fix: removing unnecessary code and adding config for observing
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ajspig
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@ -1,130 +0,0 @@
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"""
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Honcho Multi-Tool Example
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Demonstrates using Honcho tools with an Agno agent:
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- honcho_chat: Ask questions about the conversation (recommended)
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- honcho_get_context: Retrieve raw session context
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- honcho_search_messages: Semantic search through messages
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The honcho_chat tool is the recommended way to understand users
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It reasons over conversation context and provides synthesized insights.
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Environment Variables:
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LLM_OPENAI_API_KEY: OpenAI API key (matches honcho .env)
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OPENAI_MODEL: Model to use
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HONCHO_API_KEY: Required for Honcho API access
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"""
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import os
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import uuid
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from dotenv import load_dotenv
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from honcho import Honcho
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from honcho_agno import HonchoTools
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load_dotenv()
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# Use LLM_OPENAI_API_KEY from honcho .env
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if llm_key := os.getenv("LLM_OPENAI_API_KEY"):
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os.environ["OPENAI_API_KEY"] = llm_key
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def main():
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print("=" * 70)
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print("HONCHO TOOLS + AGNO EXAMPLE")
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print("=" * 70 + "\n")
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# Initialize Honcho client
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honcho = Honcho(workspace_id="travel-app")
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# Setup Honcho tools - creates peer and session internally
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# Generate unique session ID to avoid message accumulation across runs
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honcho_tools = HonchoTools(
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peer_id="travel-assistant",
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session_id=str(uuid.uuid4()),
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honcho_client=honcho,
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)
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# Create user peer (the toolkit's peer is "travel-assistant")
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user_peer = honcho.peer("traveler-42")
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# Pre-populate with user's travel preferences
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print("Adding user's travel preferences to memory...")
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messages = [
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"I'm planning a trip to Japan in March",
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"I love trying authentic local cuisine",
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"My budget is around $3000 for 10 days",
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"I prefer ryokans over hotels",
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"I'm interested in both traditional temples and modern Tokyo",
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]
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for msg in messages:
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honcho_tools.session.add_messages([user_peer.message(msg)])
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print(f" [traveler-42]: {msg[:50]}...")
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print("\n" + "-" * 70 + "\n")
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# Create travel planning agent with memory tools
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agent = Agent(
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name="Travel Planner",
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model=OpenAIChat(id=os.getenv("OPENAI_MODEL", "gpt-4o")),
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tools=[honcho_tools],
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description=(
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"A travel planning expert with access to Honcho memory tools. "
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"Use honcho_chat to understand the traveler's preferences and travel style."
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),
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instructions=[
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"Use the honcho_chat tool to understand the user's preferences and travel style",
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"Ask both broad and specific questions like 'What is their travel style?' or 'What is their budget?'",
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"Only use honcho_get_context or honcho_search_messages if you need raw message history",
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"Be specific and actionable in your recommendations",
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],
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)
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# Run the agent with a planning request
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print("Asking agent to create a personalized itinerary...\n")
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response = agent.run(
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"Create a 3-day Tokyo itinerary for me. Use the honcho_chat tool to ask about "
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"my budget, accommodation preferences, and interests, then create "
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"a personalized plan that matches my travel style."
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)
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# Save the assistant's response to Honcho (using toolkit's peer and session)
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assistant_response = str(response.content) if response.content else ""
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if assistant_response:
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honcho_tools.session.add_messages([honcho_tools.peer.message(assistant_response)])
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print("=" * 70)
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print("RESPONSE")
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print("=" * 70)
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print(response.content)
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# Demonstrate chat (recommended)
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print("\n" + "=" * 70)
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print("DIRECT TOOL USAGE: honcho_chat (recommended)")
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print("=" * 70)
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chat_result = honcho_tools.honcho_chat(
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"What are the traveler's key preferences and constraints?"
