cayu 0.1.0a1

A Python runtime for production AI agents with durable sessions, controlled tool execution, context management, reviewed knowledge, recovery, evals, and replay.

Cayu

Cayu is a runtime for building production AI agents in Python.

It provides the durable execution layer—sessions, model calls, tool execution, approvals, context management, budgets, recovery, events, and evals—while applications retain control of their UI, authentication, domain logic, and workflows.

Cayu is designed for agents that do consequential or long-running work. It is not a prompt-chain DSL, a visual workflow builder, or an application frontend; you compose its runtime primitives directly.

Why Cayu

Agent prototypes are easy to start. Production failures happen at the boundaries:

• a process dies after a side effect but before state is recorded;
• a model requests a valid tool with the wrong authority;
• a run needs human input or approval halfway through;
• context grows until a provider rejects the next request;
• retries, forks, or subagents lose cost and causal attribution;
• operators cannot reconstruct what happened from prompt text alone; or
• evals test final prose while missing the runtime trajectory.

Cayu treats these as runtime contracts. Important actions become structured events; tool authority and recovery are explicit; configured durable stores let transcripts and checkpoints survive process boundaries; and the same public seams support local development, tests, control-plane inspection, and hosted deployments.

What Cayu provides

Need	Cayu primitive
Long-running work	Durable sessions, transcripts, events, resume, fork, interruption
Safe effects	Typed tools, effect declarations, policies, approvals, idempotency keys
Human interaction	User-input checkpoints, approval resolution, manual recovery
Context pressure	Token counting, projection, compaction, overflow recovery
Cost control	Usage events, run limits, budgets, pricing, causal-budget summaries
Execution boundaries	Environments, workspaces, runners, artifacts, vaults, egress
Reviewed knowledge	Durable entries, approval state, keyword/vector retrieval, recall tools
Provider flexibility	OpenAI, Anthropic, Bedrock, Vertex, OpenAI-compatible APIs
Durable automation	Tasks, dispatchers, event watchers, subagents, runtime hooks
Behavioral proof	Runtime tests, trajectory assertions, replay, eval reports
Operations	FastAPI control plane and a packaged inspection dashboard

Quickstart

Start a project

The generated project is the recommended path for both humans and coding agents. Cayu requires Python 3.11 or newer.

pip install cayu pytest
cayu new myagent
cd myagent

cayu inspect --json
cayu check --json
pytest
cayu eval run evals.assistant:build_eval

The scaffold is credential-free and includes:

• a process-scoped build_app() factory with bounded local-store initialization;
• one safe example tool;
• a hermetic runtime test and trajectory eval; and
• a project-local AGENTS.md with the exact build and verification contract.

Open the generated project, replace the tracer-bullet behavior with your domain behavior, and keep the public test/eval seam intact.

Run an agent

This compact example shows the core API. Real projects should put the same registrations in the generated build_app() factory instead of constructing a module-global app.

import asyncio

from cayu import (
    AgentSpec,
    CayuApp,
    Message,
    OpenAIProvider,
    RunRequest,
    run_to_completion,
)


async def main() -> None:
    app = CayuApp()
    app.register_provider(OpenAIProvider(), default=True)  # reads OPENAI_API_KEY
    app.register_agent(AgentSpec(name="assistant", model="gpt-5.6"))

    outcome = await run_to_completion(
        app,
        RunRequest(
            agent_name="assistant",
            messages=[Message.text("user", "Explain durable agent sessions.")],
        ),
    )

    if outcome.ok:
        print(outcome.final_text)
    else:
        print(f"{outcome.status}: {outcome.error}")


asyncio.run(main())

CayuApp() uses in-memory stores by default, which is appropriate for this one-shot example and for tests. The generated project configures SQLite so its sessions survive process restarts. Multi-process production deployments should select a conforming shared store such as PostgreSQL.

CayuApp.run(...) is the lower-level event-stream API. Runtime failures are terminal session.failed events, not exceptions raised from iteration. run_to_completion(...) consumes that same stream and returns a typed outcome when an application only needs the result. It retains the complete event stream in RunOutcome.events; use it for bounded runs. Consume CayuApp.run(...) incrementally for long-lived or high-volume runs.

For a runnable example with no API key, use examples/echo_tool_runtime.py. To add workspace tools and command execution, see examples/local_environment_runtime.py.

Build with a coding agent

The generated AGENTS.md is the project-local source of truth. Ask the coding agent to read it first, then use Cayu's package-shipped guides and structured inspection:

cayu guide anatomy
cayu guide authoring
cayu inspect --json
cayu check --fail-on warning --json

The supported authoring loop is:

understand -> inspect -> plan -> change -> test -> eval -> exercise -> report evidence

Use cayu generate slice ... --dry-run --json to plan a small vertical slice. Generated slices are intentionally unfinished tracer bullets; replace their placeholder prompt, tool behavior, runtime test, and eval before treating them as application functionality.

Mental model

Cayu separates the agent's identity from the resources and durable state used for one execution:

AgentSpec
  identity, model, instructions, defaults, runtime policies

Environment
  workspace, runner, artifacts, vault, proxy, knowledge, MCP

Session
  durable identity, transcript, events, status, checkpoints

ToolContext
  the active environment services and call identity for one tool execution

• Agent describes who is acting and how model work is configured.
• Environment describes what that agent can touch.
• Session records one durable execution and its lineage.
• Tool is an explicitly registered capability, not arbitrary model code.
• Task is an optional durable unit of background or orchestrated work.
• Workflow is deterministic application orchestration around agent steps.

An environment is optional for a conversational agent. It becomes important when tools need files, commands, artifacts, secrets, network policy, or a sandbox. Static environments are useful for trusted local work; EnvironmentFactory creates or reattaches session-specific environments in production.

