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opik Prompt模板
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Prompt模板

opik Prompt模板

基于 Python · 专业级提示词模板,解锁 AI 的真实潜力
英文名:opik
⭐ 19.3k Stars 🍴 1.5k Forks 💻 Python 📄 Apache-2.0 🏷 AI 8.5分
8.5AI 综合评分
LLM监控Prompt管理评估框架RAG系统Agent工具
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:opik Prompt模板 是一款优质的Prompt模板。在 GitHub 上收获超过 19.3k 颗 Star,AI 综合评分 8.5 分,在同类工具中表现稳健。如果你正在寻找可靠的Prompt模板解决方案,这是一个值得深入了解的选择。

📚 深度解析
opik Prompt模板 是经过精心设计和实践验证的专业 Prompt 模板。Prompt 工程(Prompt Engineering)是充分发挥 Claude、ChatGPT 等大型语言模型潜力的关键技能,而一套经过优化的 Prompt 模板可以将 AI 输出质量提升数倍。

优质 Prompt 模板的核心价值在于其结构化设计:明确的角色设定、精确的任务描述、具体的输出格式要求和必要的边界条件,这些要素共同构成了一个能够持续产出高质量结果的 Prompt 框架。opik Prompt模板 提供的模板经过反复迭代和用户验证,能够有效减少 AI 的"幻觉"(Hallucination)和输出不稳定问题。

无论你使用 Claude 3.5 Sonnet、GPT-4、Gemini 还是国内的文心一言、智谱 AI,优质的 Prompt 设计都能跨模型复用。AI Skill Hub 建议将本模板保存为个人 Prompt 库的标准组件,根据具体场景调整参数后反复使用,形成自己的 AI 提效工作流。
📋 工具概览

opik Prompt模板 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。

GitHub Stars
⭐ 19.3k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
活跃维护,更新频繁
开源协议
Apache-2.0
AI 综合评分
8.5 分
工具类型
Prompt模板
Forks
1.5k
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

opik Prompt模板 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。

📌 核心特色
  • 精心设计的 Prompt 框架,快速激活 AI 的深层能力
  • 支持参数化替换,灵活适配多种业务场景
  • 经过反复验证的指令结构,显著提升 AI 输出质量和一致性
  • 适用于 Claude、ChatGPT 等主流大语言模型
  • 可作为团队标准 Prompt 模板复用和二次开发
🎯 主要使用场景
  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# Prompt 无需安装,直接复制使用
# 支持:Claude / ChatGPT / Gemini / 通义千问 等主流模型

# 使用步骤
# 1. 复制 Prompt 模板内容
# 2. 粘贴到 AI 对话框
# 3. 替换 [占位符] 为实际内容
# 4. 发送后获取结构化输出

# 获取原始文件
git clone https://github.com/comet-ml/opik
📋 安装步骤说明
  1. 复制本工具的 Prompt 模板内容
  2. 打开 Claude、ChatGPT 或其他 AI 对话工具
  3. 将 Prompt 粘贴到对话框开头
  4. 根据实际需求替换 [占位符] 中的内容
  5. 发送后 AI 将按照模板格式执行,获得结构化输出
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 粘贴到 Claude/ChatGPT 使用
# 示例 Prompt 结构:

你是一位 [角色],擅长 [领域]。
请根据以下要求完成任务:

任务背景:[描述背景]
具体要求:[详细说明]
输出格式:[期望格式]

# 将 [] 内内容替换为实际需求
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# opik 配置文件示例(config.yml)
app:
  name: "opik"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
opik --config config.yml

# 或通过环境变量配置
export OPIK_API_KEY="your-key"
export OPIK_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 64/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

Comet Opik logo
Opik

Open-source AI Observability, Evaluation, and Optimization

Opik helps you build, test, and optimize generative AI applications that run better, from prototype to production. From RAG chatbots to code assistants to complex agentic systems, Opik provides comprehensive tracing, evaluation, and automatic prompt and tool optimization to take the guesswork out of AI development.

