Failproof AI
End-to-end reliability for AI agents. Traces every run at the agent runtime, scores every finished session in the cloud with code checks, LLM judges and the eval set you already have (DeepEval, Ragas, promptfoo or your own), brought in as it is, finds where agents fail on its own, and lets you author and enforce a policy that stops the bad action in realtime - deployed on-prem or in the cloud.
LangSmith
Tracing and evals for LangChain and LangGraph. Deeply integrated with that stack - datasets, prompt hub, annotation. Best inside the LangChain ecosystem.
| capability | Failproof AI | LangSmith |
|---|---|---|
| Built for | AI agents | LangChain apps |
| Works with any framework | Any agent / harness | LangChain & LangGraph |
| Agent-level tracing | Agent runtime, deeper | Chain / model level |
| Autonomous failure finding | Automatic | No |
| Policy authoring | Yes | No |
| Realtime policy enforcement | Yes, realtime | No |
| Prompt hub & eval datasets | No | Yes |
| Deployment | Local, on-prem, cloud | Cloud (self-host Enterprise) |
Choose Failproof AI when
- you run high-impact autonomous agents that take real actions
- a wrong action is costly or irreversible
- you need to stop failures at runtime, not just trace them
- you want an end-to-end reliability solution, on-prem or cloud
Choose LangSmith when
- you are all-in on LangChain / LangGraph
- you want a prompt hub and dataset-based evals
- you need chain- and graph-aware tracing
FAQ
Does LangSmith enforce policies?
It traces and evaluates; enforcing an action at runtime is not its job (LangGraph recursion limits are in-framework config, not a policy across agents). Failproof AI authors and enforces policy at the runtime, framework-agnostic.
Does Failproof AI work without LangChain?
Yes - it is framework-agnostic and hooks into any agent runtime, LangChain or not.
Can I use both?
Yes - trace LangChain apps in LangSmith, enforce every agent with Failproof AI.
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