Use the smallest runtime shape

Do not add every Cayu primitive to every application.

Desired behavior	Start with
One model-driven interaction	CayuApp, AgentSpec, provider, RunRequest
Deterministic model-callable action	Tool, ToolSpec, explicit ToolEffect
Authority over an effect	ToolPolicy; approval only where a human gate is required
Mutable files or commands	Explicit Environment, Workspace, and Runner
Durable uploaded or generated files	ArtifactStore
Long-lived conversation or recovery	Durable SessionStore and checkpoint APIs
Background durable work	TaskStore plus an explicitly started worker
Delegated model work	Subagent tools with bounded child-session policy
Behavioral regression proof	EvalSuite and trajectory assertions

A conversation agent does not automatically need a workflow, task queue, environment, memory store, server, or multi-agent topology. A coding agent does not automatically need unrestricted shell access. Prefer narrow domain tools and add authority only when the behavior requires it.

Application UI and control plane

Your application should own:

• end-user prompts and domain forms;
• product authentication and authorization;
• business-specific workflow and state;
• user-facing streaming, notifications, and presentation; and
• decisions about when a run, task, approval, or interruption is allowed.

Cayu owns runtime execution and the operational state recorded by the application's configured stores. Its optional dashboard is a control plane for developers and operators: inspect sessions, events, transcripts, tasks, usage, artifacts, pending actions, and recovery state. It is not intended to replace the product experience your users need.

Start work through the API that matches the trigger:

• run for an immediate new session;
• resume for a deliberate continuation;
• dispatch for placement through a dispatcher;
• a task worker for durable queued work;
• a subagent for model-selected bounded delegation; or
• an event watcher for durable reactions to already-persisted events.

See Triggering runs for the decision guide and lifecycle responsibilities.

Providers and environments

The base package includes the provider contracts and built-in OpenAI, Anthropic, and OpenAI-compatible HTTP adapters. Optional extras add integrations without forcing their dependencies into every deployment:

Extra	Adds
cayu[server]	FastAPI control plane and packaged dashboard
cayu[postgres]	PostgreSQL session, task, knowledge, and related stores
cayu[aws]	Amazon Bedrock and Lambda MicroVM support
cayu[vertex]	Anthropic models through Google Cloud Vertex AI
cayu[e2b]	E2B runner and workspace
cayu[microsandbox]	Local microVM-backed untrusted-code runner
cayu[egress]	Virtual egress and credential-broker primitives
cayu[files]	Image and PDF inspection
cayu[console]	Interactive application console

Providers normalize text, thinking, tool calls, usage, completion reasons, and typed failures behind one runtime contract. Cayu does not infer a provider from an arbitrary model name: applications register providers explicitly and may add deterministic model-pattern routing.

The same agent can run in a local workspace, trusted Docker container, E2B, Microsandbox, Lambda MicroVM, or an application-owned runner without changing its identity or transcript contract.

Production boundaries

Cayu makes safety boundaries explicit, but configuration still matters:

• LocalRunner is trusted local execution, not a sandbox.
• DockerRunner is useful for development and CI; ordinary Docker isolation is not presented as a secure untrusted-code boundary.
• Environment registration does not imply selection: mark a default explicitly or name the environment on the request. Provider defaults and model-pattern routing should likewise be configured deliberately and kept unambiguous.
• Tool effects do not authorize themselves. Use policies, approvals, scoped credentials, and destination controls where consequences require them.
• SQLite is appropriate for local and single-writer deployments. Use PostgreSQL or another conforming shared store for sustained multi-process concurrency.
• The FastAPI control plane requires authentication outside explicit dev=True; protect it as an operator surface, not an end-user endpoint. Generated API documentation is a separate exposure decision.
• Usage is derived from recorded events and survives restarts when those events use a durable store; cost remains an estimate against the price book your application selects.
• Recovery never invents the outcome of an ambiguous external side effect. Reconcile it through the typed recovery APIs.

Read Runtime contracts before changing persistence, replay, approval, interruption, budget, provider, runner, or recovery behavior.

Documentation

Start with the document that matches the job:

Goal	Guide
Build an application, by hand or with an AI coding agent	Authoring guide
Classify tool mutation and replay behavior	Tool effects
Understand factories, process roles, and lifecycle	Application anatomy
Choose how work starts	Triggering runs
Create per-session workspaces and runners	Environment factories
Implement a runner for your platform	Build a runner
Configure network and credential boundaries	Virtual egress
Design assertions and trajectory evals	Evals
Estimate and govern cost	Cost optimization
Use the application console	Console
Inspect supported model metadata	Model catalog
Look up exact runtime behavior	Runtime contracts
Track prerelease behavior and migrations	Release notes

Maintainer-facing architecture is documented in Architecture, Project layout, and the Glossary.

Examples

• Echo tool runtime — credential-free model/tool loop.
• Local environment runtime — files and commands.
• Server example — protected API and control plane.
• Cloud PR reviewer — durable task, isolated workspace, QA, and an explicit external effect.
• Business approvals — domain approval routing over the binary runtime primitive.
• Advanced runtime examples — forks, compaction, taint isolation, speculative approval, and measured evidence.

Advanced examples are executable runtime specifications, not claims that one strategy fits every workload. Their evidence boundaries and measured results are described in Advanced runtime strategies.

Contributing and security

Framework contributors should read CONTRIBUTING.md for placement policy, setup, validation commands, and pull-request requirements. New third-party integrations normally live in their own packages against Cayu's public extension contracts.

Report suspected vulnerabilities privately as described in SECURITY.md. Do not open a public issue or pull request for a suspected security vulnerability.

For questions and project discussion, join Discord. Use GitHub issues for actionable bugs and concrete feature proposals.

License

Cayu is licensed under the Apache License 2.0.