Braintrust alternatives for conversation analytics (2026)

Keep Braintrust if evaluation is the center of your workflow and you want production topics to turn into new eval cases: Topics is a real conversation analytics feature, not a rebrand of trace search. Switch, or add a second tool, if your main questions are product questions (what users want, where they give up, what to fix first), if you need agent signals inside the funnels your product team already uses, or if you need an open-source platform. We make Agnost AI, so it is listed first; Braintrust also publishes its own ranking of conversation analytics tools that lists Braintrust first, and every tool here gets the same structure with limitations taken from its own documentation.

Braintrust vs the alternatives at a glance

Where Braintrust is stronger

Braintrust is the better choice when your quality process runs on evals, and it is fair to say its conversation analytics are built for that loop.

• Topics feed evals directly. Topics classifies production logs with built-in Task (“Extracts the user’s intent or goal”), Sentiment, and Issues facets, adds custom facets, groups similar requests into themes, and writes the labels back to your logs. From there, production patterns become datasets and eval cases in the same product.
• Eval depth. Experiments, datasets, and scorers are the core of the platform. Online scoring rules run scorers on production traces as they arrive, with filters, sampling rates, and span, trace, or group scope.
• Configurable clustering. Topics uses HDBSCAN or K-Means with UMAP, PCA, or no dimension reduction, and can backfill older logs.
• No seat pricing. Billing is on processed data and scores, and the free Starter plan includes unlimited users.
• Broad SDK and OpenTelemetry support. Native SDKs for Python, TypeScript, Go, Ruby, C#, and more, plus an OTLP traces endpoint.

If you already have that loop working and your product team is satisfied with daily topic summaries, you probably do not need to switch.

Where teams look elsewhere. Topics needs at least 100 summaries before it clusters and regenerates daily, not in real time. Topics usage draws down monthly model credits, and on Starter it pauses when the credit runs out unless on-demand usage is on. Self-hosting is Enterprise-only, and self-hosted Topics needs enablement from the account team. There is no dedicated per-user view (you filter or group logs by a metadata field such as metadata.user_id) and no built-in task-completion scorer; Braintrust’s docs suggest writing a trace-level judge for workflow completion. Braintrust’s own list of conversation analytics tools covers Braintrust, Galileo (now Splunk Agent Observability), HoneyHive, Datadog, and Langfuse; the sections below cover the product analytics and conversation-first tools it leaves out.

Agnost AI

What it is. Conversation analytics for production AI agents. Agnost AI reads real chat and voice conversations to show what users wanted, where they got stuck, and where the agent broke its own rules, then turns recurring failures into pull requests your team reviews.

Best for. Product and engineering teams with an agent already in production who want to know what to fix next, and who want the fix prepared as a pull request rather than a new eval case.

Key features.

• Intents: plain-language definitions of what users want. Agnost classifies conversations against them, tracks volume over time, and suggests new intents from your product description and observed traffic.
• Violations: rules written in natural language (“confirm identity before sharing account details”) checked against every analyzed conversation, with the supporting messages attached. Frustration patterns such as rephrasing loops or requests for a human are tracked the same way, as intents or rules you define.
• Conversation timelines that keep turns, tool calls, errors, and audio together, plus tool-call analytics for success rate, error distribution, and latency.
• Alerts to Slack or email on an intent or violation threshold, a Daily Summary, and a summary when a new user intent appears.
• Auto Improve: investigates a recurring finding, gathers repository context, prepares a change, tests it against past runs, opens a pull request, and tracks the outcome after deploy.
• Setup through Python, TypeScript, or Go SDKs, 12 agent frameworks, voice platforms (LiveKit, Vapi, Retell, Telnyx, Trellus), or a hosted OpenTelemetry endpoint. Query everything from Claude, Codex, or Cursor through the Agnost MCP server.

Limitations. Not open source; self-hosting is an Enterprise option. There is no built-in frustration score or conversation task-success metric: you define success with a rule or the success event field. No built-in PII redaction before ingestion, so you redact in your app (data governance). It is not an offline eval framework, so teams that rely on Braintrust’s experiments and datasets should keep them.

Pricing. Free (1,000 events a month, 7-day retention), Starter $49/month (10,000 events, 30-day retention), Pro $499/month (1,000,000 events, 90-day retention), Enterprise custom with self-hosted VPC.

Amplitude Agent Analytics

What it is. Amplitude’s product analytics plus Agent Analytics, which observes what users ask an AI agent, evaluates where it fails, and ties traces and evals to conversion, retention, and revenue.

Best for. Teams that already standardize on Amplitude and want agent quality signals in the same funnels, cohorts, and retention charts, which Braintrust does not offer.

