Agents that keep working after you close the tab: a13n gives a team one shared place to define them, run them, and watch what they did. In the Console, an administrator adds models from OpenAI, Anthropic, Gemini, Bedrock, Azure, OpenRouter, DeepSeek, Ollama, and others, then creates agents with instructions, skills, memories, connections, browsers, and sandbox environments. A Try agent button opens a conversation, and the Debug view lays out every run step by step, with model calls, tool calls, token counts, cache rates, and durations. Sessions and schedules keep a history of conversations and recurring jobs. The Usage page charts spend, runs, requests, and tokens by agent, model, or member across a time range. Findings analyzes traces, flags answer quality and tool failures, and lets reviewers confirm or dismiss each one with a suggested improvement. Each agent has a Connect to app screen with its ID, service URL, and API key, so applications can call it over an HTTP API or SDK. Workers lease runs from PostgreSQL and save checkpoints to object storage, so interrupted work resumes. Customer support teams, internal platform groups, and product builders use it to ship agent features without writing their own orchestration layer. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Change an agent's prompt, model, or audience from a dashboard and watch it go live without a single redeploy. MATE opens in a Work Room where any root agent can be picked from a card grid and chatted with directly. The Studio holds the builder: a drag-and-drop canvas draws parent and child agents, attaches tools and MCP servers, and keeps every configuration versioned with one-click rollback. Trace Replay plays a past conversation back across that canvas so each hand-off is visible. A template library imports ready-made agent teams, from research pipelines to support desks. The Control Room covers governance: role-based access per agent, dollar costs per agent and project, budgets that warn or block, guardrails for personal data and prompt injection, and alerts to Slack, Discord, email, or a webhook. Eval suites score replies by exact match, similarity, or an LLM judge, and a Playground compares models or prompts side by side on score, speed, and cost. Agents can be embedded as a chat widget, called through an OpenAI-compatible endpoint, or exposed over MCP and A2A. Agencies running client agents and platform teams juggling dozens of them can manage the lot from one dashboard. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.