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.
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.
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.
Test your AI agent's quality and security in seconds. Paste a system prompt or point at an HTTP endpoint — get a multi-criteria score with actionable insights.
Send adversarial attacks to your agent's HTTP endpoint and grade its responses across 4 security criteria.
Paste your system prompt, generate test cases with AI, and get a detailed quality score.
Share your agent's score on LinkedIn or X with a beautiful OG image. Show the world your agent is production-ready.
Teams discover AI quality drops 6 weeks too late. Continuous regression testing for your AI workflows — like unit tests, but for agent quality.
Monitor your agents with your own prompts and test cases. Detect quality drift before it impacts your users.
Automatic comparison against your last run. Get Slack/email alerts the moment quality drops below your threshold.
Run monitoring checks directly from your IDE, CI/CD, or terminal. Quality monitoring as a natural step in your dev workflow.
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.
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.
32 MCP tools to build, test, grade, and deploy AI agents from your terminal or IDE. One command to install. Zero dependencies.
claude mcp add kopern -- npx -y @kopern/mcp-server{
"mcpServers": {
"kopern": {
"command": "npx",
"args": ["-y", "@kopern/mcp-server"],
"env": {
"KOPERN_API_KEY": "kpn_your_key_here"
}
}
}
}Or use Streamable HTTP directly
{
"mcpServers": {
"kopern": {
"type": "http",
"url": "https://kopern.ai/api/mcp/server",
"headers": { "Authorization": "Bearer kpn_..." }
}
}
}Every Kopern feature is available as an MCP tool. Create agents, run grading, deploy templates, connect channels — all without leaving your editor.
Agent Management
8 tools
Grading & Optimization
6 tools
Teams & Pipelines
4 tools
Connectors
7 tools
Sessions & Memory
3 tools
Utilities
4 tools
Everything you need to know about building, testing and deploying AI agents with Kopern.
AI agents autonomously execute multi-step tasks using tools (APIs, databases, code), while chatbots only reply with scripted text. Agents can reason, call external services, and decide what to do next. Use a chatbot for simple FAQs and an AI agent for multistep workflows like ticket triage, RAG pipelines, or research.
Yes. Kopern is a no-code AI agent builder: describe your goal, pick a template or model, and deploy in under an hour. No Python, no LangChain boilerplate. Use the visual workflow editor, pre-built connectors (Slack, widget, webhooks), and JSON-schema tool definitions to ship without writing a single function.
Kopern is a no-code alternative to CrewAI and LangChain with built-in grading, MCP endpoints, multi-agent teams, and one-click deployment. Unlike framework-only tools, Kopern covers the full lifecycle — build, test, grade, deploy, monitor — and supports Claude, GPT, Gemini, Mistral, and Ollama with zero boilerplate.
Running an AI agent costs roughly $0.01–$0.30 per conversation depending on the model and context size. Platform costs start free and scale to $79/month for production features. Teams typically see 30–40% lower operational costs versus chatbots once deployed, thanks to higher resolution and autonomous task handling.
Define test cases with inputs and expected behaviors, then grade responses against six criteria: output match, schema validation, tool usage, safety, custom scripts, and LLM-as-judge. Run the suite on every change to catch regressions. Kopern automates grading with AutoTune and AutoFix for continuous improvement.
MCP is an open standard for connecting AI agents to external tools and data. Claude Code, Cursor, and VS Code all speak MCP. With Kopern, expose any agent as an MCP server and call it from your IDE, CI pipeline, or custom apps — a standard protocol replaces dozens of custom integrations.
Full enforcement starts August 2, 2026. High-risk AI agents must provide technical documentation, human oversight, audit trails, and stop mechanisms. Kopern ships with built-in tool approval policies, session event logs, and a compliance report generator that cover EU AI Act Article 14 requirements out of the box.