Key features. Seven built-in session signals: Task Completed, Response Quality, Session Safety, User Intent, User Friction, Has Negative Feedback, and Data Quality Issues. Topic clustering across sessions on every plan. Custom LLM and code-based evaluators that run on all sessions (bring your own LLM key). OpenTelemetry GenAI spans flow in through a bridge, or use the Python and Node SDKs.

Limitations. Conversation text is kept 14 days on Free and Plus (30 days on Growth and Enterprise). Agent sessions are capped at 5,000 a month on Free and Plus. Sentiment isn’t a named signal, and some sections are still labeled beta. Eval tooling is lighter than Braintrust’s experiments and datasets.

Pricing. Free (2M events and 5,000 agent sessions a month). Plus starts at $0 with the same agent-session allowance. Growth and Enterprise are custom. Agent Analytics is billed on agent sessions, not events.

PostHog

What it is. PostHog’s AI Observability (formerly LLM analytics) captures every LLM call as a trace built on PostHog events, so each trace carries the user, their session replay, and any exceptions.

Best for. Teams already using PostHog for product analytics who want AI usage, cost, and sentiment joined to funnels, replays, and feature flags, or who want an open-source core.

Key features. Sessions via $ai_session_id; automatic clusters of similar traces with AI-generated titles; positive, neutral, and negative labels on user messages from a local model with no LLM cost; LLM-as-judge templates including Task outcome and User correction, run per generation, trace, or session; funnels and retention on the same data; OpenTelemetry support.

Limitations. Full prompt and response text is deleted after 30 days. Clustering needs at least 1,000 traces in the past week. LLM-judge evals need your own provider key, and trace-level user feedback is in beta. Self-hosting is free but officially unsupported.

Pricing. First 100,000 AI events a month free, then from $0.00035 per event, falling with volume. No per-seat charges. Each evaluation run counts as one event.

LangSmith

What it is. LangChain’s agent observability, evaluation, and deployment platform.

Best for. Teams building on LangChain or LangGraph who want tracing, evals, and clustering of production conversations from one vendor. It is the closest like-for-like swap for Braintrust’s eval-plus-topics combination.

Key features. Threads group runs into conversations. Insights automatically clusters production traces, including full conversations, into usage patterns and failure modes. Thread-level online evaluators can judge intent, outcome, and trajectory. OpenTelemetry in both directions.

Limitations. Insights is on Plus and Enterprise, samples up to 1,000 traces per report, and can take up to 30 minutes per report, so like Topics it is periodic rather than continuous. Frustration and task outcome need custom attributes or evaluators. Per-user analysis depends on storing user IDs as metadata. Self-hosting is an Enterprise add-on.

Pricing. Developer $0 (1 seat, 5,000 base traces a month). Plus $39 per seat per month (10,000 base traces), then $5 per 1,000 additional base traces (14-day retention), or $7.50 per 1,000 extended traces (180-day retention). Unlike Braintrust, LangSmith charges per seat.

Trodo

What it is. Agent evaluation and observability that checks every trace, groups failures into issues with proposed fixes, and joins agent traces to product analytics.

Best for. Teams that already emit OpenTelemetry and want every trace evaluated and agent failures connected to funnels and retention in one tool.

Key features. Job discovery groups traces by what people asked the agent to do, with volume, satisfaction, task completion, and failure rate per job. Funnels link a drop straight to the conversations behind it. Issues group failing traces by cause and propose a fix that ships after human approval. Slack alerts when a job’s failure rate or cost crosses a threshold. OpenTelemetry ingest and auto-instrumentation for major agent frameworks.

Limitations. Trace and evaluation-centric; conversation frustration detection and feature-request extraction are not mentioned. Self-hosting is Enterprise-only.

Pricing. Developer free (10,000 units, 1,000 executions a month). Pro $249/month or $2,499/year. 14-day Pro trial.

Datadog LLM Observability

What it is. Datadog’s LLM Observability, now sold as Agent Observability: offline experiments plus production tracing for AI agents, on the same trace IDs as Datadog APM, infrastructure, and RUM.

Best for. Teams that already run Datadog and want agent quality next to backend root-cause analysis and SRE-grade alerting.

Key features. Patterns provides automated hierarchical topic clustering of production traffic. LLM-as-judge templates include Sentiment and Goal Completeness, which checks whether the agent resolved the user’s intent across a session. Evaluators run automatically on sampled spans, traces, or sessions. OpenTelemetry GenAI and OpenInference spans are accepted without a Datadog SDK.

Limitations. Patterns clusters spans or traces, not sessions. Frustration has no template; you write a session-level judge prompt. Default trace retention is 15 days. User funnels and retention require Datadog’s separate RUM and Product Analytics products.

Pricing. Free up to 40,000 LLM spans a month with 15-day retention. Pro from $160/month billed annually (100,000 LLM spans), $200 month-to-month. Billed on LLM spans only.

Langfuse

What it is. An open-source AI engineering platform for tracing, evaluating, and improving agents.