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)
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print(chat_result)
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# Demonstrate search capability
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print("\n" + "=" * 70)
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print("DIRECT TOOL USAGE: honcho_search_messages")
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print("=" * 70)
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search_result = honcho_tools.honcho_search_messages("budget money cost", limit=5)
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print(search_result)
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# Show full conversation context
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print("\n" + "=" * 70)
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print("DIRECT TOOL USAGE: honcho_get_context")
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print("=" * 70)
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print(honcho_tools.honcho_get_context())
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if __name__ == "__main__":
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main()
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@ -20,6 +20,7 @@ from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from honcho import Honcho
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from honcho.session import SessionPeerConfig
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from honcho_agno import HonchoTools
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load_dotenv()
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@ -49,6 +50,14 @@ def main():
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assistant_peer = honcho.peer("assistant")
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session = honcho.session(session_id)
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# Configure observation settings:
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# - User is observed (assistant builds theory-of-mind of user)
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# - Assistant is NOT observed (no need for user to model the assistant)
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session.add_peers([
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(user_peer, SessionPeerConfig(observe_me=True, observe_others=False)),
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(assistant_peer, SessionPeerConfig(observe_me=False, observe_others=True)),
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])
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# Create an agent with memory tools
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agent = Agent(
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name="Programming Mentor",
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@ -1,115 +0,0 @@
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"""
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Tests for Honcho Agno Tools
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Simple tests verifying tool structure, registration, and initialization.
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These tests use minimal mocking - just enough to avoid network calls.
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"""
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import uuid
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from unittest.mock import MagicMock
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import pytest
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from honcho_agno import HonchoTools
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@pytest.fixture
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def mock_client():
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"""Create a minimal mock Honcho client."""
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client = MagicMock()
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client.peer.return_value = MagicMock()
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client.session.return_value = MagicMock()
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return client
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class TestInitialization:
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"""Tests for HonchoTools initialization."""
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def test_initializes_with_client(self, mock_client):
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"""Test initialization with a provided client."""
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tools = HonchoTools(
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peer_id="assistant",
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session_id="test-session",
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honcho_client=mock_client,
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)
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assert tools.honcho is mock_client
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assert tools.peer_id == "assistant"
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assert tools.session_id == "test-session"
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def test_default_peer_id(self, mock_client):
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"""Test default peer_id is 'assistant'."""
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tools = HonchoTools(honcho_client=mock_client)
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assert tools.peer_id == "assistant"
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def test_auto_generates_session_id(self, mock_client):
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"""Test session_id is auto-generated if not provided."""
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tools = HonchoTools(honcho_client=mock_client)
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assert tools.session_id is not None
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# Should be valid UUID
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uuid.UUID(tools.session_id)
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def test_toolkit_name(self, mock_client):
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"""Test toolkit has correct name."""
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tools = HonchoTools(honcho_client=mock_client)
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assert tools.name == "honcho"
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class TestToolRegistration:
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"""Tests verifying tools are properly registered."""
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def test_all_tools_registered(self, mock_client):
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"""Test all expected tools are registered."""
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tools = HonchoTools(honcho_client=mock_client)
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registered = [func.name for func in tools.functions.values()]
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assert "honcho_get_context" in registered
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assert "honcho_search_messages" in registered
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assert "honcho_chat" in registered
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assert len(registered) == 3
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def test_tools_are_callable(self, mock_client):
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"""Test tool methods exist and are callable."""
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tools = HonchoTools(honcho_client=mock_client)
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assert callable(tools.honcho_get_context)
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assert callable(tools.honcho_search_messages)
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assert callable(tools.honcho_chat)
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class TestMultiPeerPattern:
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"""Tests for multi-peer conversation patterns."""
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def test_multiple_toolkits_share_session(self, mock_client):
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"""Test multiple toolkits can share a session ID."""
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session_id = "shared-session"
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agent1 = HonchoTools(
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peer_id="agent-alpha",
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session_id=session_id,
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honcho_client=mock_client,
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)
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agent2 = HonchoTools(
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peer_id="agent-beta",
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session_id=session_id,
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honcho_client=mock_client,
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)
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assert agent1.session_id == agent2.session_id
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assert agent1.peer_id != agent2.peer_id
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def test_each_toolkit_has_one_peer(self, mock_client):
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"""Test each toolkit represents exactly one peer."""
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tools = HonchoTools(
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peer_id="my-agent",
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honcho_client=mock_client,
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)
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assert tools.peer is not None
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assert tools.peer_id == "my-agent"
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# Should not have multiple peer attributes
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assert not hasattr(tools, "user")
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assert not hasattr(tools, "assistant_peer")
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