This content originally appeared on HackerNoon and was authored by Sunil Kumar Dash
\ Over the past few weeks, we’ve been diligently working on a rapidly growing repository at Composio. We soon realized that tasks such as updating ReadMes, fixing docstrings, and making minor bug fixes—though repetitive and mundane—were consuming much of our bandwidth.
\ So, I thought, why not build an AI-powered autonomous agent to handle these grunt works?
We wanted an AI agent that could.
\
- Access the GitHub repository.
- Take any given issue and write a solution for it.
- If needed, run the code to check if it is good.
- Finally, push the patch files to the remote repository.
\ Finally, we built a simple and extensible meta-framework for building software engineering agents.
These agents can perform similarly to their human counterparts in many such tasks. Offloading mundane tasks to these agents makes sense to free up your developers to focus on more creative tasks.
\n 
\ In this article, I will show you how to build an SWE agent in Typescript to automate your GitHub workflows.
But before that, let’s understand what even AI and SWE agents are.
What are AI Agents?
AI agents are systems powered by AI models that can autonomously perform tasks, interact with their environment, and make decisions based on their programming and the data they process.
Components of AI Agents
An AI agent consists of three crucial components:
- LLMs: The Large Language Model is responsible for reasoning, decision-making, tool calling, etc.
- Memory: Manages short-term and long-term information to track workflows.
- Tools: Enable interaction with the external environment, such as a GitHub tool for extracting information from repositories and making necessary changes.
What are SWE Agents?
So, when do you call an AI agent a SWE agent?
SWE agents are AI agents that mimic the qualities and characteristics of a human software engineer, such as
- Long-term planning and reasoning.
- Using standard developer tools.
- Improving code quality through testing and feedback.
- Debugging and resolving issues autonomously.
Overview SWE Agent
Here are some of the characteristics of the SWE agent that we are going to build:
- It is framework agnostic, so you can use any framework, such as LangChain, OpenAI SDK, etc…
- You can add tools to extend its versatility, such as Tavily for internet access.
\ The SWE agents can access your public and private repositories, work on provided issues, and push changes to the repositories.
It can execute codes using the host machine, Docker, or any other cloud environment (E2B, FlyIo). However, it would be best if you preferred to use the latter two for sandboxing code execution.
\ Sandboxing helps prevent any unintended consequences of arbitrary code execution.
Prerequisites for SWE Agent
Here are the prerequisites to successfully build the agent:
OpenAI API key: We will use OpenAI SDK to access GPT models and orchestrate tool calls.
GitHub Access Token: You must link your GitHub account using a personal access token to enable the SWE agent to access and modify your code repository.
Composio API key: You will also need an API key from Composio. To get one, create a user account with Composio and navigate to the Settings tab on the dashboard.
Let’s Get Started 🔥
Dependencies
Begin by installing dependencies using your favorite package manager. The recommended method is pnpm, but you can also use npm or yarn. \n
pnpm install -g composio-core
Set up Environment Variables 🌐
You will need a GITHUBACCESSTOKEN, OPENAIAPIKEY, COMPOSIOAPIKEY, GITHUBUSERNAME, and GITHUBUSER_EMAIL to complete the project.
\
So, create a .envfile and add the above variables. \n
GITHUB_ACCESS_TOKEN="your access token"
OPENAI_API_KEY="openai_key"
COMPOSIO_API_KEY="composio-api-key"
GITHUB_USER_NAME="GitHub username"
GITHUB_USER_EMAIL="GitHub user email"
Project Structure 📁
The project is organized as follows:
src \n ├── agents \n │ └── swe.ts \n ├── app.ts \n ├── prompts.ts \n └── utils.ts
Here’s a brief description of the files.
- agents/swe.ts: Contains the implementation of the SWE agent.
- app.ts: The main entry point of the application.
- prompts.ts: Defines the prompts used by the agents.
- utils.ts: Utility functions used throughout the project.
To start quickly, clone this repository and install the rest of the dependencies. \n
git clone https://github.com/ComposioHQ/swe-js-template.git swe-js
cd swe-js && pnpm i
\ Now that you have finished with the whole set-up. Let’s code our AI agent.
\n 
\
Defining Prompts and Goals 🎯
We begin by defining the prompts and goals for the SWE agent. Explaining each step in detail is crucial, as these definitions significantly influence the agent's performance and execution.
So, create a prompts.ts file if you haven’t done so.
Next, define the role and goal of the agent. \n
export const ROLE = "Software Engineer";
export const GOAL = "Fix the coding issues given by the user, and finally generate a patch with the newly created files using `filetool_git_patch` tool";
\
Here, we defined the role as SWE, and the goal is to fix any coding issue and create a patch for the fix using filetool_git_patch. This is a Compsoio Action for the GitHub integration for creating patch files.
