Regular agent memory systems retrieve disconnected facts, leaving you to connect the dots.
Tortoise organizes memories into a logical model of how your business and code work. This ‘cognitive layer’ gives your agents the memory, reasoning, and learning to operate like senior leaders at machine speed.
Delegate outcomes, not just tasks.
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, chat messages, systems of record, BI, 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 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.
Define a confidence threshold or lifecycle events to fire webhooks. Automate decisions and agent actions.
Start free. Scale when your agents do — you pay for usage, not seats.
Free forever
$84 billed annually
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