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-0.2.26.tar.gz (59.4 kB view details)

Uploaded Source

Built Distribution

swekit-0.2.26-py3-none-any.whl (88.1 kB view details)

Uploaded Python 3

File details

Details for the file swekit-0.2.26.tar.gz.

File metadata

  • Download URL: swekit-0.2.26.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-0.2.26.tar.gz
Algorithm Hash digest
SHA256 354de28dffba8941aed1bad678153e4d1b0c4a0d055c89bc46a3662a09bc8168
MD5 0f53719f8ecb1072645d4f42e29a0369
BLAKE2b-256 95e00081dd1c15dc95b3216dd7b8ee6e2ac106c1df64ece712035606ae6be931

See more details on using hashes here.

File details

Details for the file swekit-0.2.26-py3-none-any.whl.

File metadata

  • Download URL: swekit-0.2.26-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-0.2.26-py3-none-any.whl
Algorithm Hash digest
SHA256 da3966e34cdf4f01f22a899362bdede95b954aaf58d7adddf43a48b6aec7e8bc
MD5 9ff21c410a959f6f0417dcd9006046f8
BLAKE2b-256 e1017b2b155b23f7e40689c3bad5b0582d48d0c6183d7756e69441ae33c16826

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