rlama
RLAMA is an open-source, cross-platform AI platform and CLI for building Retrieval-Augmented Generation (RAG) systems and orchestrating intelligent AI agents and multi-agent crews, with a focus on local processing and privacy.
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Best for: AI Agents
What it does
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rlama
RLAMA is an open-source AI platform and command-line tool for creating, managing, and interacting with Retrieval-Augmented Generation (RAG) systems and intelligent AI agents. It supports ingestion of multiple document formats, local embedding and storage, and orchestration of single agents or multi-agent crews to automate tasks from document Q&A to complex multi-step workflows. The project is offered as a cross-platform CLI and includes an HTTP API server option for application integration, with emphasis on local processing and privacy ("no data sent externally").
The platform targets developers, data analysts, researchers, and teams who need private, local RAG capabilities and agent-based automation; it provides tools for building RAGs, configuring agents with tools (e.g., RAG search, code execution, web search), and orchestrating sequential or parallel multi-agent workflows.
Open-source tool for building document question-answering systems with local AI models.
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Complete RAG systems
Create, manage, and interact with Retrieval-Augmented Generation systems that index documents and support Q&A over multiple formats.
Multiple document formats
Supports .txt, .md, .pdf, .docx, .pptx, code files (e.g., .py, .go), spreadsheets and many other formats for ingestion.
Local processing & privacy
100% local processing with no data sent externally; suitable for private knowledge bases and sensitive documents.
AI Agents & Crews
Create specialized agents with roles (researcher, writer, coder, analyst) and tools (RAG search, web search, code execution); group them into crews for collaborative tasks.
Multi-agent orchestration
Support for sequential, parallel, and hierarchical workflows allowing agents to work together on complex tasks.
Interactive CLI
Terminal-based commands to create RAGs, agents, crews, run interactive sessions, and watch directories for updates.
Visual RAG Builder
A drag-and-drop visual interface for creating RAGs without coding, with configurable chunking and source settings.
HTTP API server
Option to start an API server for integrating RLAMA functionality into applications.
Cross-platform support
Available for macOS, Linux, and Windows.
Model support
Supports local models and integrations with Ollama, OpenAI, and Hugging Face models.
Pricing
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Use Cases
Technical documentation Q&A
Index project docs and manuals to allow natural-language queries against technical documentation.
Private knowledge base
Create secure, locally-hosted RAG systems to query sensitive internal documents without sending data externally.
Research assistance
Deploy agents to query papers, analyze data, summarize findings, and generate insights.
Content creation and collaboration
Orchestrate crews of agents for drafting, reviewing, and publishing content in collaborative workflows.
Automated workflows and tooling
Automate multi-step processes with agents executing tasks sequentially or in parallel, including code execution and web search tools.
Integrations
Ollama
Local model support via Ollama is mentioned as a supported model backend.
OpenAI
OpenAI model support is listed as an available model option.
Hugging Face
Hugging Face model support is listed as a supported model source.
HTTP API
Built-in API server to integrate RLAMA into other applications.
Benefits
Limitations
Frequently Asked Questions
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Getting Started
- 1 Install RLAMA on macOS, Linux, or Windows and verify with rlama --version.
- 2 Create a RAG from a local folder: rlama rag [model] [rag-name] [folder-path] (example: rlama rag llama3 documentation ./docs).
- 3 Create agents and crews with rlama agent create and rlama crew create, then run interactive sessions with rlama run; optionally start the HTTP API server with rlama api --port <PORT>.
Support
docs
Documentation available from the site (Documentation link present on rlama.dev).
blog
Project blog is available from the site (Blog link present on rlama.dev).
source code / issues (GitHub)
View on GitHub link is present on the site for source, issues, and contribution.
API
https://rlama.dev/ (see Documentation on site)
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