Build autonomous AI agents in Python.
Hands on labs and code to help you learn, measure, and build using architectural best practices.
Chaos Engineering Toolkit & Orchestration for Developers
Chaos and resiliency testing tool for Kubernetes with a focus on improving performance under failure conditions. A CNCF sandbox project.
Intelligent error monitoring agent that uses Airweave to find context from your code, tickets, and Slack. Clusters similar errors, enriches with relevant context, and creates actionable alerts.
a general library for fatigue and reliability
Run your real agent with and without your skills, MCPs, and rules. See which ones actually help, and what they cost in tokens. Supports Claude Code, Codex, Pi, and Hermes.
[ICLR 2025] xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
Fast computation of Krippendorff's alpha agreement measure in Python.
The Active Reliability Layer for AI Agents. Catch failures, teach fixes, and automate reliability
Reliability harness for long-running OpenClaw agents: persistent state, strict task contracts, scheduled execution, and external validation.
Guardian of Kubernetes clusters. Tool to monitor clusters health and signal/alert on failures.
User-impact-weighted SLO dashboard — measure true 99.99% uptime by actual customer exposure, not raw availability
[AAAI22 Oral] Reliable Propagation-Correction Modulation for Video Object Segmentation
Unified resilience patterns for Python — retry, circuit breaker, timeout, fallback, bulkhead, rate limiter, and cache in one decorator. Python's Resilience4j.
A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.
Reconstruct an incident timeline from exported evidence and draft a cited postmortem.
Develop reliable AI apps
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