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truera/trulens

⭐ 3589 Python repository created 2020-11-02

TruLens is an open-source framework designed for the evaluation and tracking of Large Language Model (LLM) applications and AI agents. It provides tools for systematically understanding and improving the performance of LLM-powered applications, covering aspects from prototype development to iteration and comparison of different versions. The platform offers fine-grained, stack-agnostic instrumentation built on OpenTelemetry, capturing every function call, LLM generation, retrieval, and tool invocation as structured OTEL spans. This allows for interoperability with existing observability infrastructure, enabling export of traces to various OTLP-compatible backends like Jaeger, Grafana Tempo, and Datadog. Key features include purpose-built evaluators for agentic systems, measuring aspects like logical consistency, execution efficiency, plan adherence, plan quality, tool selection, tool calling, and tool quality. TruLens supports both inline evaluation, where feedback runs alongside the application, and batch evaluation for pre-collected datasets. It includes Model Context Protocol (MCP) support for instrumenting tool calls, capturing details like tool name, arguments, output, and latency. A flexible Selector API allows targeting specific span attributes for evaluation. TruLens integrates with various LLM providers (e.g., OpenAI, LiteLLM, Google Gemini, AWS Bedrock, HuggingFace) and application frameworks like LangChain and LlamaIndex, making it a versatile tool for LLMOps and MLOps workflows focused on the operational aspects of AI.

https://github.com/truera/trulens

agent-evaluationagentopsai-agentsai-monitoringai-observabilityevalsexplainable-mlllm-evalllm-evaluationllmopsllmsmachine-learningneural-networksopentelemetrytracingfeedback functionsRAGbatch evaluationinline evaluation

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