Respan offers an engineering platform that captures every LLM call, tool invocation, and response with full context, creating searchable execution traces and replayable sessions for debugging. The platform includes versioned prompt, tool, and model management, composite evaluation pipelines, a unified gateway for 500+ model providers, and real‑time monitoring dashboards with alerting for latency, cost, and custom business metrics.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents and LLM‑driven applications often suffer from hidden failures, drifting performance, and opaque execution paths as prompts, tools, and model versions evolve. Without systematic observability, teams cannot reliably trace requests, measure quality, or pinpoint regressions in production traffic.
Solution
Respan delivers an end‑to‑end engineering platform that captures every LLM call, tool invocation, and response with full context, turning raw production traffic into searchable execution traces. Integrated evaluation pipelines let teams combine human review, code checks, and LLM judges against defined quality metrics, while versioned prompt, tool, and model management enables controlled experimentation. A unified gateway abstracts over 500+ model providers, providing consistent routing, rollout, and rollback capabilities. Real‑time monitoring dashboards surface latency, cost, and custom business metrics, and trigger alerts or automated remediation when drift is detected. The platform’s SDKs and native integrations embed observability and control directly into existing codebases, allowing AI teams to ship reliable agents at scale.
Target Audience
AI product engineers, LLM‑agent developers, and enterprise data science teams that need production‑grade observability, evaluation, and deployment control for large‑scale generative AI applications.
Features
- End‑to‑end tracing of prompts, tool calls, and LLM responses with rich metadata (latency, cost, tags) searchable via the Respan UI.
- Replayable production sessions in an interactive playground for rapid debugging and hypothesis testing.
- Composite evaluation workflows that orchestrate human feedback, code validators, and LLM judges, all measured against user‑defined metrics.
- Prompt, tool, model, and workflow versioning with diff visualizations to compare changes against baseline datasets.
- Unified AI gateway supporting 500+ model providers, offering load‑balanced routing, provider abstraction, and one‑click promotion to production.
- Customizable monitoring dashboards (80+ chart types) with alerting to Slack, email, or webhook and automated actions such as dataset generation or re‑evaluation triggers.
- SDKs for Python, TypeScript, and major agent frameworks (LangChain, LlamaIndex, Vercel AI) plus pre‑built integrations for OpenAI, Anthropic, Google Gemini, and others.
- Enterprise‑grade compliance (ISO 27001, SOC 2, GDPR, HIPAA) with role‑based access control, end‑to‑end encryption, and audit logging.