v0.1Iris MCP Server — 3 tools, 12 eval rules, open source→

Comparison

Iris vs LangSmith

MCP-native, zero-code observability vs SDK-based instrumentation with cloud-first architecture. Two different philosophies for monitoring your AI agents.

TL;DR

Iris is an MCP server your agent discovers and uses automatically — zero code changes, zero SDK imports, one SQLite file for storage. LangSmith is LangChain's full-featured observability and evaluation platform with SDK integrations for multiple frameworks, LLM-as-Judge evaluation, and enterprise-grade infrastructure. If you're building with MCP-compatible agents and want the simplest possible setup with no vendor lock-in, Iris gets you there in 60 seconds. If you need advanced evaluation workflows, auto-clustering, or enterprise compliance today, LangSmith is the more established platform.

Feature Comparison

Side by side.

FeatureIrisLangSmith
Integration methodMCP config (zero code)SDK imports + @traceable decorators
Self-hosting complexitySingle SQLite fileEnterprise-only, license key required
Performance overheadZero (no SDK in hot path)Async tracing via SDK in your process
Eval rules12 built-in + 8 custom types, heuristic (<1ms)LLM-as-Judge + human review workflows
Cost trackingPer-trace USD costToken + latency per trace and tool call
MCP supportProtocol-native (IS an MCP server)A2A & MCP protocol support for deployment
LicenseMIT (fully permissive)Proprietary platform (SDK is MIT)
PricingFree + Cloud waitlistFree tier (5k traces/mo), Plus $39/seat/mo, Enterprise custom
DashboardReal-time dark-mode UIAuto-clustering, pattern detection, custom dashboards
Framework supportAny MCP-compatible agentLangChain, OpenAI, Anthropic, Vercel AI, LlamaIndex + more
Data retentionUnlimited (your SQLite, your storage)14 days (free) / 400 days (paid)
Enterprise featuresRoadmap (v0.5)SSO, BYOC, SOC 2, dedicated support

Decision Guide

Which one fits your stack?

When to choose Iris

  • You're building with MCP-compatible agents (Claude Desktop, Cursor, Windsurf)
  • You want zero-code integration — no SDK imports, no decorators
  • You want simple self-hosting — one binary, one SQLite file, no license key
  • You want fully permissive MIT licensing with no proprietary lock-in
  • You want unlimited data retention without per-trace pricing

When to choose LangSmith

  • You need LLM-as-Judge evaluation with human review workflows
  • You need enterprise compliance and dedicated support today
  • You're heavily invested in the LangChain ecosystem
  • You need auto-clustering and pattern detection across traces
  • You need broad SDK support across many non-MCP frameworks

Last verified: March 2026. This comparison is based on publicly available documentation and may not reflect recent changes to LangSmith. We aim to keep this page accurate and fair.

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