Now, define the backstory and a description of the Swe agent. \n
export const BACKSTORY = `You are an autonomous programmer; your task is to
solve the issue given in the task with the tools in hand. Your mentor gave you
the following tips.
1. Please clone the github repo using the 'FILETOOL_GIT_CLONE' tool, and if it
already exists - you can proceed with the rest of the instructions after
going into the directory using \`cd\` shell command.
2. PLEASE READ THE CODE AND UNDERSTAND THE FILE STRUCTURE OF THE CODEBASE
USING GIT REPO TREE ACTION.
3. POST THAT READ ALL THE RELEVANT READMES AND TRY TO LOOK AT THE FILES
RELATED TO THE ISSUE.
4. Form a thesis around the issue and the codebase. Think step by step.
Form pseudocode in case of large problems.
5. THEN TRY TO REPLICATE THE BUG THAT THE ISSUES DISCUSS.
- If the issue includes code for reproducing the bug, we recommend that you
re-implement that in your environment, and run it to make sure you can
reproduce the bug.
- Then start trying to fix it.
- When you think you've fixed the bug, re-run the bug reproduction script
to make sure that the bug has indeed been fixed.
- If the bug reproduction script does not print anything when it is successfully
runs, we recommend adding a print("Script completed successfully, no errors.")
command at the end of the file so that you can be sure that the script
indeed, it ran fine all the way through.
6. If you run a command that doesn't work, try running a different one.
A command that did not work once will not work the second time unless you
modify it!
7. If you open a file and need to get to an area around a specific line that
is not in the first 100 lines, say line 583, don't just use the scroll_down
command multiple times. Instead, use the goto 583 command. It's much quicker.
8. If the bug reproduction script requires inputting/reading a specific file,
such as buggy-input.png, and you'd like to understand how to input that file,
conduct a search in the existing repo code to see whether someone else has
I've already done that. Do this by running the command find_file "buggy-input.png"
If that doesn't work, use the Linux 'find' command.
9. Always make sure to look at the currently open file and the current working
directory (which appears right after the currently open file). The currently
open file might be in a different directory than the working directory! Some commands, such as 'create', open files, so they might change the
currently open file.
10. When editing files, it is easy to accidentally specify a wrong line number or write code with incorrect indentation. Always check the code after
You issue an edit to ensure it reflects what you want to accomplish.
If it didn't, issue another command to fix it.
11. When you FINISH WORKING on the issue, USE THE 'filetool_git_patch' ACTION with the
new files using the "new_file_paths" parameters created to create the final patch to be submitted to fix the issue. Example,
if you add \`js/src/app.js\`, then pass \`new_file_paths\` for the action like below,
{
"new_file_paths": ["js/src/app.js"]
}
`;
export const DESCRIPTION = `We're solving the following issue within our repository.
Here's the issue text:
ISSUE: {issue}
REPO: {repo}
Now, you're going to solve this issue on your own. When you're satisfied with all
your changes, you can submit them to the code base by simply
running the submit command. Note, however, that you cannot use any interactive
session commands (e.g. python, vim) in this environment, but you can write
scripts and run them. E.g. you can write a Python script and then run it
with \`python </path/to/script>.py\`.
If you face a "module not found error", you can install dependencies.
Example: in case the error is "pandas not found", install pandas like this \`pip install pandas\`
Respond to the human as helpfully and accurately as possible`;
In the above code block, we have carefully and clearly defined the steps the agent needs to undertake to accomplish the task. This is important to ensure the agent knows what to do when faced with common programming hurdles.
Defining Utility Functions 🛠️
In this section, we will define two main functions, from GitHub and getBranchNameFromIssue, which will extract information about an issue. \n
import * as fs from 'fs';
import * as path from 'path';
import * as readline from 'readline';
import { ComposioToolSet } from "composio-core/lib/sdk/base.toolset";
import { nanoid } from "nanoid";
type InputType = any;
function readUserInput(
prompt: string,
metavar: string,
validator: (value: string) => InputType
): InputType {
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout
});
return new Promise<InputType>((resolve, reject) => {
rl.question(`${prompt} > `, (value) => {
try {
const validatedValue = validator(value);
rl.close();
resolve(validatedValue);
} catch (e) {
console.error(`Invalid value for \`${metavar}\` error parsing \`${value}\`; ${e}`);
rl.close();
reject(e);
}
});
});
}
function createGithubIssueValidator(owner: string, name: string, toolset: ComposioToolSet) {
return async function githubIssueValidator(value: string): Promise<string> {
const resolvedPath = path.resolve(value);
if (fs.existsSync(resolvedPath)) {
return fs.readFileSync(resolvedPath, 'utf-8');
}
if (/^\d+$/.test(value)) {
const responseData = await toolset.executeAction('github_issues_get', {
owner,
repo: name,
issue_number: parseInt(value, 10),
});
return responseData.body as string;
}
return value;
};
}
export async function fromGithub(toolset: ComposioToolSet): Promise<{ repo: string; issue: string }> {
const owner = await readUserInput(
'Enter github repository owner',
'github repository owner',
(value: string) => value
);
const name = await readUserInput(
'Enter github repository name',
'github repository name',
(value: string) => value
);
const repo = `${owner}/${name.replace(",", "")}`;
const issue = await readUserInput(
'Enter the github issue ID or description or path to the file containing the description',
'github issue',
createGithubIssueValidator(owner, name, toolset)
);
return { repo, issue };
}
So, here is what is going on in the above code block.
