Overview
Respan is the full-stack AI engineering platform for LLM and agent products. It helps you route models, set up monitoring, build prompts, run evaluations, and audit agent security, all in one place.
To get started, create a free account at platform.respan.ai.
How Respan works
Everything in Respan is built on one core data structure: the span. Whether it is a single LLM call, a full agent workflow, or a tool step like retrieval (RAG), a memory lookup, an embedding, or a reranking, every interaction is stored as a span with its input, output, model, metrics, and metadata.
Every product reads and writes this same span data:
- Gateway: route any LLM call through one endpoint and log it as a span.
- Observability: trace agent workflows as a span tree, and monitor live metrics with alerts.
- Prompt management: version-control your prompts and serve them directly through the gateway.
- Evaluations: score a single span or a whole trace against your evaluators.
- Red Team: run an authorized security audit of your agent.
Gateway
Route every LLM request through a single endpoint to 1000+ models, with automatic logging, fallbacks, retries, load balancing, and caching. Point any LLM SDK at Respan, change the base URL, and the rest of your code stays the same.
Models
Fallback
Limits
Caching
Browse and route to 1000+ models across every major provider from one endpoint. Switch models without changing your code.
Set up the gateway · Model catalog · Reliability & caching
Observability
Set up tracing and monitoring for your LLM traffic: inspect every span, watch live metrics, catch error incidents, and read the behavioral signals behind them.
Tracing
Metrics
Pulse
User analytics
See agent workflows as trace trees with parent-child spans. Each span shows its input, output, latency, and cost.
Set up the SDK · Framework integrations · OpenTelemetry
Prompt management
Ship prompts without shipping code: create templates with {{variables}}, commit and compare versions, test them in the playground, and deploy instantly. Your application picks up the new version the moment you publish.
Create prompts · Deploy & version
Evaluations
Measure output quality and catch regressions before they reach users: grade with LLM judges, deterministic code checks, or human review, across curated datasets, offline experiments, and live traffic.
- Build a dataset: sample production spans or import test cases.
- Set up evaluators: LLM judges, code checks, or human review.
- Run experiments: test against different prompt versions or models, then compare scores.
- Add online evals: score live traffic and alert when quality drops.
Red Team
Run an authorized security audit of your agent: adversarial campaigns that probe for prompt injection, system-prompt leakage, secret disclosure, and goal hijacking, then report confirmed findings with severity. It runs against the same connected agent you already trace.
Only test an agent and its connected systems when you own them or have explicit authorization to assess them.