traceAI
by Community
Open Source AI Tracing Framework built on Opentelemetry for AI Applications and Frameworks
OSS
traceAI
Added 1 June 2026
Overview
traceAI is an open source Python framework for tracing AI applications and frameworks using OpenTelemetry. It provides instrumentation to capture spans and metrics for LLM calls and other AI pipeline steps. The project is maintained by the community and currently has 190 stars on GitHub.
Best for
Best for
Developers who need a lightweight, OpenTelemetry-based tracing solution for Python AI applications and are comfortable with community-maintained tools.
Use cases
- Instrument LLM calls for performance monitoring
- Debug multi-step AI pipelines with distributed tracing
- Export trace data to OpenTelemetry-compatible backends
Notes
traceAI is an open source Python framework for tracing AI applications and frameworks using OpenTelemetry. It provides instrumentation to capture spans and metrics for LLM calls and other AI pipeline steps. The project is maintained by the community and currently has 190 stars on GitHub.
190 stars on GitHub. Last updated 2026-05-27. Licensed Apache-2.0.
Use cases
- Instrument LLM calls for performance monitoring
- Debug multi-step AI pipelines with distributed tracing
- Export trace data to OpenTelemetry-compatible backends
Pros
- Built on the OpenTelemetry standard, enabling integration with existing observability stacks
- Open source with no licensing costs
- Native Python implementation for easy adoption in Python-based AI projects
Cons
- Small community and low star count indicate limited adoption and potential lack of mature features
- Documentation and support may be sparse compared to established tracing solutions
- No corporate backing, which can affect long-term maintenance and reliability
Indexed from awesome-langchain and enriched against its public facts.
Pros
- Built on the OpenTelemetry standard, enabling integration with existing observability stacks
- Open source with no licensing costs
- Native Python implementation for easy adoption in Python-based AI projects
Cons
- Small community and low star count indicate limited adoption and potential lack of mature features
- Documentation and support may be sparse compared to established tracing solutions
- No corporate backing, which can affect long-term maintenance and reliability
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