readUserInput: This function reads user input from the command line. We only need the GitHub user ID, repository name, and issue number or description.createGithubIssueValidator: This function returns a validator for GitHub issues. It can handle input as a file path, a numeric issue ID, or a plain string description. If the input is a numeric issue ID, it fetches the issue details from GitHub using Composio’sgithub_issues_getaction.fromGitHub: This function combines these elements to gather and validate the necessary information about a GitHub repository and an issue.
Now, define the getBranchNameFromIssueto create a branch name from the issue description. \n
export function getBranchNameFromIssue(issue: string): string {
return "swe/" + issue.toLowerCase().replace(/\s+/g, '-') + "-" + nanoid();
}
Defining the Swe Agent 🤖
This is the most important section, where you will define the Swe agent using the OpenAI assistants and Composio toolsets.
So, first, import the libraries and define the LLM and tools. \n
import { OpenAIToolSet, Workspace } from 'composio-core';
import { BACKSTORY, DESCRIPTION, GOAL } from '../prompts';
import OpenAI from 'openai';
// Initialize tool.
const llm = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
const composioToolset = new OpenAIToolSet({
workspaceConfig: Workspace.Docker({})
});
// To use E2B Code interpreter
/*
const composioToolset = new OpenAIToolSet({
workspaceConfig: Workspace.E2B({
apiKey: process.env.E2B_API_KEY,
})
});
*/
In the above code block,
- We created an instance of OpenAI with the API key.
- We also created an instance of
OpenAIToolSetwithworkspaceConfigset to Docker. This is to use Docker to sandbox the coding environment for the Swe agent. You can also use cloud code interpreters like E2B and FlyIo.
Now, we will define the Swe Agent. \n
export async function initSWEAgent(): Promise<{composioToolset: OpenAIToolSet; assistantThread: OpenAI.Beta.Thread; llm: OpenAI; tools: Array<any>}> {
let tools = await composioToolset.getTools({
apps: [
"filetool",
"fileedittool",
"shelltool"
],
});
tools = tools.map((a) => {
if (a.function?.description?.length || 0 > 1024) {
a.function.description = a.function.description?.substring(0, 1024);
}
return a;
});
tools = tools.map((tool) => {
const updateNullToEmptyArray = (obj) => {
for (const key in obj) {
if (obj[key] === null) {
obj[key] = [];
} else if (typeof obj[key] === 'object' && !Array.isArray(obj[key])) {
updateNullToEmptyArray(obj[key]);
}
}
};
updateNullToEmptyArray(tool);
return tool;
});
const assistantThread = await llm.beta.threads.create({
messages: [
{
role: "assistant",
content:`${BACKSTORY}\n\n${GOAL}\n\n${DESCRIPTION}`
}
]
});
return { assistantThread, llm, tools, composioToolset };
}
Here is what is going on in the above code block.
- Get Tools: Fetches tools from the Composio toolset for
filetool,file edit tool, andshelltool. As the name suggests, these will be used to access files, edit files, and use shell for executing commands. - Trim Tool Descriptions: Limits tool descriptions to a maximum of 1024 characters.
- Update Null Values: Replaces null values in tool configurations with empty arrays.
- Create Assistant Thread: Initiates an OpenAI assistant thread with predefined prompts.
- Return Statement: Provides the initialized tools, assistant thread, OpenAI instance, and Composio toolset.
Defining the Entry-point to the Application 🚀
This is the final section, where we define the application's entry point. Therefore, load the environment variables and import the required modules. \n
import dotenv from "dotenv";
dotenv.config();
import { fromGithub, getBranchNameFromIssue } from './utils';
import { initSWEAgent } from './agents/swe';
import { GOAL } from './prompts';
The code block
- Loads environment variables.
