Tortoise is the memory and reasoning system that enables your agents to make complex decisions without losing track of the variables or being swayed by the last message. They don’t just recall facts. They understand why decisions were made and reason with auditable world models.
Connect your agent →Every relationship expresses how one belief affects another. When new evidence arrives, confidence propagates through the graph and your agent’s world model evolves.
Every decision becomes auditable and programmable. Your agents build on each other’s work instead of wasting tokens reconstructing context.
Automatically ingest agent sessions, GitHub issues, documents, Slack messages, Linear tasks, or any other data you want to connect. Index files for faster search or extract and organize logical claims to feed your agents’ world model.
Turn separate facts into a world model. Skills and MCP tools to analyze sources and organize arguments, evidence, alternatives, and decisions into logical graphs. Agents can reason across complex webs of variables while new evidence automatically updates confidence throughout the graph.
Retrieve what matters, not just what matches. Combine graph traversal, vector search, and semantic retrieval with controls for time decay, confidence, and contradiction.
Set automated actions when confidence changes on a specific node. Use webhooks to trigger workflows, update systems, or coordinate agents as your world model evolves.
Start free. Scale when your agents do — you pay for usage, not seats.
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Setup is in the docs — the welcome page walks you through MCP and CLI after signup.