Forward Future Tools Library
RLAMA
RLAMA builds local RAG systems and multi-agent workflows from documents and websites, suited to developers and researchers who want to run models on their own machines.
Try RLAMA →
rlama.dev·Free
![FREE] [STANDALONE] Interact UI](/_next/image?url=https%3A%2F%2Fmch9boduqwxvwzvs.public.blob.vercel-storage.com%2Ftools%2Fenrichment-images%2Frlama-3994b97aa051.jpg&w=3840&q=75)
›What is RLAMA?
RLAMA is a cross-platform tool for creating, managing, and querying Retrieval-Augmented Generation systems with local models. It can index folders, websites, and files such as TXT, Markdown, PDF, Python, and JavaScript. It also supports role-based agents, collaborative crews, interactive sessions, and an HTTP API.
›What are the pros and cons of RLAMA?
Strengths
Keeps RAG storage and processing local, with no data sent to external servers
Supports both visual configuration and command-line workflows
Combines document Q&A with agents, crews, and multi-agent orchestration
Works across macOS, Linux, and Windows
Accepts multiple document formats and configurable chunking strategies
Trade-offs
The project is temporarily paused while its maintainers focus on full-time work and university studies
The documented command-line setup requires Go 1.21 or later and Ollama to be installed locally
›What are RLAMA’s key features?
Create RAG systems from local folders, websites, and multiple document formats
Configure semantic chunking, chunk size, overlap, and processing rules
Build agents with roles such as researcher, writer, coder, and analyst
Equip agents with RAG search, code execution, and web search tools
Orchestrate sequential, parallel, and hierarchical multi-agent workflows
Use a visual drag-and-drop RAG builder or the command-line interface
Run local processing on macOS, Linux, and Windows, with HTTP API support
›What are the best use cases for RLAMA?
Query internal documentation and other local document collections
Create research agents that search a RAG system and coordinate with writing agents
Build document analysis workflows from PDFs, Markdown files, code, and website content
Expose RAG or agent workflows to other applications through the HTTP API
›What is the pricing for RLAMA?
Free
›Who is RLAMA best for?
developersDevelopers can create local RAG systems, expose them through an HTTP API, and compose agents into automated workflows.
researchersResearchers can query document collections and assemble agents with researcher, writer, and analyst roles.
small teamSmall teams can use sequential or parallel agent crews for shared research and document-processing workflows.
Not for
- Users who need an actively maintained project should look elsewhere while RLAMA is paused.
- Users who want a fully hosted document assistant without installing local runtimes should skip it.
