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Documentation Index

Fetch the complete documentation index at: https://polos.dev/docs/llms.txt

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Conversation memory enables agents to maintain context across multiple interactions. Agents remember previous messages and use them to provide coherent, context-aware responses in multi-turn conversations.

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Enabling conversation memory

Set conversation_history to specify how many previous messages to retain: 
from polos import Agent

conversational_agent = Agent(
    id="chat-agent",
    provider="openai",
    model="gpt-4o",
    system_prompt="You are a helpful assistant. Be friendly and concise.",
    conversation_history=10  # Keep last 10 messages (i.e. 5 conversation turns)
)
The agent automatically manages conversation history - no manual bookkeeping required. Defaults to conversation_history=10 when you don’t specify anything.

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Using conversation IDs

Group related messages using conversation_id. Works with agent.run(): 
client = PolosClient()
result = await research_agent.run(
    client,
    user_message,
    conversation_id=conversation_id
)
and agent.stream(): 
stream = await research_agent.stream(
    client,
    user_message,
    conversation_id=conversation_id
)
How it works:
  1. Agent retrieves past messages for this conversation_id
  2. Includes them in the LLM context
  3. Stores the new message after generating a response

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Example

import asyncio
from polos import PolosClient

async def main():
    conversation_id = "user-123-session-1"
    client = PolosClient()

    # Turn 1
    response1 = await my_agent.run(
        client,
        "My name is Alice and I love Python.",
        conversation_id=conversation_id
    )
    print(response1.result)

    # Turn 2 - Agent remembers Alice and Python
    response2 = await my_agent.run(
        client,
        "What's my favorite programming language?",
        conversation_id=conversation_id
    )
    print(response2.result)  # "Your favorite programming language is Python"

    # Turn 3 - Agent remembers the name
    response3 = await my_agent.run(
        client,
        "What's my name?",
        conversation_id=conversation_id
    )
    print(response3.result)  # "Your name is Alice"

if __name__ == "__main__":
    asyncio.run(main())

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History limits

The conversation_history parameter controls the maximum number of messages retained: 
# Keep last 20 messages
agent = Agent(
    id="agent",
    conversation_history=20
)
What happens when the limit is exceeded:
  • Oldest messages are dropped
  • Most recent messages (in this example, 20 messages) are kept
  • Agent always has the latest context