VegaDūtaAI · Memory
Graph Memory on VegaDūta
Graph memory is how a VegaDūta agent remembers you next week. Instead of stuffing past transcripts into the prompt, the platform extracts entities and facts from conversations into a knowledge graph — subject-relation-object triples — and agents recall from that graph as a tool when a conversation needs it.
What is remembered is yours to inspect. The Memory page in the console lists what your assistants have saved about you, and every entry can be edited or deleted. An optional tenant-shared graph lets all of a team's agents draw on the same pool of facts.
By the VegaDūta team · Last updated
Conversations become facts
As agents talk, an extraction step identifies entities and the relationships between them and writes them into the graph as triples. A customer's preferences, an order's status, a project's owner — these become structured facts rather than sentences buried in a transcript.
Recall is a tool, not a prompt dump
When an agent needs context, it queries the graph through a recall tool and gets back the relevant facts. This keeps prompts small and answers grounded: the agent retrieves the three facts that matter instead of rereading every previous conversation.
A Memory page you control
The Memory page shows what your assistants remember about you, and everything on it can be read, edited, and deleted. It has three parts: short notes that are added to conversations with agents that have shared memory turned on (how you want to be spoken to, who you are, what you are working on), a note per project that is used only when your message mentions that project's name, and the facts assistants saved during conversations, each marked as shared or learned by one agent.
- Edit a remembered fact that is wrong, or delete it; deletion cannot be undone
- Notes left empty are left out of conversations
- A workspace can turn long-term memory off entirely
Shared memory for teams
Optionally, a tenant can enable a shared graph so facts learned by one agent are recallable by the others. Your sales agent and your support agent then agree on who the customer is and what was promised, instead of each keeping a private, diverging picture.
Frequently asked questions
How does an AI agent remember past conversations?
On VegaDūta, facts are extracted from conversations into a knowledge graph as entity-relation triples. In later conversations the agent queries that graph through a recall tool, retrieving just the relevant facts instead of replaying old transcripts.
What is graph memory, compared to a vector database?
A vector database retrieves chunks of text that are semantically similar to a query; a knowledge graph stores discrete facts and their relationships. VegaDūta uses both — RAG over your documents for reference material, graph memory for structured facts about people, things, and commitments learned in conversation.
Can multiple agents share the same memory?
Yes, optionally. A tenant-shared graph lets all agents on a team read the same facts, so knowledge learned by one agent is available to the rest. It is a per-tenant setting, not the default.
Can I see and delete what an agent remembers about me?
Yes. The Memory page in the console lists the notes and facts your assistants hold about you. Each one can be edited or deleted, and a deleted memory is gone for good.
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