Respan

Respan is an open-source gateway designed for teams developing and deploying AI agents using large language models. Instead of juggling multiple APIs and model providers, users can route all their requests through Respan’s unified endpoint, which supports over a thousand models from leading companies. This streamlines development, enables easy switching between models, and automates fallbacks when providers fail or rate-limit.

A major strength of Respan is its deep observability: teams can trace, monitor, and analyze every request and response. Built-in logging, error tracking, and budget controls help manage usage, monitor model health, and ensure cost predictability. Evaluation tools let teams score outputs using configurable benchmarks—leveraging LLM judges, rule-based checks, or even human review—to compare model quality and ship changes only when they outperform previous versions.

Collaboration is built in, with workspaces, prompt versioning, and integrations for Python, JavaScript, and popular AI frameworks. Advanced enterprise features are available, such as custom SLAs, dedicated support, private deployments, and compliance packages for healthcare and finance use cases.

Whether running a single AI app or orchestrating complex agent workflows, Respan helps teams scale, optimize, and control AI deployments with confidence.

Respan is aimed at technical teams, such as software developers, machine learning engineers, and product managers, working on AI or LLM-based applications in startups, SaaS companies, or enterprise product organizations.

It’s especially useful for those managing multiple language models, needing robust monitoring and evaluation of AI agent outputs, or seeking to streamline integration across providers. Teams that require cost controls, compliance, and observability for their AI deployments—such as in regulated industries—will benefit from its built-in monitoring and reporting features.