Information
# Open Agent RL Dataset: High Quality AI Agent | Tool Use & Function Calls | Reinforcement Learning Datasets
DeepNLP website provides **high quality, genuinue, online users' request** of Agent & RL datasets to help LLM foundation/SFT/Post Train to get more capable models at function call, tool use and planning. The datasets are collected and sampled
from users' requests on our various clients (Web/App/Mini App) and [Open OneKey Agent Router](https://www.deepnlp.org/agent/onekey-mcp-router) and [Open OneKey MCP Router](https://www.deepnlp.org/agent/onekey-mcp-router). Some datasets requires credit to deduct and you can easily gain more credit by activities such as commenting and discussion and uploading your own datasets to the communities (https://www.deepnlp.org/workspace/billing).
Visit Our AI Store Dataset Tab to Select [Dataset](https://www.deepnlp.org/store/dataset).
**Disclaimer**: Safe privacy preserving or personalized information are marked and filtered out.
## 1. Dataset Features
**Genuinue Users' Queries**: Most of the high quality datasets are collected from query logs of our live AI Agents, such as [MCP Tool Use Agent](https://agent.deepnlp.org/agent/mcp_tool_use), [Open OneKey Agent Router](https://www.deepnlp.org/agent/onekey-mcp-router) and [Open OneKey MCP Router](https://www.deepnlp.org/agent/onekey-mcp-router).
**Function Call and MCP Servers Support**: The datasets covers wide range of MCP servers from the Open MCP Marketplace() and Playgrounds.
**Users Action and Humans' Feedback**: Users' actual feedbacks are crucial in improving the AI Agents training process. We collects users' genuine actions, such as **ACCEPT/REJECT** in confirming the function call results, **Upvote/Downvote** action of the final responses, and many other users' feedback on clickable elements.
**Various Domains and Tasks**: We covers 40+ categories of AI agents' tool use scenarios, ranging from information seeking (AI search, map search, etc) to autonomous AI agents browser use, computer use, Data Analysis, Excel Spreadsheet and Powerpoint creation and generation, etc.
**Example AI Agent Dataset Dialogues**
| Domain | Related MCP Server| Demo |
| ---- | ---- | ---- |
| Office File Agent | Excel Spreadsheet, Powerpoint, PDF, etc | [Example](https://agent.deepnlp.org/agent/mcp_tool_use/share/ee640008-6bc1-4c3a-832b-2557f985b540) [MCP]() |
| AI Search/Deep Research | Bing/Google Custom/Perplexity/Tavily/Firecrawl | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use?server=tavily-ai/tavily-mcp) [MCP]() |
| Map Trip Planning | GoogleMap, Amap(Gaode), BaiduMap, etc. | [Example](https://agent.deepnlp.org/agent/mcp_tool_use/share/8ab0b25c-b72d-4cae-9c86-a852df8c6541) [MCP](https://agent.deepnlp.org/agent/mcp_tool_use?server=amap-mcp/amap-mcp-%E9%AB%98%E5%BE%B7%E5%9C%B0%E5%9B%BE-mcp) [Use MCP]() |
| Browser Usage | Playwright, Puppeteer, etc. | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use?server=puppeteer/puppeteer) [MCP]() |
| Chart,Graph,Image | everart,mcp-server-charts(AntV),canva-mcp | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use/share/93d94694-e673-49d3-b805-820c4ef842bd) [MCP]() |
## 2. Dataset Introduction
We provide main below types of AI agents datasets in List of Messages Json Formats and scalar data such as rewards, etc.
| Dataset Name | Description | User Feedback | Example Dataset Download | Full DataSet Download |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ---- |-------|-----------------------------------------------------------------------------------------------------------------|
| Tool Use Multi-Turn Dialogue | The tool use multi-turn dialogue dataset is in the list of messages formats, Useful for AI Search/Deep Research/Map/Financial Data/etc | YES | 50 instances, [Download](https://www.deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-tool-use-dialogue-open-dataset-example) | 1k, [Download](https://www.deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-tool-use-dialogue-open-dataset) |
| Function Calling Tool Use | The dataset contains **messages** and **available tools** as input and output the choosen **tool_call** result indicating which tool to use and the arguments. The datasets are collected from calling SOTA LLM such as GPT, OpenAI o-series, Claude, Qwen, Kimi, etc. | No | 50 instances, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-function-calling-open-dataset-example) | 1k, [Download](https://deepnlp.org/store/ai-agent/ai-agent/pub-deepnlp/agent-function-calling-open-dataset) |
| Reinforcement Learning | Sessions of user and assistant' multi-dialogues, rewards from users' feedback in this session, such click of confirmation (Accept/Reject), Upvote, Downvote on the responses, etc. | YES | 50 instances, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-reinforcement-learning-open-dataset-example) | 1k, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-reinforcement-learning-open-dataset) |
### Dataset 1 Tool Use Multi-Turn Dialogue Dataset
**Dataset Description**
| KEY | Type | Description |
| ---- |---------------------|-----------------------------------------------------------------------------------------------------------------------------|
| trace_id | String | Identify each unique new user request or API calling |
| session_id | String | The identifier of each dialogue, which consists of multiple turns of dialogues and every user input produces a new trace_id |
| messages | List of Json Object | Dialogue Messages |
This data instances indicates a multi-turn dialogues of users' calling Google Maps **get_weather** tool to know
the recent weather in San Francisco. The dialogues contains three types of messages:
\`\`\`
User: query, original question that user asks,
User: available_tools, List of Json that user provides to LLM,
Assistant: message, content.type='tool_use', LLM output which tool to use and its parameters,
User: message, content.type='tool_result', Users' actual function call running results.
