--- title: "openllmetry vs trulens" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/traceloop-openllmetry-vs-truera-trulens" tools: ["traceloop-openllmetry", "truera-trulens"] --- # openllmetry vs trulens Neutral, constraint-first comparison with live GitHub stats. | | [openllmetry](/tools/traceloop-openllmetry.md) | [trulens](/tools/truera-trulens.md) | | --- | --- | --- | | Tagline | Open-source observability for your LLM application | Evaluation and Tracking for LLM Experiments and AI Agents | | Stars | 7,281 | 3,429 | | Forks | 1,016 | 309 | | Open issues | 591 | 104 | | Language | Python | Python | | Adopt for | OpenLLMetry is an open-source observability tool based on OpenTelemetry for monitoring and metrics in GenAI and LLM applications. | TruLens provides fine-grained instrumentation and agentic evaluations to identify failure modes in LLM experiments. It supports multiple providers and integrates with various app frameworks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 license grants permission to use, study, modify, and distribute the tool freely. | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [openllmetry](/tools/traceloop-openllmetry.md) | [trulens](/tools/truera-trulens.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 7d | | Open issues (now) | 591 | 104 | | Security scan | 29 low (29 low) | No criticals | | Full report | [trust report](/tools/traceloop-openllmetry/trust.md) | [trust report](/tools/truera-trulens/trust.md) | **Typed relationship:** openllmetry _(alternative)_ trulens OpenLLMetry provides open-source observability for LLMs, similar to TruLens, which focuses on evaluation and tracking of performance across developmental iterations. ## Shared compatibility - **Python**: [openllmetry](/tools/traceloop-openllmetry.md) - Python runtime; [trulens](/tools/truera-trulens.md) - Python runtime ## Decision facts: openllmetry - **Requirements:** Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb - **Adopt for:** OpenLLMetry is an open-source observability tool based on OpenTelemetry for monitoring and metrics in GenAI and LLM applications. - **License detail:** Apache-2.0 license grants permission to use, study, modify, and distribute the tool freely. ## Decision facts: trulens - **Pricing:** freemium - TruLens offers its core functionality as open-source under the MIT license. However, additional services and integrations might come with commercial tiers not specified in the repository. - **Requirements:** Min 4 GB RAM; TruLens requires installation via pip for Python and supports multiple integrations with LLM providers and app frameworks through additional packages. - **Adopt for:** TruLens provides fine-grained instrumentation and agentic evaluations to identify failure modes in LLM experiments. It supports multiple providers and integrates with various app frameworks. ## Choose when ### Choose openllmetry if… - License: openllmetry is Apache-2.0, trulens is MIT. - Requirements: Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb. - OpenLLMetry provides open-source observability for LLMs, similar to TruLens, which focuses on evaluation and tracking of performance across developmental iterations. - Tags unique to openllmetry: llmops, ml, artifical-intelligence, llm. - - Use it if you are working with Large Language Model (LLM) or Generative AI applications that require detailed observations, including model oversight and performance metrics. ### Choose trulens if… - License: trulens is MIT, openllmetry is Apache-2.0. - Pricing: TruLens offers its core functionality as open-source under the MIT license. However, additional services and integrations might come with commercial tiers not specified in the repository.. - Requirements: Min 4 GB RAM; TruLens requires installation via pip for Python and supports multiple integrations with LLM providers and app frameworks through additional packages.. - OpenLLMetry provides open-source observability for LLMs, similar to TruLens, which focuses on evaluation and tracking of performance across developmental iterations. - Tags unique to trulens: neural-networks, llm-eval, agent-evaluation, machine-learning. - - When you need a stack-agnostic tool to systematically evaluate your LLM applications, especially those involving complex interactions such as retrieval-augmented generation (RAG) or multi-tool based ## When NOT to use openllmetry - - Avoid using it for applications where existing comprehensive proprietary observability solutions already meet all requirements without the need for integration flexibility offered by OpenTelemetry. - - Not recommended if your team has limited experience with OpenTelemetry, which could complicate adoption and integration with other tools unless there is a dedicated effort to adapt. ## When NOT to use trulens - - If your project strictly adheres to one model and provider which meets all your observation needs without requiring detailed performance metrics or cross-provider comparisons - - When you are working on very simple LLM applications that do not involve complex prompts, retrievers, or multiple knowledge sources where such fine-grained evaluation is unnecessary ## Common questions ### What is the difference between openllmetry and trulens? openllmetry: Open-source observability for your LLM application. trulens: Evaluation and Tracking for LLM Experiments and AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose openllmetry over trulens? Choose openllmetry over trulens when License: openllmetry is Apache-2.0, trulens is MIT; Requirements: Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb; OpenLLMetry provides open-source observability for LLMs, similar to TruLens, which focuses on evaluation and tracking of performance across developmental iterations; Tags unique to openllmetry: llmops, ml, artifical-intelligence, llm; - Use it if you are working with Large Language Model (LLM) or Generative AI applications that require detailed observations, including model oversight and performance metrics. ### When should I choose trulens over openllmetry? Choose trulens over openllmetry when License: trulens is MIT, openllmetry is Apache-2.0; Pricing: TruLens offers its core functionality as open-source under the MIT license. However, additional services and integrations might come with commercial tiers not specified in the repository.; Requirements: Min 4 GB RAM; TruLens requires installation via pip for Python and supports multiple integrations with LLM providers and app frameworks through additional packages.; OpenLLMetry provides open-source observability for LLMs, similar to TruLens, which focuses on evaluation and tracking of performance across developmental iterations; Tags unique to trulens: neural-networks, llm-eval, agent-evaluation, machine-learning; - When you need a stack-agnostic tool to systematically evaluate your LLM applications, especially those involving complex interactions such as retrieval-augmented generation (RAG) or multi-tool based. ### When should I avoid openllmetry? - Avoid using it for applications where existing comprehensive proprietary observability solutions already meet all requirements without the need for integration flexibility offered by OpenTelemetry. - Not recommended if your team has limited experience with OpenTelemetry, which could complicate adoption and integration with other tools unless there is a dedicated effort to adapt. ### When should I avoid trulens? - If your project strictly adheres to one model and provider which meets all your observation needs without requiring detailed performance metrics or cross-provider comparisons - When you are working on very simple LLM applications that do not involve complex prompts, retrievers, or multiple knowledge sources where such fine-grained evaluation is unnecessary ### Is openllmetry or trulens more popular on GitHub? openllmetry has more GitHub stars (7,281 vs 3,429). Stars measure visibility, not whether either tool fits your constraints. ### Are openllmetry and trulens open source? Yes - both are open-source projects on GitHub (openllmetry: Apache-2.0, trulens: MIT). ### Where can I find alternatives to openllmetry or trulens? GraphCanon lists graph-backed alternatives at /tools/traceloop-openllmetry/alternatives and /tools/truera-trulens/alternatives (/tools/traceloop-openllmetry/alternatives.md, /tools/truera-trulens/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/traceloop-openllmetry-vs-truera-trulens.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, openllmetry or trulens? openllmetry: Very active. trulens: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for openllmetry and trulens? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: openllmetry: /tools/traceloop-openllmetry/trust; trulens: /tools/truera-trulens/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=traceloop-openllmetry`](/api/graphcanon/graph?tool=traceloop-openllmetry) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_