Langfuse Alternatives 2026: 7 Top Picks for Agent Observability

Langfuse is a good open-source LLM observability tool. It shines at prompt versioning, typed observations, and an eval harness that plugs into notebook workflows. The trouble starts when your app grows teeth.

A production agent runs for ten minutes, calls fifteen tools, spawns a sub-agent, and fails four tool calls deep. You open the Langfuse trace and get a list of observations. You want to know what the agent said to the user, what the user said back, and which tool call threw. That is a different product.

This article ranks the top Langfuse alternatives for 2026, ordered by how well they solve agent observability and debugging rather than prompt-first logging. We score each on trace UX for long runs, OpenTelemetry support, self-host story, pricing model, and eval/dataset workflows.

1. Laminar. Open-source (Apache 2.0), OpenTelemetry-native, built for long-running agents. Transcript view, Signals, SQL over traces, agent rollout debugger, browser-agent session replay. The direct Langfuse alternative if you are debugging agents, not iterating on prompts.
2. LangSmith. Closed source, LangChain-first. LangGraph Studio is genuinely the best agent IDE if you live in that stack.
3. Arize Phoenix. Open-source (Elastic 2.0), OpenTelemetry-native via OpenInference. Best for notebook and eval-heavy workflows.
4. Braintrust. Closed source, eval-first. Strong regression harness, tracing bolted on.
5. Weights & Biases Weave. Closed source. Fits if your ML team already lives in W&B.
6. Helicone. Open-source proxy-based logging. Fine for quick request/response capture on raw LLM calls.
7. Traceloop / OpenLLMetry. Vendor-neutral OpenTelemetry instrumentation plus a backend. Useful as a Langfuse-compatible ingest if you care about portability.

One-line rule: pick Laminar if your workload is agents, LangSmith if you are locked to LangGraph, Phoenix if you are already on Arize, Braintrust if evaluation regression is your bottleneck.

Langfuse is not broken. It is specialized. The friction points that push teams to look elsewhere are specific and worth naming so you know whether they apply to you.

• Observation-first data model. Langfuse traces are a list of observations (generations, spans, events). Great for prompt-centric apps. Slower to read when the "trace" is a 30-minute agent run with nested tool calls and sub-agents.
• No SQL over traces in product. Analysis is API-first. You export to your warehouse or write code. Fine for scheduled reporting, painful for the 2 a.m. question "why did this agent fail."
• No natural-language outcome tracking. You cannot say "agent asked the user for clarification" and get every matching run backfilled as a structured event. You tag manually or write code.
• Unit-based pricing. Langfuse Cloud bills on traces + observations + scores. Agents with thousands of small spans per run add up fast.
• Trace UX not built for long runs. Reading a 2,000-span agent trace as a flat observation list is slow. There is no transcript view.

If none of this hurts, Langfuse is fine. If any of it hurts, the platforms below solve it.

Most "LLM observability" tools were designed for a single prompt/completion pair. Agent observability is a different problem:

• Long traces. Thousands of spans across LLM calls, tool calls, retries, and sub-agent invocations.
• Non-deterministic control flow. The agent decides the next step. Every run has a different shape.
• Nested causality. A failure at span 1,800 can be caused by a bad retrieval at span 42. You follow the chain, not the list.
• Session continuity. Agents pause and resume. A task spans multiple process runs. The trace has to stitch.

Everything below claims to handle this. The ranking reflects how well they actually do.

License: Apache 2.0. Deployment: Cloud, or self-hosted via the official Helm chart in minutes. Repo: github.com/lmnr-ai/lmnr.

Laminar was built from day one for long-running agents. Where Langfuse organizes around observations and prompts, Laminar organizes around the agent conversation and the spans that produced it.

The transcript view is the default way to read a trace in Laminar. You see what the agent said, what the user said back, and what each tool call did, rendered as a conversation. The span tree is still one click away when you want it. Langfuse renders traces as observations; Laminar renders them as the work the agent did.

This alone is the difference between a ten-second read and a ten-minute read on a 2,000-span run. We wrote about why transcript is the right default in a separate post.

