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See the full context behind any change.

A single feature spans the spec, the threads that shaped it, the tickets that scoped it, and the PRs that shipped it. Virgo assembles that connected history on demand. You start with the whole picture instead of reconstructing it tool by tool.

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The context behind a feature is scattered across your tools

One checkout rewrite lives in the spec, the threads that shaped it, the tickets that scoped it, and the PRs that shipped it. To understand it, you open every tool and rebuild the picture by hand.

Virgo assembles the whole picture from one question.

Virgo connects these sources into one reasoning graph. When you ask about the checkout rewrite, it queries each one, pulls the piece it holds, and returns the connected context. You stop reconciling tabs by hand.

How context retrieval works

Behind one question, Virgo walks the reasoning graph and returns the connected context behind it, already assembled.

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Give every agent the real history behind the change.

The engineer reads the assembled change context while the agent acts on it.

For engineers

Understand a feature, including the reasoning behind it.

The spec, the discussion, the tickets, and the code arrive connected. You see how a change came to be and why it works the way it does before you touch it.

  • What it is: the spec and the scope behind it.
  • How it works: the code paths and the decisions that shaped them.
  • Why it exists: the threads, tickets, and trade-offs on the record.
For AI agents

The agent reasons from the assembled context.

An agent handed a snippet writes against the snippet. With the assembled context (the decision, the constraint, the reverted attempt), it reasons about the real change. Virgo retrieves across the graph at query time, over MCP, for any connected agent.

97%context retrieval accuracy

Early evaluations reached 97% context retrieval accuracy, compared with about 68% for baseline RAG or hybrid search.