OpenAI builds and deploys advanced AI models like GPT-4o for autonomous agents and workflows.
Rebuno
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About Rebuno
Rebuno sits between an AI agent and the tools it calls, recording every model and tool interaction and enforcing user-defined policies before execution. It provides crash recovery by replaying saved results after failures, reducing redundant work. Human approvals can be required for sensitive actions, pausing execution until explicitly allowed or denied. Policies restrict which tools or commands an agent may use, limiting access to specific functions or data. The system works with existing agent frameworks and does not replace the agent’s control loop. Typical use cases include coding agents that resume after crashes, support agents that require approval for refunds, and incident agents restricted to read-only operations.
Key features
- Crash recovery and result replay
- Pre-execution policy checks
- Human approvals for actions
- Tool and model call recording
- Policy-based access control
- Multi-framework compatibility
- Idempotency enforcement for tools
- Async execution support
Use cases
- Coding agents that resume after crashes without redoing work
- Support agents requiring human approval for refunds
- Incident agents restricted to read-only Kubernetes commands
Pros
- Durable execution with crash recovery and result replay
- Policy enforcement before tool or model calls
- Human-in-the-loop approvals for sensitive actions
- Works with multiple agent frameworks (LangChain, Pydantic AI, CrewAI, Vercel AI SDK)
- Records all model and tool calls with results
Cons
- Requires integration into existing agent code
- No free tier or open registration mentioned
- Policies must be defined manually
- Limited to supported agent frameworks
Frequently asked questions about Rebuno
What does Rebuno do?
Rebuno sits between an AI agent and the tools it calls, recording every model and tool interaction while enforcing user-defined policies before execution. It provides crash recovery by replaying saved results after failures and supports human approvals for sensitive actions.
Who is Rebuno for?
Rebuno is designed for teams running AI agents in production environments, such as coding agents, support agents, and incident response agents. It is particularly useful for scenarios requiring durable execution, policy enforcement, or human oversight.
How does Rebuno integrate with existing agent frameworks?
Rebuno works with existing agent frameworks like LangChain, Pydantic AI, CrewAI, and the Vercel AI SDK by intercepting tool calls and model interactions without replacing the agent’s control loop.
Can Rebuno enforce policies on tool usage?
Yes, Rebuno allows users to define policies that restrict which tools or commands an agent may use, limiting access to specific functions or data. Policies can also require human approval for sensitive actions.
Does Rebuno support crash recovery?
Rebuno provides crash recovery by saving the results of each model and tool call. If a run crashes, it can resume from the last saved result, avoiding redundant work.
How do I get started with Rebuno?
To get started, add Rebuno to your agent code by importing its components and configuring the agent to route calls through Rebuno. The tool provides examples for integrating with popular frameworks like LangChain and CrewAI.
