CLI
There are two main ways to interact with the system:
- Through web UI with the server running.
- Using CLI scripts for specific tasks.
Running the server & UI
Run the following commands from the typedai git repo root directory.
Local install
In one terminal run
In a second terminal runDocker
Run docker compose up
The UI will be available at http://localhost:4200
CLI commands
Running in Docker
To run the CLI scripts when using the Docker container, run the script ./bin/container from the repo root to first open a bash shell inside the TypedAI development container.
Running outside the repository
To run TypedAI agents/workflows via the CLI script described below, in a folder outside the typedai repository, the script at bin/path/ss allows you to invoke the TypedAI package.json scripts from any directory.
The bin/configure script will update your shell configuration files to include it in your PATH.
Either run the bin/configure script from the TypedAI repository root or copy and run the required section from the script. This will add to your .bashrc and .zshrc files (if they exist) the output of:
Then from any folder you can run commands like:
ss query what test frameworks does this repository use
Where query is the TypedAI package.json script. For all the examples in the CLI scripts section above you can replace npm run with ai
CLI scripts
There are a number of convenience scripts in the package.json for running agents and other scripts such as benchmarks, where the entrypoint file matches /src/cli/<script-name>.ts
agent
npm run agent or ai agent will run the autonomous agent configured in src/cli/agent.ts. Note that the agent will have the functions available configured in the agent.ts file.
If no arguments are supplied the user prompt will be read from src/cli/agent-in
Alternatively you can provide the prompt with additional arguments. It's not necessary to quote the prompt unless it has special characters that the shell would interpret.
For example, you could run:
npm run agent research the latest news about large language models and write a report to the file ai-news.md
code
npm run code or ai code runs the CodeEditingAgent configured in src/cli/code.ts
Without arguments the prompt is read from src/cli/code-in otherwise it uses the provided arguments for the prompt.
This is a useful for editing the typedai codebase. You could run a command like:
npm run code In the anthropic vertex class update the pricing for claude 3.5 sonnet to be 3 dollars per million input tokens and 15 dollars per million output tokens
When editing other repositories you will need use the ss command to run the agent with its virtual filesystem working
directory set to the current shell directory.
index
npm run index or ai index
This runs the agent which indexes a repository, and stores the summary index docs under .typedai/docs
slack
npm run slack or ai slack starts the Slack chatbot. The chatbot will have the functions available defined in src/modules/slack/slackChatBotService.ts
swe
npm run swe or ai swe runs the SoftwareDeveloperAgent configured in src/cli/swe.ts
Without arguments the prompt is read from src/cli/swe-in otherwise it uses the provided arguments for the prompt.
The agent is used for editing a remote repository which it will clone, branch and push a merge/pull request.
This agent can be used for process automation and handling requests within the limits of the CodeEditingAgent capabilities in a multi-repo environment.
gen
npm run gen or ai gen runs the script at src/cli/gen.ts
This simply generates text from a prompt. As with the other scripts you can provide arguments for a quick prompt.
Otherwise, prepare the prompt in src/cli/gen-in and don't provide any other arguments.
The output is written to src/cli/gen-out and the console
gaia
npm run gaia <task_id> runs an agent which completes a task from the GAIA agent benchmark suite and
completes with the answer in the required format, read and appending from the files defined by the tasksFile and resultsFile consts in gaia.ts.
Without the task_id argument it will attempt to complete all questions in the dataset.
You will first need to download the dataset and convert it to the format required (more detailed steps soon...)
It is extremely important you don't commit and push to a public repository the test/validation dataset. Make sure the directory you save the files to is in the .gitignore.
scrape
npm run scrape <url> or ai scrape <url> runs the PublicWeb.getWebPage function which uses a headless browser to scrape a web page, and then converts
it to a slim format by using the @mozilla/readability module to first extract the main contents of the page, and then the turndown
package to convert the HTML to Markdown, further reducing the token count.
By default, it writes the output to scrape.md. Alternatively you can provide an argument for the file to write to.
query
npm run query <question> or ai query <question> runs the codebase query agent at src/swe/discovery/fileSelectionAgent.ts which can answer ad hoc
questions about a codebase/folder contents.
Development
Running tests
Keep the Firestore emulator running in a separate shell or in the background
Speech-to-text
Speech-to-text is useful writing longer prompts with additional details to guide the agents.
On Mac's you can enable Dictation for quick-access speech-to-text
