agentops

Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.

Transparent telemetry collector for hi-fidelity agent traces

oly runs coding agents and long-running commands in persistent, supervised terminal sessions. The daemon owns the PTY, so you can close your terminal and come back from the browser or your phone.

The open-source AI agent control plane and software factory: govern every tool and model call.

Geniusrise: Framework for building geniuses

A collection of cookbooks to help developers get started quickly with the Firecrawl API.

A radically simple, reliable, and high performance template to enable you to quickly get set up building multi-agent applications

Learn AI, ML, and NLP with interactive Jupyter Notebook tutorials.

Observable diagnostics, failure attribution, metrics, and no-key replay for multi-agent runtimes.

Companion code for Foundations of Agentic AI for Retail, Third Edition (2026): agent architectures, decision frameworks, multi-agent protocols, guardrails, evaluation and runnable demos, as printed in the book.

A curated list of the best AgentOps tools for 2026 — observability, tracing, evaluation, cost monitoring, and guardrails for LLM agents. Covering open-source and SaaS tools with feature benchmarks and architecture guidance.

Shogun AFM is Agent Fleet Management for self-improving AI agents — combining agent orchestration, persistent memory, fleet monitoring, governance, security posture, and Gensui command control.

AgentOps control plane for AI agent execution. Rust policy plane, hash-chained audit, deny-by-default tool allowlists and budget enforcement, eval gating in CI. Single maintainer, moving fast, read the README before you deploy it. Docs: https://sattyamjjain.github.io/ferrumdeck/

Complete hands-on Agentic AI bootcamp repository covering LangGraph, LangChain, AI Agents, multi-agent workflows, RAG pipelines, tool-augmented LLM systems, memory architectures, and production-grade AI application engineering for real-world intelligent systems.

Demonstrate LLMOps in TypeScript

Local AgentOps for cross-vendor CLI coding agents: route work, recover state, and accept only with evidence.

Distill claude-mem into CLAUDE.md, skills, and an Obsidian long-term-memory vault — with built-in AgentOps (monitoring, alerting, adoption metrics, redaction). Set up from Claude Cowork chat.

A framework-neutral taxonomy of agentic-AI artifacts, organized around what matters in production: versioning, inspectability, and governability.