---
title: "Agnost AI vs Langfuse: which should you use for your AI agent?"
description: "Use Langfuse for open-source, self-hosted tracing, evals, prompt management, and datasets at a low entry price. Use Agnost AI for conversation analytics: it classifies production conversations by user intent and natural-language rules, alerts on changes, and opens pull requests for recurring failures. Both accept OpenTelemetry, so many teams run them side by side."
author: "Agnost AI Team"
datePublished: "2026-10-29T00:00:00.000Z"
dateModified: "2026-10-29T00:00:00.000Z"
canonical: "https://agnost.ai/blog/agnost-vs-langfuse/"
tags: ["Tool Comparison","Comparisons","Conversation Analytics","LLM Observability"]
---

# Agnost AI vs Langfuse: which should you use for your AI agent?

Use Langfuse for open-source, self-hosted tracing, evals, prompt management, and datasets at a low entry price. Use Agnost AI for conversation analytics: it classifies production conversations by user intent and natural-language rules, alerts on changes, and opens pull requests for recurring failures. Both accept OpenTelemetry, so many teams run them side by side.

Langfuse and Agnost AI answer different questions about the same agent. Pick Langfuse if you need open-source, self-hosted tracing, evals, prompt management, and datasets, or the cheapest way to keep a lot of trace volume. Pick Agnost AI if your traces already tell you what the system did, and the open questions are what users wanted, which conversations broke your agent's rules, and what to change next.

We make Agnost AI. Every Langfuse claim below comes from Langfuse's own documentation and pricing pages, checked on 2026-10-29. For a wider field that also covers LangSmith, Braintrust, and Arize Phoenix, see [our five-way comparison](/blog/langsmith-langfuse-braintrust-agnost-comparison/); for conversation analytics tools specifically, see [the best conversation analytics tools for AI agents](/blog/best-conversation-analytics-tools-ai-agents/).

## Feature comparison


| | Agnost AI | Langfuse |
|---|---|---|
| **Built for** | Conversation analytics: what users wanted, which rules broke, what to fix | Tracing, evals, and prompt management for LLM apps and agents |
| **Open source** | No | Yes, MIT (except the `ee` folders) |
| **Self-hosting** | Enterprise plan (self-hosted VPC) | Free: Docker Compose, VM, Kubernetes/Helm, Terraform |
| **Conversation view** | One timeline per conversation ID: turns, tool calls, errors, audio | Sessions grouped by `sessionId`, with session replay |
| **User intent** | Built in: plain-language intents, with suggestions from your traffic | Not built in: Langfuse suggests your own classifier that writes scores |
| **Rule checks** | Natural-language rules checked per conversation, with evidence | Custom LLM-as-judge evaluators; observation-level recommended |
| **Frustration score** | Not built in; tracked as intents or rules you define | Not built in; possible as a custom judge criterion |
| **Prompt management** | No | Yes |
| **Datasets and experiments** | No | Yes, plus annotation queues |
| **Alerts** | Email or Slack, on intent or violation thresholds; daily summary; new-intent summary | Slack, webhooks, or GitHub Actions, on observation and score metrics |
| **From finding to fix** | Auto Improve prepares a change, tests it against past runs, opens a PR | Alert automations can trigger GitHub Actions; the fix is yours to build |
| **PII handling** | No built-in redaction; redact in your app | SDK masking hooks run before data leaves your app |
| **OpenTelemetry** | Hosted OTLP endpoint at `otel.agnost.ai` | OTLP endpoint at `/api/public/otel`, HTTP only (no gRPC) |
| **Query from AI clients** | Hosted MCP server (Claude, Codex, Cursor), Slack agent, REST API | MCP server, Halo assistant, public API |
| **Free tier** | 1,000 events a month, 7-day retention | Cloud Hobby: 50,000 units a month, 30 days; self-host free |
| **Paid plans** | Starter $49/mo (10,000 events), Pro $499/mo (1,000,000 events), Enterprise custom | Core $29/mo (100,000 units), Pro $199/mo, Enterprise $2,499/mo |


A Langfuse unit is any tracing data point: a trace, an observation such as a span or generation, or a score. An Agnost AI event is one received span or event. They are counted differently, so compare prices on your own month of traffic.