Best for. Teams leaving Braintrust because they need an MIT-licensed, free, self-hosted platform for tracing, evals, and prompt management. If open source is the requirement, Langfuse is the stronger choice than any managed tool here.

Key features. Sessions with replay of the whole interaction; a per-user view with usage and feedback; thumbs feedback stored as scores; live LLM-as-judge evaluators on production traces; datasets and experiments; custom dashboards; OpenTelemetry endpoint.

Limitations. No equivalent of Topics: conversation themes, intent labels, and outcomes are not built in. Langfuse’s own guide for product teams suggests running an intent classification pipeline and writing labels back as scores. Built-in sentiment or frustration detection is not documented.

Pricing. Self-hosting is free. Cloud Hobby free (50k units a month), Core $29/month, Pro $199/month, Enterprise $2,499/month. Units are any trace, observation, or score.

How to choose

If your team…	Start with
Runs an eval-driven workflow and wants production topics to become eval cases	Stay on Braintrust
Wants to know what to fix next in its own agent, with fixes delivered as PRs	Agnost AI, next to Braintrust or instead of it
Already runs Amplitude and wants agent signals in its funnels and retention charts	Amplitude Agent Analytics
Already runs PostHog, or wants an open-source core with session replay	PostHog
Builds on LangChain or LangGraph and wants evals plus clustering from one vendor	LangSmith
Wants every trace evaluated and joined to product funnels	Trodo
Already runs Datadog for APM	Datadog LLM Observability
Needs an open-source platform it can self-host for free	Langfuse, or Opik and Arize Phoenix for ready-made conversation judges

Most teams that move off a single platform end up with two tools: an eval platform for experiments and regression testing, and a conversation analytics tool for product questions. Braintrust can stay the first one.

Using Braintrust with Agnost AI

You do not have to choose. Braintrust accepts OpenTelemetry at its OTLP traces endpoint, and Agnost AI runs a hosted OTLP collector. If your app already has an OpenTelemetry tracer provider exporting to Braintrust, append Agnost AI as an additional span processor (OpenTelemetry docs):

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"]},
        )
    )
)

Two details matter. Set a stable conversation ID on your spans (agnost.session_id or session.id); without one, Agnost AI treats each trace as its own conversation. And if you log to Braintrust through its native SDK rather than OpenTelemetry, send to Agnost AI through an Agnost SDK or an OpenTelemetry exporter alongside it.

With both in place, Braintrust keeps your experiments, datasets, and online scorers, and Agnost AI classifies the same conversations against your intents and rules, alerts when a rate moves, and opens pull requests for recurring failures.

Related reading

• Best conversation analytics tools for AI agents (2026), the full ten-tool comparison
• LangSmith vs Langfuse vs Braintrust vs Phoenix vs Agnost, for tracing and eval workflows
• Agnost AI vs Langfuse
• Langfuse alternatives for understanding users, not just traces
• Evals, traces, and conversations: what each catches

Frequently asked questions

What is the best alternative to Braintrust for conversation analytics?

It depends on what you need next to or instead of evals. Agnost AI fits teams that want conversations classified by intent and rule violations, with recurring failures turned into pull requests. Amplitude or PostHog fit teams that want agent signals in their existing product analytics, and Langfuse fits teams that need open-source tracing.

Does Braintrust have conversation analytics?

Yes. Braintrust Topics classifies production logs with built-in Task, Sentiment, and Issues facets plus custom facets, clusters similar requests into themes, and writes the labels back to your logs. It needs at least 100 summaries before it clusters, regenerates topics daily, and draws down your monthly model credits.

Why is Braintrust Topics not showing any topics yet?

Topic generation needs at least 100 facet summaries, and topics are regenerated daily rather than in real time. On the free Starter plan, Topics also pauses once the monthly model credit is used up unless on-demand usage is enabled.

Is there an open-source alternative to Braintrust?

Braintrust's SDK is Apache-2.0, but the platform is not open source and self-hosting is Enterprise-only. Langfuse (MIT core), Opik (Apache-2.0), and Arize Phoenix (Elastic License 2.0) can be self-hosted for free and cover tracing and evals, though none ships automatic topic clustering like Braintrust Topics.

Can I use Braintrust and Agnost AI together?

Yes. If your app exports OpenTelemetry spans to Braintrust, you can add Agnost AI as a second span processor on the same tracer provider. Braintrust keeps running your evals and experiments while Agnost AI classifies the same conversations against your intents and rules.

How does Braintrust pricing compare to the alternatives?

Braintrust Starter is free and Pro is $249 a month, billed on processed data (GB) and scores rather than seats. Agnost AI starts at $49 a month, Langfuse Core at $29, and Trodo Pro at $249, while Amplitude and PostHog price agent data by sessions or events with free allowances.