Python SDK License Build

</div>

<p align="center"> <a href="https://www.comet.com/site/products/opik/?from=llm&utm_source=opik&utm_medium=github&utm_content=website_button&utm_campaign=opik"><b>Website</b></a> • <a href="https://chat.comet.com"><b>Slack Community</b></a> • <a href="https://x.com/Cometml"><b>Twitter</b></a> • <a href="https://www.comet.com/docs/opik/changelog"><b>Changelog</b></a> • <a href="https://www.comet.com/docs/opik/?from=llm&utm_source=opik&utm_medium=github&utm_content=docs_button&utm_campaign=opik"><b>Documentation</b></a> </p>

<br>

Opik platform screenshot (thumbnail)

<a id="-what-is-opik"></a>

🛠️ Opik Server Installation

Get your Opik server running in minutes. Choose the option that best suits your needs:

install using pip

pip install opik

or install with uv

uv pip install opik


Configure the python SDK by running the `opik configure` command, which will prompt you for your Opik server address (for self-hosted instances) or your API key and workspace (for Comet.com):
bash opik configure ```

[!TIP] You can also call opik.configure(use_local=True) from your Python code to configure the SDK to run on a local self-hosted installation, or provide API key and workspace details directly for Comet.com. Refer to the Python SDK documentation for more configuration options.

You are now ready to start logging traces using the Python SDK.

<a id="-logging-traces-with-integrations"></a>

Python SDK Quick Start

To get started with the Python SDK:

Install the package:

```bash

Option 2: Self-Host Opik for Full Control

Deploy Opik in your own environment. Choose between Docker for local setups or Kubernetes for scalability.

Self-Hosting with Docker Compose (for Local Development & Testing)

This is the simplest way to get a local Opik instance running. Note the new ./opik.sh installation script:

On Linux or Mac Environment:

```bash

💻 Opik Client SDK

Opik provides a suite of client libraries and a REST API to interact with the Opik server. This includes SDKs for Python, TypeScript, and Ruby (via OpenTelemetry), allowing for seamless integration into your workflows. For detailed API and SDK references, see the Opik Client Reference Documentation.

📝 Logging Traces with Integrations

The easiest way to log traces is to use one of our direct integrations. Opik supports a wide array of frameworks, including recent additions like Google ADK, Autogen, AG2, and Flowise AI:

