Velane

Product Introduction

1. Definition: Velane is an open-source, agent-native integration lifecycle infrastructure and runtime platform. Technically, it functions as a Model Context Protocol (MCP) server that provides AI agents (like those in Claude, Cursor, or Codex) with a managed environment to autonomously discover, code, test, and deploy API integrations.
2. Core Value Proposition: Velane exists to eliminate the backend infrastructure and OAuth complexity developers face when building AI agents that need to interact with external SaaS tools. Its primary value is enabling AI agents to directly write, execute, and ship production-ready integration code from within a chat or IDE interface, creating a seamless "agent-first" deployment pipeline.

Main Features

1. Agent-Native MCP Interface: Velane exposes its entire functionality via the Model Context Protocol (MCP), allowing AI agents to directly control it. Agents can call tools like list_connections to discover OAuth accounts, get_logs to debug executions, and publish_snippet to promote workflows to production, all without human intervention.
2. Managed Sandboxed Runtimes: The platform provides isolated, ephemeral execution environments for Bun and Python. Each workflow invocation runs in a fresh, sandboxed container with zero cold start, ensuring security and performance isolation between different executions and tenants.
3. Unified Integration Catalog & Auth Proxy: Velane integrates with Nango to offer instant access to over 800 pre-built OAuth integrations (e.g., Stripe, Slack, GitHub). The platform acts as a credential proxy, automatically injecting live OAuth tokens into the sandbox, so agent-generated code contains zero hardcoded secrets or API keys.
4. Multi-Stage Deployment Pipeline: It provides three distinct environments: development, staging, and production. AI agents can write and test code in the dev sandbox, validate it in staging, and then promote versioned workflows to a stable, versioned HTTP endpoint in production, enabling safe rollouts and rollbacks.

Problems Solved

1. Pain Point: The high friction and manual backend work required to give AI agents the ability to call external APIs. Traditionally, developers must manually set up OAuth, write glue code, manage credentials, and deploy serverless functions, which breaks the agent's autonomous workflow.
2. Target Audience: The primary user is a developer building or working with AI agents. Key personas include solo indie hackers building AI-powered tools, startup engineering teams implementing AI workflows, and developers using AI-powered IDEs like Cursor or agents like Claude who need to ship functional integrations quickly.
3. Use Cases: An AI agent autonomously creating a customer support ticket in Zendesk based on an email; an agent in Cursor IDE writing and deploying a script to sync new Stripe customers to a HubSpot CRM; a team using an agent to build, test, and promote a production workflow that posts GitHub commit summaries to a Slack channel.

Unique Advantages

1. Differentiation: Unlike generic iPaaS (Integration Platform as a Service) tools like Pipedream or Zapier, which are designed for human-centric workflow builders, Velane is architected from the ground up for control by AI agents via MCP. Unlike raw serverless platforms (AWS Lambda), Velane provides the integrated OAuth layer, agent-accessible tooling, and a managed promotion pipeline specifically for autonomous AI operations.
2. Key Innovation: The core innovation is treating the AI agent itself as the primary developer and deployment mechanism. By combining a comprehensive MCP server interface with isolated, fast-start runtimes and a managed credential layer, Velane creates a closed loop where an agent can conceive, code, test, and ship an integration entirely within a conversational or IDE context, which is a paradigm shift from traditional development tooling.

Frequently Asked Questions (FAQ)

1. Is Velane really controlled by AI agents? Yes, Velane is designed as an "agent-first" platform. AI agents connect via the Model Context Protocol (MCP) and use specific MCP tools to discover connected apps, write Bun/Python code, execute it in sandboxes, view logs, and promote workflows to production, all through API calls initiated by the agent.
2. How does Velane handle security and OAuth tokens? Velane manages all OAuth flows and token storage securely on its backend (or your self-hosted instance). When an agent's code runs in the sandbox, the platform's runtime automatically injects the appropriate live credentials for integrations like Stripe or Slack, meaning no tokens or secrets ever appear in the source code written by the agent.
3. Can I self-host Velane for my proprietary project? Yes, Velane is open-source under the AGPL-3.0 license, allowing free self-hosting for personal or open-source projects. For commercial proprietary use, a separate commercial license is available to avoid AGPL copyleft obligations, including full source access and deployment configurations for infrastructure like Amazon EKS.
4. What is the difference between Velane's dev, staging, and prod environments? Each environment is fully isolated. The dev environment is for writing and immediate testing with sandboxed execution. staging is for pre-production validation, often with test API credentials. prod is for live deployments, where published workflows receive a stable, versioned HTTP endpoint and use live OAuth connections.
5. Which AI agents and IDEs are compatible with Velane? Velane is compatible with any AI agent or developer tool that supports the Model Context Protocol (MCP) standard. This includes Anthropic's Claude Desktop, Cursor IDE, Windsurf, and any other MCP client. It works by adding the Velane MCP server URL to the client's configuration file (mcp.json).