AgentOps
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. Track, save, and monitor every token your agent sees. Manage and visualize agent spending with up-to-date price monitoring. Fine-tune specialized LLMs up to 25x cheaper on saved completions. Build your next agent with evals, observability, and replays. With just two lines of code, you can free yourself from the chains of the terminal and instead visualize your agents’ behavior in your AgentOps dashboard. After setting up AgentOps, each execution of your program is recorded as a session and the data is automatically recorded for you.
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Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more.
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Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed.
Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning.
Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production.
Features:
Agent Simulation
Agent Evaluation
Prompt Playground
Logging/Tracing Workflows
Custom Evaluators- AI, Programmatic and Statistical
Dataset Curation
Human-in-the-loop
Use Case:
Simulate and test AI agents
Evals for agentic workflows: pre and post-release
Tracing and debugging multi-agent workflows
Real-time alerts on performance and quality
Creating robust datasets for evals and fine-tuning
Human-in-the-loop workflows
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Base AI
The easiest way to build serverless autonomous AI agents with memory. Start building local-first, agentic pipes, tools, and memory. Deploy serverless with one command. Developers use Base AI to develop high-quality AI agents with memory (RAG) using TypeScript and then deploy serverless as a highly scalable API using Langbase (creators of Base AI). Base AI is web-first with TypeScript support and a familiar RESTful API. Integrate AI into your web stack as easily as adding a React component or API route, whether you're using Next.js, Vue, or vanilla Node.js. With most AI use cases on the web, Base AI helps you ship AI features faster. Develop AI features on your machine with zero cloud costs. Git integrates out of the box, so you can branch and merge AI models like code. Complete observability logs let you debug AI-like JavaScript, and trace decisions, data points, and outputs. It's like Chrome DevTools for your AI.
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