Memory Graph
Lithora builds a connected memory of your work. Search in plain language and the agent surfaces the related tasks, PRs, docs, decisions, and people as a navigable graph.
A search hub with its related work, animated as a graph — click a node to jump straight to it.
How it works
- Semantic search: Ask for a topic, not exact keywords — results are ranked by meaning across every surface.
- Connected context: Each result links to the tasks, PRs, docs, and people around it, so you see the whole thread of work, not one isolated item.
- Agent recall: The AI Workspace draws on this graph to answer questions with the right context already in hand.
What gets connected
The graph is built from the work you already do in Lithora and the GitHub activity linked to it. Nodes are the things you work on; edges are the relationships between them.
Tasks
Work items with their status, owners, and history.
Pull requests
Linked PRs, reviews, and CI outcomes from GitHub.
Docs & notes
Workspace documents and the context written around work.
Decisions
The calls the team made and why, kept next to the work.
People
Who owns what, and who has touched a given thread.
Searching the graph
Phrase a search the way you'd ask a teammate — by intent, not by remembering the exact title. A few examples:
- “what did we decide about the billing migration?”
- “everything related to the auth rewrite”
- “who has worked on the checkout flow?”
From any result, follow an edge to jump to the connected task, PR, doc, or person — the graph is a map you navigate, not just a list.
The agent uses it too
The same graph is what powers Workspace Q&A. When you ask the agent a question, it pulls the relevant nodes as context and answers with its sources cited, so you can click straight through to the work it drew from.
What it is — and isn't
Availability