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I'm building Rice (docs.tryrice.com). Think of Rice as a managed state machine for AI agents with long term memory.
Rice is a platform that unifies long term memory and short term state management for AI agents. Effectively, Rice solves the context compounding issue in the immediate sense - by using Rice Slate (our state management service), the context consumption was down 60%. This makes the agents more efficient. The state management layer also allows agents to share context without the conventional "message passing" approach meaning you can run parallel AI agents.
The memory layer enables the agents to have a broader contextual understand of the data and relationships - personalisation and automation at scale for agents.
How we're different (https://docs.tryrice.com/rice-vs) and working on some cool aspects.
The core value prop -
1. Auditable Agentic executions out of the box 2. Shared state for AI agents (not using message passing approach) for efficient executions 3. Persistent memory for historical data and more.
Currently in beta phase, so looking for beta testers. Appreciate any thoughts and tests.
Please enter your email at tryrice.com if you'd like to get in the beta.