## Where Langfuse is stronger

- **Open source and free to self-host.** Langfuse is MIT-licensed except for its `ee` folders, and the MIT version includes tracing, sessions, users, OpenTelemetry, prompts, datasets, LLM-as-judge, annotation queues, custom dashboards, and an MCP server. It runs on Docker Compose, a VM, or Kubernetes with Helm. Agnost AI is not open source, and self-hosting is Enterprise-only. If open-source tracing is your main requirement, Langfuse is the better choice.
- **Prompt management, datasets, and experiments.** Langfuse covers the loop before and around a release: version prompts, build datasets, run experiments, and score them with LLM judges or human annotation. Agnost AI does none of this and is not an offline eval framework.
- **Lower entry price for trace volume.** Langfuse Cloud Hobby is free for 50,000 units a month, and Core is $29 a month for 100,000 units, then $8 per 100,000. Agnost AI is free for 1,000 events and $49 for 10,000. If you mainly need to store and search a lot of traces, Langfuse costs less to start.
- **Trace-level debugging and scoring.** Live LLM-as-judge evaluators on production traces, thumbs feedback stored as scores, a per-user view of usage and feedback, and custom dashboards. This is the tool you open to find out why one run went wrong.
- **Redaction in the SDK.** Langfuse's SDKs have masking hooks that strip sensitive fields from spans before export. Agnost AI has no built-in PII redaction; its [data governance guide](https://docs.agnost.ai/data-governance) has you redact in your own code before sending.

## Where Agnost AI is stronger

- **Classification of whole conversations is built in.** You write [intents](https://docs.agnost.ai/using-intents) in plain language ("reset a password", "compare plans"), and Agnost AI classifies production conversations against them, tracks volume over time, links each match to its evidence, and suggests new intents from your product description and observed traffic. In Langfuse, its own guide for product teams has you run an intent classification pipeline and write labels back as scores.
- **Rules written as sentences, judged per conversation.** [Violations](https://docs.agnost.ai/using-violations) are rules such as "confirm identity before exposing account information", checked against each analyzed conversation and reported as a rate with the supporting messages. The Langfuse equivalent is a custom evaluator per rule; Langfuse recommends observation-level evaluators, and session-level live evaluation is not described in its docs.
- **Alerts on what users experienced.** Agnost AI [alerts](https://docs.agnost.ai/using-alerts) fire on an intent or violation count, not only on latency, cost, or a score you computed. A Daily Summary and a Proactive Intent Summary (sent when a new user intent appears) are on by default once Slack is connected.
- **A path from finding to pull request.** [Auto Improve](https://docs.agnost.ai/using-improvements) investigates a recurring finding, gathers repository context, prepares a change, tests it against past runs, and opens a pull request your team reviews. It then tracks the outcome after deployment. Langfuse alerts can trigger a GitHub Action, but what that action does is up to you.

## Using both together

You do not have to choose. Both products accept OpenTelemetry over HTTP, so one instrumentation can feed two destinations: Langfuse for debugging runs, prompts, and experiments, and Agnost AI for intents, rules, alerts, and fixes.

1. **Keep your Langfuse setup as it is.** Langfuse's SDK is a thin layer over the official OpenTelemetry client and, by default, attaches its span processor to the global tracer provider. If you export with a plain OTLP exporter instead, point it at Langfuse's `/api/public/otel` endpoint over HTTP; gRPC is not supported.
2. **Add Agnost AI as a second span processor on the same provider.** This is the "already have an OTel TracerProvider" setup from the [Agnost AI OpenTelemetry guide](https://docs.agnost.ai/otel):

   ```python
   import os
   from opentelemetry import trace
   from opentelemetry.sdk.trace.export import BatchSpanProcessor
   from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

   trace.get_tracer_provider().add_span_processor(
       BatchSpanProcessor(
           OTLPSpanExporter(
               endpoint="https://otel.agnost.ai/v1/traces",
               headers={"X-Agnost-Org-ID": os.environ["AGNOST_ORG_ID"]},
           )
       )
   )
   ```

