AgentReader is an open source AI agent that connects to LLMs to ingest PDFs, text files, markdown, and URLs. It provides interactive question-answering, streaming responses, web scraping, and plugin extensions for custom workflows.
AI Platforms & Frameworks
Document Processing
#AI agent framework
#Interactive Q&A
#data indexing
#PDF ingestion
#Web Scraping
#real-time data interaction
#plugin architecture
#custom workflows
#Knowledge Retrieval
#Self-Hosted Solutions
#Large Language Models
#Easy Integration
#Developer Tools
#data processing
#chat interface
#streaming responses
#multi-source data
#Open-source AI
#Knowledge Base
#custom extensions
...
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AgentReader
AgentReader is an open source AI agent that connects to LLMs to ingest PDFs, text files, markdown, and URLs. It provides interactive question-answering, streaming responses, web scraping, and plugin extensions for custom workflows.
AI Platforms & Frameworks
Document Processing
#AI agent framework
#Interactive Q&A
#data indexing
#PDF ingestion
#Web Scraping
#real-time data interaction
#plugin architecture
#custom workflows
#Knowledge Retrieval
#Self-Hosted Solutions
#Large Language Models
#Easy Integration
#Developer Tools
#data processing
#chat interface
#streaming responses
#multi-source data
#Open-source AI
#Knowledge Base
#custom extensions
...
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What is AgentReader?
AgentReader is a developer-friendly AI agent framework that enables you to load and index various data sources such as PDFs, text files, markdown documents, and web pages. It integrates seamlessly with major LLM providers to power interactive chat sessions and question-answering over your knowledge base. Features include real-time streaming of model responses, customizable retrieval pipelines, web scraping via headless browser, and a plugin architecture for extending ingestion and processing capabilities.
Who will use AgentReader?
- Data scientists
- Researchers
- Knowledge management teams
- Developers building AI assistants
- Technical writers
How to use the AgentReader?
- Step1: Clone the repository from GitHub.
- Step2: Install dependencies (Node.js or Python environment).
- Step3: Configure your LLM API key in the config file.
- Step4: Run the ingestion command to load documents or URLs.
- Step5: Start the chat interface and ask questions about your data.
Platform
- mac
- windows
- linux
AgentReader's Core Features & Benefits
The Core Features
- Ingest PDFs, text, markdown, and URLs
- Interactive Q&A chat interface
- Streaming LLM responses
- Web scraping with headless browser
- Plugin architecture for custom extensions
The Benefits
- Rapid knowledge retrieval from multiple sources
- Real-time interaction with your data
- Highly customizable workflows
- Open source and self-hosted
- Easy integration into existing projects
AgentReader's Main Use Cases & Applications
- Building an AI-powered document search assistant
- Automating customer support knowledge retrieval
- Research paper summarization and Q&A
- Internal knowledge base query interface
- Technical documentation chatbot
FAQs of AgentReader
AgentReader Company Information
AgentReader Reviews
5/5
AgentReader's Main Competitors and alternatives?
- LangChain
- Haystack
- GPT-Index
- Vectara
- Chat with PDF
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