Competitive comparison

LangSmith alternative that works with any framework

LangSmith ties tracing and evaluation to the LangChain ecosystem. LLMWise is framework-agnostic with an OpenAI-compatible API, so you keep full control of your stack.

Start Free Back to overview

Teams switch because

Locked into LangChain abstractions just to get tracing and evaluation tooling

Teams switch because

Need model routing and optimization without adopting an opinionated framework

Teams switch because

Need production orchestration modes like compare, blend, and judge without custom chain code

LangSmith vs LLMWise

Capability	LangSmith	LLMWise
Framework requirement	LangChain preferred	Any framework or none
OpenAI-compatible API	No	Yes
Multi-model orchestration	Via custom chains	Built-in five modes
Failover mesh routing	No	Built-in circuit breaker
Optimization policy + replay	Evaluation only	Policy + replay + snapshots

Migration path in 15 minutes

Keep your OpenAI-style request payloads.
Switch API base URL and auth key.
Start with one account instead of separate model subscriptions.
Set routing policy for cost, latency, and reliability.
Run replay lab, then evaluate and ship with snapshots.

OpenAI-compatible request

POST /api/v1/chat
{
  "model": "auto",
  "optimization_goal": "cost",
  "messages": [{"role": "user", "content": "..." }],
  "stream": true
}

Common questions

Do I need LangChain to use LLMWise?

No. LLMWise uses an OpenAI-compatible API. You can call it from any HTTP client, SDK, or framework without vendor lock-in.

How does evaluation differ from LangSmith?

LangSmith focuses on trace-level evaluation within LangChain runs. LLMWise evaluates at the routing level with replay lab, optimization snapshots, and drift alerts to improve model selection over time.

Try it yourself

500 free credits. One API key. Nine models. No credit card required.

Get 500 free credits Run traffic replay

Generic LLM Gateways OpenRouter Portkey LiteLLM Cloudflare AI Gateway Vercel AI Gateway