Advanced Memory Systems for AI Agents
Giving Agents a Past: The Memory Challenge
A core challenge for truly useful AI agents is memory. Without it, they are stateless calculators, unable to learn from past interactions or build context over time. At Interacly, we believe robust memory is non-negotiable for building sophisticated digital entities.
Types of Agent Memory
- Short-Term / Working Memory: Handled within the context window of the LLM. Fast but limited and transient.
- Long-Term / Episodic Memory: Storing summaries of past conversations, user preferences, or world knowledge. Typically involves external storage.
- Semantic / Knowledge Memory: Accessing vast external knowledge bases (like documentation or the web) to answer questions or ground responses. Often powered by Retrieval-Augmented Generation (RAG).
Interacly’s Approach: Pluggable Memory Modules
Instead of locking you into one memory type, Interacly allows you to compose memory strategies using flexible tools and integrations.
- Vector Stores: Connect tools for Pinecone, ChromaDB, or Weaviate to store and retrieve semantic information based on similarity.
- SQL/NoSQL Databases: Use database tools to store structured logs, user profiles, or conversation histories.
- Knowledge Graphs: Integrate with graph databases for complex relationship mapping.
Example: Building a RAG Agent
With Interacly, creating an agent that can answer questions about your private documentation using RAG is straightforward:
- Ingestion: Use a tool to chunk and embed your documents into a chosen vector store.
- Retrieval: Add a vector search tool to the agent’s allowed actions.
- Augmentation: Configure the agent’s prompt to:
- Receive the user query.
- Use the vector search tool to find relevant document chunks.
- Synthesize an answer based only on the retrieved context.
This pattern prevents hallucination and ensures answers are grounded in your specific knowledge base.
The Future: Adaptive Memory
We envision agents that dynamically choose the right memory type for the task at hand, perhaps even learning to summarize sessions into long-term storage automatically. Interacly’s composable architecture is designed to support these future advancements.
Explore our memory tools and start building agents that learn and remember today.
