What is AI agent analytics?

AI agent analytics is the practice of tracking and measuring AI agent behavior in production — capturing every prompt, tool call, agent trace, and user outcome. Trodo provides AI agent analytics that connects agent performance to product KPIs like retention and conversion.

What is agent observability?

Agent observability is the engineering discipline of capturing detailed telemetry from AI agents: traces, spans, prompts, completions, tool calls, latencies, errors, and cost. Trodo combines agent observability with product analytics in one unified platform.

How does Trodo track AI agents?

Trodo tracks AI agents by instrumenting every agent run with a lightweight SDK. Each run captures the full trace — planner steps, tool calls, LLM calls, retrieved context, and final output — plus user identity and session context, so every agent execution is connected to a real product outcome.

What is AI product analytics?

AI product analytics is product analytics purpose-built for AI-native applications. Unlike traditional product analytics that tracks clicks and page views, AI product analytics tracks prompts, tool calls, agent traces, and AI feature adoption — giving product teams the behavioral data they need to improve AI products.

Is Trodo a LangSmith alternative?

Trodo and LangSmith solve adjacent but different problems. LangSmith is an LLM observability and evaluation tool aimed at engineers; Trodo is an AI product analytics and AI agent analytics platform aimed at product, growth, and engineering teams together. Many teams use both — LangSmith for prompt-level evaluations and Trodo for agent analytics joined to user behavior. Some teams replace LangSmith with Trodo if their primary need is product analytics rather than evaluations.

What does LangSmith do that Trodo does not?

LangSmith has deep evaluation, prompt experimentation, and dataset management features built around LLM call quality. If your main job is to A/B test prompts, run regression evaluations against datasets, or tune individual model calls, LangSmith is purpose-built for that. Trodo focuses on the product side — funnels, retention, cohorts, agent runs joined to user outcomes.

What does Trodo do that LangSmith does not?

Trodo natively models product analytics on top of agent data: AI feature adoption, retention curves, funnels through multi-step agent flows, revenue attribution by agent, and natural-language querying for non-engineers. It also unifies classic product events with AI traces in a single layer, which LangSmith does not.

Can I use Trodo with my existing LangChain or LangGraph stack?

Yes. Trodo ingests traces from LangChain, LangGraph, OpenAI agents, the Anthropic SDK, and OpenTelemetry-compatible sources. You can keep LangSmith for engineering-side evaluation and stream the same traces (or a Trodo SDK call) into Trodo for product analytics.

Which is better for production AI agents?

For production agents at scale, you usually want both: LangSmith (or a similar LLM observability tool) for engineering-side evaluation, and Trodo for AI agent analytics, agent observability, and product KPIs. If you have to pick one, choose based on which audience suffers more without it — engineering teams without LangSmith struggle to evaluate prompt quality; product and growth teams without Trodo struggle to know whether the agents are creating value.

Trodo

Trodo vs LangSmith: AI Product Analytics or LLM Observability?

A direct comparison of Trodo and LangSmith — what each does, who it serves, and how to decide which one (or both) your AI team needs in 2026.

Published April 26, 202610 min read

Trodo vs LangSmithLangSmith alternativeLangSmith comparisonAI product analyticsLLM observabilityAI agent analyticsagent observability

Trodo and LangSmith come up together constantly in 2026 — both are products that touch AI traces, both target teams running AI in production, and both market themselves as essential infrastructure. But they sit in different categories. This post lays out exactly what each does, who it is for, and how to decide which (or both) your team needs.

Short version

LangSmith is an LLM observability and evaluation platform. Trodo is an AI product analytics, AI agent analytics, and agent observability platform. LangSmith is built for ML and platform engineers improving LLM call quality. Trodo is built for product, growth, and engineering teams aligning on whether the AI is actually creating value for users.

What LangSmith does well

LangSmith's core strength is engineering-side evaluation. It captures prompts and completions, supports detailed traces, and offers excellent tooling for prompt versioning, dataset-driven evaluations, and regression testing across model versions. It is widely used by ML engineers who want to systematically improve prompt quality and catch model regressions before they reach production.

For deep prompt experimentation — running a new prompt against a curated dataset of 500 examples, scoring outputs, comparing to a baseline — LangSmith is one of the strongest tools on the market.

What Trodo does that LangSmith does not

Trodo is built for the product analytics layer. It models prompts, tool calls, and agent runs as first-class objects in a product analytics graph, joined to users, sessions, and revenue. That means Trodo natively answers questions like:

Which AI features drive long-term retention, and for which user cohorts?
Where do users drop off inside multi-step agent flows?
Which prompts produce users who convert to paid plans?
How does AI feature adoption correlate with revenue?
Which agent runs failed silently and how many users were affected?

These are product analytics questions. LangSmith is not designed to answer them; Trodo is.

Audience and workflow

LangSmith is opened most often by ML and platform engineers. Trodo is opened by product managers, growth leads, marketers, and engineers — usually all on the same week, looking at the same data. The audience difference is the simplest way to predict which tool your team will get value from first.

Side-by-side capability comparison

Prompt versioning and experiments — LangSmith: yes; Trodo: limited (focus is product analytics, not prompt experimentation).
Dataset-driven evaluations — LangSmith: yes; Trodo: limited.
Trace and span capture — both, with LangSmith deeper on engineering metadata, Trodo deeper on product context.
Tool-call analytics — LangSmith: present; Trodo: yes, with product KPIs joined.
User and session join — Trodo: yes, native; LangSmith: limited.
Funnel and retention analysis — Trodo: yes, native; LangSmith: not designed for it.
AI feature adoption metrics — Trodo: yes, native; LangSmith: not designed for it.
Natural-language querying for non-engineers — Trodo: yes; LangSmith: no.
AI-generated PRDs and prioritization — Trodo: yes; LangSmith: no.
Cost and latency monitoring — both.

When to use both

Most teams running serious AI in production benefit from a layered stack. LangSmith handles engineering-side prompt and evaluation work. Trodo handles AI product analytics, AI agent analytics, and the bridge to product KPIs. The two complement each other; nothing about using one prevents using the other.

When to choose one over the other

Choose LangSmith first if your immediate pain is engineering-side: prompt regressions, model evaluation, debugging individual LLM calls. Choose Trodo first if your immediate pain is product-side: you cannot tell which AI features are working, your PM team has no visibility into agent flows, or product, growth, and engineering are looking at different dashboards.

Bottom line

Trodo and LangSmith are complementary tools, not direct competitors. If you have to start with one, start with the one whose audience is your bigger blind spot today. For most AI-native product teams, that audience is product and growth — and Trodo is built specifically for them.

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