Skip to main content

Tools for running a SWE agent using Composio platform

Project description

Composio logo Composio Logo

PyPI NPM Downloads

Production Ready Toolset for AI Agents

Build Software engineering Agents fast and easy!


✨ Socials >> Discord | Youtube | Twitter | Linkedin

⛏️ Contribute >> Report Bugs | Request Feature | Contribute

📋 Table of contents

Table of Contents

Overview

swekit is a framework for building SWE agents on by utilising composio tooling ecosystem. SWE Kit allows you to

  • Scaffold agents which works out-of-the-box with choice of your agentic framework, crewai, llamaindex, etc...
  • Tools to add or optimise your agent's abilities
  • Benchmark your agents against SWE-bench

Dependencies

Before getting started, ensure you have the following set up:

  1. Installation:

    pip install swekit composio-core
    
  2. Install agentic framework of your choice and the Composio plugin for the same: Here we're using crewai for the example:

    pip install crewai composio-crewai
    
  3. GitHub Access Token:

    The agent requires a github access token to work with your repositories, You can create one at https://github.com/settings/tokens with necessary permissions and export it as an environment variable using export GITHUB_ACCESS_TOKEN=<your_token>

  4. LLM Configuration: You also need to setup API key for the LLM provider you're planning to use. By default the agents scaffolded by swekit uses openai client, so export OPENAI_API_KEY before running your agent

Getting Started

Creating a new agent

  1. Scaffold your agent using:

    swekit scaffold crewai -o <path>
    

    This creates a new agent in <path>/agent with four key files:

    • main.py: Entry point to run the agent on your issue
    • agent.py: Agent definition (edit this to customise behaviour)
    • prompts.py: Agent prompts
    • benchmark.py: SWE-Bench benchmark runner
  2. Run the agent:

    cd agent
    python main.py
    

    You'll be prompted for the repository name and issue.

Workspace Environment

The SWE-agent runs in Docker by default for security and isolation. This sandboxes the agent's operations, protecting against unintended consequences of arbitrary code execution.

The composio toolset has support for different types of workspaces.

  1. Host - This will run on the host machine.
from composio import ComposioToolSet, WorkspaceType

toolset = ComposioToolSet(
    workspace_config=WorkspaceType.Host()
)
  1. Docker - This will run inside a docker container
from composio import ComposioToolSet, WorkspaceType

toolset = ComposioToolSet(
    workspace_config=WorkspaceType.Docker()
)

On the docker container you can configure and expose the port for development as per your requirements. You can also use workspace.as_prompt() method to generate a workspace description for setting up your agent.

from composio import ComposioToolSet, WorkspaceType

toolset = ComposioToolSet(
    workspace_config=WorkspaceType.Docker(
        ports={
            8001: 8001,
        }
    )
)

You can read more about configuring docker ports here.

  1. E2B - This will run inside a E2B Sandbox
from composio import ComposioToolSet, WorkspaceType

toolset = ComposioToolSet(
    workspace_config=WorkspaceType.E2B(),
)
  1. FlyIO - This will run inside a FlyIO machine
from composio import ComposioToolSet, WorkspaceType

toolset = ComposioToolSet(
    workspace_config=WorkspaceType.FlyIO(),
)

FlyIO also allows for configuring ports for development/deployment.

from composio import ComposioToolSet, WorkspaceType

composio_toolset = ComposioToolSet(
    workspace_config=WorkspaceType.FlyIO(
        image="composio/composio",
        ports=[
            {
                "ports": [
                    {"port": 443, "handlers": ["tls", "http"]},
                ],
                "internal_port": 80,
                "protocol": "tcp",
            }
        ],
    )
)

You can read more abour configuring network ports on flyio machine here

Customising the workspace environment

The workspace environment contains following environment variables by default

  • COMPOSIO_API_KEY: The composio API key for interacting with composio API.
  • COMPOSIO_BASE_URL: Base URL for composio API server.
  • GITHUB_ACCESS_TOKEN: Github access token for the agent.
  • ACCESS_TOKEN: Access token for composio tooling server.

If you want to provide additional environment configuration you can use environment argument when creating a workspace configuration.

composio_toolset = ComposioToolSet(
    workspace_config=WorkspaceType.Docker(
        environment={
            "SOME_API_TOKEN": "<SOME_API_TOKEN>",
        }
    )
)

Running the Benchmark

SWE-Bench is a comprehensive benchmark designed to evaluate the performance of software engineering agents. It comprises a diverse collection of real-world issues from popular Python open-source projects, providing a robust testing environment.

To run the benchmark:

  1. Ensure Docker is installed and running on your system.
  2. Execute the following command:
    cd agent
    python benchmark.py --test-split=<test_split>
    
    • By default, python benchmark.py runs only 1 test instance.
    • Specify a test split ratio to run more tests, e.g., --test-split=1:300 runs 300 tests.

To run the benchmarks in E2B or FlyIO sandbox, you can set the workspace_env in the evaluate function call in benchmark.py

from composio import WorkspaceType

(...)

    evaluate(
        bench,
        dry_run=False,
        test_range=test_range,
        test_instance_ids=test_instance_ids_list,
        workspace_env=WorkspaceType.E2B
    )

To use E2B or FlyIO sandboxes you'll require API key for respective platforms, to use E2B export your API key as E2B_API_KEY and to use FlyIO export your API token as FLY_API_TOKEN.

Note: We utilize SWE-Bench-Docker to ensure each test instance runs in an isolated container with its specific environment and Python version.

To extend the functionality of the SWE agent by adding new tools or extending existing ones, refer to the Development Guide.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

swekit-0.2.23.tar.gz (59.4 kB view details)

Uploaded Source

Built Distribution

swekit-0.2.23-py3-none-any.whl (88.1 kB view details)

Uploaded Python 3

File details

Details for the file swekit-0.2.23.tar.gz.

File metadata

  • Download URL: swekit-0.2.23.tar.gz
  • Upload date:
  • Size: 59.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for swekit-0.2.23.tar.gz
Algorithm Hash digest
SHA256 f416e959b9478be1a92fb4ce13c873af19c5fcc5fa1b422826467e5225dea1d7
MD5 591d465cabf4eeb347cc608641c642e5
BLAKE2b-256 76b6ae78d86bd9552e9a09dd1dc1c0f1eba353c3dd9d23935b867b74402a3f58

See more details on using hashes here.

File details

Details for the file swekit-0.2.23-py3-none-any.whl.

File metadata

  • Download URL: swekit-0.2.23-py3-none-any.whl
  • Upload date:
  • Size: 88.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for swekit-0.2.23-py3-none-any.whl
Algorithm Hash digest
SHA256 37934a1a8b77ac7f0b0a074123d69bbd8b19034a4a68b796e0d63fb0a12cc6ed
MD5 a50e1749ccd51ec5b64816ee2067035a
BLAKE2b-256 248e4d47781d7e62152fa339c576628415aece9283f28e7cc0db743f37f8403d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page