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Personal AI agent that builds custom mini-apps and tools on request and remembers your preferences to help with daily life.
No-code builder for custom ChatGPT-style website chatbots trained on your data for support, lead gen and engagement.
Prompt engineering, management, and LLM observability platform.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
- ✦Personal AI agent for daily life
- ✦Deep Memory that remembers user preferences
- ✦Builds custom tools and mini-apps on request
- ✦Personality test for personalization
- ✦Mobile app
- ✦Content and template library
- ✦No-code chatbot creation
- ✦Train on files/URLs/Notion/Zendesk
- ✦Website embed widget
- ✦Conversation analytics
- ✦~95 language support
- ✦Custom branding
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- →Getting personalized help with daily tasks
- →Generating small custom tools on demand
- →Building an AI that remembers your context
- →Creative writing and story generation
- →Website customer support
- →Lead generation
- →FAQ and audience engagement
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents