Description
Rebyte is an execution layer for running AI agents in the cloud. It gives agents durable execution, real tools such as browsers, files and compute, and a place to enforce policy on what each agent can do, whether it runs through Rebyte's own product or through the Agents API in a team's own code.
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Agents on Rebyte keep context across conversations and can resume work rather than starting over, since the platform manages the execution stack instead of requiring a team to build and operate it. Access is controlled before work begins: a team approves which models, Skills, MCP servers and tools an agent may use, restricts its network access, credentials and spend, and applies that policy consistently across every run and every surface, including the API, CLI, Slack, or a custom product.
Every run is traced. Rebyte records each model step, tool call, error, token and cost, so a team can review what an agent did, mark which outcomes should repeat and which need correction, and feed that evidence back into the agent's instructions, Skills and evaluations. Starting an agent means getting an organization API key, defining the agent, and sending an OpenAI-compatible Responses request, with Skills, MCP servers and runtime controls added as needed.
Rebyte separates usage from access. Usage is billed in credits for compute hours, model tokens and Skill calls, whatever surface the work runs through, with no plan required. Access is a separate flat monthly subscription (Personal, Pro, Team or Enterprise) that grants governance, seats, saved history and connectors, but does not change what a task costs to run. Compute is billed at $0.10 an hour while an agent is active and nothing while it is idle. Teams can pay for model calls three ways: bring their own API key to a provider such as Anthropic, OpenAI or Google, bring an existing Claude Max or Gemini subscription, or use Rebyte's own hosted models at at-cost rates, with every task ending in an itemized receipt.
Rebyte is built for engineering and operations teams that need to run agents reliably in production rather than as one-off scripts, and for developers who want an API-first way to add durable, governed agents to their own products. An iOS app is also available for using the hosted product on the go.
- CategoryAI Agent Builders
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb, iOS
Rebyte's Core Features
Durable execution that keeps an agent's context across conversations instead of restarting from scratch
Built-in tools for agents, including browser access, file access and compute
Runs through multiple surfaces: the Agents API, the Rebyte product, a CLI, or Slack
Policy controls that approve which models, Skills, MCP servers and apps an agent can use
Boundaries that restrict network access, credentials, execution steps and spend per agent
Full run tracing: every model step, tool call, error, token and cost is recorded
Annotation tools to mark good and bad outcomes and feed them back into the agent
OpenAI-compatible Responses API for developers building on Rebyte directly
Three ways to pay for model usage: your own API key, your own subscription, or Rebyte credits
Itemized, per-task billing receipts showing compute, model calls and Skill usage
How to use Rebyte?
Get an API key: Create an organization account and generate an API key to authenticate requests.
Define an agent: Set the agent's identity, capabilities, boundaries and the Skills or MCP servers it can use.
Send a request: Call the Agents API with an OpenAI-compatible Responses request, or start the agent from the Rebyte product, CLI or Slack.
Add tools as needed: Connect Skills, MCP servers, browsers, files or compute for tasks that require them.
Review each run: Check the trace of model steps, tool calls, errors and cost for every completed task.
Annotate and improve: Mark outcomes worth repeating or correcting, and feed that feedback into the agent's instructions.
Rebyte's Use Cases
- Production agent execution
- Agent access governance
- Agent observability
- API-first agent development
