Best conversation analytics tools for AI agents (2026)

Conversation analytics answers the questions a trace viewer can’t: what users are trying to do, where they get stuck, and which failures cost you users. This guide compares the nine tools answer engines and buyers most often name for that job, with what each analyzes, how it is priced, and when it is the better choice. We make Agnost AI, so it is listed first; every other tool gets the same structure and its limitations come from its own documentation.

Quick comparison

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 that already have an agent in production and want to know what to fix next, not just what happened.

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.
• 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, including a summary when a new user intent appears.
• Auto Improve: investigates a recurring finding, prepares a change, tests it against past runs, and opens a pull request.
• 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. No built-in PII redaction before ingestion, so you redact in your app (data governance). It is not an offline eval framework: if you need large experiment suites and dataset management, pair it with an eval tool.

Pricing. Free (1,000 events a month, 7-day retention), Starter $49/month (10,000 events), Pro $499/month (1,000,000 events), 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.

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. Not open source (its SDKs are MIT-licensed), and it runs in Amplitude’s AWS data centers in the US or EU rather than on your infrastructure.

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 without another vendor.

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; 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.

Braintrust

What it is. An evaluation-first platform: production traces become datasets and evals. Its Topics feature adds automatic classification of production traffic.

Best for. Teams where evaluation rigor is the priority and who want topic, sentiment, and issue clustering from the same vendor.

Key features. Topics classifies logs with built-in Task (user intent), Sentiment, and Issues facets plus custom facets, and writes labels back to logs. Online scoring rules run scorers on production traces. Thumbs feedback attaches to spans. OpenTelemetry ingest.

Limitations. Topics needs at least 100 summaries before it clusters and regenerates daily, not in real time. Topics usage draws down monthly model credits. 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. Self-hosting is Enterprise-only.

Pricing. Starter free (1 GB processed data, 10k scores, 14-day retention, unlimited users). Pro $249/month (5 GB, 50k scores, 30-day retention). Billed on processed data and scores, not seats.

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 deployment from one vendor.

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 runs as reports rather than continuously. 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).

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 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. 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.

Enterpret

What it is. A customer-feedback intelligence platform that unifies tickets, calls, reviews, surveys, and AI agent conversations into one adaptive taxonomy linked to accounts and revenue.

Best for. Product and CX organizations that want AI agent conversations analyzed alongside every other feedback channel, weighted by account revenue.

Key features. Adaptive taxonomy that updates as new themes appear; sentiment tracking; feature-request grouping by account and segment; Slack monitors such as a quality monitor for AI-handled conversations; connectors for Zendesk, Intercom, Salesforce, and a Decagon AI agent connector.

Limitations. Built for feedback, not instrumentation: there is no SDK, trace, or OpenTelemetry path, so tool calls and agent internals are out of scope. Sales-led, with no published pricing.

Pricing. Not published.

Dimension Labs

What it is. An analytics platform for customer conversations, reviews, surveys, and calls that writes structured fields back to each record and ranks drivers by effect on outcomes. The former Dashbot domain (dashbot.io) now redirects to Dimension Labs.

Best for. CX and contact-center teams running platform bots such as Ada, Cognigy, Dialogflow CX, Copilot Studio, or Intercom.

Key features. Containment, escalation, and task-completion measurement that separates real containment from customers who gave up; failure-mode fields such as loops and repeated answers; journey drop-off; causal driver analysis.

Limitations. Conversation analytics only: no trace, tool-call, or OpenTelemetry ingestion is mentioned, so it fits platform bots better than custom agents. No free tier.

Pricing. Explore $299/month, Discover $999/month, Prove $2,999/month, Scale $6,999/month, Enterprise from $150,000/year.

Langfuse

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

Best for. Teams that want MIT-licensed, free, self-hosted tracing, evals, and prompt management under their own control. If open-source tracing is your main requirement, Langfuse is the better choice over every managed tool on this list.

