SafeRun vs LangSmith.
Two tools, two jobs. Here's the honest breakdown — and why most production teams end up running both.
LLM observability & evals
Tracing, evals, datasets, and prompt iteration for LLM apps and chains. Built by the LangChain team. Best-in-class for understanding what your model said and how to make it say it better.
Inline reliability gate for agents
Sits between agents and tools. Validates tool calls, blocks risky actions, routes ambiguous calls to a human, breaks loops, and gives engineers a replayable timeline of every decision.
LangSmith tells you what your LLM said. SafeRun controls what your agent does.
Where each tool actually plays.
| Capability | LangSmith | SafeRun |
|---|---|---|
| LLM call tracing | Tool calls only | |
| Prompt & token analytics | ||
| Evals & dataset testing | ||
| Inline tool-call validation | ||
| Block risky actions before execution | ||
| Human-in-the-loop approvals (Slack/email) | ||
| Loop & circuit breakers | ||
| Per-agent reliability score | LLM-level | Action-level |
| Replay debugger for full agent runs | Trace-level | Decision-level |
| Tamper-evident audit log | ||
| Policy as code, versioned per agent | ||
| Runs inline in your stack | Async tracing | Inline gate |
When to pick which.
Pick LangSmith if…
Your team is iterating on prompts and chains, you need eval pipelines, and 'something might break in production' is a future-quarter problem.
Pick SafeRun if…
Your agent calls real tools — refunds, emails, deploys, DB writes — and you need to stop bad actions before they ship, not trace them after.
Pick both if…
You're running agents in production at scale. LangSmith for prompt and eval iteration; SafeRun as the inline reliability gate for tool calls.
Different layers. They don't compete.
LangSmith optimizes the model. SafeRun governs the action. Run LangSmith on the LLM call, run SafeRun on the tool call, and you get prompt-level iteration plus production-grade control. We've designed SafeRun to drop in next to your existing tracing — not replace it.
Give production agents a checkpoint before they act.
Wrap risky tool calls, pause or block what shouldn't run, and replay the decision so teams can turn each near-miss into a rule.