126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
"""
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Honcho Tools with CrewAI Example
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Demonstrates how to equip CrewAI agents with Honcho's memory tools:
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- HonchoGetContextTool: Retrieve session context with token limits
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- HonchoDialecticTool: Query representations about peers
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- HonchoSearchTool: Perform semantic search across session messages
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These tools give agents explicit control over memory retrieval, beyond the
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automatic memory provided by ExternalMemory.
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"""
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew, Process
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from honcho import Honcho
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from honcho_crewai import (
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HonchoGetContextTool,
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HonchoDialecticTool,
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HonchoSearchTool,
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)
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load_dotenv()
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def main():
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"""Demonstrate Honcho tools with CrewAI agents."""
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print("=" * 70)
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print("HONCHO TOOLS + CREWAI EXAMPLE")
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print("=" * 70 + "\n")
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# Step 1: Setup session with conversation history
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print("1. Setting up session with conversation history...\n")
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honcho = Honcho()
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user_id = "demo-user-45"
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session_id = "tools-demo-session"
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user = honcho.peer(user_id)
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session = honcho.session(session_id)
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# Add conversation history
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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, especially ramen and sushi",
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"My budget is around $3000 for a 10-day trip",
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"I'm interested in visiting both Tokyo and Kyoto",
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"I prefer staying in traditional ryokans over hotels",
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]
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for msg in messages:
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print(f" • {msg}")
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session.add_messages([user.message(msg)])
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print("\n ✓ Session created with 5 messages\n")
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# Step 2: Create Honcho tools
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print("2. Creating Honcho memory tools...\n")
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context_tool = HonchoGetContextTool(
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honcho=honcho, session_id=session_id, peer_id=user_id
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)
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print(" ✓ get_session_context - Retrieve conversation context")
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dialectic_tool = HonchoDialecticTool(
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honcho=honcho, session_id=session_id, peer_id=user_id
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)
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print(" ✓ query_peer_knowledge - Ask about user preferences")
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search_tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
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print(" ✓ search_session_messages - Semantic search messages\n")
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# Step 3: Create agent with tools
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print("3. Creating travel planning agent with memory tools...\n")
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travel_agent = Agent(
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role="Travel Planning Specialist",
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goal="Create personalized travel recommendations using memory tools",
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backstory=(
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"You are an expert travel planner with access to conversation memory tools. "
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"Use the tools to understand the user's preferences before making recommendations."
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),
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tools=[context_tool, dialectic_tool, search_tool],
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verbose=True,
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allow_delegation=False
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)
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print(" ✓ Agent created with 3 Honcho tools\n")
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# Step 4: Create task
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print("4. Creating task...\n")
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task = Task(
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description=(
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"Create a personalized 3-day Tokyo itinerary. "
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"Use the memory tools to understand:\n"
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" • Food preferences (search for 'cuisine' or 'food')\n"
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" • Travel style and budget (query user knowledge)\n"
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" • Recent context (get conversation context)\n"
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"Then create a detailed plan matching their interests."
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),
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expected_output=(
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"A 3-day Tokyo itinerary with:\n"
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" • Daily activities matching user interests\n"
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" • Restaurant recommendations\n"
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" • Accommodation suggestions\n"
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" • Budget considerations"
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),
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agent=travel_agent
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)
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print(" ✓ Task created\n")
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# Step 5: Execute
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print("5. Executing crew (agent will use tools to retrieve memory)...\n")
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print("-" * 70 + "\n")
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crew = Crew(
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agents=[travel_agent],
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tasks=[task],
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process=Process.sequential,
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verbose=True
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)
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crew.kickoff()
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if __name__ == "__main__":
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main()
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