[Instrumentation] Contribute loongsuite GenAI instrumentations to OTel Python GenAI

Summary

We would like to contribute a set of GenAI instrumentation packages from alibaba/loongsuite-python to this repository. These instrumentations follow the OpenTelemetry GenAI Semantic Conventions and have been validated through the genai-otel-conformance test suite.

Motivation

There is a growing ecosystem of GenAI agent frameworks and LLM libraries that lack official OTel instrumentation. We have developed and tested instrumentations for 26 additional libraries, covering:

• Inference Spans (chat completions, text generation)
• Agent Spans (create agent, invoke agent client/internal)
• Embeddings Spans
• Execute Tool Spans
• Client Operation Duration Metric (gen_ai.client.operation.duration)
• Client Token Usage Metric (gen_ai.client.token.usage)

Conformance Test Results

All proposed instrumentations have passed the GenAI OTel Conformance Tests. The full conformance dashboard showing coverage across Required, Conditionally Required, Recommended, and Opt-In attributes for each signal type is attached below:

dashboard-full.pdf

Proposed Instrumentations

Below is the full list of instrumentations available for contribution, grouped by priority:

Priority 1: Popular Open-Source Frameworks (High Community Value)

Instrumentation	Target Package	Description
loongsuite-instrumentation-crewai	crewai >= 0.80.0	Multi-agent orchestration framework
loongsuite-instrumentation-langgraph	langgraph >= 0.2	LangChain agent orchestration
loongsuite-instrumentation-litellm	litellm >= 1.0.0	Universal LLM proxy/gateway
loongsuite-instrumentation-google-adk	google-adk >= 0.1.0	Google Agent Development Kit
loongsuite-instrumentation-dashscope	dashscope >= 1.0.0	Alibaba Cloud model serving platform
loongsuite-instrumentation-qwen-agent	qwen-agent >= 0.0.20	Qwen agent framework
loongsuite-instrumentation-mcp	mcp >= 1.3.0	Model Context Protocol
loongsuite-instrumentation-mem0	mem0ai >= 1.0.0, < 2.0.0	Memory layer for AI apps
loongsuite-instrumentation-dify	dify	LLMOps platform
loongsuite-instrumentation-agno	agno >= 2.0.0, < 3	Agent framework
loongsuite-instrumentation-agentscope	agentscope >= 1.0.0	Multi-agent platform

Priority 2: Agent Evaluation & Benchmark Frameworks

Instrumentation	Target Package	Description
loongsuite-instrumentation-openhands	openhands	AI software engineering agent
loongsuite-instrumentation-minisweagent	mini-swe-agent >= 2.2.0	Minimal SWE agent
loongsuite-instrumentation-bfclv4	bfcl-eval >= 4.0.0	Function calling evaluation
loongsuite-instrumentation-claw-eval	claw-eval >= 0.1.0	Agent evaluation framework
loongsuite-instrumentation-algotune	algotune	Algorithm tuning benchmark
loongsuite-instrumentation-webarena	webarena >= 0.0.1	Web agent benchmark
loongsuite-instrumentation-widesearch	widesearch >= 0.1.0	Search agent benchmark
loongsuite-instrumentation-wildtool	openai >= 1.0.0	Tool-use evaluation

Priority 3: Domain-Specific Agent Instrumentations

Instrumentation	Target Package	Description
loongsuite-instrumentation-claude-agent-sdk	claude-agent-sdk >= 0.1.0	Anthropic Claude agent SDK
loongsuite-instrumentation-hermes-agent	openai >= 1.0.0	Hermes agent framework
loongsuite-instrumentation-qwenpaw	qwenpaw >= 1.1.0	QwenPaw personal assistant
loongsuite-instrumentation-slop-code	slop-code-bench >= 0.1	Code agent benchmark
loongsuite-instrumentation-terminus2	terminal-bench >= 0.1.0	Terminal agent benchmark
loongsuite-instrumentation-vita	vita >= 0.0.1	Vita agent
loongsuite-instrumentation-langchain	langchain_core >= 0.1.0	Enhanced LangChain instrumentation

Semantic Convention Coverage

Each instrumentation emits telemetry aligned with the GenAI semantic conventions. Example coverage for Inference Spans:

• Required: gen_ai.operation.name, gen_ai.system
• Conditionally Required: error.type, gen_ai.request.model, server.address, server.port
• Recommended: gen_ai.response.finish_reasons, gen_ai.response.id, gen_ai.response.model, gen_ai.usage.input_tokens, gen_ai.usage.output_tokens
• Opt-In: gen_ai.input.messages, gen_ai.output.messages, gen_ai.system_instructions, gen_ai.tool.definitions

Contribution Plan

We propose to contribute these instrumentations in phases:

1. Phase 1: Submit Priority 1 instrumentations (popular open-source frameworks) as individual PRs
2. Phase 2: Submit Priority 2 instrumentations (evaluation/benchmark frameworks)
3. Phase 3: Submit Priority 3 instrumentations (domain-specific agents)

Each PR will include:

• Instrumentation source code (following existing package structure)
• Unit tests
• Updated conformance test data in genai-otel-conformance

Source Repository

• Instrumentation code: https://github.com/alibaba/loongsuite-python/tree/main/instrumentation-loongsuite
• Conformance tests: https://github.com/trask/genai-otel-conformance

Questions for Maintainers

1. Is there a preference for batching PRs (e.g., one PR per instrumentation, or grouping related ones)?
2. For libraries that already have instrumentations in this repo (e.g., langchain, claude-agent-sdk), should we propose enhancements to existing packages or submit as alternatives?
3. Are there any specific requirements for evaluation/benchmark-focused instrumentations vs. production-library instrumentations?

Start by reviewing the existing package structure in this repository and comparing it with the instrumentation code in alibaba/loongsuite-python. Read the genai-otel-conformance results and contribution plan, then clarify with maintainers whether to submit individual packages or enhancements to existing instrumentations; completion depends on that scope decision.