Threads in Marvin provide a powerful mechanism for managing conversation history and context across your AI workflows. Unlike ControlFlow’s flows, threads in Marvin are focused on conversation management and persistence rather than workflow orchestration.

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Understanding Threads

A thread in Marvin represents a conversation context that can span multiple interactions. Each thread:
  • Maintains its own conversation history
  • Can be persisted to a database
  • Can be referenced across different parts of your application
  • Supports attaching memories for long-term information storage

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Creating and Using Threads

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Basic Thread Creation

The simplest way to create a thread is to instantiate the Thread class: 
import marvin

thread = marvin.Thread()
# A new thread is created with a unique ID

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Specifying a Thread ID

You can create a thread with a specific ID, which is useful for resuming conversations: 
thread = marvin.Thread(id="user_123_lesson")
# This thread will use the specified ID

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Using Threads with Tasks

Threads can be passed to tasks to maintain conversation context: 
import marvin

thread = marvin.Thread()

task = marvin.Task(
    instructions="Write a poem about AI",
    result_type=str
)

# Run the task with the thread
result = task.run(thread=thread)

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Adding Messages

You can manually add messages to a thread: 
# Add a user message
thread.add_user_message("Hello!")

# Add multiple messages
from marvin.engine.llm import UserMessage, AssistantMessage
thread.add_messages([
    UserMessage(content="What's the weather?"),
    AssistantMessage(content="It's sunny today!")
])

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Retrieving History

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Getting Messages

You can retrieve messages from a thread with various filters: 
# Get all messages
messages = thread.get_messages()

# Get messages with filters
messages = thread.get_messages(
    include_system_messages=False,  # Exclude system messages
    before=datetime.now(),          # Messages before a timestamp
    after=some_timestamp,           # Messages after a timestamp
    limit=10                        # Limit number of messages
)

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Tracking LLM Usage

Threads also track LLM API usage: 
# Get all LLM calls
llm_calls = thread.get_llm_calls()

# Get usage statistics
usage = thread.get_usage()

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Thread Context Management

Marvin provides context management for threads, making it easy to set the current thread: 
with thread:
    # This thread is now the current thread
    task1.run()  # Uses the thread automatically
    task2.run()  # Same thread is used

# Thread context is restored after the block
You can also get the current thread: 
current_thread = marvin.get_current_thread()

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Parent-Child Relationships

Threads can have parent-child relationships: 
parent_thread = marvin.Thread()
child_thread = marvin.Thread(parent_id=parent_thread.id)

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Database Integration

Threads are automatically persisted to a database when configured: 
# Messages and LLM calls are automatically saved
thread.add_user_message("Save this message")

# Later, create a thread with the same ID to access history
same_thread = marvin.Thread(id=thread.id)
messages = same_thread.get_messages()  # Includes saved messages

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Threads and Memory

While threads provide short-term conversation context within a session, Memory offers long-term persistence across multiple sessions. The two can work together: 
import marvin
from marvin import Thread, Memory, Agent

# Create a memory for storing user preferences
user_preferences = Memory(key="user_preferences")

# Create an agent with this memory
assistant = Agent(
    name="Personal Assistant",
    instructions="You are a helpful assistant",
    memories=[user_preferences]
)

# Use the agent in a thread
with Thread() as thread:
    # The agent can access both thread context (recent conversation)
    # and memory (long-term stored information)
    assistant.run("Remember that I prefer dark mode")
    assistant.run("What are my UI preferences?")  # Will recall from memory
Threads maintain the flow of conversation, while Memory stores specific information that should persist across different threads and sessions. See the Memory documentation for more details on working with persistent memory.