Morph

Human-centric & Coherent Whole Program Synthesis aka your own personal junior developer

Build the thing that builds the thing! a smol dev for every dev in every situation

This is a "junior developer" agent (aka smol dev) that either:

1. scaffolds an entire codebase out for you once you give it a product spec
2. gives you basic building blocks to have a smol developer inside of your own app.

Instead of making and maintaining specific, rigid, one-shot starters, like create-react-app, or create-nextjs-app, this is basically is or helps you make create-anything-app where you develop your scaffolding prompt in a tight loop with your smol dev.

After the successful initial v0 launch, smol developer was rewritten to be even smol-ler, and importable from a library!

# install
git clone https://github.com/smol-ai/developer.git
cd developer
poetry install # install dependencies. pip install poetry if you need

# run
python main.py "a HTML/JS/CSS Tic Tac Toe Game" # defaults to gpt-4-0613
# python main.py "a HTML/JS/CSS Tic Tac Toe Game" --model=gpt-3.5-turbo-0613

# other cli flags
python main.py --prompt prompt.md # for longer prompts, move them into a markdown file
python main.py --prompt prompt.md --debug True # for debugging

In this way you can use your clone of this repo itself to prototype/develop your app.

This is the new thing in smol developer v1! Add smol developer to your own projects!

pip install smol_dev

Here you can basically look at the contents of main.py as our "documentation" of how you can use these functions and prompts in your own app:

from smol_dev.prompts import plan, specify_file_paths, generate_code_sync

prompt = "a HTML/JS/CSS Tic Tac Toe Game"

shared_deps = plan(prompt) # returns a long string representing the coding plan

# do something with the shared_deps plan if you wish, for example ask for user confirmation/edits and iterate in a loop

file_paths = specify_file_paths(prompt, shared_deps) # returns an array of strings representing the filenames it needs to write based on your prompt and shared_deps. Relies on OpenAI's new Function Calling API to guarantee JSON.

# do something with the filepaths if you wish, for example display a plan

# loop through file_paths array and generate code for each file
for file_path in file_paths:
    code = generate_code_sync(prompt, shared_deps, file_path) # generates the source code of each file

    # do something with the source code of the file, eg. write to disk or display in UI
    # there is also an async `generate_code()` version of this

To start the server run:

poetry run api

or

python smol_dev/api.py

and then you can call the API using either the following commands:

To create a task run:

curl --request POST \
  --url http://localhost:8000/agent/tasks \
  --header 'Content-Type: application/json' \
  --data '{
	"input": "Write simple script in Python. It should write '\''Hello world!'\'' to hi.txt"
}'

You will get a response like this:

{"input":"Write simple script in Python. It should write 'Hello world!' to hi.txt","task_id":"d2c4e543-ae08-4a97-9ac5-5f9a4459cb19","artifacts":[]}

Then to execute one step of the task copy the task_id you got from the previous request and run:

curl --request POST \
  --url http://localhost:8000/agent/tasks/<task-id>/steps

or you can use Python client library:

from agent_protocol_client import AgentApi, ApiClient, TaskRequestBody

...

prompt = "Write simple script in Python. It should write 'Hello world!' to hi.txt"

async with ApiClient() as api_client:
    # Create an instance of the API class
    api_instance = AgentApi(api_client)
    task_request_body = TaskRequestBody(input=prompt)

    task = await api_instance.create_agent_task(
        task_request_body=task_request_body
    )
    task_id = task.task_id
    response = await api_instance.execute_agent_task_step(task_id=task_id)

...

• 6 minute video demo - (sorry for sped up audio, we were optimizing for twitter, bad call)

  • this was the original smol developer demo - going from prompt to full chrome extension that requests and stores and apikey, generates a popup window, reads and transmits page content, and usefully summarizes any website with Anthropic Claude, switching models up to the 100k one based on length of input
  • the prompt is located in prompt.md and it outputs /exampleChromeExtension

• smol-plugin - prompt to ChatGPT plugin (tweet, fork)

  

• Prompt to Pokemon App

  

• Political Campaign CRM Program example

• Lessons from Creating a VSCode Extension with GPT-4 (also on HN)

• 7 min Video: Smol AI Developer - Build ENTIRE Codebases With A Single Prompt produces a full working OpenAI CLI python app from a prompt

  

• 12 min Video: SMOL AI - Develop Large Scale Apps with AGI in one click scaffolds a surprisingly complex React/Node/MongoDB full stack app in 40 minutes and $9

  

I'm actively seeking more examples, please PR yours!

sorry for the lack of examples, I know that is frustrating but I wasnt ready for so many of you lol

please send in alternative implementations, and deploy strategies on alternative stacks!

