RagLeap
AI employees that ask before they act.
RagLeap is a free platform for building AI employees — agents that learn from your business documents and talk to your customers on WhatsApp, Telegram, Discord, and voice. Every real action they take waits for your approval first.
# clone and run locally $ git clone https://github.com/antonyrag/ragleap-core.git $ cd ragleap-core && docker compose up -d # or install just the retrieval engine $ pip install ragleap-rag
One sentence, no fine print
RagLeap is open-source software you run on your own server. It is not a SaaS product, there is no RagLeap account, and no RagLeap-hosted database — your documents and conversations stay on infrastructure you control.
What it does
Turns your documents — PDFs, policies, catalogs, FAQs — into AI employees that can answer questions accurately, hold a conversation across chat and voice channels, and optionally take real actions like sending an email or logging a task.
What it doesn't do
It doesn't act without permission. It doesn't browse the open web or run shell commands by default. It doesn't lock you into one AI provider, one vector database, or one deployment target.
A staff of AI employees, not one chatbot
Instead of a single assistant that tries to do everything, RagLeap ships with 46 pre-built roles — each with its own guardrails, memory, and skill tags — plus the tools to define your own. Route a request to the right one directly, or let a supervisor model pick for you.
Not another agent framework
A personal, always-on agent — broad reach across shell, browser and channels, built for one person's own machine. No concept of a business owner approving what it's allowed to do.
An orchestration layer that sits above agents like OpenClaw — org charts, budgets, governance. Closer to a management console than to a set of employees who actually do the work.
AI employees that already know your business — your documents, your policies — and a hard rule underneath every one of them: anything that reaches outside a chat reply waits for you to approve it first.
Every action passes through one gate
A chat reply is free. Anything that sends an email, posts to Slack, calls a webhook, or changes a setting is not — it stops here first, every time.
Employee proposes
One action, from what you've configured — never a destination it invents itself.
Owner is asked
A message goes to the exact channel and contact you set as the approver.
YES or NO
Checked against that one configured owner — a reply from anyone else changes nothing.
Runs, or doesn't
Only on YES. Every step is logged either way.
Sensitive roles can't opt out
Legal, medical, tax and compliance-flavored employees are permanently held to semi-automatic or fully manual mode — including any role you create yourself.
Every call is metered
A usage ledger records every LLM call an employee makes, with optional daily and monthly token caps — per employee or across the whole deployment.
Webhooks verify who's really talking
Platform signatures are checked on every inbound message; an unsigned request is rejected rather than silently trusted.
What's actually in the repo
Reasoning that shows its work
Optional chain-of-thought and tree-of-thought modes, plus a self-correction pass that checks an answer against your documents before it's sent.
A supervisor that routes for you
Send a request to the office as a whole — a supervisor model picks the right employee, or splits it across a small team and merges the results.
Bring your own model
Gemini, Anthropic, OpenAI, Groq, or a local Ollama model — with automatic fallback between providers if one is down or rate-limited.
Six vector backends
pgvector by default, or swap in FAISS, Pinecone, Weaviate, Qdrant or Milvus without changing how you call it.
Talks where your customers already are
WhatsApp, Telegram, Discord, and voice calls (Twilio, real-time transcription and speech), plus an n8n workflow integration.
Contributor-ready by design
Automated guard tests catch a half-added role before it ships unguarded, and a CONTRIBUTING guide walks through adding a new employee.
Shipped, in progress, and next
Every item below reflects the real state of the repo, not a projection. See the latest release notes for exact PR references.
Shipped
In progress
Next up
Built in the open
Every contributor is pulled live from GitHub — including community fixes like improved short-query language detection. Open an issue before a large change; the project deliberately stays narrow in scope.
View all contributorsCommon questions
Is RagLeap free to use?
Yes. It's MIT licensed and free to self-host. You only pay for the AI provider you connect — or nothing, if you run a local model through Ollama.
Can it take real actions on its own?
Only with your approval. It can propose one action from tools you've configured, but nothing runs until you reply YES from the exact channel and contact you set as the approver.
Is this a cloud service?
No. There's no RagLeap account and no RagLeap-hosted database. It runs on your own server via Docker Compose.
What if I want to build my own AI employee role?
Add one through the API at runtime, or contribute a built-in role — both paths are covered in the CONTRIBUTING guide, and a guard test stops a half-configured role from shipping without safety checks.
Which AI providers are supported?
Gemini, Anthropic, OpenAI, Groq, and local Ollama models, with automatic fallback if one provider is down.
Running in under five minutes
Clone and start the stack
git clone https://github.com/antonyrag/ragleap-core.git
cd ragleap-core && docker compose up -dAdd your provider key
Copy .env.example to .env and add a key for Gemini, Anthropic, OpenAI, or point it at a local Ollama instance.
Upload a document, ask a question
The app runs at localhost:8000. Ingest a file, then chat with any of the built-in employee roles.