# The Developer Platform for Enterprise AI Agents

The control of building in-house. The speed of buying off-the-shelf. The governance to operate at enterprise scale.

## Open source foundation

- On-prem / private cloud / air-gapped
- LLM-agnostic — bring your own model

## Build agents that don't just respond — they resolve

### WHY DEVELOPERS CHOOSE RASA

### Strong Architecture
Patented dialogue manager orchestrates autonomous reasoning and guided workflows in one system. LLM fluency meets deterministic logic — no hallucinations in your business rules.

### Open Framework
Full access to prompts, policies, and codebase. Swap your LLM, your infra, your integration layer. The framework adapts to your requirements — not the other way around.

### Multi-Agent Orchestration
Coordinate agents across systems with shared state, clean handoffs, and unified memory. Scale from a single agent to a decentralized enterprise network without architectural rework.

### Full Observability
Inspectable dialogue state. Event-based tracker. Every orchestrator decision is traceable — not buried in logs. Debug, test, and improve with confidence.

### Enterprise Dev Tooling
Work with Cursor, Copilot, VS Code, GitHub, Jenkins, and OpenTelemetry alongside Rasa. Git versioning, CI/CD pipelines, and rollback support built-in.

### Deployment Control
Your infrastructure, your LLM provider, air-gapped if required. On-prem is the default — not an add-on. Built for regulated environments from the start.

## Three layers. One coherent system.

### How It Works

#### Orchestrator
The coordination layer that decides what happens next. Manages context, routes to the right skill, and keeps journeys consistent across steps, systems, and channels.

#### Skills
Reusable capability units the agent invokes on demand. A skill has a purpose, inputs it needs, tools it can use, and boundaries it respects. Build once, reuse across agents.

#### Memory
The continuity layer that carries context over time. Decides what should be remembered, when it should be used, and when it must be ignored. Session memory for coherence. Long-term memory for relationships.

## HOW WE COMPARE

### Stop rebuilding the platform layer

DIY frameworks give you flexibility but consume engineering capacity building the foundation. Low-code tools are fast to configure but closed to customize. Rasa gives you both.

| Capability                                     | Rasa           | DIY               | Low-Code         |
|-----------------------------------------------|----------------|------------------|------------------|
| On-prem / air-gapped deployment                | Yes            | No               | No               |
| LLM-agnostic (swap providers)                 | Yes            | No               | No               |
| Multi-agent orchestration built-in            | Yes            | Build yourself    | No               |
| Guided + autonomous skill mixing               | Yes            | Build yourself    | Config only      |
| Inspectable dialogue state                     | Yes            | Build yourself    | No               |
| Open source foundation                         | Yes            | No               | No               |
| Time to production                             | 4-8 weeks      | 6–18 months      | 2–6 weeks        |

## What engineering teams say

"We evaluated six platforms. Rasa was the only one that let us deploy on-prem, swap LLM providers, and maintain full observability. Our security and compliance teams signed off in weeks, not months."

— Marcus K., Lead Architect · Allianz

"With LangChain we were spending 60% of our time building orchestration and memory systems from scratch. Rasa gave us that layer out of the box — we shipped to production in 8 weeks."

— Sophie L., Platform Engineering Lead · Deutsche Telekom

"The inspectable dialogue state changed how we debug. Every decision is traceable — we can reproduce issues, understand why the agent behaved a certain way, and fix it with confidence."

— Thomas R., CTO · European Insurance Group

## AI that adapts to your business, not the other way around

## Ready to build enterprise AI agents you actually own?

Get started with docs, explore the open source framework, or talk to our solutions engineering team about your deployment requirements.