Laminar vs Langfuse vs LangSmith: LLM Observability Compared (2026)

Choosing an observability platform is rarely academic; you are usually debugging an agent or RAG pipeline that is already in motion. This LLM observability comparison focuses on Langfuse, Laminar, and LangSmith for developers actively evaluating tools.

I grounded feature and pricing details in each vendor's own docs and pricing pages; verify anything budget- or compliance-critical against the latest pages before you commit.

Laminar uses a span-based model that captures the execution flow of LLM calls, tool executions, and custom functions. It supports automatic instrumentation across LLM SDKs and frameworks once you initialize the SDK, and it highlights real-time traces for long-running or multi-step workflows. The tracing docs emphasize a tree of spans and explicit relationships between calls, so you can follow a request end-to-end.

Causality tracing (Laminar strength): Laminar doesn't label it this way, but the parent/child span tree and the documented focus on relationships between calls effectively give you a causal chain (routing -> tool use -> final answer).

Langfuse frames tracing as structured logs for every request, capturing prompts, responses, token usage, latency, and intermediate steps like tool calls or retrieval. Its data model is traces -> observations -> sessions, and observations can be nested and typed (generations, tool calls, retrieval steps), which makes complex flows tractable.

LangSmith provides end-to-end tracing that captures every significant operation as a "run" within a trace. Each trace represents a complete execution of your application chain or agent. LangSmith tracks prompts, tool calls, LLM outputs, token usage, latency, and cost at granular levels.

The platform offers real-time monitoring dashboards with alerts for issues like latency spikes, error rates, and cost anomalies. LangSmith also provides "clusters" of similar conversations to help identify systemic issues across your application.

Key differentiator: LangSmith works with any framework (not just LangChain/LangGraph), though integration is simplest with LangChain apps--just set one environment variable to enable tracing.

Laminar positions itself as observability built for AI agents and provides automatic tracing for LangChain and LangGraph so agent steps, tool usage, and LLM calls show up in detail. It also visualizes LangGraph executions and supports browser-agent observability with synchronized session replay alongside traces--useful for debugging what the agent actually saw in a browser automation context.

Langfuse provides an agent graph view (beta) that visualizes agentic workflows, either inferred from observation types/timing or via LangGraph integration. This makes complex agent behavior easier to inspect when you structure traces with agentic observation types.

LangSmith offers LangGraph Studio, a specialized agent IDE that enables visualization, interaction, and debugging of agentic applications. Studio lets you:

• Visualize agent graphs and execution paths
• Add breakpoints/interrupts to pause agent execution
• Modify agent state mid-trajectory for iterative debugging
• View real-time token usage, latency, and status during runs

LangSmith also integrates tightly with LangSmith Deployment (formerly LangGraph Platform) for 1-click agent deployment with built-in checkpointing, memory, and horizontal scaling.

Laminar makes replay a first-class workflow. The overview highlights replaying agents from parts of a captured trace, and the Playground lets you open an LLM span and inherit the original model, tool, and prompt configuration for rapid iteration. The Playground also preserves session history, and browser-agent session replay is synced with spans to debug what the agent actually saw.

Langfuse's docs emphasize observability plus evaluations, datasets, and prompt management as core platform features, which supports an iterate-and-test loop even when debugging happens offline or in batch.

LangSmith provides the Playground for prompt iteration--you can open LLM runs directly from traces and experiment with different prompts, models, and parameters while preserving the original context. As of April 2026, LangGraph Studio integrates with the Playground, letting you apply prompt changes directly back to your agent.

LangSmith's tagging system lets you label runs by feature, prompt version, or experiment name, then compare traces by tag. Combined with datasets for regression testing, this supports systematic debugging workflows.

Langfuse also offers a free self-host option (MIT licensed) with all core features.

LangSmith trace pricing:

• Base traces: $0.50 per 1k traces (14-day retention)
• Extended traces: $2.50 per 1k to upgrade (400-day retention)
• Traces with user feedback automatically upgrade to extended

Net: Langfuse and LangSmith use observation/trace-based pricing with user tiers; Laminar uses data-volume pricing. LangSmith's seat-based model ($39/user) adds up for larger teams but includes generous free trace allotments. Model cost using your expected trace volume, retention needs, and team size.