\`\`\`
\`\`\`
[
\{
"role": "user",
"content": "What is the weather like in San Francisco?"
\},
\{
"role": "assistant",
"content": [
\{
"type": "text",
"text": "I need to use get_weather, and the user wants SF, which is likely San Francisco, CA."
\},
\{
"type": "tool_use",
"id": "toolu_01A09q90qw90lq917835lq9",
"name": "get_weather",
"input": \{
"location": "San Francisco, CA",
"unit": "celsius"
\}
\}
]
\},
\{
"role": "user",
"content": [
\{
"type": "tool_result",
"tool_use_id": "toolu_01A09q90qw90lq917835lq9",
"content": "15 degrees"
\}
]
\}
]
\`\`\`
Note that the function call comes in different formats when calling various models. We are mainly collecting in the OpenAI and anthroupic function calling formats.
We supported both and you can see the differences from the offical documentations.
**Multi-modal and Files** formats are also attached:
The images and raw descriptions of the files such as path are also attached for context variables.
**OpenAI/Qwen/etc Function Call Formats**
\`\`\`
\{
"tool_call": \{
"id": "call_d6f4ed29ce614390b99a05",
"function": \{
"arguments": "\{\"url\": \"https://www.stackoverflow.com\", \"browserType\": \"chromium\"\}",
"name": "playwright_navigate"
\},
"type": "function",
"index": 0
\}
\}
\`\`\`
**Anthroupic Tool Use Formats**
\`\`\`
\{
"type": "tool_use",
"id": "toolu_01A09q90qw90lq917835lq9",
"name": "get_weather",
"input": \{
"location": "San Francisco, CA",
"unit": "celsius"
\}
\}
\`\`\`
## 1. Dataset Features
**Genuinue Users' Queries**: Most of the high quality datasets are collected from query logs of our live AI Agents, such as [MCP Tool Use Agent](https://agent.deepnlp.org/agent/mcp_tool_use), [Open OneKey Agent Router](https://www.deepnlp.org/agent/onekey-mcp-router) and [Open OneKey MCP Router](https://www.deepnlp.org/agent/onekey-mcp-router).
**Function Call and MCP Servers Support**: The datasets covers wide range of MCP servers from the Open MCP Marketplace() and Playgrounds.
**Users Action and Humans' Feedback**: Users' actual feedbacks are crucial in improving the AI Agents training process. We collects users' genuine actions, such as **ACCEPT/REJECT** in confirming the function call results, **Upvote/Downvote** action of the final responses, and many other users' feedback on clickable elements.
**Various Domains and Tasks**: We covers 40+ categories of AI agents' tool use scenarios, ranging from information seeking (AI search, map search, etc) to autonomous AI agents browser use, computer use, Data Analysis, Excel Spreadsheet and Powerpoint creation and generation, etc.
**Example AI Agent Dataset Dialogues**
| Domain | Related MCP Server| Demo |
| ---- | ---- | ---- |
| Office File Agent | Excel Spreadsheet, Powerpoint, PDF, etc | [Example](https://agent.deepnlp.org/agent/mcp_tool_use/share/ee640008-6bc1-4c3a-832b-2557f985b540) [MCP]() |
| AI Search/Deep Research | Bing/Google Custom/Perplexity/Tavily/Firecrawl | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use?server=tavily-ai/tavily-mcp) [MCP]() |
| Map Trip Planning | GoogleMap, Amap(Gaode), BaiduMap, etc. | [Example](https://agent.deepnlp.org/agent/mcp_tool_use/share/8ab0b25c-b72d-4cae-9c86-a852df8c6541) [MCP](https://agent.deepnlp.org/agent/mcp_tool_use?server=amap-mcp/amap-mcp-%E9%AB%98%E5%BE%B7%E5%9C%B0%E5%9B%BE-mcp) [Use MCP]() |
| Browser Usage | Playwright, Puppeteer, etc. | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use?server=puppeteer/puppeteer) [MCP]() |
| Chart,Graph,Image | everart,mcp-server-charts(AntV),canva-mcp | [Demo](https://agent.deepnlp.org/agent/mcp_tool_use/share/93d94694-e673-49d3-b805-820c4ef842bd) [MCP]() |
## 2. Dataset Introduction
We provide main below types of AI agents datasets in List of Messages Json Formats and scalar data such as rewards, etc.
