Orqis

Tagline: Turn any API into an AI agent in 60 seconds. Description: Most developers have been there. You want to connect an LLM to an API — maybe your own backend, a third-party service, or an internal tool. So you start writing. You parse the OpenAPI spec. You define typed tools for each endpoint. You handle authentication. You wire up the chat loop. You debug why the agent sent a partial payload and wiped half a database record. You figure out why it's confidently telling the user the operation succeeded when it actually returned a 404. Three days later you have something that barely works in a demo but breaks in production. That's the problem Orqis solves. Paste a URL to your OpenAPI spec and get a fully working conversational AI agent in under 60 seconds — one that actually calls your API, handles errors correctly, and gives users real answers based on real data. What Orqis does Orqis reads your OpenAPI or Swagger spec and automatically generates a complete AI agent that understands every endpoint in your API. You don't write a single line of code. You don't craft system prompts. You don't define tools manually. Orqis does all of it. Once your agent is built, you can talk to it in plain English. Ask it to fetch records, create resources, update data, or run complex multi-step operations. It figures out which endpoints to call, in what order, with what parameters — and it gives you back a clean, formatted answer based on the actual API response. Key Features Instant agent generation from any OpenAPI spec Point Orqis at any publicly accessible OpenAPI 3.x or Swagger 2.0 spec URL and it will parse the entire spec, extract every endpoint, infer the right tool definitions, and build a working agent automatically. No configuration required. The whole process takes under 60 seconds regardless of how large or complex the spec is. If your spec has 5 endpoints or 500, Orqis handles it the same way. It reads the path parameters, query parameters, request bodies, and response schemas and translates them into structured tools the LLM can call reliably. Visual agent graph Every agent gets a visual graph showing all its tools as nodes. You can see at a glance which endpoints your agent has access to, how they're named, and what they do. This isn't just cosmetic — it's how you understand and audit what your agent can do before you run it in production. You can click any node in the graph to see the full tool details. And if you want to change how a tool is named or described, you can edit it inline directly from the graph. No need to go back to the spec or modify any configuration files. Inline tool editing Tool names and descriptions matter more than most people realize. The LLM uses them to decide which tool to call and when. If a tool is named something generic or its description is ambiguous, the agent will make wrong decisions. Orqis lets you edit tool names and descriptions directly from the agent interface — both in the playground and in the dashboard. Click a tool, type a better name or a clearer description, and save. The agent immediately starts using the updated definition. Tool names are validated to ensure they only contain letters and underscores, which is what the underlying LLM expects. Secure API header storage Most APIs require authentication — an Authorization header, an API key, an X-API-Key, or something custom. Orqis lets you store any set of HTTP headers securely, encrypted at rest, tied to your agent. When the agent makes API calls, it automatically includes those headers without you having to pass them every time. You can add, modify, or remove headers at any time. You can store multiple headers for a single agent. And you never have to expose your credentials in a chat message or a config file. Human-in-the-loop confirmation for dangerous operations Not every API call should happen automatically. DELETE requests, POST requests that create records, PUT requests that overwrite data — these are operations w