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Creates concise summaries of any input text.

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Code

Create text-summarizer.py with the code below, or save it directly from your editor. 
from bindu.penguin.bindufy import bindufy
from agno.agent import Agent
from agno.models.openrouter import OpenRouter
from dotenv import load_dotenv
import os

load_dotenv()

agent = Agent(
    instructions="You are a professional summarization assistant. Create clear, concise summaries that capture the main points and essential information from any input text. Aim for 2-3 sentences that preserve the core meaning while being significantly shorter than the original.",
    model=OpenRouter(
        id="openai/gpt-oss-120b",
        api_key=os.getenv("OPENROUTER_API_KEY")
    ),
)

def handler(messages):
    user_input = messages[-1]["content"]
    result = agent.run(input=user_input)
    return result

config = {
    "author": "gaurikasethi88@gmail.com",
    "name": "summarizer_agent",
    "description": "Professional text summarization agent using OpenRouter's openai/gpt-oss-120b model.",
    "deployment": {
        "url": "http://localhost:3773",
        "expose": True,
        "cors_origins": ["http://localhost:5173"]
    },
    "skills": ["skills/text-summarization-skill"],
}

bindufy(config, handler)

#bindufy(config, handler, launch=True)
# This will create a tunnel to your agent and expose it on port 3773

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Skill Configuration

Create skills/text-summarization-skill/skill.yaml: 
# Text Summarization Skill
# Advanced text summarization with intelligent content condensation

id: text-summarization-skill
name: Text Summarization Skill
version: 1.0.0
author: gaurikasethi88@gmail.com

description: |
  Advanced text summarization agent that creates concise, coherent summaries
  of any input text while preserving key information and context.

  Features:
  - Intelligent content condensation
  - Key point extraction and preservation
  - Context-aware summarization
  - Coherent narrative flow
  - Multi-format text support
  - Rapid processing with OpenRouter integration

  Capabilities:
  - Long-form text summarization
  - Article and document condensation
  - Key point extraction
  - Context preservation
  - Coherent summary generation

  Uses OpenRouter's `openai/gpt-oss-120b` model for high-quality
  summarization with excellent comprehension and generation capabilities.

  Perfect for quickly understanding lengthy documents, articles,
  reports, or any text content that needs to be condensed efficiently.
tags:
  - summarization
  - text-processing
  - content-condensation
  - information-extraction
  - document-analysis
  - reading-assistance
  - productivity
input_modes:
  - application/json
output_modes:
  - application/json
examples:
  - "Summarize this article about climate change"
  - "Create a summary of the quarterly report"
  - "Condense this research paper into key points"
  - "Summarize the meeting transcript"
  - "Extract main points from this email thread"
capabilities_detail:
  text_summarization:
    supported: true
    description: "Creates concise 2-3 sentence summaries of any input text"
  key_point_extraction:
    supported: true
    description: "Identifies and preserves the most important information"
  context_preservation:
    supported: true
    description: "Maintains context and coherence in summaries"
  multi_format_support:
    supported: true
    description: "Handles various text formats and structures"
  rapid_processing:
    supported: true
    description: "Fast summarization using OpenRouter's efficient model"

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How It Works

Summarization Instructions
  • Clear directive: “Create clear, concise summaries”
  • Target length: 2-3 sentences
  • Preserves core meaning while reducing length
Handler
  • Extracts user input: messages[-1]["content"]
  • Passes to agent for summarization
  • Returns condensed version
Model
  • openai/gpt-oss-120b: Advanced text understanding
  • Via OpenRouter API
  • Optimized for text transformation tasks
Skills
  • text-summarization-skill: Defines summarization capabilities
  • Enables skill-based discovery

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Dependencies

uv init
uv add bindu agno python-dotenv

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Environment Setup

Create .env file: 
OPENROUTER_API_KEY=your_openrouter_api_key_here

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Run

uv run text-summarizer.py
Try: “Summarize this text about climate change and its effects on the environment”

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Example API Calls

{
  "jsonrpc": "2.0",
  "method": "message/send",
  "params": {
    "message": {
      "role": "user",
      "kind": "message",
      "messageId": "9f11c870-5616-49ad-b187-d93cbb100001",
      "contextId": "9f11c870-5616-49ad-b187-d93cbb100002",
      "taskId": "9f11c870-5616-49ad-b187-d93cbb100003",
      "parts": [
        {
          "kind": "text",
          "text": "Summarize this text about climate change and its effects on the environment"
        }
      ]
    },
     "skillId": "text-summarization-skill",
    "configuration": {
      "acceptedOutputModes": ["application/json"]
    }
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100003"
}

{
  "jsonrpc": "2.0",
  "method": "tasks/get",
  "params": {
    "taskId": "9f11c870-5616-49ad-b187-d93cbb100003"
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100004"
}

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Frontend Setup

# Clone the Bindu repository
git clone https://github.com/GetBindu/Bindu

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start frontend development server
npm run dev
Open http://localhost:5173 and try to chat with the text summarizer