RLAMARLAMA
📣 New

Introducing RLAMA Chat

RLAMA

The complete AI platform for creating RAG systems and intelligent agents. Build, deploy, and manage AI-powered solutions with local models - from document Q&A to autonomous agent crews.

Install Now

Available for macOS, Linux, and Windows

$ rlama --version
RLAMA v0.1.29
$ rlama rag llama3 documentation ./docs
Embeddings generated successfully!
RAG system "documentation" created successfully!
$ rlama agent create researcher --role="Data Analyst"
Agent "researcher" created with RAG search tools!
$ rlama crew create research-team researcher writer
Crew "research-team" ready for collaborative tasks!

What is RLAMA?

A complete AI platform that combines RAG systems with intelligent agents, creating powerful automated workflows for any task - from document analysis to complex multi-agent collaboration.

Complete RAG Solution

Create, manage, and interact with Retrieval-Augmented Generation systems tailored to your documentation.

  • Multiple document formats (.txt, .md, .pdf, etc.)
  • Advanced semantic chunking strategies
  • Local storage and processing with no data sent externally
AI Agents & Crews

Create specialized AI agents that can perform specific tasks or collaborate as crews to solve complex problems.

  • Multiple agent roles (researcher, writer, coder, analyst)
  • Agent tools (RAG search, code execution, web search)
  • Collaborative workflows with sequential or parallel steps
Multi-Agent Orchestration

Orchestrate multiple agents working together in sophisticated workflows for complex automation tasks.

  • Sequential workflows for step-by-step processes
  • Parallel execution for concurrent task processing
  • Hierarchical delegation with manager agents
Flexible Integration

Adapt RLAMA to your workflow with multiple integration options and extensive tooling support.

  • HTTP API server for application integration
  • Cross-platform support (macOS, Linux, Windows)
  • OpenAI model support alongside Ollama

Key Features

Everything you need to build powerful RAG systems and intelligent AI agent workflows

RAG Systems

Create and manage Retrieval-Augmented Generation systems with multiple document formats.

AI Agents & Crews

Build specialized AI agents that collaborate as crews to solve complex problems.

Local Processing

100% local processing with no data sent to external servers for maximum privacy.

Intelligent Automation

Automate workflows with AI agents equipped with tools and collaborative capabilities.

Multi-Agent Workflows

Orchestrate multiple agents working together in sequential or parallel workflows.

Interactive Sessions

Chat with your RAG systems and agents through intuitive terminal interfaces.

Visual RAG Builder

Create powerful RAG systems in minutes without writing a single command

Create RAGs visually in 2 minutes

No coding required. Our intuitive interface makes RAG creation accessible to everyone.

  • Easy drag-and-drop document upload
  • Configure advanced settings with simple controls
  • Save and share your configurations
my-rag
Choose a name for your new RAG
Model
llama3.2
Ollama, OpenAI, or Hugging Face models
Source Type
Local Folder
Website
Source Configuration
Configure local folder source
Local Folder Path
./documents
Path to your document folder
Exclude Directories
node_modules,dist
Exclude Extensions
.log,.tmp
Process Extensions
.md,.py,.js
Chunking Settings
Controls document splitting
Strategy
Hybrid
Chunk Size
1000
Chunk Overlap
200

RLAMA in Action

See how simple it is to create and use RAG systems with our intuitive CLI

Create a RAG System

Index a folder of documents to create a new RAG system. Supports multiple file formats and embedding models.

rlama rag llama3 documentation ./docs
Processing file: docs/installation.md
Processing file: docs/commands.md
Processing file: docs/troubleshooting.pdf
Processing file: docs/api/endpoints.md
Processing file: docs/examples/basic_usage.md
Processing file: docs/examples/advanced_usage.md
Generating embeddings for 6 documents...
Embeddings generated successfully!
RAG system "documentation" created successfully!

Popular Use Cases

Discover how RLAMA powers both RAG systems and AI agent workflows

RAGPDFDOCXMD
Question: Question about docs...

Technical Documentation

Query your project documentation, manuals, and specifications with intelligent RAG systems.

LocalPrivateSecure
100% local processing

Private Knowledge Base

Create secure RAG systems for sensitive documents with full privacy and local processing.

AgentsResearchAnalysis
Command: Summarize key concepts...

Research Assistant

Deploy AI agents to query research papers, analyze data, and generate insights.

AgentsAutomationTools
Agent: Analyzing data sources...

AI Agent Workflows

Create specialized agents for coding, writing, analysis, and other automated tasks.

CrewsContentCollaboration
Multi-agent collaboration

Content Creation Crews

Orchestrate teams of AI agents for content creation, review, and publishing workflows.

WorkflowsAutomationMulti-Agent
Workflow: Sequential task execution...

Automated Workflows

Build complex multi-step workflows with agents working in sequence or parallel.

Command Reference

A complete reference of all available commands to master RLAMA

rag

Create a new RAG system from documents

rlama rag [model] [rag-name] [folder-path]
Example: rlama rag llama3 documentation ./docs
agent

Create and manage AI agents with specific roles

rlama agent [create|run|list] [agent-name] [options]
Example: rlama agent create researcher --role='Data Analyst' --tools=web_search,rag_search
crew

Create and orchestrate multi-agent crews for complex tasks

rlama crew [create|run|list] [crew-name] [agents...]
Example: rlama crew create research-team researcher writer reviewer
run

Start an interactive session with a RAG system or agent

rlama run [rag-name|agent-name|crew-name]
Example: rlama run documentation
list

List all available RAG systems, agents, and crews

rlama list [--type=rag|agents|crews]
Example: rlama list --type=agents
agent create

Create a new specialized AI agent

rlama agent create [agent-name] --role=[role] --tools=[tools]
Example: rlama agent create coder --role='Senior Developer' --tools=code_execution,rag_search
crew workflow

Define collaborative workflows between agents

rlama crew workflow [crew-name] --process=[sequential|parallel|hierarchical]
Example: rlama crew workflow content-team --process=sequential
agent tools

Manage tools available to agents (RAG search, web search, code execution)

rlama agent tools [list|add|remove] [agent-name]
Example: rlama agent tools add researcher web_search
watch

Set up directory watching for a RAG system

rlama watch [rag-name] [directory-path]
Example: rlama watch documentation ./docs
watch-off

Disable directory watching for a RAG system

rlama watch-off [rag-name]
Example: rlama watch-off documentation
check-watched

Check a RAG's watched directory for new files

rlama check-watched [rag-name]
Example: rlama check-watched documentation
api

Start API server

rlama api [--port PORT]
Example: rlama api --port 8080
update

Update RLAMA to the latest version

rlama update [--force/-f]
Example: rlama update
version

Display RLAMA version

rlama --version
Example: rlama -v

Common Issues & Solutions

Quick fixes for the most frequently encountered problems

Supported File Formats

RLAMA supports a wide variety of document formats to meet all your needs

Text

.txt
.md
.html
.json
.csv
.yaml
.yml
.xml
.org

Code

.go
.py
.js
.java
.c
.cpp
.cxx
.h
.rb
.php
.rs
.swift
.kt
.ts
.f
.F
.F90
.el
.svelte

Documents

.pdf
.docx
.doc
.rtf
.odt
.pptx
.ppt
.xlsx
.xls
.epub

Ready to streamline your document question-answering?