Signals turn a description of an outcome into a structured event on every trace it matches. You write "agent asked the user for clarification and got a useful answer." Laminar extracts it, backfills it across history, and fires on every new trace that matches the pattern.

This is the primitive Langfuse does not have. You do not re-tag old data when a new failure mode shows up. You name the failure, and Laminar finds it.

Laminar includes a SQL editor that queries traces, spans, events, and metadata directly. "How many runs called tool X more than five times and then errored" is one query. No warehouse export, no API loop.

Re-run an agent from any span in a captured trace. Change the prompt, swap the model, edit the tool call, and see what would have happened. Not replay-as-playback, but rollout-as-iteration. Docs: platform/debugger.

Native SDKs for Python and TypeScript. Auto-instrumentation for LangChain, LangGraph, CrewAI, AutoGen, Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK, and Browser Use. Because it is OTel-native, OpenInference and OpenLLMetry instrumentation also work. No lock-in: you can switch backends without re-instrumenting.

Laminar is genuinely easy to self-host. The repo ships a production-ready Helm chart: clone, apply, and you are running. No enterprise sales call, no proprietary operator, no "contact us for self-host." All features ship on the OSS image, including Signals, the SQL editor, and the debugger. That is unusual in this category.

Data-volume pricing with no seat fees and no per-span unit counting. Free: 1GB/month, 15-day retention. Hobby: $30/month for 3GB and 30-day retention. Pro: $150/month for 10GB and 90-day retention, unlimited seats. Enterprise is custom. Self-hosting is free. Data-volume pricing tracks actual payload size, so it stays predictable as traces grow; the absence of per-seat charges is unusual in this market.

• You do not have nested tool use or agents. A single-call logging tool is enough.
• Your entire workflow is versioning and testing prompts. Langfuse still wins there.

License: Closed source. Deployment: Cloud, hybrid, self-hosted (Enterprise only).

If your stack is LangChain or LangGraph, LangSmith fits like a glove. One environment variable, and runs are traced. LangGraph Studio is the best agent IDE available: visualize the graph, set breakpoints, modify state mid-run, resume from a checkpoint.

Strengths:

• LangGraph Studio (real agent IDE, not just a viewer).
• Managed deployment with checkpointing and memory.
• OpenTelemetry support added in March 2026.

Weaknesses:

• Closed source. Self-hosting is Enterprise-only.
• Seat-based pricing ($39/seat/month on Plus) gets expensive with larger teams.
• Tightest fit is still LangChain. Teams on other frameworks get less.

Pricing: Developer free with 5k base traces/month. Plus $39/seat/month plus $0.50 per 1k base traces. Extended retention traces cost $2.50 per 1k.

License: Elastic License 2.0. Deployment: Self-host (pip install), Arize AX managed option.

Phoenix is the open-source side of Arize. It uses OpenInference, a widely adopted set of OTel semantic conventions for LLM spans.

Strengths:

• OpenTelemetry-native with OpenInference. Instrument once, send anywhere.
• Strong evaluation harness (Phoenix Evals).
• Notebook-friendly; runs in Colab or locally.

Weaknesses:

• Trace UX is span-tree-first. No transcript view.
• Less purpose-built for long agent runs than Laminar.
• Commercial Arize AX has a different cost curve. Plan ahead if you need to graduate.

Pricing: Phoenix is free. Arize AX pricing is custom.

License: Closed source. Deployment: Cloud, on-prem for Enterprise.

Braintrust is eval-first. Tracing exists to feed the eval loop, not to stand alone.

Strengths:

• Mature scorers, comparisons, regression detection.
• Clean prompt playground tied to eval sets.
• Strong if your bottleneck is "did this change break behavior X."

Weaknesses:

• Not a debugger. You will not be faster at finding what broke in production.
• Lighter agent-specific UX.
• Closed source.

Pricing: Free tier available. Pro scales with usage. Enterprise custom.

License: Closed source. Deployment: Cloud, on-prem for Enterprise.

Weave plugs tracing into the existing W&B console. If your ML team already lives there, it is the path of least friction.

Strengths:

• Native W&B integration.
• Strong eval framework with scorers and comparisons.
• Good for teams evaluating models and agents on the same platform.

