Agnes 2.0 Flash

Agnes 2.0 Flash is a fast and efficient Agnes AI language model. It is designed for high-frequency production use cases such as agent workflows, tool calling, coding, multi-turn conversations, reasoning, and image understanding.

Model

agnes-2.0-flash

API Endpoints

Chat Completions: POST /v1/chat/completions
Responses: POST /v1/responses

Context Window

512K

Current Price

Input and output tokens are currently $0 / 1M tokens.

Overview

Agnes 2.0 Flash is optimized for fast, reliable, and cost-effective language generation, agent task execution, tool orchestration, and image understanding. The model performs strongly on the Claw-Eval benchmark. It ranks No. 9 on the general leaderboard with a Pass^3 score of 60.9%, demonstrating strong autonomous agent capabilities.

Core Capabilities

Chat Completions

Generate high-quality responses for conversations, applications, and business systems.

Multi-turn Conversations

Maintain context consistency across continuous interactions.

Image URL Input

Accept visual content through publicly accessible image URLs.

Image Understanding

Analyze screenshots, describe images, answer visual questions, and extract visual information.

Tool Calling

Support function calling and external tool orchestration.

Agent Workflows

Suitable for planning, execution, and multi-step task completion.

Coding Tasks

Assist with code generation, debugging, explanation, and refactoring.

Streaming

Return responses in real time for a better interactive experience.

Use Cases

AI Assistants

General Q&A, productivity assistants, personal assistants, and in-app copilots.

Autonomous Agents

Multi-step task execution, planning, tool use, and workflow scheduling.

Coding Assistants

Code generation, bug fixing, refactoring suggestions, and code explanation.

Customer Support

FAQ automation, support chatbots, and service workflow automation.

Search and Q&A

Retrieval-based answers, summarization, and information extraction.

Image Understanding

Screenshot analysis, image description, visual Q&A, and structured extraction.

API Reference

Endpoint

POST https://apihub.agnes-ai.com/v1/chat/completions

Headers

-H "Authorization: Bearer YOUR_API_KEY"
-H "Content-Type: application/json"

Request Parameters

Parameter	Type	Required	Description
`model`	string	Yes	Model name. Use `agnes-2.0-flash`.
`messages`	array	Yes	Conversation messages, including `system`, `user`, and `assistant` messages.
`messages[].content`	string / array	Yes	Message content. It can be plain text or an array of content blocks containing `text` and `image_url`.
`temperature`	number	No	Controls randomness. Lower values produce more deterministic results.
`top_p`	number	No	Controls nucleus sampling. Lower values make output more focused.
`max_tokens`	number	No	Maximum number of tokens to generate in the response.
`stream`	boolean	No	Whether to enable streaming output.
`tools`	array	No	Tool definitions for tool-calling workflows.
`tool_choice`	string / object	No	Controls whether and how the model uses tools.
`chat_template_kwargs`	object	No	Extension field for enabling Thinking and other features in OpenAI-compatible requests.
`thinking`	object	No	Field for enabling Thinking mode in Anthropic-compatible requests.

Image URL Input

Agnes 2.0 Flash supports passing text and image URLs in the same messages request.

Input Type	Format	Description
Text	`text`	Plain text instruction or question.
Image URL	`image_url`	Pass image content through a publicly accessible image URL.

{
  "role": "user",
  "content": [
    {
      "type": "text",
      "text": "Describe the content of this image."
    },
    {
      "type": "image_url",
      "image_url": {
        "url": "https://example.com/image.jpg"
      }
    }
  ]
}

Request Examples

Basic Chat
Streaming
Tool Calling
Image Understanding

curl https://apihub.agnes-ai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful AI assistant."
      },
      {
        "role": "user",
        "content": "Explain how autonomous agents use tools to complete tasks."
      }
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

curl https://apihub.agnes-ai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "messages": [
      {
        "role": "user",
        "content": "Write a short product introduction for an AI assistant app."
      }
    ],
    "stream": true
  }'

curl https://apihub.agnes-ai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "messages": [
      {
        "role": "user",
        "content": "What is the weather like in Singapore today?"
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and country"
              }
            },
            "required": ["location"]
          }
        }
      }
    ]
  }'

curl https://apihub.agnes-ai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Describe the content of this image."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/image.jpg"
            }
          }
        ]
      }
    ]
  }'

Response Format

{
  "id": "chatcmpl_xxx",
  "object": "chat.completion",
  "created": 1774432125,
  "model": "agnes-2.0-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Autonomous agents use tools by understanding the user's goal, breaking it into steps, selecting the right tools, executing actions, and using the results to complete the task."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 35,
    "completion_tokens": 58,
    "total_tokens": 93
  }
}

Response Fields

Field	Type	Description
`id`	string	Unique ID of the completion request.
`object`	string	Object type, usually `chat.completion`.
`created`	integer	Request timestamp.
`model`	string	Model used for the request.
`choices`	array	List of generated responses.
`choices[].message.role`	string	Role of the message sender.
`choices[].message.content`	string	Content generated by the model.
`choices[].finish_reason`	string	Reason generation stopped.
`usage`	object	Token usage information.

Responses API

In addition to Chat Completions, this model supports the OpenAI Responses API. Use input instead of messages.

Responses endpoint

POST https://apihub.agnes-ai.com/v1/responses

Responses request parameters

Parameter	Type	Required	Description
`model`	string	Yes	Model name. Use `agnes-2.0-flash`.
`input`	string / array	Yes	A plain text prompt or an array of structured input messages.
`max_output_tokens`	integer	No	Maximum output budget. Use a larger value for reasoning models to avoid an `incomplete` response.

