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[...] tps://masterofcode.com/blog/top-chatbot-analytics-tools). - **Langfuse**: Provides observability for chatbot systems with logging, tracing, performance metrics (e.g., response times, error rates), anomaly detection, and unified data aggregation. Use the `@observe()` decorator for Python LLM apps or OpenAI integration; supports any LLM. Official site: https://langfuse.com/[Chatbot Monitoring with Advanced Observability - Langfuse](https://langfuse.com/faq/all/chatbot-analytics). - **Botium**: Automates testing and tracks conversation flows across various chatbots/virtual assistants. Free version incl [...]

December 24, 2025

[...] bot-analytics-tools)[14 chatbot metrics to track whether your AI is performing good ...](https://www.agilitypr.com/pr-news/branding-reputation/14-chatbot-metrics-to-track-whether-your-ai-is-performing-good-customer-service/). ### Implementation Steps 1. Integrate via APIs or SDKs (e.g., Langfuse's OpenAI wrapper or Dashbot's platform connectors)[The Top Chatbot Analytics Tools to Drive Conversion and ...](https://masterofcode.com/blog/top-chatbot-analytics-tools)[Chatbot Monitoring with Advanced Observability - Langfuse](https://langfuse.com/faq/all/chatbot-analytics). 2. Log user inputs/responses [...]

December 24, 2025

[...] ls/) - **New Relic AI Monitoring** ([https://newrelic.com/platform/ai-monitoring](https://newrelic.com/platform/ai-monitoring)): Provides full-stack observability with real-time metrics (response time, quality, tokens), custom alerts for costs/performance, model drift tracking, and integrations for OpenAI, LangChain, Pinecone.[New Relic AI Monitoring](https://newrelic.com/platform/ai-monitoring) - **UptimeRobot** ([https://uptimerobot.com](https://uptimerobot.com/knowledge-hub/monitoring/ai-monitoring-guide/)): Monitors AI endpoints/APIs with instant alerts via Slack/email/SMS for latency, errors, o [...]

December 24, 2025

[...] ssment, self-harm, hate, sexual content) - Azure AI Content Safety — text/image moderation with granular categories: https://azure.microsoft.com/products/ai-services/ai-content-safety - Perspective API (Google/Jigsaw) — toxicity and harassment scoring for user text: https://www.perspectiveapi.com - OpenAI Moderation — safety classifiers for text content: https://platform.openai.com/docs/guides/safety-moderation - Hive Moderation — scalable text/image/video content moderation: https://thehive.ai/moderation - Spectrum Labs — context-aware trust and safety for communities: https://www.spectrumlabsai.co [...]

December 24, 2025

[...] image moderation with granular categories: https://azure.microsoft.com/products/ai-services/ai-content-safety - Perspective API (Google/Jigsaw) — toxicity and harassment scoring for user text: https://www.perspectiveapi.com - OpenAI Moderation — safety classifiers for text content: https://platform.openai.com/docs/guides/safety-moderation - Hive Moderation — scalable text/image/video content moderation: https://thehive.ai/moderation - Spectrum Labs — context-aware trust and safety for communities: https://www.spectrumlabsai.com LLM chatbot guardrails and observability (monitor AI chatbot interactio [...]

December 24, 2025

[...] to monitor support chat quality at scale: start with Observe.AI, Level AI, Cresta, or Klaus. - Need policy/compliance/DLP for internal chat: start with Theta Lake, Aware, SafeGuard Cyber, or Nightfall AI. - Need community safety/toxicity filters: start with Azure AI Content Safety, Perspective API, OpenAI Moderation, Hive, or Spectrum Labs. - Need guardrails/observability for your LLM chatbot: start with Langfuse, Helicone, Guardrails AI, or Lakera Guard. If you share your primary use-case, team size, and must-have integrations, I can narrow this to a 2–3 vendor shortlist and a feature checklist. [...]

December 24, 2025

[...] e-bot-service-4.0) - Dialogflow CX (https://cloud.google.com/dialogflow/cx) - Rasa (open source) (https://rasa.com/docs/rasa) - Botpress (https://botpress.com/) 3) Attach model-level telemetry for LLM calls - Persist the provider request ID to correlate failures and measure latency/cost: - OpenAI x-request-id (https://platform.openai.com/docs/guides/rate-limits/retrying-requests) - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenA [...]

December 24, 2025

[...] https://cloud.google.com/dialogflow/cx) - Rasa (open source) (https://rasa.com/docs/rasa) - Botpress (https://botpress.com/) 3) Attach model-level telemetry for LLM calls - Persist the provider request ID to correlate failures and measure latency/cost: - OpenAI x-request-id (https://platform.openai.com/docs/guides/rate-limits/retrying-requests) - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.co [...]

December 24, 2025

[...] penAI x-request-id (https://platform.openai.com/docs/guides/rate-limits/retrying-requests) - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.com/docs) - Google AI (Gemini) docs (https://ai.google.dev/) - Azure OpenAI Service (https://learn.microsoft.com/azure/ai-services/openai/) 4) Pipe events into a centralized store - Open-source observability path: - OpenTelemetry Collector → Elasticsearch + [...]

December 24, 2025

[...] rm.openai.com/docs/guides/rate-limits/retrying-requests) - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.com/docs) - Google AI (Gemini) docs (https://ai.google.dev/) - Azure OpenAI Service (https://learn.microsoft.com/azure/ai-services/openai/) 4) Pipe events into a centralized store - Open-source observability path: - OpenTelemetry Collector → Elasticsearch + Kibana or Grafana Loki + Grafana [...]

December 24, 2025

[...] Ds (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.com/docs) - Google AI (Gemini) docs (https://ai.google.dev/) - Azure OpenAI Service (https://learn.microsoft.com/azure/ai-services/openai/) 4) Pipe events into a centralized store - Open-source observability path: - OpenTelemetry Collector → Elasticsearch + Kibana or Grafana Loki + Grafana - Elasticsearch (https://www.elastic.co/elasticsearch/) - Logstash (optional [...]

December 24, 2025