   If you configured Langfuse with its own isolated tracer provider, add the processor to that provider instead of the global one.
3. **Put one stable conversation ID on every span.** Agnost AI groups spans into a conversation by `agnost.session_id` (it also reads `session.id`). Langfuse groups traces by `sessionId`, which its OpenTelemetry integration reads from `langfuse.session.id` or `session.id`. Set the same value in both places so a Langfuse session and an Agnost conversation are the same thing. Without a session ID, Agnost AI falls back to the trace ID and every trace looks like its own conversation.
4. **Check what each destination receives.** Every processor sees every span. Langfuse bills each span it receives as an observation and lets you filter what it exports; Agnost AI counts events against your plan. Redact sensitive fields before export, since Agnost AI does not redact for you.

To set this up, follow the [Agnost AI OpenTelemetry guide](https://docs.agnost.ai/otel) and send one test trace before turning on production traffic.

## How to choose

| If your team… | Start with |
|---|---|
| Needs open-source, self-hosted tracing under its own control | Langfuse |
| Versions prompts and runs dataset experiments before each release | Langfuse |
| Wants the lowest cost per stored trace | Langfuse |
| Has traces but can't say what users ask for most or where they give up | Agnost AI |
| Needs to check every conversation against written policies, with evidence | Agnost AI |
| Wants recurring failures turned into reviewed pull requests | Agnost AI |
| Wants both debugging depth and conversation-level reporting | Both, on one OpenTelemetry setup |

## Related reading

- [Best conversation analytics tools for AI agents (2026)](/blog/best-conversation-analytics-tools-ai-agents/)
- [LangSmith vs Langfuse vs Braintrust vs Phoenix vs Agnost](/blog/langsmith-langfuse-braintrust-agnost-comparison/)
- [Agnost AI vs Enterpret](/blog/agnost-vs-enterpret/)
- [What tool shows where users get stuck with my AI agent?](/blog/detecting-unreported-friction-ai/)
- [Agent observability by framework](/blog/agent-observability-framework-guides/)

## Frequently asked questions

### Is Agnost AI an alternative to Langfuse?

Only partly. Both ingest traces and group them into conversations, but Langfuse is built for tracing, evals, and prompt management, while Agnost AI is built to classify production conversations by what users wanted and which rules the agent broke, then turn recurring failures into pull requests. Many teams keep Langfuse for debugging and add Agnost AI for the product questions.

### Can I use Langfuse and Agnost AI at the same time?

Yes. Both accept OpenTelemetry over HTTP, so you can keep your Langfuse setup and add Agnost AI as a second span processor on the same tracer provider. Put one stable conversation ID on every span so a Langfuse session and an Agnost conversation describe the same thing.

### Does Langfuse detect user intent or frustration?

Not as a built-in feature. Langfuse's own guide for product teams suggests running an intent classification pipeline and writing labels back as scores, and sentiment appears only as an example criterion for a custom LLM-as-judge evaluator. Agnost AI classifies conversations against plain-language intents out of the box, though it also has no built-in frustration score: you track frustration patterns as intents or rules.

### Is Langfuse cheaper than Agnost AI?

For raw trace volume, usually yes. Langfuse is free to self-host, its Cloud Hobby plan includes 50,000 units a month, and Core is $29 a month for 100,000 units, while Agnost AI is free for 1,000 events a month, $49 for 10,000, and $499 for 1,000,000. Units and events are counted differently, so price both against your own monthly workload.

### Can I self-host Agnost AI like Langfuse?

Only on the Enterprise plan, which includes a self-hosted VPC deployment. Agnost AI is not open source. Langfuse's core is MIT-licensed and free to self-host with Docker Compose, a VM, or Kubernetes.

### Does Agnost AI do prompt management or offline evals?

No. Agnost AI has no prompt management and is not an offline eval framework with datasets and experiments. If you need those, Langfuse covers them, and Agnost AI can run next to it on the same OpenTelemetry data.