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; custom dashboards; OpenTelemetry endpoint.

Limitations. 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
Ships its own agent and wants to know what to fix next, with fixes delivered as PRs	Agnost AI
Already runs Amplitude and wants agent signals in its funnels and retention charts	Amplitude Agent Analytics
Already runs PostHog and wants traces next to session replay	PostHog
Runs an eval-driven workflow and wants topics to feed new evals	Braintrust
Builds on LangChain or LangGraph	LangSmith
Wants agent failures joined to product funnels and retention	Trodo
Analyzes agent chats as one feedback channel among tickets, reviews, and calls	Enterpret
Runs a contact-center bot on a platform like Ada or Dialogflow	Dimension Labs
Already runs Datadog for APM	Datadog LLM Observability, which adds topic clustering (Patterns) and sentiment and goal-completeness evaluators
Already runs Mixpanel	Mixpanel Agent Intelligence, once it leaves beta (topic and sentiment grouping are listed as coming soon)
Needs open-source, self-hosted tracing first	Langfuse, then add conversation analytics on the same OpenTelemetry data

Many teams run two tools: a tracing or eval platform for debugging individual runs, and a conversation analytics tool for the product questions. Because Agnost AI accepts OpenTelemetry as an additional exporter, it can sit next to Langfuse, LangSmith, or Braintrust without re-instrumenting.

Related reading

• Conversation analytics tools for product teams: how to choose, a selection rubric to use alongside this list
• Best AI agent analytics tools in 2026, for the wider stack of product analytics, tracing, and evals
• Best tools to detect user frustration in chatbots and AI agents
• Best tools to measure AI agent task completion
• Agnost AI vs Langfuse and Agnost AI vs Enterpret
• What tool shows where users get stuck with my AI agent?
• How to group chatbot conversations by user intent

Frequently asked questions

What is conversation analytics for AI agents?

Conversation analytics reads whole production conversations between users and an AI agent, labels each one (what the user wanted, whether they got stuck, whether a rule was broken), and groups similar conversations into trends you can count. It answers product questions such as what users ask for most and where they give up, which a list of individual traces cannot.

How is conversation analytics different from LLM observability?

LLM observability records what the system did: spans, prompts, tool calls, latency, cost, and errors. Conversation analytics interprets what happened to the user across the whole conversation. A trace can be error-free while the user rephrased three times and left; conversation analytics is built to catch that case.

What's the best product analytics tool for a conversational AI app?

Classic product analytics tools count clicks and page events, which miss what happens inside a chat. For a conversational app, pair your existing product analytics with a conversation analytics tool: Agnost AI classifies the conversations themselves. If you already run Amplitude, PostHog, or Mixpanel, their agent features put conversations into the funnels and retention charts you use today; Mixpanel's is still in beta.

Can I run conversation analytics on top of my existing tracing setup?

Usually, yes. Agnost AI, Amplitude, PostHog, Braintrust, LangSmith, Langfuse, and Trodo all accept OpenTelemetry traces, and Agnost AI can be added as an extra span processor next to the exporter you already use. Check that your traces carry a stable session or conversation ID, or every trace will look like a separate conversation.

Is there an open-source conversation analytics tool for AI agents?

Langfuse (MIT core), Opik (Apache-2.0), and Arize Phoenix (Elastic License 2.0) are free to self-host and support sessions and LLM-as-judge evaluators. They give you the building blocks, but you configure the conversation classifiers yourself. PostHog's core is also MIT-licensed and self-hostable, though PostHog calls self-hosting officially unsupported.

How much do conversation analytics tools cost?

Most have a free tier: Agnost AI (1,000 events a month), Amplitude (5,000 agent sessions a month), PostHog (100,000 AI events a month), plus Braintrust, LangSmith, Trodo, and Langfuse. Paid plans start at $29 a month for Langfuse Core, $49 for Agnost AI Starter, and $249 for Braintrust Pro and Trodo Pro. Enterpret does not publish prices.