Open
Description
1. Overview:
Provide an integration with MongoDB databases (self-hosted or managed like MongoDB Atlas) to enable persistent storage for agent memory, primarily conversational history. Leveraging MongoDB's flexible document model allows developers to store complex, evolving agent interactions efficiently and scale horizontally.
2. Goals:
- Implement a
MemoryStorageadapter specifically for MongoDB. - Utilize the official MongoDB Node.js driver (
mongodb). - Allow configuration of MongoDB connection details (connection string).
- Map the conversational memory structure (
AgentHistoryEntry, messages, steps) to MongoDB collections and documents. - Support efficient CRUD operations using MongoDB's query language.
- Provide guidance on indexing strategies for common query patterns (e.g., retrieving history by
conversationId). - (Optional) Explore using MongoDB Atlas Vector Search for storing and querying vector embeddings if integrated with Chunking & Embedding support.
3. Proposed Architecture & Components:
MongoMemoryStorage: A class implementing theMemoryStorageinterface. This class will:- Use the official
mongodbNode.js driver. - Manage database connections.
- Translate memory operations into MongoDB operations (e.g.,
insertOne,findOne,find,updateOne,deleteOne). - Handle the mapping between
voltagentmemory objects and MongoDB documents.
- Use the official
MemoryManagerIntegration: ModifyMemoryManagerto accept and use theMongoMemoryStorageadapter.- Configuration: Extend agent or global configuration to include the MongoDB connection string and potentially database/collection names.
- Schema Guidance: While MongoDB is schema-flexible, document the recommended document structure and indexing strategies for optimal performance.
4. Affected Core Modules:
MemoryManager: Needs to support the newMongoMemoryStorageadapter.Agent Options/ Configuration: Needs to accommodate MongoDB connection settings.- Requires adding the
mongodbdriver as a dependency (likely optional).
5. Acceptance Criteria (Initial MVP):
- Users can configure
voltagentto use a MongoDB database for storing conversational history via a connection string. - Agent conversations (input, output, basic metadata) are successfully saved as documents in a specified MongoDB collection.
- Conversation history can be retrieved from MongoDB based on
conversationId. - Documentation provides basic setup instructions, connection string format, and recommended indexes.
- The integration uses the official
mongodbdriver.
6. Potential Challenges & Considerations:
- Managing connections efficiently.
- Designing appropriate indexes for performant queries as data grows.
- Handling potential schema evolution over time, even with flexibility.
- Security: Users are responsible for securing their MongoDB instance/Atlas cluster and managing credentials.
- Cost considerations for managed MongoDB services like Atlas.
- Error handling for connection issues and database operations.
- Potentially more complex aggregation queries if advanced data analysis on memory is needed.