betaNot just another AI agent. Mine.
WhatsApp, Telegram, Slack, GitHub, webhooks, MCP, embedded widgets 9 connectors to deploy your agents where your users already work.
Reach your customers on WhatsApp — the #1 messaging app. Meta Cloud API, read receipts, 1000 free conversations/month.
Deploy your agent as a Telegram bot. Private chats and groups, MarkdownV2 formatting, instant setup via @BotFather.
Your agent joins Slack conversations. Mention it in channels, DM it directly, full thread context preserved.
Agents read your code, review PRs, analyze dependencies, and suggest fixes — directly from your repositories.
Connect n8n, Zapier, or Make to orchestrate complex workflows. Your agent becomes a node in your automation stack.
Connect any external service via the Model Context Protocol. Slack, Linear, Notion, databases — your agents access them all.
Embed a fully-featured AI chat bubble on any website with a single script tag. Shadow DOM isolation, SSE streaming, mobile-ready.
Trigger your agent from any service — Stripe, Jira, Zapier, n8n. Inbound JSON responses, outbound event notifications, HMAC security.
Chain integrations: an agent reads a GitHub PR, checks Jira for context, posts a review, and updates Slack — fully automated.
Enterprise-grade security, EU AI Act compliance, and battle-tested infrastructure from day one.




Configure agents with system prompts, skills, tools and extensions. Multi-model support.
6 criterion types: output match, schema validation, tool usage, safety check, LLM judge and more.
Expose agents as JSON-RPC endpoints with API key auth, rate limiting and usage tracking.
Owner-only Firestore rules, hashed API keys, server-side key management, sandboxed execution.
Build sophisticated workflows with agent teams, pipelines, and meta-agents that create other agents.
Run multiple specialized agents in parallel, sequential, or conditional modes. A coordinator synthesizes their outputs into unified results.
Chain agents into step-by-step workflows where each agent's output feeds the next. Built-in error handling and input mapping.
Describe what you need in plain language. The meta-agent creates a fully configured agent — system prompt, skills, tools, and grading suite.
6 optimization modes to push your agents further — hill-climbing prompt tuning, one-click bug fixing, adversarial stress testing, model tournaments, cost distillation, and multi-dimensional evolution. Each mode runs experiments, grades results, and keeps only what improves performance.
Iteratively mutate system prompts using LLM-guided strategies. Each iteration is graded, and only improvements are kept — hill-climbing to the best config.
Analyze failing grading cases, diagnose root causes, and automatically patch the system prompt to fix specific weaknesses.
Red team your agent automatically — generate adversarial attacks (prompt injections, jailbreaks, hallucination traps, edge cases) and harden its defenses until it passes.
Pit multiple model and config combinations against each other. A multi-round tournament reveals the best quality/cost/latency trade-off.
Explore every combination of prompt, model, and config simultaneously. Runs parallel candidates, compares their grading scores, and converges on the best-performing setup for your use case.
Transfer knowledge from expensive teacher models to cheaper students. Maintain quality while dramatically reducing inference costs.
Track every token, every call, every cost — with full session tracing and pay-per-use billing.
Full observability with event timelines, token counts, cost tracking, and tool execution traces for every agent session.
Transparent usage-based billing with per-agent breakdown, historical charts, and multi-provider cost tracking.