Laminar can run as managed cloud or be self-hosted via Docker Compose, and the SDK can be pointed at a local instance. Enterprise mentions on-premise deployment. This enables hybrid patterns (dev in cloud, prod self-hosted).

Langfuse offers cloud plans and a free self-host open-source option (MIT licensed) with core features. Enterprise options add governance controls like RBAC and audit logs for self-hosted deployments.

LangSmith offers three deployment models:

• Cloud: Fully managed by LangChain (data in GCP us-central-1, or EU region)
• Hybrid: SaaS control plane with self-hosted data plane
• Self-hosted: Fully self-managed on your Kubernetes cluster (AWS, GCP, Azure)

Important: Self-hosting is an Enterprise plan add-on only--there is no open-source self-host option for LangSmith. This contrasts with Langfuse's MIT-licensed self-host option.

Laminar supports native OpenTelemetry ingestion, which matters if you already have OTel pipelines. This allows unified observability across your stack.

Langfuse operates as an OpenTelemetry backend and can receive traces on its /api/public/otel (OTLP) endpoint. This extends compatibility to frameworks and languages beyond native SDKs, including OpenLLMetry and OpenLIT integrations for Java, Go, AutoGen, and Semantic Kernel.

LangSmith offers full end-to-end OpenTelemetry support for LangChain and LangGraph applications. This includes:

• Native OTel support in the LangSmith SDK
• OTLP endpoint for standard OTel exporters
• Support for OpenLLMetry semantic conventions
• Vercel AI SDK integration via client-side trace exporter

You can send traces to LangSmith alongside other OTel-compatible backends, or exclusively via OTel by setting LANGSMITH_OTEL_ONLY=true.

• Deep agent debugging with span-tree structure for causal reasoning
• Real-time trace visibility during long-running operations
• Replay from spans as a first-class workflow
• Browser-agent session replay tied directly to traces
• Open-source core with self-hosting options
• OpenTelemetry ingestion for existing OTel pipelines

• Open-source self-host (MIT licensed) with all core features
• Very explicit trace data model (traces -> observations -> sessions)
• Agent graph view (beta) for visualizing agentic workflows
• Strong evaluation, prompt management, and dataset features as first-class citizens

• LangGraph Studio: Purpose-built agent IDE for visualization, debugging, and iteration
• LangSmith Deployment: 1-click agent deployment with checkpointing, memory, and scaling
• Tightest integration with LangChain/LangGraph ecosystem
• Real-time monitoring dashboards with alerting
• Polly AI assistant for trace analysis
• Conversation clustering to identify patterns and systemic issues
• Hybrid and self-hosted options (Enterprise only)

Choose Laminar if:

• You need deep, causal debugging for complex agents
• Real-time visibility during long-running operations is a must-have
• Replay from captured spans is critical to your iteration workflow
• You're building browser-automation agents and need synchronized session replay
• You prefer open-source tooling with self-host flexibility
• Data-volume pricing fits your usage pattern better than trace-based

Choose Langfuse if:

• You want a free, open-source self-host option
• You prioritize a clear trace schema with strong evaluation/prompt/dataset workflows
• You need agent graph visualization without vendor lock-in
• You're working across multiple frameworks (not just LangChain)

Choose LangSmith if:

• You're heavily invested in the LangChain/LangGraph ecosystem
• You want a purpose-built agent IDE (LangGraph Studio) for development
• You need managed agent deployment with built-in infrastructure (checkpointing, scaling)
• Real-time monitoring dashboards and alerting are priorities
• You're an enterprise with resources for the Plus/Enterprise tiers

Pilot approach: Many teams pilot two platforms with a single agent or RAG flow and keep the one that best matches their iteration loop. All three offer free tiers that let you evaluate core functionality before committing.

• Start with Laminar if you want to validate real-time debugging and replay on a single agent run. Free tier ->
• Start with Langfuse if you prefer a broader evaluation and prompt-management workflow with open-source self-hosting. Cloud Hobby or self-host ->
• Start with LangSmith if you're building with LangChain/LangGraph and want the tightest integration plus agent deployment capabilities. Developer plan ->

Last updated: January 2026. Verify pricing and features against each vendor's current documentation before making decisions.