Weaknesses:

• Trace UX borrowed from ML experiment tracking. Not agent-first.
• Weak on realtime trace viewing during long runs.
• Closed source.

Pricing: Free tier with limited storage. Paid plans scale with volume and seats.

License: Apache 2.0. Deployment: Cloud, self-host.

Helicone is a proxy that sits in front of the LLM provider and logs every request. Simplest integration of any tool in this list: change a base URL.

Strengths:

• Zero-code proxy integration.
• Caching, rate-limit, and retry built into the proxy.
• Cheap to get started.

Weaknesses:

• Request/response focused, not span-based. Multi-step agents are stitched after the fact.
• No transcript view, no Signals, no agent rollout.
• Proxy model adds a hop to every LLM call.

Pricing: Free tier. Paid plans scale with request volume.

License: Apache 2.0 (OpenLLMetry SDK). Deployment: Cloud backend, vendor-neutral SDK.

Traceloop's value is the OpenLLMetry SDK: vendor-neutral OpenTelemetry instrumentation for LLMs. Traceloop's own backend is one place the traces can go. Most backends in this list (Laminar, Langfuse, Phoenix, LangSmith) can also ingest OpenLLMetry spans, which makes OpenLLMetry the safest instrumentation choice for teams that want portability.

Strengths:

• OTel-native. Works with any compatible backend.
• Active open-source community.

Weaknesses:

• The backend UX is less agent-specific than Laminar or LangSmith.
• Primary value is the SDK, not the product.

Criterion	Winner	Why
Agent-specific UX	Laminar	Transcript view, Signals, agent rollout, browser-agent session replay.
LangGraph integration	LangSmith	LangGraph Studio is the best agent IDE today.
Open-source self-host	Laminar	Apache 2.0, Helm chart, all features on the OSS image. Langfuse (MIT) is a close second.
OpenTelemetry support	Laminar / Phoenix	Both OTel-native from day one.
Evaluation harness	Braintrust / Langfuse	Purpose-built scorers and comparison flows.
Vendor-neutral instrumentation	OpenLLMetry / OpenInference	Instrument once, switch backends later.
Pricing predictability	Laminar	Data-volume pricing tracks actual payload, not trace counts or seats.

Platform	Free tier	Paid entry	Enterprise / self-host
Laminar	1GB, 15-day retention	$30/mo Hobby (3GB), $150/mo Pro (10GB, 90-day retention)	Custom. Self-host free via Helm chart, all features included
Langfuse	50k observations, 30-day retention	$29/mo Core, $199/mo Pro	$2,499/mo Enterprise, self-host all features
LangSmith	5k base traces	$39/seat/mo + $0.50 per 1k traces	Enterprise self-host
Phoenix	Free open-source	Arize AX (custom)	Arize AX / self-host
Braintrust	Free tier	Pro scales with usage	Custom, on-prem
Weave	Limited storage	Scales with volume and seats	On-prem for Enterprise
Helicone	Free tier	Scales with requests	Self-host

Data-volume pricing (Laminar) tracks actual payload size. Unit-based pricing (Langfuse) counts traces plus observations plus scores; agent traces with many small spans hit thresholds faster. Seat-based pricing (LangSmith) scales with team size independent of usage.

Matters if you self-host, run in air-gapped environments, or want to own the trace data.

Platform	License	Self-host	All features on self-host
Laminar	Apache 2.0	Yes, Helm chart, one command	Yes
Langfuse	MIT	Yes	Yes
Phoenix	Elastic 2.0	Yes	Yes
Helicone	Apache 2.0	Yes	Yes
OpenLLMetry SDK	Apache 2.0	N/A (SDK)	N/A
LangSmith	Closed	Enterprise only	N/A
Braintrust	Closed	Enterprise only	N/A
Weave	Closed	On-prem Enterprise	N/A

Answer these in order. Stop at the first yes.

1. Are you debugging long-running agents in production and want realtime traces, Signals, and agent rollout? → Laminar.
2. Are you committed to LangChain or LangGraph and want an agent IDE? → LangSmith.
3. Is your primary pain regression testing, not debugging? → Braintrust.
4. Are you already on Arize or need OpenInference? → Phoenix.
5. Does your ML team live in W&B? → Weave.
6. Do you just need cheap request/response logs for raw LLM calls? → Helicone.
7. Do you want vendor-neutral instrumentation and will decide the backend later? → OpenLLMetry plus any of the above.

