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Best Open‑Source Chatbot Analytics Tools (2025): Self‑Hosted Options

· 2 min read
Daniel Garcia
Daniel Garcia
CEO @ Optimly

Open‑source analytics can be a strong fit when you need data control, on‑premise deployments, or deep customization. This guide reviews leading open‑source options and how they compare to fully managed chatbot analytics platforms. For Botpress‑specific guidance, see: /blog/botpress-analytics

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Updated September 2025
Top Picks (Quick)
  • Best event analytics stack: PostHog — flexible pipeline, funnels, retention
  • Best open‑source flow analytics: Botpress — visual flows, developer‑friendly
  • Best NLU control: Rasa — custom intents, on‑premise, strong community
  • Best helpdesk hybrid: Chatwoot — inbox metrics + bot/human handoff

Need LLM‑native metrics (costs, RAG, frustration)? Pair with Optimly.

Why Go Open Source?​

  • Data control and residency
  • Custom pipelines and event schemas
  • On‑premise or private cloud deployments
  • Cost predictability at scale

If you’re comparing click‑based analytics vs. conversation analytics, start with: /blog/llm-chatbot-analytics-vs-web-analytics

Top Open‑Source Options (2025)​

PostHog​

  • What it is: Open‑source product analytics with event pipelines
  • Use for: Chatbot events (session_start, intent, escalation), funnels, retention
  • Pros: Flexible, self‑hostable, good visualization
  • Cons: Not conversation‑native; transcripts and LLM metrics require custom build

Botpress (Community)​

  • What it is: Open‑source conversational framework
  • Use for: Flow analytics and basic conversation metrics
  • Pros: Visual flows, developer‑friendly
  • Cons: Less focus on LLM token/cost visibility and ROI

Rasa + Rasa X​

  • What it is: Open‑source NLU + assistant stack
  • Use for: Intent analytics and training data iteration
  • Pros: Custom NLU, on‑premise control
  • Cons: Requires engineering time; LLM usage and costs not first‑class

Chatwoot​

  • What it is: Open‑source customer engagement/helpdesk
  • Use for: Bot + human hybrid workflows, inbox metrics
  • Pros: Self‑hostable, broad channel support
  • Cons: Analytics are helpdesk‑oriented vs. LLM‑specific

When to Choose a Managed Platform​

If you need:

  • LLM‑native metrics (token costs, RAG usage, frustration detection)
  • Out‑of‑the‑box ROI views (deflection, conversion)
  • Simple setup across web + messaging

…a managed platform like Optimly gives faster time‑to‑value while you keep PostHog/GA4 for broader product analytics.

See also: /blog/best-analytics-platforms-for-website-chatbots

Hybrid Approach​

  • Use PostHog/GA4 for site‑wide events and attribution
  • Use a chatbot analytics layer for transcripts, LLM costs, and outcomes
  • Sync key events both ways for a unified view

FAQs​

Can I store full transcripts on‑prem?
Yes—self‑hosted stacks enable transcript storage control, but ensure consent and retention policies.

What about PII and privacy laws?
Adopt data minimization, redact PII in logs, and add a DPA. More here: /blog/privacy-friendly-chatbot-analytics

Can I start open source and add Optimly later?
Absolutely. Many teams begin with PostHog and later add Optimly for conversation‑native insights.

Get the Best of Both Worlds​

Combine open‑source flexibility with an LLM‑native analytics layer. Try Optimly free.

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