AI agents — long-term memory on object storage.
Give your autonomous agents persistent memory, tool retrieval, and per-user context without managing a vector database cluster. Namespaced per user or session, stored in your own bucket, queryable in under 10ms.
Three types of agent memory ManyVector enables.
Episodic memory
Past conversation recall
Store embeddings of past interactions. Before each turn, retrieve the most relevant memories for the current context. Agents can recall user preferences, prior decisions, and past tasks.
Semantic memory
Knowledge base retrieval
Store facts, documents, and tool descriptions. Agents retrieve relevant knowledge before acting — grounding decisions in verified information rather than hallucinated context.
Tool memory
Dynamic tool selection
Embed tool descriptions and retrieve the most relevant tools for a given task. Scale to hundreds of tools without stuffing all descriptions into the context window.
One namespace per user. Zero cross-contamination.
Create a dedicated namespace per user or per agent session. Memories are isolated — user A cannot retrieve user B's data. Namespaces are lightweight (metadata-only until first write), so creating thousands per day has no overhead.
Namespace branching lets you fork an agent's memory for testing and experimentation — without disrupting the live session.
Long-term memory for autonomous agents.
<10ms
memory recall
∞
memory horizon
Any
agent framework
Multi-agent
shared memory
Common questions
How do agents read and write memory with ManyVector?
Use the SDK to upsert memory embeddings on write and query nearest-neighbour memories on recall. Works with LangChain, LlamaIndex, AutoGen, or any agent framework.
How do I prevent memory from growing unboundedly?
Set TTL on memory items, use namespace branching for session-scoped memories, or prune low-relevance memories periodically using a scheduled query.
Can multiple agents share a memory namespace?
Yes. A shared namespace lets agent teams build collective memory. Use metadata filtering to scope queries by agent ID, session, or task.
How does tool retrieval differ from RAG retrieval?
Conceptually identical — embed tool descriptions, query for the most relevant tools given the current task context. ManyVector works the same for both; the difference is what you embed.