traceAI: Finally, LLM Observability That Speaks Agent, Not HTTP
The observability space for AI applications has been a mess. Every framework has its own tracing format. Every vendor wants you locked into their dashboard. traceAI takes a different approach. It's an open-source framework built natively on OpenTelemetry that traces LLM calls, agent decisions, tool invocations, and retrieval steps, then sends structured traces to whatever backend you already use. Datadog, Grafana, Jaeger, whatever. No new vendor required.
The project launched on Product Hunt today and hit number 3 with 216 upvotes, which is strong signal for a developer infrastructure tool. It supports Python, TypeScript, Java, and C# with consistent APIs across all four. Over 50 integrations cover the major LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Mistral), agent frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and vector databases.
What makes this different from the dozen other tracing tools? The semantic conventions. traceAI doesn't just capture HTTP request-response pairs. It understands what a tool call is, what a retrieval step looks like, what an agent decision means. The traces have semantic structure that maps to how agents actually work, not how web servers work. That's a meaningful distinction when you're debugging why your agent chose tool A over tool B, or why retrieval returned irrelevant context.
MIT licensed, zero configuration to get started, and vendor-agnostic by design. In a world where every coding agent, every multi-agent swarm, and every MCP-connected workflow needs observability, having an open standard for agent tracing matters more than having the fanciest dashboard.
https://github.com/future-agi/traceAI
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The project launched on Product Hunt today and hit number 3 with 216 upvotes, which is strong signal for a developer infrastructure tool. It supports Python, TypeScript, Java, and C# with consistent APIs across all four. Over 50 integrations cover the major LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Mistral), agent frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and vector databases.
What makes this different from the dozen other tracing tools? The semantic conventions. traceAI doesn't just capture HTTP request-response pairs. It understands what a tool call is, what a retrieval step looks like, what an agent decision means. The traces have semantic structure that maps to how agents actually work, not how web servers work. That's a meaningful distinction when you're debugging why your agent chose tool A over tool B, or why retrieval returned irrelevant context.
MIT licensed, zero configuration to get started, and vendor-agnostic by design. In a world where every coding agent, every multi-agent swarm, and every MCP-connected workflow needs observability, having an open standard for agent tracing matters more than having the fanciest dashboard.
https://github.com/future-agi/traceAI
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