Zespan is an AI agent observability and engineering platform. It traces every agent decision, tool call, handoff, and delegation in production. It also provides prompt versioning, built-in LLM-as-judge evaluations, guardrails, cost optimization, and an AI ops assistant called ZespanPilot.

How do I instrument my AI agent with Zespan?

Zespan requires 2 lines of code. Import zespan and call zespan.init({ apiKey: process.env.LT_KEY }). This auto-patches OpenAI, Anthropic, Gemini, Bedrock, and Mistral. For framework-level tracing, add one handler: ZespanCallbackHandler for LangChain, ZespanCrewAIListener for CrewAI, or ZespanADKHandler for Google ADK.

Does Zespan support prompt versioning?

Yes. Zespan includes prompt management with versioning, a playground for iteration, and A/B testing to compare prompt versions against each other in production.

What evaluations does Zespan support?

Zespan ships 12 built-in LLM-as-judge evaluation templates including faithfulness, relevance, toxicity, groundedness, and more. Evaluations run automatically on every trace with no custom scoring functions required.

How does Zespan compare to Langfuse?

Zespan is agent-native: every span carries agent identity, delegations are first-class trace events, and an agent map is built automatically. Langfuse was built for LLM pipelines and extended to agents later. Zespan also ships 12 built-in eval templates (Langfuse has none), includes an AI cost optimizer, and ZespanPilot for AI ops. Langfuse has open-source self-hosting; Zespan does not.