Text Input
Structured Input

curl https://apihub.agnes-ai.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "input": "Explain how autonomous agents use tools.",
    "max_output_tokens": 1024
  }'

curl https://apihub.agnes-ai.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.0-flash",
    "input": [
      {
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "Explain how autonomous agents use tools."
          }
        ]
      }
    ],
    "max_output_tokens": 1024
  }'

Responses output format

{
  "id": "resp_xxx",
  "object": "response",
  "status": "completed",
  "model": "agnes-2.0-flash",
  "output": [
    {
      "type": "reasoning",
      "summary": []
    },
    {
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Autonomous agents use tools to retrieve data and perform actions."
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 40,
    "output_tokens": 80,
    "total_tokens": 120
  },
  "error": null,
  "incomplete_details": null
}

Field	Type	Description
`id`	string	Unique response ID.
`object`	string	Object type, usually `response`.
`status`	string	Response state, such as `completed` or `incomplete`.
`output`	array	Ordered response items, including reasoning and assistant messages.
`output[].type`	string	Item type, such as `reasoning` or `message`.
`output[].content[].type`	string	Content type. Generated text uses `output_text`.
`output[].content[].text`	string	Generated assistant text.
`usage`	object	Token usage information.
`error`	object / null	Error details when the request fails.
`incomplete_details`	object / null	Explains why a response stopped before completion.

The current response does not include a top-level output_text convenience field. Extract generated text from message items where output[].type is message and output[].content[].type is output_text.

Reasoning items are optional and can use either content[].reasoning_text or summary[].summary_text. Token usage field names can also vary by model: support both input_tokens / output_tokens and prompt_tokens / completion_tokens.

If status is incomplete, inspect incomplete_details and retry with a larger max_output_tokens value. Reasoning models can consume part of the output budget before producing assistant text.

Thinking Mode

For coding, debugging, reasoning, and agent workflows, you can enable Thinking mode to improve task decomposition and problem-solving quality.

OpenAI-compatible Format
Anthropic-compatible Format

{
  "model": "agnes-2.0-flash",
  "messages": [
    {
      "role": "user",
      "content": "Help me write a Python script to process a CSV file."
    }
  ],
  "chat_template_kwargs": {
    "enable_thinking": true
  }
}

{
  "model": "agnes-2.0-flash",
  "messages": [
    {
      "role": "user",
      "content": "Help me refactor this TypeScript function and explain the changes."
    }
  ],
  "thinking": {
    "type": "enabled",
    "budget_tokens": 2048
  }
}

For regular coding tasks, start with budget_tokens: 2048. For complex debugging, refactoring, or multi-step agent workflows, increase the budget as needed.

Best Practices

Prompt Structure

[Role] + [Task] + [Context] + [Requirements] + [Output Format]

Product Copywriting

You are a product marketing expert. Write a concise App Store description for an AI assistant app. The tone should be clear, professional, and user-friendly.

Coding Tasks

Help me debug this React component. The issue is that the button state does not update after clicking. Explain the cause and provide the corrected code.

Agent Workflows

You are an autonomous research agent. Search for relevant information, summarize the key findings, and return the result in a structured format with source links.

Image Understanding Tasks

Analyze this screenshot. Identify the main UI elements, explain the possible issue, and provide suggestions to improve the user experience.

Limits and Pricing

Item	Value
Context window	`512K`
Maximum output	`65.5K`

Type	Standard Price	Current Price
Input tokens	`$0.03 / 1M tokens`	`$0 / 1M tokens`
Output tokens	`$0.15 / 1M tokens`	`$0 / 1M tokens`

Integration Checklist

Use agnes-2.0-flash as the model name.

Basic chat completion requests must include model and messages.

Image inputs must use publicly accessible image_url values.

Set stream to true when you need streaming responses.

Get Started

Use Guides

Model

API Endpoints

Context Window

Current Price

Overview

Core Capabilities

Chat Completions

Multi-turn Conversations

Image URL Input

Image Understanding

Tool Calling

Agent Workflows

Coding Tasks

Streaming

Use Cases

AI Assistants

Autonomous Agents

Coding Assistants

Customer Support

Search and Q&A

Image Understanding

API Reference

Endpoint

Headers

Request Parameters

Image URL Input

Request Examples

Response Format

Response Fields

Responses API

Responses endpoint

Responses request parameters

Responses output format

Thinking Mode

Best Practices

Limits and Pricing

Integration Checklist

Model

API Endpoints

Context Window

Current Price

​Overview

​Core Capabilities

Chat Completions

Multi-turn Conversations

Image URL Input

Image Understanding

Tool Calling

Agent Workflows

Coding Tasks

Streaming

​Use Cases

AI Assistants

Autonomous Agents

Coding Assistants

Customer Support

Search and Q&A

Image Understanding

​API Reference

​Endpoint

​Headers

​Request Parameters

​Image URL Input

​Request Examples

​Response Format

​Response Fields

​Responses API

​Responses endpoint

​Responses request parameters

​Responses output format

​Thinking Mode

​Best Practices

​Limits and Pricing

​Integration Checklist

Overview

Core Capabilities

Use Cases

API Reference

Endpoint

Headers

Request Parameters

Image URL Input

Request Examples

Response Format

Response Fields

Responses API

Responses endpoint

Responses request parameters

Responses output format

Thinking Mode

Best Practices

Limits and Pricing

Integration Checklist