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Comparison

LangSmith vs Fleet: LLM Observability vs Agent Orchestration

LangSmith vs Fleet compares LLM tracing and evaluation with governed Claude Code agent workflows. They sit at different layers and can be used together.

LangSmith, from the LangChain team, is a platform for debugging, testing, evaluating, and monitoring LLM applications. You instrument your app, and LangSmith captures every trace — prompts, tool calls, token usage, latency — so you can inspect runs, build evaluation datasets, and track quality over time. It is framework-agnostic and not limited to coding agents; teams use it for chatbots, RAG pipelines, and any LLM workload.

Fleet is not an observability platform. It is the governed workflow layer that runs Claude Code agents against your repositories. You save a typed workflow graph, start a run manually, on a schedule, or from a GitHub label, and route code, review, approval, publish, and merge steps through explicit bounded edges. Where LangSmith answers 'what did my LLM calls do and how good were they?', Fleet answers 'who does what, in what order, and with what guardrails?'.

Choose Fleet if

Teams that want governed Claude Code workflows with explicit graph steps, bounded routing, approval gates, and auditable run history against real repositories.

Choose LangSmith if

Teams building any kind of LLM application who need deep tracing, evaluation datasets, prompt experimentation, and production monitoring of their model calls.

Fleet vs. LangSmith: side by side

FeatureFleetLangSmith
Primary functionGoverned Claude Code workflows — typed steps, approval gates, artifacts, and run historyTracing, evaluation, and observability for LLM apps
ScopeSoftware and document workflows: code, review, approvals, publish, and mergeAny LLM application — chatbots, RAG, agents, pipelines
DeploymentSelf-hosted Go binary on your infrastructureHosted SaaS (self-hosted available on Enterprise)
EvaluationBuilt-in 6-dimension agent evaluation, plus a separate risk model that drives auto-quarantineDataset-based evals, LLM-as-judge, custom evaluators, regression testing
Tracing depthWorkflow run history, step artifacts, decisions, and provenance (not LLM-call tracing)Full call-level traces: prompts, tool calls, token usage, latency
Agent runnerRuns Claude Code as the agent runnerNot a runner — observes whatever LLM/framework you wrap
GitHub automationLabel-triggered workflow runs with review, approval, and merge gatesNot applicable

Where Fleet is the better fit

  • Actually runs and coordinates the agents — LangSmith observes LLM calls but does not orchestrate a team of agents or drive a GitHub workflow
  • Saved workflow graphs make code, review, approval, and merge steps explicit, with bounded routing between outcomes
  • Self-hosted single binary; your source code stays local, going only to your model backend and GitHub
  • Built-in governance: per-agent run-time budgets, 6-dimension evaluation, and a separate risk model that auto-quarantines at critical risk

Where LangSmith is the better fit

  • Purpose-built, mature LLM observability: full call-level traces with prompts, tool calls, token usage, and latency that Fleet does not capture
  • Evaluation datasets, LLM-as-judge scoring, and prompt experimentation across any LLM workload, not just coding agents
  • Framework-agnostic — works with LangChain, LangGraph, or any instrumented app, in any domain
  • Production monitoring dashboards and alerting on quality and cost regressions at the model-call level

Pricing

LangSmith has a free Developer tier and paid Plus and Enterprise plans (seat-based with usage on traces); self-hosting is an Enterprise option. See LangSmith's pricing page for current figures. Fleet offers a free tier (500 hosted starts) and Business at $299/org/month.

Do they compete, or coexist?

These are complementary layers. Fleet runs and coordinates your Claude Code agents; LangSmith can observe and evaluate the underlying model calls those agents make if you instrument them. A team can run Fleet for orchestration and governance while using LangSmith for deep LLM-call tracing and evaluation. Fleet's own 6-dimension evaluation is built for agent-level scoring, not a replacement for LangSmith's call-level observability.

Frequently asked questions

Does Fleet replace LangSmith?

No. Fleet orchestrates and governs coding-agent workflows; LangSmith traces and evaluates LLM calls. Fleet includes a built-in 6-dimension agent evaluation plus workflow run history, artifacts, decisions, and provenance, but it does not provide call-level prompt/token/latency tracing the way LangSmith does. If you need LLM observability, keep LangSmith and run Fleet alongside it.

Does Fleet track tokens like LangSmith?

No. Fleet's enforced budget is run time (cumulative seconds), and it meters agent status, run counts, and run duration — not token counts. LangSmith captures token usage per call. If token-level cost visibility is your goal, that is LangSmith's job, not Fleet's.

Keep your AI agents from escaping

Jail Fleet-launched Linux agents, then run saved workflows with review, approvals, and an audit trail. Prove it at /security/#containment.