LangSmith is the default choice for LangChain teams. But if your stack has moved beyond LangChain — or you're using MCP servers — you're working around LangSmith, not with it.
Feature Comparison
| Feature | TraceHawk | LangSmith |
|---|---|---|
| MCP server name captured | ✅ Always | ⚠️ Requires manual tagging |
| Per-server latency (p50/p95) | ✅ Built-in | ❌ Not tracked |
| MCP error details | ✅ Full error + stack | ❌ Not available |
| MCP server health dashboard | ✅ Built-in | ❌ Not available |
| OTEL-native ingest | ✅ OTLP endpoint | ⚠️ LangChain-first, OTEL adapter |
| LLM call tracing | ✅ | ✅ |
| Cost attribution | ✅ Per agent/trace/org | ✅ Per run |
| Prompt versioning / hub | ⚠️ Roadmap | ✅ LangSmith Hub |
| Agent replay timeline | ✅ Step-by-step | ✅ Run timeline |
| Dataset / eval harness | ❌ Not in scope | ✅ Built-in |
| Retry loop detection | ✅ Automatic badge | ❌ Not available |
| OTEL dual-write re-export | ✅ Built-in fan-out | ❌ Not available |
| Self-host option | ✅ Open source core | ❌ Cloud only (Enterprise) |
| Free tier | 50K spans/month | Limited (Developer) |
| Pro tier | $99/month | $39/month (25 seats) |
| Framework support | Any (OTEL-compatible) | LangChain/LangGraph-first |
The core difference
LangSmith was built to observe LangChain chains. Everything else is a wrapper around that mental model. TraceHawk was built around OpenTelemetry from day one — which means any framework, any language, and first-class support for Model Context Protocol.
This isn't a criticism of LangSmith. It's the right tool if your entire stack is LangChain/LangGraph and you want deep eval/dataset tooling. The question is whether that describes your stack in 2026.
MCP support: built-in vs bolted on
Model Context Protocol is now the dominant way AI agents use tools — Claude Code, LangGraph, CrewAI, OpenAI Agents SDK all support it natively. LangSmith doesn't have a concept of "MCP server" — you can log the spans manually, but there's no:
- Per-server health dashboard (error rate, p95 latency, call frequency)
- Automatic tool name extraction from
mcp.tool_nameattributes - Server degradation alerts
- MCP-aware retry loop detection
- Agent → server dependency graph
In TraceHawk, all of this is automatic. If you emit standard OTLP spans with mcp.server_name and mcp.tool_name attributes, the dashboard populates itself. No configuration required.
Framework independence
LangSmith works best with LangChain. The tracing callbacks are tightly coupled to the LangChain execution model — on_llm_start, on_tool_end, etc. If you switch to OpenAI Agents SDK, CrewAI, or write a custom agent, you're on your own.
TraceHawk uses OTLP as the ingest protocol. Any framework that emits OpenTelemetry spans works out of the box — including LangChain, LangGraph, CrewAI, OpenAI Agents SDK, Claude Code hooks, and custom agents. One endpoint, everything traces.
When LangSmith wins
LangSmith has capabilities TraceHawk doesn't aim to replicate:
- Prompt Hub — version-controlled prompt management with deployment
- Evaluation datasets — structured datasets for regression testing
- LangChain-native callbacks — zero-config if your stack is 100% LangChain
- LangGraph Studio integration — visual graph debugging
If your workflow is "build in LangGraph, test with eval datasets, iterate on prompts in Hub" — LangSmith is genuinely great. TraceHawk doesn't try to replace that.
When TraceHawk wins
- Your stack uses MCP servers (Claude Code, custom MCP, any framework)
- You want OTEL-native ingest without framework lock-in
- You need cost attribution per agent/trace/organization
- You want to self-host (open source core, Docker-deployable)
- You need retry loop detection and server health alerts
- You want to dual-write to Datadog/Grafana simultaneously
Pricing
LangSmith Developer tier is free with limited traces. Their paid plans start at $39/month for a team of 25. TraceHawk is $0 for 50K spans/month, $99/month for unlimited — no per-seat pricing, no surprise overages.
For production AI agent teams, the relevant comparison is: LangSmith Plus ($99–$499/month, per-seat) vs TraceHawk Pro ($99/month flat). If your team is 5+ people, TraceHawk is cheaper.
The bottom line
LangSmith is excellent if you're all-in on LangChain. TraceHawk is the right choice if you're using MCP, want framework independence, or need production-grade observability without per-seat pricing.
They're not direct competitors — LangSmith is a LangChain-native eval platform that includes tracing. TraceHawk is an OTEL-native observability platform that focuses on what matters for AI agent teams in 2026: MCP visibility, cost attribution, and production alerting.
Try TraceHawk free: 50K spans/month, no credit card. tracehawk.dev
Top comments (2)
Good comparison. The observability gap for AI agents is one of the biggest unsolved problems in the space right now.
What's missing from both tools IMO: cost attribution per task. When you're running Claude Code or any agent framework in a team setting, you need to know not just "this trace took 45 seconds" but "this trace consumed 12k tokens at $0.08 and the agent made 3 tool calls that could have been 1." That cost-per-action granularity is what turns observability from a debugging tool into a governance tool.
The MCP integration angle is interesting too — as agents get more tools via MCP servers, the trace complexity explodes. You need observability that understands the MCP protocol natively, not just generic HTTP spans.
Exactly this — cost-per-action granularity is the core of what we built. TraceHawk shows cost per tool call, per decision node, and yes — MCP spans are parsed natively, not as generic HTTP. Would love your feedback if you give it a try.