Helicone vs Langfuse for AI Agents

Helicone and Langfuse are both LLM-specific observability platforms. They differ in depth. Langfuse provides nested agent chain tracing, evaluation pipelines, and prompt management. Helicone provides proxy-based request logging with cost tracking and session grouping. Langfuse wins on trace depth, evaluation, and prompt management. Helicone wins on zero-code integration and AI gateway features.

Where Langfuse wins

Nested agent chain tracing with 90+ framework integrations. Langfuse captures traces with nested observations: spans, generations, and events. Orchestrator decisions, tool calls, retrieval steps, and LLM calls are shown with full input/output, token counts, and latency. Auto-instrumentation for LangChain, LlamaIndex, OpenAI, Anthropic, Vercel AI SDK, LiteLLM, CrewAI, and 80+ other frameworks provides agent chain visibility without manual annotation. Helicone's Sessions group requests into hierarchical paths. The hierarchy is defined manually via Helicone-Session-Path headers. Helicone does not auto-instrument agent frameworks—the developer defines the trace structure via HTTP headers on each request.
Evaluation pipelines with LLM-as-judge, human annotation, and datasets. Langfuse provides LLM-as-judge evaluators, human annotation queues, custom scoring via API/SDK, datasets for systematic testing, and experiments for prompt/model comparison. All attach to production traces. Live evaluators monitor production quality. Helicone accepts evaluation scores from external systems via API (RAGAS, custom evaluators, LLM-as-judge). It does not run evaluators itself. The evaluation logic must be built and executed outside Helicone. For teams that need integrated evaluation workflows, Langfuse runs the evaluators; Helicone stores the results.
Prompt management with versioning, playground, and runtime fetching. Langfuse manages prompts as versioned artifacts with a playground for testing, deployment labels, and runtime fetching via SDK. Prompt metrics (latency, cost, scores) are tracked per version. Helicone does not document prompt management, versioning, or playground features.

Where Helicone wins

Zero-code proxy integration with automatic logging. Helicone integrates by changing the base URL in the OpenAI SDK—no new library, no callbacks, no decorators. Every request is logged automatically with prompts, completions, tokens, latency, and cost. This works with any language or runtime that can make HTTP requests. Langfuse requires installing a Python or JavaScript SDK and adding callbacks, decorators, or wrapper functions to capture traces—more integration effort, but also more control over trace structure.
AI gateway features: caching, fallbacks, and rate limiting. Helicone's proxy model enables request caching (repeated identical prompts return cached responses), automatic provider fallbacks when a provider is down, and rate limiting. These gateway-level features reduce cost and improve reliability at the request level. Langfuse is an observability platform, not a gateway—it does not intercept or modify requests. Caching, fallbacks, and rate limiting require separate infrastructure.
Cost tracking with unified provider billing. Helicone's proxy model captures cost data automatically for every request across 100+ models with zero configuration. Unified billing allows teams to pay through Helicone rather than managing separate provider accounts. Langfuse tracks cost via framework integrations but does not act as a billing intermediary or proxy gateway.

The agentic difference

Langfuse is an LLM engineering platform: trace, evaluate, iterate, deploy. Evaluation and prompt management workflows drive agent improvement over time. Helicone is an LLM gateway with observability: log, track cost, cache, route. Gateway features reduce operational overhead at the request level.

For agent teams whose primary workflow is "observe production quality → evaluate outputs → improve prompts → redeploy," Langfuse provides the complete loop. For teams whose primary workflow is "log all LLM requests, track costs, and optimize at the gateway level," Helicone provides that with less integration effort. Both are self-hostable (Langfuse open-source, Helicone enterprise on-premises).

When to pick which

Pick Langfuse when the team needs agent chain tracing with auto-instrumented framework integrations, evaluation pipelines (LLM-as-judge, human annotation), prompt management with versioning, or open-source self-hosting.
Pick Helicone when the priority is zero-code LLM request logging with cost tracking, the team benefits from gateway features (caching, fallbacks, rate limiting), and evaluation and prompt management are handled by separate tools or not yet required.