#python #agent #agentops #agents_sdk #ai #anthropic #autogen #cost_estimation #crewai #evals #evaluation_metrics #groq #langchain #llm #mistral #ollama #openai #openai_agents
AgentOps is a tool that helps developers monitor and improve AI agents. It provides features like session replays, cost management for Large Language Models (LLMs), and security checks to prevent data leaks. This platform allows you to track how your agents perform, interact with users, and use external tools. By using AgentOps, you can quickly identify problems, optimize agent performance, and ensure compliance with safety standards. It integrates well with popular platforms like OpenAI and AutoGen, making it easy to set up and use[1][3][5].
https://github.com/AgentOps-AI/agentops
AgentOps is a tool that helps developers monitor and improve AI agents. It provides features like session replays, cost management for Large Language Models (LLMs), and security checks to prevent data leaks. This platform allows you to track how your agents perform, interact with users, and use external tools. By using AgentOps, you can quickly identify problems, optimize agent performance, and ensure compliance with safety standards. It integrates well with popular platforms like OpenAI and AutoGen, making it easy to set up and use[1][3][5].
https://github.com/AgentOps-AI/agentops
GitHub
GitHub - AgentOps-AI/agentops: Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most…
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and...
#jupyter_notebook #a2a #agentic_ai #dapr #dapr_pub_sub #dapr_service_invocation #dapr_sidecar #dapr_workflow #docker #kafka #kubernetes #langmem #mcp #openai #openai_agents_sdk #openai_api #postgresql_database #rabbitmq #rancher_desktop #redis #serverless_containers
The Dapr Agentic Cloud Ascent (DACA) design pattern helps you build powerful, scalable AI systems that can handle millions of AI agents working together without crashing. It uses Dapr technology with Kubernetes to efficiently manage many AI agents as lightweight virtual actors, ensuring fast response, reliability, and easy scaling. You can start small using free or low-cost cloud tools and grow to planet-scale systems. The OpenAI Agents SDK is recommended for beginners because it is simple, flexible, and gives you good control to develop AI agents quickly. This approach saves costs, avoids vendor lock-in, and supports resilient, event-driven AI workflows, making it ideal for developers aiming to create advanced, cloud-native AI applications[1][2][3][4].
https://github.com/panaversity/learn-agentic-ai
The Dapr Agentic Cloud Ascent (DACA) design pattern helps you build powerful, scalable AI systems that can handle millions of AI agents working together without crashing. It uses Dapr technology with Kubernetes to efficiently manage many AI agents as lightweight virtual actors, ensuring fast response, reliability, and easy scaling. You can start small using free or low-cost cloud tools and grow to planet-scale systems. The OpenAI Agents SDK is recommended for beginners because it is simple, flexible, and gives you good control to develop AI agents quickly. This approach saves costs, avoids vendor lock-in, and supports resilient, event-driven AI workflows, making it ideal for developers aiming to create advanced, cloud-native AI applications[1][2][3][4].
https://github.com/panaversity/learn-agentic-ai
GitHub
GitHub - panaversity/learn-agentic-ai: Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native…
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kuberne...