IntegrationDescriptionDocumentation
ADKLog traces for Google Agent Development Kit (ADK)[Documentation](https://www.comet.com/docs/opik/integrations/adk?utm_source=opik&utm_medium=github&utm_content=google_adk_link&utm_campaign=opik)
AG2Log traces for AG2 LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/ag2?utm_source=opik&utm_medium=github&utm_content=ag2_link&utm_campaign=opik)
Agent SpecLog traces for Agent Spec calls[Documentation](https://www.comet.com/docs/opik/integrations/agentspec?utm_source=opik&utm_medium=github&utm_content=agentspec_link&utm_campaign=opik)
AIsuiteLog traces for aisuite LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/aisuite?utm_source=opik&utm_medium=github&utm_content=aisuite_link&utm_campaign=opik)
AgnoLog traces for Agno agent orchestration framework calls[Documentation](https://www.comet.com/docs/opik/integrations/agno?utm_source=opik&utm_medium=github&utm_content=agno_link&utm_campaign=opik)
AnthropicLog traces for Anthropic LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/anthropic?utm_source=opik&utm_medium=github&utm_content=anthropic_link&utm_campaign=opik)
AutogenLog traces for Autogen agentic workflows[Documentation](https://www.comet.com/docs/opik/integrations/autogen?utm_source=opik&utm_medium=github&utm_content=autogen_link&utm_campaign=opik)
BedrockLog traces for Amazon Bedrock LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/bedrock?utm_source=opik&utm_medium=github&utm_content=bedrock_link&utm_campaign=opik)
BeeAI (Python)Log traces for BeeAI Python agent framework calls[Documentation](https://www.comet.com/docs/opik/integrations/beeai?utm_source=opik&utm_medium=github&utm_content=beeai_link&utm_campaign=opik)
BeeAI (TypeScript)Log traces for BeeAI TypeScript agent framework calls[Documentation](https://www.comet.com/docs/opik/integrations/beeai-typescript?utm_source=opik&utm_medium=github&utm_content=beeai_typescript_link&utm_campaign=opik)
BytePlusLog traces for BytePlus LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/byteplus?utm_source=opik&utm_medium=github&utm_content=byteplus_link&utm_campaign=opik)
Cloudflare Workers AILog traces for Cloudflare Workers AI calls[Documentation](https://www.comet.com/docs/opik/integrations/cloudflare-workers-ai?utm_source=opik&utm_medium=github&utm_content=cloudflare_workers_ai_link&utm_campaign=opik)
CohereLog traces for Cohere LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/cohere?utm_source=opik&utm_medium=github&utm_content=cohere_link&utm_campaign=opik)
CrewAILog traces for CrewAI calls[Documentation](https://www.comet.com/docs/opik/integrations/crewai?utm_source=opik&utm_medium=github&utm_content=crewai_link&utm_campaign=opik)
CursorLog traces for Cursor conversations[Documentation](https://www.comet.com/docs/opik/integrations/cursor?utm_source=opik&utm_medium=github&utm_content=cursor_link&utm_campaign=opik)
DeepSeekLog traces for DeepSeek LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/deepseek?utm_source=opik&utm_medium=github&utm_content=deepseek_link&utm_campaign=opik)
DifyLog traces for Dify agent runs[Documentation](https://www.comet.com/docs/opik/integrations/dify?utm_source=opik&utm_medium=github&utm_content=dify_link&utm_campaign=opik)
DSPYLog traces for DSPy runs[Documentation](https://www.comet.com/docs/opik/integrations/dspy?utm_source=opik&utm_medium=github&utm_content=dspy_link&utm_campaign=opik)
Fireworks AILog traces for Fireworks AI LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/fireworks-ai?utm_source=opik&utm_medium=github&utm_content=fireworks_ai_link&utm_campaign=opik)
Flowise AILog traces for Flowise AI visual LLM builder[Documentation](https://www.comet.com/docs/opik/integrations/flowise?utm_source=opik&utm_medium=github&utm_content=flowise_link&utm_campaign=opik)
Gemini (Python)Log traces for Google Gemini LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/gemini?utm_source=opik&utm_medium=github&utm_content=gemini_link&utm_campaign=opik)
Gemini (TypeScript)Log traces for Google Gemini TypeScript SDK calls[Documentation](https://www.comet.com/docs/opik/integrations/gemini-typescript?utm_source=opik&utm_medium=github&utm_content=gemini_typescript_link&utm_campaign=opik)
GroqLog traces for Groq LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/groq?utm_source=opik&utm_medium=github&utm_content=groq_link&utm_campaign=opik)
GuardrailsLog traces for Guardrails AI validations[Documentation](https://www.comet.com/docs/opik/integrations/guardrails-ai?utm_source=opik&utm_medium=github&utm_content=guardrails_link&utm_campaign=opik)
HaystackLog traces for Haystack calls[Documentation](https://www.comet.com/docs/opik/integrations/haystack?utm_source=opik&utm_medium=github&utm_content=haystack_link&utm_campaign=opik)
HarborLog traces for Harbor benchmark evaluation trials[Documentation](https://www.comet.com/docs/opik/integrations/harbor?utm_source=opik&utm_medium=github&utm_content=harbor_link&utm_campaign=opik)
InstructorLog traces for LLM calls made with Instructor[Documentation](https://www.comet.com/docs/opik/integrations/instructor?utm_source=opik&utm_medium=github&utm_content=instructor_link&utm_campaign=opik)
LangChain (Python)Log traces for LangChain LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/langchain?utm_source=opik&utm_medium=github&utm_content=langchain_link&utm_campaign=opik)
LangChain (JS/TS)Log traces for LangChain JavaScript/TypeScript calls[Documentation](https://www.comet.com/docs/opik/integrations/langchainjs?utm_source=opik&utm_medium=github&utm_content=langchainjs_link&utm_campaign=opik)
LangGraphLog traces for LangGraph executions[Documentation](https://www.comet.com/docs/opik/integrations/langgraph?utm_source=opik&utm_medium=github&utm_content=langgraph_link&utm_campaign=opik)
LangflowLog traces for Langflow visual AI builder[Documentation](https://www.comet.com/docs/opik/integrations/langflow?utm_source=opik&utm_medium=github&utm_content=langflow_link&utm_campaign=opik)
