Why Cayu?
Your expertise.
An agent that puts it to work.
Domain-specific long-horizon agents need a system shaped around the work. Cayu brings the framework, harness components, and durable runtime together in Python.
Start with the work
The domain gives the agent its job.
A document review agent needs to know which fields matter, which sources to trust, what counts as an exception, and when a person needs to decide. Those choices determine whether the work is useful.
The model proposes a next step. The harness supplies the context, tools, domain logic, memory, policies, and checks around it. With Cayu, you build that harness using Python components and run it on the framework’s integrated runtime.
The result is your agent: a product that performs a defined job in your domain.
Two parts of the same job
Specific to your domain.
Built for a long horizon.
Domain-specific describes how the agent works: the knowledge, tools, rules, and verification tailored to its job. Long-horizon describes how it executes: many decisions over hours or days, with new evidence, feedback, and interruptions along the way.
“Review these 5,000 documents”
has a finish line. Getting there takes a system.
The agent may need to inspect thousands of records, investigate exceptions, wait for an approval, and continue after a worker stops. Its state and evidence must remain available throughout that process.
See how execution continuesWhy one framework
Build the behavior and
its execution together.
Domain decisions have operational consequences. A tool needs an authority policy. A human review needs a durable pause. A completion check needs evidence from the run. Cayu gives those concerns a place in the same application.
Your domain shapes the behavior.
Compose the context, tools, memory, policies, and verification that define the job. Keep business logic in Python, alongside the libraries your team already uses.
Execution is part of the framework.
Sessions, state, workers, recovery, approvals, and budgets are integrated. Configure them as you build the harness, so they support the same application.
You can inspect and change the system.
Start with defaults, extend or replace components, and choose providers and infrastructure. Evaluate the effect of each change against work from your domain.
A practical place to start
Generate a project, define the job, assemble its harness, and evaluate representative cases. Add the capabilities the work needs as you develop the agent.
Walk through building an agentWhy Python
Build on the expertise
your team already has.
Bring existing data pipelines, calculations, models, integrations, and evaluation code into the agent. The harness remains ordinary Python that your team can read, test, and change.
Models and providers can evolve while your domain logic stays in the application. Cayu exposes replaceable components for those choices; your evaluations tell you whether a change improves the work.
When Cayu fits
When the work needs
more than one response.
Cayu is especially useful when a domain-specific job involves real systems, persistent context, many model and tool calls, human decisions, cost limits, or recovery across a long execution.
For a single request with no persistent state or operational lifecycle, you may need fewer of these capabilities. Start from the requirements of the job.
Inspect the implementation comparisonFrom our work to yours
Built while building agents.
We arrived at Cayu while developing agents for finance, accounting, and enterprise operations. The recurring work included both the domain-specific harness and the infrastructure needed to keep execution durable, visible, and controlled.
We brought those pieces together in an open-source framework. Our background in ML and industrial systems shapes the approach: domain knowledge, verification, and the system surrounding the model are part of the product.
Run Cayu on your own infrastructure, with its packaged operator dashboard. Cayu Cloud is an optional shared workspace for building, deploying, operating, and evaluating agents across a team.
Cayu for the enterpriseDomain-specific long-horizon agents