u/zamir_akimbekov

Hi there!

TL;DR: https://github.com/cayu-dev/cayu — Apache-2.0 Python package for production agent runtimes (durable sessions, controlled tools, budgets, recovery, evals). Looking for GitHub-minded feedback.

Context

Cayu is the durable execution layer for AI agents in Python. You compose runtime primitives directly — not a prompt-chain DSL or visual workflow builder.

• Homepage: https://cayu.dev
• PyPI: `pip install cayu`

Problem

Prototypes fail in production at boundaries: crash after side effects, wrong tool authority, mid-run human approvals, context overflow, lost cost attribution, evals that only score final text.

Solution

Runtime contracts instead of prompt spaghetti:

• AgentSpec / Environment / Session / ToolContext mental model
• durable stores, approvals, budgets, recovery APIs
• cayu new → inspect / check / pytest / eval
• optional extras: server dashboard, postgres, e2b, microsandbox, aws, egress

Try:

pip install cayu pytest

cayu new demo && cd demo

cayu check --json

CTA

⭐ / issues / PRs welcome: https://github.com/cayu-dev/cayu

Docs start at README + docs/runtime-contracts.md

Early release — tell us what you’d break or skip.

Hi there!

TL;DR: I open-sourced Cayu (https://github.com/cayu-dev/cayu) — a Python runtime for production AI agents: durable sessions, controlled tools, approvals, budgets, recovery, and evals. Not another LangChain-style framework. Looking for roast-level feedback from builders.

Context

At Cayu we’re focused on the durable execution layer for agents that do consequential or long-running work. Apps keep their own UI, auth, and domain logic; Cayu owns sessions, tool authority, recovery, cost attribution, and trajectory evals. Python ≥3.11, Apache-2.0, on PyPI.

Problem

Agent prototypes are easy. Production fails at the boundaries:

• process dies after a side effect but before state is recorded
• model calls a valid tool with the wrong authority
• run needs human approval halfway through
• context blows up / cost attribution gets lost across retries and subagents
• evals score final prose and miss the runtime trajectory

Most “agent frameworks” optimize the happy path. We wanted a runtime that treats those failure modes as contracts.

Solution

Cayu is that runtime:

• durable sessions / resume / fork / interruption
• typed tools + policies + approvals
• budgets + pricing + causal cost summaries
• environments / runners / vaults / egress boundaries
• inspect / check / eval CLI (cayu new scaffolds a testable project)

Quick try:

pip install cayu pytest

cayu new myagent && cd myagent

cayu check --json && pytest

Credential-free example: examples/echo_tool_runtime.py

CTA

• GitHub: https://github.com/cayu-dev/cayu
• Site: https://cayu.dev
• PyPI: https://pypi.org/project/cayu/

Still early (0.1.x). What feels wrong as Python builders — install friction, API weirdness, docs, or “I’d never use this because ___”? Happy to answer how this differs from LangGraph / Claude Agent SDK / Temporal.