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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Muse Code
Muse Code is Meta’s terminal coding agent, powered by Muse Spark 1.2, for handling complex software engineering tasks across large repositories. The agent can plan changes, write code, validate results, and coordinate multiple persistent subagents during development sessions. Muse Code uses async background agents that stay active throughout a session to reduce repeated information gathering and help complete multi-step tasks with less steering. Its runtime uses a local event log that records model calls, tool runs, approvals, and edits so sessions can be replayed and resumed after failures. Muse Code includes bundled skills such as /plan for approval-gated planning, /grill for stress-testing plans, and /goal for working toward completion. Built for AI developers and software teams, Muse Code helps automate coding workflows, long-running engineering tasks, debugging, and repository-level development.
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Loadmill
Create automated API tests from real user sessions. Loadmill helps companies unclog the Dev pipeline, boost velocity and automate manual testing by replaying real user behavior. Eliminate regression bugs by replaying thousands of tests based on real user flows. Reduce your regression cycle duration and keep your team focused on what matters. Cut costs on ineffective testing efforts and maintenance. Automatically generate regression tests from real user recordings. Move fast and keep your team focused on what matters. Automate the entire QA process by replaying real user behavior. Integrate Loadmill into your continuous delivery pipeline and start shipping faster. Loadmill is using real traffic from websites around the world to test your server's performance. Get results fast - launch tests within seconds & detect bottle-necks before production. Truly distributed - unlimited unique IPs and locations per test. Record and replay your own user scenarios at scale.
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Metorial
Metorial is an open source, developer-centric integration platform that streamlines the creation, deployment, monitoring, and scaling of agentic AI applications by connecting models to tools, data, and APIs via the Model Context Protocol. With a catalog of over 600 verified MCP “servers,” developers can give their agents capabilities like interacting with Slack, Google Calendar, Notion, APIs, databases, or other systems in just a few clicks or one API call. Metorial’s infrastructure is serverless and built for scale, deploying MCP servers in three clicks or an API call, supporting “zero to millions” of requests, and offering out-of-the-box observability including detailed logging, tracing, session replay, and error alerts. A full set of SDKs (Python, TypeScript) is provided, and every interaction is traceable so teams can audit and optimize agent behaviour. Whether self-hosted or cloud-powered, Metorial offers enterprise-grade security and multi-tenant support.
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