Future AGI vs LangWatch

Generate personas, scenarios and synthetic datasets to stress-test text and voice agents across many conversation branches before launch.

Combine always-free heuristic metrics, code evaluators and LLM-as-judge scoring, including agentic evaluators that localise errors in RAG and tool calls.

Route traffic across 15+ LLM providers with load balancing, failover and caching, plus 15 built-in rule-based guardrails and ML content moderation.

End-to-end traces, sessions, agent graphs, custom dashboards and alert monitors give real-time visibility into production behaviour.

Full GenAI spec support means traces from LangChain, LangGraph, CrewAI, Mastra, Pydantic AI, the Vercel AI SDK and any OTel-compatible stack land in the same view, with tokens, cost and cache accounted per span.

Score single outputs or entire conversations with an LLM judge, custom code or a workflow, run evals from a notebook or UI, and evaluate live production traffic in real time.

Simulated text and voice users push agents turn after turn in realistic scenarios, runnable locally and in CI, with tools, skills and MCP servers traced and mockable.

Route OpenAI, Anthropic, Google, Bedrock, Mistral and other providers through one endpoint using your own keys, with budgets per person, team and customer and around 11 microseconds of overhead.