• JS/TS: https://github.com/PicoCreator/smol-dev-js A pure JS variant of smol-dev, allowing even smoler incremental changes via prompting (if you dun want to do the whole spec2code thing), allowing you to plug it into any project live (for better or worse)
• C#/Dotnet: https://github.com/colhountech/smol-ai-dotnet in C#!
• Golang: https://github.com/tmc/smol-dev-go in Go
• https://github.com/gmchad/smol-plugin automatically generate @openai plugins by specifying your API in markdown in smol-developer style
• your fork here!

Please subscribe to https://latent.space/ for a fuller writeup and insights and reflections

• Markdown is all you need - Markdown is the perfect way to prompt for whole program synthesis because it is easy to mix english and code (whether variable_names or entire ``` code fenced code samples)
  • turns out you can specify prompts in code in prompts and gpt4 obeys that to the letter
• Copy and paste programming
  • teaching the program to understand how to code around a new API (Anthropic's API is after GPT3's knowledge cutoff) by just pasting in the curl input and output
  • pasting error messages into the prompt and vaguely telling the program how you'd like it handled. it kind of feels like "logbook driven programming".
• Debugging by cating the whole codebase with your error message and getting specific fix suggestions - particularly delightful!
• Tricks for whole program coherence - our chosen example usecase, Chrome extensions, have a lot of indirect dependencies across files. Any hallucination of cross dependencies causes the whole program to error.
  • We solved this by adding an intermediate step asking GPT to think through shared_dependencies.md, and then insisting on using that in generating each file. This basically means GPT is able to talk to itself...
  • ... but it's not perfect, yet. shared_dependencies.md is sometimes not comperehensive in understanding what are hard dependencies between files. So we just solved it by specifying a specific name in the prompt. felt dirty at first but it works, and really it's just clear unambiguous communication at the end of the day.
  • see prompt.md for SOTA smol-dev prompting
• Low activation energy for unfamiliar APIs
  • we have never really learned css animations, but now can just say we want a "juicy css animated red and white candy stripe loading indicator" and it does the thing.
  • ditto for Chrome Extension Manifest v3 - the docs are an abject mess, but fortunately we don't have to read them now to just get a basic thing done
  • the Anthropic docs (bad bad) were missing guidance on what return signature they have. so just curl it and dump it in the prompt lol.
• Modal is all you need - we chose Modal to solve 4 things:
  • solve python dependency hell in dev and prod
  • parallelizable code generation
  • simple upgrade path from local dev to cloud hosted endpoints (in future)
  • fault tolerant openai api calls with retries/backoff, and attached storage (for future use)

Please subscribe to https://latent.space/ for a fuller writeup and insights and reflections

We were working on a Chrome Extension, which requires images to be generated, so we added some usecase specific code in there to skip destroying/regenerating them, that we haven't decided how to generalize.

We dont have access to GPT4-32k, but if we did, we'd explore dumping entire API/SDK documentation into context.

The feedback loop is very slow right now (time says about 2-4 mins to generate a program with GPT4, even with parallelization due to Modal (occasionally spiking higher)), but it's a safe bet that it will go down over time (see also "future directions" below).

things to try/would accept open issue discussions and PRs:

• specify .md files for each generated file, with further prompts that could finetune the output in each of them
  • so basically like popup.html.md and content_script.js.md and so on
• bootstrap the prompt.md for existing codebases - write a script to read in a codebase and write a descriptive, bullet pointed prompt that generates it
  • done by smol pm, but its not very good yet - would love for some focused polish/effort until we have quine smol developer that can generate itself lmao
• ability to install its own dependencies
  • this leaks into depending on the execution environment, which we all know is the path to dependency madness. how to avoid? dockerize? nix? web container?
  • Modal has an interesting possibility: generate functions that speak modal which also solves the dependency thing https://twitter.com/akshat_b/status/1658146096902811657
• self-heal by running the code itself and use errors as information for reprompting
  • however its a bit hard to get errors from the chrome extension environment so we did not try this
• using anthropic as the coding layer
  • you can run modal run anthropic.py --prompt prompt.md --outputdir=anthropic to try it
  • but it doesnt work because anthropic doesnt follow instructions to generate file code very well.
• make agents that autonomously run this code in a loop/watch the prompt file and regenerate code each time, on a new git branch
  • the code could be generated on 5 simultaneous git branches and checking their output would just involve switching git branches