ClawPipe vs LangSmith

When you want to spend less, not just see what you spent. LangSmith is the tracing and evals home for LangChain-first teams. ClawPipe sits one layer down — it changes the call before LangSmith ever traces it.

Honest feature matrix

Feature	ClawPipe	LangSmith
Multi-provider routing	Yes	n/a (not a router)
Semantic cache	Yes	No
Pre-LLM skip ("Booster")	Yes (unique)	No
Self-learning router	Yes	No
Tracing / evals	Per-request analytics only	Best-in-class for LangChain
PII / guardrails	No	n/a
Observability dashboard	Partial	Yes (trace-centric)
OpenAI-compatible shim	Yes	n/a (callback-based)
Open-source gateway	No	n/a
SDKs shipped	6 languages	2 (Python, JS)
Pricing floor (paid)	$79/mo (Dev)	$39/seat (Plus)

Choose ClawPipe if...

You want lower bills, not richer traces — Booster + Cache + Packer run on every call. Measured per-bucket numbers coming from the public benchmark in progress.
You're not LangChain-first. ClawPipe ships drop-in OpenAI/Anthropic shims; LangSmith is built around LangChain primitives.
You want one tool for routing + cache + skip, instead of stitching together a router and a tracer.

Choose LangSmith if...

Your stack is LangChain or LangGraph — LangSmith is the native tracing layer and the integration is unbeatable.
Eval pipelines and dataset versioning are your primary need — LangSmith ships both, ClawPipe ships neither.
You need fine-grained span-level debugging across multi-step agents.

Pricing snapshot

Tier	ClawPipe	LangSmith
Free	1,000 calls/day, all features	5K traces/mo, 1 user
Entry paid	$79/mo (Dev)	$39/seat (Plus)
Enterprise	Custom, SSO + audit	Custom

Migration from / pairing with LangSmith

# they're complementary — keep LangSmith tracing,
# point your LLM client at ClawPipe.
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
from openai import OpenAI
client = OpenAI(api_key="cp_...", base_url="https://api.clawpipe.ai/v1")
# LangSmith still traces every call. ClawPipe still optimizes every call.

Try the free tier (1,000 calls/day, no card) →