While threads maintain conversation history within a single interaction, Marvin’s memory feature allows agents to store and recall information across multiple conversations. This is useful for:
  • Remembering user preferences across sessions
  • Building up knowledge bases over time
  • Sharing information between different agents
  • Maintaining context across multiple interactions
  • Creating more personalized user experiences

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How Memory Works

Marvin memories are implemented as vector stores that allow agents to store and retrieve information using natural language. Each memory has a unique key that identifies it and instructions that guide how it should be used.

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Creating Memory Modules

To create a memory module, you need to provide a key and instructions: 
import marvin

# Create a memory for user preferences
preferences = marvin.Memory(
    key="user_preferences",
    instructions="Store and retrieve user preferences and settings."
)

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Memory Keys

The key uniquely identifies a memory module and must be consistent across uses to access the same stored information. Choose descriptive, unique keys for different types of information: 
# Different memories for different purposes
user_prefs = marvin.Memory(key="user_preferences")
knowledge_base = marvin.Memory(key="documentation")
project_info = marvin.Memory(key="project_details")

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Memory Instructions

The instructions tell agents how to use the memory - what to store, when to access it, and how to format information: 
weather_memory = marvin.Memory(
    key="weather",
    instructions="""
    Store daily weather information including:
    - Temperature
    - Conditions
    - Location
    Format: 'Location: temp, conditions'
    """
)

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Using Memories

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With Tasks

Attach memories to tasks to provide context: 
import marvin

# Create a memory module
weather = marvin.Memory(key="weather")

# Use it in a task
task = marvin.Task(
    instructions="What was yesterday's weather?",
    memories=[weather]
)

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With Agents

Give agents persistent memory across all their tasks: 
import marvin

# Create an agent with memory
agent = marvin.Agent(
    name="WeatherBot",
    memories=[weather]
)

# The agent can access weather memory in any task
marvin.run("Record today's weather", agents=[agent])
marvin.run("Compare to yesterday's weather", agents=[agent])

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Example: Weather Tracking

Here’s a complete example of using memory to track weather information: 
import marvin

# Create a memory module
weather = marvin.Memory(
    key="weather",
    instructions="Store and retrieve daily weather information."
)

# Record today's weather
marvin.run(
    "Record that it's 72°F and sunny today",
    memories=[weather]
)

# Later, retrieve the information
result = marvin.run(
    "What was the weather like today?",
    memories=[weather]
)
print(result)

Today's weather was 72°F and sunny.

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Memory Providers

Marvin supports several vector store backends for memory storage:

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ChromaDB (Default)

ChromaDB is the default provider, offering a simple, file-based vector store: 
pip install "marvin[chroma]"
Configure Chroma: 
import marvin
from marvin.memory.providers import chroma

provider = chroma.ChromaEphemeralMemory()
# or provider = chroma.ChromaPersistentMemory()
# or provider = chroma.ChromaCloudMemory()

memory = marvin.Memory(
    key="knowledge",
    provider=provider
)

​
LanceDB

LanceDB provides a fast, efficient vector store with columnar storage: 
pip install "marvin[lance]"
Configure LanceDB: 
import marvin
from marvin.memory.providers import LanceProvider

provider = LanceProvider(uri="data/memories.lance")
memory = marvin.Memory(
    key="knowledge",
    provider=provider
)

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PostgreSQL

Use PostgreSQL with pgvector for scalable, production-ready vector storage: 
pip install "marvin[postgres]"
Configure PostgreSQL: 
import marvin
from marvin.memory.providers import PostgresProvider

provider = PostgresProvider(
    connection_string="postgresql://user:pass@localhost/db"
)
memory = marvin.Memory(
    key="knowledge",
    provider=provider
)

​
Qdrant

Qdrant is a high-performance vector search engine built with Rust. 
pip install "marvin" "qdrant-client[fastembed]"
Configure Qdrant: 
import marvin
from marvin.memory.providers import QdrantProvider
from qdrant_client import AsyncQdrantClient

provider = QdrantProvider(client=AsyncQdrantClient())

memory = marvin.Memory(
    key="knowledge",
    provider=provider
)

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All Providers

Install all memory providers at once: 
pip install "marvin[memory]"  # Includes chroma, lance, postgres, qdrant