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Laminar vs Langfuse

Side by side, from each listing’s own data. A row where every column says the same is shown once, dimmed.

Laminar vs Langfuse, side by side
Laminarby LaminarRemove Laminar from this comparisonLangfuseby LangfuseRemove Langfuse from this comparison
Sets it apart
  • Only one with an MCP server
  • Streams responses
  • Newest listing (added Sep 13, 2026)
  • Official Langfuse listing
  • Most skills (5)
What it isTrace, evaluate, and debug AI agents with open-source observability — captures LLM calls, tool calls, and sub-agent spans, then clusters recurring failure modes into eval datasets.Open-source LLM observability and analytics. Trace, evaluate, and debug LLM apps with prompt versioning, score tracking, and a full analytics dashboard.
ProviderLaminarLangfuse
Official vendorNot an official listingLangfuse
VerifiedYesSame for all
AccessAPI, MCP, CLIAPI, CLI
AuthAPI KeySame for all
MCP serverIn its setup guideNo MCP server
CLI installIn its docsnpmnpm i -g @langfuse/cli
API base URLIn its docsSame for all
SkillsAgent Tracing, Failure Clustering, Eval DatasetsTrace every LLM call and chain step with full input/output capture and latency, Version and manage prompts with a/b testing and rollback across environments, Score traces with custom evaluation rubrics to track quality over time, Analyze user session funnels, latency distributions, and token cost trends, Self-host on any infrastructure or use Langfuse Cloud with zero configuration
StreamingYesNo
Push notificationsNoSame for all
CategoriesCode & DevTools, Infrastructure & OpsInfrastructure & Ops
Tagsagent-observability, tracing, evaluations, failure-analysis, opentelemetry, self-hosted, span-inspectionobservability, open-source, llm-tracing, prompt-management, evaluation, self-hosted
Listed sinceSep 13, 2026Apr 8, 2026
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/compare?a=laminar&b=langfuse
2 listings side by side · "same" marks a row every column shares
listings
laminarapi+cli+mcpOnly one with an MCP server; Streams responses; Newest listing (added Sep 13, 2026)langfuseapi+cliOfficial Langfuse listing; Most skills (5)
rows
descriptionTrace, evaluate, and debug AI agents with open-source observability — captures LLM calls, tool calls, and sub-agent spans, then clusters recurring failure modes into eval datasets. | Open-source LLM observability and analytics. Trace, evaluate, and debug LLM apps with prompt versioning, score tracking, and a full analytics dashboard.
providerLaminar | Langfuse
officialNot an official listing | Langfuse
verified (same)Yes
accessAPI, MCP, CLI | API, CLI
auth (same)API Key
mcpIn its setup guide | No MCP server
cliIn its docs | npm: npm i -g @langfuse/cli
api (same)In its docs
skillsAgent Tracing, Failure Clustering, Eval Datasets | Trace every LLM call and chain step with full input/output capture and latency, Version and manage prompts with a/b testing and rollback across environments, Score traces with custom evaluation rubrics to track quality over time, Analyze user session funnels, latency distributions, and token cost trends, Self-host on any infrastructure or use Langfuse Cloud with zero configuration
streamingYes | No
push (same)No
categoriesCode & DevTools, Infrastructure & Ops | Infrastructure & Ops
tagsagent-observability, tracing, evaluations, failure-analysis, opentelemetry, self-hosted, span-inspection | observability, open-source, llm-tracing, prompt-management, evaluation, self-hosted
addedSep 13, 2026 | Apr 8, 2026
add a third
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