Respan
Unified LLM engineering platform for observability, evaluations, prompt optimization, and AI gateway services.
Score
60
User Rating
3.0
Free/Trial Support
Not Supported
Starting Price
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Last Updated
Aug 14, 2026
Overview
What is Respan?
Respan (formerly Keywords AI) is a comprehensive LLM engineering platform designed to help teams build, monitor, and optimize AI agents. It unifies observability, evaluations, prompt optimization, and a unified AI gateway into a single interface. The platform allows developers to trace agent execution paths, evaluate performance using LLM-as-judge or human review, manage prompt versions, and access over 500 models through a single API endpoint. It is built to handle massive scale, supporting trillions of tokens while maintaining rigorous security standards like SOC 2 and HIPAA.
What is Respan used for?
Tags
Top Features
- Self-driving observability and agent tracing for debugging production issues.
- Integrated evaluation workflows combining human review, code checks, and LLM judges.
- Unified AI Gateway supporting 500+ models with built-in routing and load balancing.
- Prompt management system with version control and collaborative IDE.
- Real-time monitoring with custom dashboards and automated alerts (Slack/Email).
- Enterprise-grade security including SOC 2, HIPAA, ISO 27001, and GDPR compliance.
How to use Respan?
To use Respan, developers integrate the platform into their AI applications using Python or JS/TS SDKs. Once connected, they can trace production logs in the dashboard, set up evaluation workflows to measure agent quality, use the collaborative IDE to optimize and version prompts, and route traffic through the unified gateway to access various LLM providers with built-in failovers.
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Pros & Cons
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Use Cases
- Tracing and debugging complex AI agent execution paths in real-time.
- Running A/B tests on prompts and models against production baselines.
- Scaling AI traffic across multiple providers using a single, reliable gateway API.
- Building automated evaluation pipelines to ensure AI quality before deployment.
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