telemetry.dev is an OpenTelemetry-based observability platform for AI agents, providing end-to-end tracing across model calls, tool invocations, and MCP servers.
What the product does and how it is positioned
telemetry.dev provides observability and performance monitoring for AI applications and autonomous agents. Built on OpenTelemetry standards, the system consolidates model invocations, retrieval events, and tool executions into unified traces, allowing engineering teams to evaluate execution flow, locate tool timeouts, and diagnose operational failures.
The platform tracks token consumption, latency benchmarks, and cost metrics broken down by model provider, session, framework, and user. In addition to trace visualization, telemetry.dev includes email alerting for budget thresholds and agent loops, environment-level prompt capture controls, and a read-only MCP server for automated telemetry analysis.
Source-supported ways to use the product
Software teams trace complex agent workflows across multi-step model responses and tool executions to identify latency bottlenecks and pinpoint failed calls.
Developers analyze span counts, failure rates, and p95 latency for MCP tools on both client and server interfaces to maintain tool reliability.
Engineering managers monitor token volumes across input, cached, and output tokens to evaluate model costs across different providers and users.
The documented workflow, where available
Identify the application framework, model providers, and runtime environment using a coding agent prompt or direct setup documentation.
Install the appropriate TypeScript SDK, Python SDK, framework-specific package, or configure an existing OpenTelemetry OTLP/HTTP exporter.
Configure the project API key and required telemetry endpoint settings within the target environment.
Execute AI requests to transmit spans, logs, and metric data points directly to the telemetry dashboard.
telemetry.dev models the execution of generative AI applications by grouping individual operations into structured traces. Each trace captures the end-to-end execution path of an agent or request, mapping individual model prompts, completions, retrieval steps, and tool calls. Detailed metadata reveals step-level durations, token counts across cached and non-cached inputs, and error states, enabling engineers to locate latency bottlenecks and broken integrations.
The platform provides specialized observability for Model Context Protocol systems. It tracks tool executions from both the client and server perspective, aggregating metrics such as span counts, p95 durations, and failure percentages by tool method. Additionally, telemetry.dev provides a read-only MCP server endpoint, enabling external coding or analysis agents to inspect traces and error reports programmatically.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
telemetry.dev is an OpenTelemetry-based observability platform for AI applications and agents that monitors model calls, tool executions, retrievals, latency, errors, and token costs.
Applications can ingest traces using the official TypeScript SDK, Python SDK, integration packages for OpenAI and Vercel AI SDK, or any exporter supporting OTLP/HTTP.
Yes, prompt capture can be disabled on a per-environment basis before data is stored, helping reduce prompt and completion contents from being retained.
The platform provides email notifications triggered by budget thresholds, agent infinite loops, cost anomalies, and model or tool operational errors.
telemetry.dev monitors MCP tool spans, failure rates, and p95 latency, while also offering a read-only MCP server for AI agents to query telemetry data.
