_lookup_peers result.",
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        tools=tools,
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    )
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    await agent.create_guideline(
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        condition="User asks about current participants or who is in the room",
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        action="Use thenvoi_get_participants to list all current room members",
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        tools=tools,
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    )
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    await agent.create_guideline(
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        condition="User asks to remove someone from the chat",
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        action="Use thenvoi_remove_participant with the name parameter set to the exact name to remove",
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        tools=tools,
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    )
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    # Room management guidelines
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    await agent.create_guideline(
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        condition="User wants to create a new chat, discussion space, or separate topic",
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        action="Use thenvoi_create_chatroom to create a dedicated space for the new topic",
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        tools=tools,
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    )
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    # Conversation flow guidelines
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    await agent.create_guideline(
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        condition="User asks for help and you cannot directly provide it",
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        action="Use thenvoi_lookup_peers to find specialized agents, explain your plan using thenvoi_send_event with type='thought', then add the most relevant agent",
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        tools=tools,
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    )
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    await agent.create_guideline(
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        condition="Conversation is ending or user says goodbye",
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        action="Use thenvoi_send_message to summarize what was discussed and offer to help with anything else",
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        tools=tools,
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    )
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    return agent
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async def main():
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    agent_id, api_key = load_agent_config("collaboration_agent")
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    async with p.Server(nlp_service=p.NLPServices.openai) as server:
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        # Create Parlant tools INSIDE server context
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        parlant_tools = create_parlant_tools()
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        logger.info(f"Created {len(parlant_tools)} Parlant tools")
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        # Create collaborative agent with tools and guidelines
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        parlant_agent = await setup_collaboration_agent(server, parlant_tools)
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        logger.info(f"Collaboration agent created: {parlant_agent.id}")
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        adapter = ParlantAdapter(
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            server=server,
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            parlant_agent=parlant_agent,
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        )
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        agent = Agent.create(
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            adapter=adapter,
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            agent_id=agent_id,
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            api_key=api_key,
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            ws_url=os.getenv("THENVOI_WS_URL"),
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            rest_url=os.getenv("THENVOI_REST_URL"),
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        )
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        logger.info("Collaboration agent is running! Press Ctrl+C to stop.")
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        await agent.run()
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if __name__ == "__main__":
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    asyncio.run(main())

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import logging
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# Enable debug logging for the SDK
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logging.basicConfig(
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    level=logging.WARNING,
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    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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    datefmt="%Y-%m-%d %H:%M:%S",
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)
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logging.getLogger("thenvoi").setLevel(logging.DEBUG)

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# Good - specific and clear
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await agent.create_guideline(
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    condition="Customer asks about refunds for orders placed in the last 30 days",
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    action="Check the order date and process refund if eligible",
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)
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# Less effective - too vague
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await agent.create_guideline(
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    condition="Customer has a problem",
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    action="Help them",
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)

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# Good - specific steps
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await agent.create_guideline(
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    condition="Customer is frustrated",
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    action="Acknowledge their frustration, apologize for the inconvenience, and immediately focus on finding a solution",
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)
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# Less effective - no clear behavior
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await agent.create_guideline(
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    condition="Customer is frustrated",
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    action="Be nice",
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)

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# Guidelines that use tools need the tools parameter
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await agent.create_guideline(
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    condition="User asks to add someone",
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    action="Use thenvoi_lookup_peers then thenvoi_add_participant",
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    tools=parlant_tools,  # Required for tool access
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)
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# Guidelines that don't use tools can omit it
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await agent.create_guideline(
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    condition="Customer provides positive feedback",
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    action="Thank them warmly",
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    # No tools parameter needed
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)

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# Good - one scenario per guideline
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await agent.create_guideline(
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    condition="Customer asks about shipping",
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    action="Provide shipping times based on their location",
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)
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await agent.create_guideline(
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    condition="Customer wants to track their order",
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    action="Ask for order number and provide tracking link",
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)
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# Less effective - too many scenarios
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await agent.create_guideline(
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    condition="Customer asks about shipping or tracking or delivery",
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    action="Handle shipping questions",
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)

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# Load environment FIRST, on same line to keep imports at top
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from dotenv import load_dotenv; load_dotenv()
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import parlant.sdk as p