AgentWach vs LangSmith
LangSmith helps you debug. AgentWach helps you stop runaway spend.
LangSmith is built for LangChain teams who want to trace, debug, and evaluate prompts. AgentWach is built for anyone running AI agents in production who needs to cap spend, detect loops, and stop runaway agents before the bill arrives.
Where LangSmith shines
- Deep LangChain / LangGraph tracing with prompt-level inspection.
- Evaluation datasets, regression tests, and human feedback loops.
- Strong prompt playground for iterating on chains.
- Mature offline analytics for ML / prompt engineers.
Where AgentWach wins
- Hard budget caps that physically stop an agent — not just dashboards after the burn.
- Loop detection across tool calls, not just chain traces.
- Works across any framework (LangGraph, CrewAI, AutoGen, Vercel AI SDK, raw OpenAI).
- GitHub Copilot premium-credit tracking in the same dashboard.
- Prompt-injection scanner on every ingested event.
Feature comparison
| Feature | AgentWach | LangSmith |
|---|---|---|
| Real-time token & cost tracking | Yes | Yes |
| Cross-provider rollup (OpenAI, Anthropic, Gemini, Copilot) | Yes | LangChain-centric |
| GitHub Copilot premium-request credit tracking | Yes | No |
| Hard budget caps that stop agents mid-run Returns stop=true on the next ingest call. | Yes | No |
| Tool-call loop detection | Yes | No |
| Prompt-injection scanner (heuristic, 20+ rules) | Yes | No |
| Slack / Discord / PagerDuty / SMS alerts | Yes | Email/Slack only |
| Tracing & evaluation tooling | Replay + diagnose | Best-in-class |
| Browser extension (ChatGPT / Claude / Gemini capture) | Yes | No |
| Free tier | Yes | Yes |
The bottom line
If you live inside LangChain and your bottleneck is prompt quality, keep LangSmith — and run AgentWach next to it for the budget firewall. AgentWach doesn't replace your tracer; it makes sure a misbehaving agent can't bankrupt the project while you're iterating on it.