LiteLLMLog traces for LiteLLM model calls[Documentation](https://www.comet.com/docs/opik/integrations/litellm?utm_source=opik&utm_medium=github&utm_content=litellm_link&utm_campaign=opik)
LiveKit AgentsLog traces for LiveKit Agents AI agent framework calls[Documentation](https://www.comet.com/docs/opik/integrations/livekit?utm_source=opik&utm_medium=github&utm_content=livekit_link&utm_campaign=opik)
LlamaIndexLog traces for LlamaIndex LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/llama_index?utm_source=opik&utm_medium=github&utm_content=llama_index_link&utm_campaign=opik)
MastraLog traces for Mastra AI workflow framework calls[Documentation](https://www.comet.com/docs/opik/integrations/mastra?utm_source=opik&utm_medium=github&utm_content=mastra_link&utm_campaign=opik)
Microsoft Agent Framework (Python)Log traces for Microsoft Agent Framework calls[Documentation](https://www.comet.com/docs/opik/integrations/microsoft-agent-framework?utm_source=opik&utm_medium=github&utm_content=agent_framework_link&utm_campaign=opik)
Microsoft Agent Framework (.NET)Log traces for Microsoft Agent Framework .NET calls[Documentation](https://www.comet.com/docs/opik/integrations/microsoft-agent-framework-dotnet?utm_source=opik&utm_medium=github&utm_content=agent_framework_dotnet_link&utm_campaign=opik)
Mistral AILog traces for Mistral AI LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/mistral?utm_source=opik&utm_medium=github&utm_content=mistral_link&utm_campaign=opik)
n8nLog traces for n8n workflow executions[Documentation](https://www.comet.com/docs/opik/integrations/n8n?utm_source=opik&utm_medium=github&utm_content=n8n_link&utm_campaign=opik)
Novita AILog traces for Novita AI LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/novita-ai?utm_source=opik&utm_medium=github&utm_content=novita_ai_link&utm_campaign=opik)
OllamaLog traces for Ollama LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/ollama?utm_source=opik&utm_medium=github&utm_content=ollama_link&utm_campaign=opik)
OpenAI (Python)Log traces for OpenAI LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/openai?utm_source=opik&utm_medium=github&utm_content=openai_link&utm_campaign=opik)
OpenAI (JS/TS)Log traces for OpenAI JavaScript/TypeScript calls[Documentation](https://www.comet.com/docs/opik/integrations/openai-typescript?utm_source=opik&utm_medium=github&utm_content=openai_typescript_link&utm_campaign=opik)
OpenAI AgentsLog traces for OpenAI Agents SDK calls[Documentation](https://www.comet.com/docs/opik/integrations/openai_agents?utm_source=opik&utm_medium=github&utm_content=openai_agents_link&utm_campaign=opik)
OpenClawLog traces for OpenClaw agent runs[Documentation](https://www.comet.com/docs/opik/integrations/openclaw?utm_source=opik&utm_medium=github&utm_content=openclaw_link&utm_campaign=opik)
OpenRouterLog traces for OpenRouter LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/openrouter?utm_source=opik&utm_medium=github&utm_content=openrouter_link&utm_campaign=opik)
OpenTelemetryLog traces for OpenTelemetry supported calls[Documentation](https://www.comet.com/docs/opik/tracing/opentelemetry/overview?utm_source=opik&utm_medium=github&utm_content=opentelemetry_link&utm_campaign=opik)
OpenWebUILog traces for OpenWebUI conversations[Documentation](https://www.comet.com/docs/opik/integrations/openwebui?utm_source=opik&utm_medium=github&utm_content=openwebui_link&utm_campaign=opik)
PipecatLog traces for Pipecat real-time voice agent calls[Documentation](https://www.comet.com/docs/opik/integrations/pipecat?utm_source=opik&utm_medium=github&utm_content=pipecat_link&utm_campaign=opik)
PredibaseLog traces for Predibase LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/predibase?utm_source=opik&utm_medium=github&utm_content=predibase_link&utm_campaign=opik)
Pydantic AILog traces for PydanticAI agent calls[Documentation](https://www.comet.com/docs/opik/integrations/pydantic-ai?utm_source=opik&utm_medium=github&utm_content=pydantic_ai_link&utm_campaign=opik)
RagasLog traces for Ragas evaluations[Documentation](https://www.comet.com/docs/opik/integrations/ragas?utm_source=opik&utm_medium=github&utm_content=ragas_link&utm_campaign=opik)
Semantic KernelLog traces for Microsoft Semantic Kernel calls[Documentation](https://www.comet.com/docs/opik/integrations/semantic-kernel?utm_source=opik&utm_medium=github&utm_content=semantic_kernel_link&utm_campaign=opik)
SmolagentsLog traces for Smolagents agents[Documentation](https://www.comet.com/docs/opik/integrations/smolagents?utm_source=opik&utm_medium=github&utm_content=smolagents_link&utm_campaign=opik)
Spring AILog traces for Spring AI framework calls[Documentation](https://www.comet.com/docs/opik/integrations/spring-ai?utm_source=opik&utm_medium=github&utm_content=spring_ai_link&utm_campaign=opik)
Strands AgentsLog traces for Strands agents calls[Documentation](https://www.comet.com/docs/opik/integrations/strands-agents?utm_source=opik&utm_medium=github&utm_content=strands_agents_link&utm_campaign=opik)
Together AILog traces for Together AI LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/together-ai?utm_source=opik&utm_medium=github&utm_content=together_ai_link&utm_campaign=opik)
Vercel AI SDKLog traces for Vercel AI SDK calls[Documentation](https://www.comet.com/docs/opik/integrations/vercel-ai-sdk?utm_source=opik&utm_medium=github&utm_content=vercel_ai_sdk_link&utm_campaign=opik)
VoltAgentLog traces for VoltAgent agent framework calls[Documentation](https://www.comet.com/docs/opik/integrations/voltagent?utm_source=opik&utm_medium=github&utm_content=voltagent_link&utm_campaign=opik)
WatsonXLog traces for IBM watsonx LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/watsonx?utm_source=opik&utm_medium=github&utm_content=watsonx_link&utm_campaign=opik)
xAI GrokLog traces for xAI Grok LLM calls[Documentation](https://www.comet.com/docs/opik/integrations/xai-grok?utm_source=opik&utm_medium=github&utm_content=xai_grok_link&utm_campaign=opik)
[!TIP] If the framework you are using is not listed above, feel free to open an issue or submit a PR with the integration.

