LangSmith Alternative

The LangSmith alternative
without the LangChain lock-in

LangSmith is the native tracing tool for LangChain apps. If you're not using LangChain — or you need CI enforcement that actually fails the PR — Refine AI was built for that.

Get started free View docs →

At a glance

LangSmith Refine AI
CI/CD gate (fails PR) ✗ No ✓ Yes
Framework requirement LangChain / LangGraph Any framework
Evaluation method LLM-as-judge Deterministic structural
Setup time Minutes (if LangChain) 5 minutes (any stack)
CI flakiness High (judge-dependent) None
Production tracing ✓ Excellent ✗ Not included
Pricing Free + paid tiers Free + CI usage
Self-hostable ✓ Yes ✗ SaaS only

Why teams switch from LangSmith

LangSmith is the right tool for LangChain teams. These are the moments it stops being the right tool.

You're not using LangChain

LangSmith's value drops sharply outside the LangChain ecosystem. On CrewAI, AutoGen, a custom framework, or plain Python — you lose most of the deep integrations. Refine AI wraps any agent: you run it in CI and assert on the output regardless of stack.

Observability doesn't enforce anything

LangSmith's traces are excellent. But viewing a trace is not the same as blocking a PR. After you see an anomaly in LangSmith, someone still has to decide what to do. Refine AI removes that step: the assertion fails, the PR fails, the engineer is notified at the exact commit.

LLM judges cost money at CI scale

LangSmith's evaluators use LLM judges — fine for periodic evals, expensive at CI scale. 100 scenarios × 3 judges = 300 LLM calls per PR. Refine AI's structural checks run in milliseconds and cost zero tokens.

How Refine AI is different

Framework-agnostic

LangChain, CrewAI, AutoGen, custom Python — the assertion wraps any agent that runs in CI.

Fails the PR automatically

The GitHub check goes red. The PR is blocked. No human in the loop for routine regression checks.

Baseline delta comparison

Compare HEAD vs main. See exactly what changed: step_count 6 → 22, loop_risk spike, new tool calls.

Zero LLM judge cost

Structural analysis of run traces. No judge API calls, no token cost, no flakiness.

Who each tool is built for

Use LangSmith if…

  • →Your stack is LangChain or LangGraph and you want native tracing
  • →You need excellent debugging and replay within the LangChain ecosystem
  • →Dataset management and prompt versioning are part of your workflow

Use Refine AI if…

  • →You're framework-agnostic or not using LangChain
  • →You want automatic PR gating on behavioral regressions
  • →You need CI assertions without LLM judge cost or flakiness

Get started in 5 minutes

Works with any agent framework — no LangChain required.

.github/workflows/agent-regression.yml 
- name: Assert agent behavior
  uses: agentdbg/agentdbg-action@v1
  with:
    baseline: main
    checks: step_count,tool_calls,loop_risk,cost,latency
Read the full setup guide →

Any framework. Automatic enforcement.

Stop manually checking traces. Let CI fail the PR when your agent regresses — regardless of your stack.

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