| Dataset Name | Description | User Feedback | Example Dataset Download | Full DataSet Download |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ---- |-------|-----------------------------------------------------------------------------------------------------------------|
| Tool Use Multi-Turn Dialogue | The tool use multi-turn dialogue dataset is in the list of messages formats, Useful for AI Search/Deep Research/Map/Financial Data/etc | YES | 50 instances, [Download](https://www.deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-tool-use-dialogue-open-dataset-example) | 1k, [Download](https://www.deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-tool-use-dialogue-open-dataset) |
| Function Calling Tool Use | The dataset contains **messages** and **available tools** as input and output the choosen **tool_call** result indicating which tool to use and the arguments. The datasets are collected from calling SOTA LLM such as GPT, OpenAI o-series, Claude, Qwen, Kimi, etc. | No | 50 instances, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-function-calling-open-dataset-example) | 1k, [Download](https://deepnlp.org/store/ai-agent/ai-agent/pub-deepnlp/agent-function-calling-open-dataset) |
| Reinforcement Learning | Sessions of user and assistant' multi-dialogues, rewards from users' feedback in this session, such click of confirmation (Accept/Reject), Upvote, Downvote on the responses, etc. | YES | 50 instances, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-reinforcement-learning-open-dataset-example) | 1k, [Download](https://deepnlp.org/store/dataset/dataset/pub-deepnlp/agent-reinforcement-learning-open-dataset) |
### Dataset 1 Tool Use Multi-Turn Dialogue Dataset
**Dataset Description**
| KEY | Type | Description |
| ---- |---------------------|-----------------------------------------------------------------------------------------------------------------------------|
| trace_id | String | Identify each unique new user request or API calling |
| session_id | String | The identifier of each dialogue, which consists of multiple turns of dialogues and every user input produces a new trace_id |
| messages | List of Json Object | Dialogue Messages |
This data instances indicates a multi-turn dialogues of users' calling Google Maps **get_weather** tool to know
the recent weather in San Francisco. The dialogues contains three types of messages:
\`\`\`
User: query, original question that user asks,
User: available_tools, List of Json that user provides to LLM,
Assistant: message, content.type='tool_use', LLM output which tool to use and its parameters,
User: message, content.type='tool_result', Users' actual function call running results.
\`\`\`
\`\`\`
[
\{
"role": "user",
"content": "What is the weather like in San Francisco?"
\},
\{
"role": "assistant",
"content": [
\{
"type": "text",
"text": "I need to use get_weather, and the user wants SF, which is likely San Francisco, CA."
\},
\{
"type": "tool_use",
"id": "toolu_01A09q90qw90lq917835lq9",
"name": "get_weather",
"input": \{
"location": "San Francisco, CA",
"unit": "celsius"
\}
\}
]
\},
\{
"role": "user",
"content": [
\{
"type": "tool_result",
"tool_use_id": "toolu_01A09q90qw90lq917835lq9",
"content": "15 degrees"
\}
]
\}
]
\`\`\`
Note that the function call comes in different formats when calling various models. We are mainly collecting in the OpenAI and anthroupic function calling formats.
We supported both and you can see the differences from the offical documentations.
**Multi-modal and Files** formats are also attached:
The images and raw descriptions of the files such as path are also attached for context variables.
**OpenAI/Qwen/etc Function Call Formats**
\`\`\`
\{
"tool_call": \{
"id": "call_d6f4ed29ce614390b99a05",
"function": \{
"arguments": "\{\"url\": \"https://www.stackoverflow.com\", \"browserType\": \"chromium\"\}",
"name": "playwright_navigate"
\},
"type": "function",
"index": 0
\}
\}
\`\`\`
**Anthroupic Tool Use Formats**
\`\`\`
\{
"type": "tool_use",
"id": "toolu_01A09q90qw90lq917835lq9",
"name": "get_weather",
"input": \{
"location": "San Francisco, CA",
"unit": "celsius"
\}
\}
\`\`\`
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