A jerry-rigged collection of tools and scripts for fast-feedback SWML development. Mostly stopgap: many of the features will likely be implemented officially at some point without relying on these hacks or roundabout ways.

The tools in this repo let me write and run SWML without having to leave my code-editor window too much. They might help you do the same.

1. SWML caller (./swml_caller_helper)

   Lets you call the phone number associated with the SWML you're writing. Exists as a tiny GUI widget you can leave floating on your screen near your code editor, and call your budding AI Agent when you like.

   

   Internally this is a webpage being served by Node/Express.js, made into a free-floating window (instead of a chrome tab) using Tauri. Phone calls powered by SignalWire Relay v2 JS SDK.

2. SWML server (./swml_server)

   When your SWML-designated phone number receives a call, a request is made to the URL you've configured (more on this below). We'll configure the phone number to call the particular URL that serves the fresh SWML we'll be writing.

3. SWML backend(./swml_backend)

   If your AI agent is expected to interact with the wider internet world (talking to third-party APIs or your coffee machine), it is best to stand a kind of proxy server: something that translates the simple requests from SWML into possibly complex interactions and API requests to third party services.

   This SWML backend server serves that purpose. In this particular instance, it proxies some of the ZenDesk API functions used by our agent.

4. LocalTunnel

   The SWML server and the SWML backend parts are expected to be accessible to SignalWire servers, so having them running on your local machine is not enough.

   LocalTunnel is an free service (open-source, self-hostable), that makes servers running in your localhost accessible to the wider internet world for development purposes.

1. Install LocalTunnel

   npm install -g localtunnel

2. Set up SWML caller

      cd swml_caller_helper
      npm install
      # edit env.sample to add your SignalWire credentials,
      # and then
      mv env.sample .env

   For a platform other than OSX, you'll have to build the Tauri codebase at ./swml_caller_helper/desktop/aicaller yourself.

3. Set up SWML server

   cd ./swml_server
   npm install

   The SWML you'll work with is located at ./swml_server/YAML/swml.yaml.

   In addition to this, edit the SWMLPROXY_SD variable in ./start.osx.sh to a random subdomain that is available in localtunnel (lt -h "https://localtunnel.me" -p 3500 -s yourchosensubdomain should be successful).

   Finally, in the SignalWire dashboard, edit the number you're using to look like this:

4. Set up SWML Backend

   cd ./swml_backend
   npm install

   This particular backend is written to serve the ZenDesk support assistant, but this can be anything you need it to be, if you need it at all.

When everything has been configured, simply calling ./start.osx.sh from the root of the repo will:

1. Open the window for SWML caller
2. Launch the SWML server and start LocalTunnel to point <yourchosensubdomain>.loca.lt to the server
3. Launch the SWML backend, start localtunnel and point your SWML file to the backend

1. Validate your schema using the schema at gh/olajhidey/swml-validator/.

   If you're using VSCode, install the YAML extension, then start your SWML file with the modeline:

   # yaml-language-server: $schema=https://raw.githubusercontent.com/olajhidey/swml-validator/master/schema/swml-schema.json

2. To call an endpoint at localhost:3500/get_data, use SWML in a way similar to this:

   data_map:
   webhooks:
      - url: https://__SUBDOMAIN__.loca.lt/get_data
         output:
         response: "data was %{data.message}"

   The __SUBDOMAIN__ part will be replaced with your backend subdomain.

3. Use any webhook logger to read callback logs of the functions you call like so:

   main:
   - label: ai
      ai:
         post_prompt_url: https://webhook.site/<uuid>
         prompt: ...

4. Every time you make a call to the number SWML-designated phone number, a fresh copy of swml.yaml will be requested. So as soon as you save your SWML file, your next call will use the freshest version.