Getting Started

This guide takes you from an empty folder to a working, tested agent. You’ll install Mantle, then use the Rasa Copilot, a set of MCP tools that plug into your IDE, to build, evaluate, and refine your first skill in a tight loop.

The build loop

Agent development works in a loop. Rasa provides you with a harness that allows you to accelerate that loop and if you’re ready for it - run it on autopilot.

Stage	What happens
Requirements + References	You give the Copilot real conversation transcripts or a plain-language description of the skill you want.
Agent	The Copilot scaffolds skills, tools, and config into your project.
Evals + Simulations	An LLM plays the user and runs multi-turn conversations against your agent, scored against goals you define.
Insights + Recommendations	The Copilot reads the results and proposes concrete changes to instructions, constraints, and tools.
Test / Inspect	You talk to the agent in the Inspector and step through what happened turn by turn.

1. Install Mantle

Mantle ships in the rasa-pro package. Install it into a fresh virtual environment on Python 3.10 to 3.13:

python3 -m venv .venv
source .venv/bin/activate
pip install rasa-pro==3.21.0.dev7

Check it worked:

rasa --version

Mantle is currently pre-release, so pin the exact version rather than letting pip resolve the latest stable one.

You will also need a Rasa license key and an API key for your LLM provider. The setup wizard in the next step asks for both.

2. Create a project

Run rasa init to start the setup wizard:

rasa init --engine mantle

The wizard walks you through the full setup:

Asks where to create your project
Collects your keys and writes them to a .env
Installs Cursor and Claude Code compatible skills
Validates and trains your agent
Opens the Inspector so you can chat with it immediately

Once it completes, your project looks like this:

my-agent/
├── agent.yml                     # identity, persona, orchestrator, global rules
├── integrations.yml              # model groups, channels, optional tracing and metrics
├── AGENTS.md                     # context for your coding agent
├── .env                          # secrets, written by the wizard
└── skills/
    └── view_transactions/
        ├── skill.md              # instructions + control declarations
        ├── memory.yml            # what this skill remembers
        └── tools.py              # auto-discovered tools

That is what the scaffold writes. A project can hold more: a project-root memory.yml for cross-skill state, responses.yml for verbatim wording, references/ for knowledge, and tools/ for tools shared between skills. See Project structure for the full layout and which files are required.

3. Set your environment variables

The wizard collects your keys and writes a .env. To change them later, edit it directly:

.env

RASA_LICENSE=""
OPENAI_API_KEY=""

To use a different model, edit the group named by orchestrator.model_group in integrations.yml.

4. Connect your coding agent

If you created your project with rasa init, Claude Code and Cursor compatible skills are already installed, and the full Mantle documentation is bundled at .rasa/docs/mantle/. Open the project in either tool and your coding agent has the context it needs to build and iterate on Mantle skills, plus the reference to look up anything the skills do not cover. For an existing project that wasn’t created with rasa init, add both:

rasa tools skills install mantle
rasa tools init docs mantle

Install the skills without the documentation and your agent still works, but it falls back to fetching the reference over the network — which many IDE sandboxes block.

5. Build your first skill from a prompt

With the Copilot connected, describe the skill in plain language, or paste in real transcripts of the conversations you want it to handle:

Describe it or paste transcripts

In your IDE chat, tell the Copilot what you want:

Build a skill that lets a customer check their account balance.
Ask for the account number, then look it up and report it clearly.

Or paste a handful of real support transcripts and ask the Copilot to turn them into a skill.

Let the Copilot scaffold the agent

The Copilot writes the skill.md, any tools/, and wires up config. Review the diff before accepting.

skills/check_balance/skill.md

---
name: Check Balance
description: Look up a customer's account balance
---

Help the customer check their account balance.

Ask for their account number, then call check_balance to look it up and
report the balance clearly.

rasa train is the validation gate. Before packaging, it checks the whole project for undeclared memory writes, unknown response names, and malformed conditions. Treat its errors as authoring feedback and fix them before debugging conversation behaviour.

6. Evaluate with simulations

Rather than scripting every turn, let an LLM simulate a real user and score whether the agent met its goals. Ask the Copilot to generate and run a scenario:

Generate and run a simulation for the check_balance skill where the user
knows their account number. Assert the balance is reported.

The Copilot writes a scenario, validates it, runs the simulation against your running agent, and returns a pass/fail summary with a link to the full transcript. When something fails, it reads the result and recommends concrete fixes: a missing constraint, an ambiguous instruction, a tool that should be gated.

7. Test and inspect

Start your agent and open a browser window where you can talk to it directly:

rasa inspect

Try the conversation yourself and step through each turn in the Inspector to see which skill was active, what memory was set, and which tools ran. Those moments are recorded as tracker events — see Tracker events.

8. Loop

Feed what you learn back into the prompt: tighten the instructions, add a tool constraint, or add scoped instructions. Re-run the simulations, inspect again, and repeat until the agent behaves the way you need.

What’s next

Skills

Understand the building block you just created.

Instructions

Write the prose that drives your skill.

Tools

Give your skill actions it can take.

Progressive Control

Add guarantees where mistakes are expensive.

Getting Started

Building Skills

Progressive Control

Best Practices

Orchestration

More

Getting Started

The build loop

1. Install Mantle

2. Create a project

3. Set your environment variables

4. Connect your coding agent

5. Build your first skill from a prompt

6. Evaluate with simulations

7. Test and inspect

8. Loop

What’s next

Skills

Instructions

Tools

Progressive Control

​The build loop

​1. Install Mantle

​2. Create a project

​3. Set your environment variables

​4. Connect your coding agent

​5. Build your first skill from a prompt

​6. Evaluate with simulations

​7. Test and inspect

​8. Loop

​What’s next

Skills

Instructions

Tools

Progressive Control

The build loop

1. Install Mantle

2. Create a project

3. Set your environment variables

4. Connect your coding agent

5. Build your first skill from a prompt

6. Evaluate with simulations

7. Test and inspect

8. Loop

What’s next