Run & observe
Chat Playground
The playground at /playground is where you talk to agents directly. It looks like a chat app; the difference is that every message produces a trace you can inspect, and the agent runs with its full saved configuration — same tools, same guardrails, same memory as anywhere else on the platform.
Chatting with an agent
Pick an agent from the selector and the conversation runs against its saved configuration: provider, model, system prompt, knowledge bases, skills, tools, guardrails, and memory. Conversations are persisted, so you can leave and pick a thread back up later.
- Attachments — drop images (sent to vision-capable models as image input) or documents (parsed and added to the conversation context) directly into a message.
- Citations — when the agent answers from a knowledge base, the message carries the source chunks it used, so grounded answers are visibly grounded.
- Memory chip — when long-term memory is enabled, each reply shows how many memory items were recalled (with a preview), so you can see exactly what the agent "remembered" rather than guessing.
- Fallback override — if the primary model fails, the playground offers a fallback model picker; your choice sticks for the rest of the session and is shown explicitly.
The trace behind every message
Every response carries a trace ID, and the inspector panel opens the full execution record for any message: the resolved prompt, tool calls with their arguments and results, tokens, cost, and latency. This is the playground's real purpose — the fastest loop from "I changed something in my agent" to "I can see exactly what that change did". The same traces are queryable later from Logs & traces.
Skill-sample agents
The first time you open the playground, a small set of sample agents built around skills is seeded into your workspace, with a short tour overlay that demonstrates how attached skills change an agent's behaviour. They are ordinary agents in your library afterwards — edit or delete them freely.