{{ entity.value }}

[...] 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

[...] 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