If you are not using any of the frameworks above, you can also use the track function decorator to log traces:

import opik

opik.configure(use_local=True) # Run locally

@opik.track
def my_llm_function(user_question: str) -> str:
    # Your LLM code here

    return "Hello"
[!TIP] The track decorator can be used in conjunction with any of our integrations and can also be used to track nested function calls.

<a id="-llm-as-a-judge-metrics"></a>

🎯 aiskill88 AI 点评 A 级 2026-05-21

19.3k星高热度开源项目,功能完整覆盖LLM应用全生命周期,社区活跃维护好,是LLM工程化必备工具。

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 想快速复用高质量提示词模板的 AI 用户
最佳实践
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • embedding 模型与查询模型不一致导致检索失效
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
opik 中文教程opik 安装报错怎么办opik Agent 工作流opik 与同类工具对比opik 最佳实践opik 适合谁用
⚡ 核心功能
👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 想快速复用高质量提示词模板的 AI 用户
⭐ 最佳实践
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • embedding 模型与查询模型不一致导致检索失效
  • Python 依赖冲突:建议用 venv / uv 隔离环境
👥 适合人群
内容创作者和自媒体人职场人士和学生ChatGPT / Claude 重度用户希望提升 AI 使用效率的普通用户
🎯 使用场景
  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板
⚖️ 优点与不足
✅ 优点
  • +GitHub 19.3k Star,社区高度认可
  • +Apache-2.0 协议,可免费商用
  • +无需安装,立即可用
  • +适配所有主流 AI 工具
  • +经社区验证的最佳实践
⚠️ 不足
  • 效果依赖使用者对 Prompt 工程的熟悉程度
  • 不同模型和版本的响应效果可能存在差异
  • 复杂场景需结合实际需求二次调整
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。

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🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合
❓ 常见问题 FAQ
支持LangChain、LlamaIndex等主流框架,并提供通用API接口。
💡 AI Skill Hub 点评

总体来看,opik Prompt模板 是一款质量优秀的Prompt模板,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ Apache-2.0 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 opik Prompt模板
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 opik
原始描述 开源Prompt模板:Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic wor。⭐19.3k · Python
Topics LLM监控Prompt管理评估框架RAG系统Agent工具
GitHub https://github.com/comet-ml/opik
License Apache-2.0
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/comet-ml/opik 🌐 官方网站  https://www.comet.com/docs/opik/

收录时间:2026-05-13 · 更新时间:2026-05-16 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。