- Imports the necessary utility functions.
- Imports the Swe Agent and the agent Goal that we defined earlier.
Now, define the mainfunction. \n
async function main() {
/**Run the agent.**/
const { assistantThread, llm, tools, composioToolset } = await initSWEAgent();
const { repo, issue } = await fromGithub(composioToolset);
const assistant = await llm.beta.assistants.create({
name: "SWE agent",
instructions: GOAL + `\nRepo is: ${repo} and your goal is to ${issue}`,
model: "gpt-4o",
tools: tools
});
await llm.beta.threads.messages.create(
assistantThread.id,
{
role: "user",
content: issue
}
);
const stream = await llm.beta.threads.runs.createAndPoll(assistantThread.id, {
assistant_id: assistant.id,
instructions: `Repo is: ${repo} and your goal is to ${issue}`,
tool_choice: "required"
});
await composioToolset.waitAndHandleAssistantToolCalls(llm as any, stream, assistantThread, "default");
const response = await composioToolset.executeAction("filetool_git_patch", {
});
if (response.patch && response.patch?.length > 0) {
console.log('=== Generated Patch ===\n' + response.patch, response);
const branchName = getBranchNameFromIssue(issue);
const output = await composioToolset.executeAction("SHELL_EXEC_COMMAND", {
cmd: `cp -r ${response.current_working_directory} git_repo && cd git_repo && git config --global --add safe.directory '*' && git config --global user.name ${process.env.GITHUB_USER_NAME} && git config --global user.email ${process.env.GITHUB_USER_EMAIL} && git checkout -b ${branchName} && git commit -m 'feat: ${issue}' && git push origin ${branchName}`
});
// Wait for 2s
await new Promise((resolve) => setTimeout(() => resolve(true), 2000));
console.log("Have pushed the code changes to the repo. Let's create the PR now", output);
await composioToolset.executeAction("GITHUB_PULLS_CREATE", {
owner: repo.split("/")[0],
repo: repo.split("/")[1],
head: branchName,
base: "master",
title: `SWE: ${issue}`
})
console.log("Done! The PR has been created for this issue in " + repo);
} else {
console.log('No output available - no patch was generated :(');
}
await composioToolset.workspace.close();
}
main();
\
This is our complete app.ts file, which will be used to execute the agentic workflow.
\ So, here is what is happening in the above code.
- Initialize SWE Agent: Calls
initSWEAgentto get the assistant thread, OpenAI instance, tools, and Composio toolset. - Fetch Repository and Issue: Fetches repository and issue details from
fromGithub. - Create Assistant: Initializes an OpenAI assistant with instructions, tools, and the language model.
- Send Issue to Assistant: Sends the issue content as a message to the assistant thread.
- Run Assistant and Poll: Runs the assistant and polls for tool call responses. For more information about polling responses, refer to the OpenAI SDK repository.
- Execute Patch Action: Executes
filetool_git_patchto generate a patch. - Handle Patch Response: If a patch is generated, log it, create a branch, commit, and push changes. Wait for 2 seconds before creating a pull request. Create a pull request on GitHub.
- Close Workspace: Closes the Composio toolset workspace.
- Run Main Function: Calls
main()to execute the above steps.
\
Now, run the application using pnpm start.
\ This will prompt you to enter the GitHub user ID, repository name, and the issue ID or description of the issue you want to address.
Once completed, it will pull a Composio Docker container from the registry and start working on the issue.
\
Finally, when the workflow is completed, the patch will be pushed to the remote repository. Now, when you open your GitHub repository, you will see a new branch with the proposed fix for the issue. You can compare it with the main branch and create a pull request. \n 
You can find the complete code here on GitHub.
Next Steps ⏭️
The best thing about this SWE agent is that you can extend its capabilities using Composio tools and integrations.
You can add Slack or Discord to your agent to notify you when the execution is completed. You can also connect Jira or Linear to automatically create and update tasks based on the agent's activities.
Let's connect! 🔌
You can join our community to engage with maintainers and contribute as an open-source developer. Don't hesitate to visit our GitHub repository to contribute and create issues related to Composio. Dev. \n \n
Star the Composio. dev repository ⭐
\ Thank you for reading!
\
This content originally appeared on HackerNoon and was authored by Sunil Kumar Dash
Sunil Kumar Dash | Sciencx (2024-08-01T21:26:01+00:00) How I Built an AI Programmer to Automate GitHub Issue Fixes 🤯. Retrieved from https://www.scien.cx/2024/08/01/how-i-built-an-ai-programmer-to-automate-github-issue-fixes-%f0%9f%a4%af/
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