If you are on Langfuse and the pain points above apply, the migration is straightforward:

1. Switch the instrumentation. Laminar's Python and TypeScript SDKs follow the same auto-instrumentation pattern. If you are already on OpenLLMetry or OpenInference, point the OTLP endpoint at Laminar and traces flow in. See the Laminar quickstart.
2. Map the data model. Langfuse observations map to OTel spans in Laminar. Sessions map to trace sessions. Scores map to Signals or explicit events.
3. Keep Langfuse running side-by-side during the transition. Send traces to both backends until you trust the new pipeline.
4. Move prompt management. If you used Langfuse prompts, keep them there during migration or move to your prompt registry of choice. Laminar is not a prompt manager; it is a debugger.

We built Laminar because none of the Langfuse alternatives solved our own problem: debugging a 30-minute browser agent that failed at minute 18, with no idea which of 2,000 spans to look at first.

The transcript view was the first thing we built. It is the thing most tools still do not have. Signals came next, because the failure mode you care about today is not the one your dashboards captured a month ago. Agent rollout came last, because replay is not enough when you want to change a prompt mid-run and see what would have happened.

If you are looking at alternatives to Langfuse because your agents broke the prompt-first model, these three primitives are the reason to try Laminar first.

Start with the free tier: 1GB of traces, 15-day retention. Instrument one agent. If you do not see the difference in the first hour, come back and tell us why.

Try Laminar free · Read the docs · Star on GitHub

For agent debugging and long-running agent observability, Laminar is the best Langfuse alternative. It is open-source (Apache 2.0), OpenTelemetry-native, and built specifically for multi-step agents with a transcript view, Signals, SQL over traces, and an agent rollout debugger. LangSmith is the best alternative if you are committed to LangGraph; Braintrust is the best alternative for eval-first workflows.

Close, but not identical. Laminar ingests OpenTelemetry, which means if you already use OpenLLMetry or OpenInference you can point the exporter at Laminar without re-instrumenting. Langfuse observations map cleanly to OTel spans. Prompt management is the one area where Laminar does not overlap with Langfuse; treat that workflow separately.

Yes. Langfuse is MIT-licensed with a free self-host that includes all core features. Laminar is Apache 2.0 and ships a Helm chart for one-command self-host with every feature on the OSS image. Phoenix is Elastic 2.0. Helicone is Apache 2.0. If open-source self-host is the requirement, all four are valid options.

Langfuse is optimized for prompt versioning, evaluation, and structured observation logging on single LLM calls or short chains. Laminar is optimized for debugging long-running agents: transcript view instead of observation lists, Signals for outcome tracking across history, SQL over traces for ad-hoc analysis, and agent rollout for re-running from any span. Full comparison: Laminar vs Langfuse.

You cannot re-export Langfuse-stored traces to another backend directly. What you can do is instrument your app with OpenTelemetry (OpenLLMetry or OpenInference) and send to multiple OTLP endpoints at once, keeping Langfuse alongside Laminar, Phoenix, or another backend during migration.

Agent observability is the practice of capturing and debugging the full execution of an AI agent, including every LLM call, tool call, retrieval, and sub-agent invocation. It differs from classical LLM observability because agent runs are long, non-deterministic, and deeply nested. Agent-specific tooling renders the run as a transcript, supports natural-language outcome tracking, and lets you re-run the agent from any point. See our ranked list of the top agent observability platforms for the full field.

Pricing varies by model. Laminar: data-volume pricing, free tier 1GB, Hobby $30/month for 3GB, Pro $150/month for 10GB. LangSmith: seats plus traces, $39/seat/month plus $0.50 per 1k base traces. Phoenix: free open-source, Arize AX custom. Braintrust: free tier plus usage-based Pro. Weave: scales with volume and seats. Helicone: free tier plus request-based paid plans. For agents with large traces, data-volume pricing is the most predictable. Self-hosting Laminar is free.

Last updated: April 2026. Verify features and pricing against each vendor's current documentation before committing.