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4 Commits

Author SHA1 Message Date
Alex Bäuerle 240db83901 Deploy website - based on cd58194d2a 2024-04-29 10:04:03 -07:00
Alex Bäuerle 1896aa92b3 Deploy website - based on 9fbfdb7724 2024-04-26 14:41:47 -07:00
Alex Bäuerle 4b82b71d44 Deploy website - based on 9fbfdb7724 2024-04-26 14:29:58 -07:00
Alex Bäuerle ce2677d794 Deploy website - based on 055f2dda4a 2024-04-26 14:19:50 -07:00
683 changed files with 2189 additions and 78777 deletions
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frontend/node_modules
config.toml
.envrc
.env
.git
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*.ipynb linguist-vendored
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codecov:
notify:
wait_for_ci: true
coverage:
status:
patch:
default:
threshold: 100% # allow patch coverage to be lower than project coverage by any amount
project:
default:
threshold: 5% # allow project coverage to drop at most 5%
comment: false
github_checks:
annotations: false
ignore:
- "agenthub/SWE_agent/**" # SWE agent is deprecated
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@@ -1,75 +0,0 @@
name: Bug
description: Report a problem with OpenDevin
title: '[Bug]: '
labels: ['bug']
body:
- type: markdown
attributes:
value: Thank you for taking the time to fill out this bug report. We greatly appreciate your effort to complete this template fully. Please provide as much information as possible to help us understand and address the issue effectively.
- type: checkboxes
attributes:
label: Is there an existing issue for the same bug?
description: Please check if an issue already exists for the bug you encountered.
options:
- label: I have checked the troubleshooting document at https://opendevin.github.io/OpenDevin/modules/usage/troubleshooting
required: true
- label: I have checked the existing issues.
required: true
- type: textarea
id: bug-description
attributes:
label: Describe the bug
description: Provide a short description of the problem.
validations:
required: true
- type: textarea
id: current-version
attributes:
label: Current OpenDevin version
description: What version of OpenDevin are you using? If you're running in docker, tell us the tag you're using (e.g. ghcr.io/opendevin/opendevin:0.3.1).
render: bash
validations:
required: true
- type: textarea
id: config
attributes:
label: Installation and Configuration
description: Please provide any commands you ran and any configuration (redacting API keys)
render: bash
validations:
required: true
- type: textarea
id: model-agent
attributes:
label: Model and Agent
description: What model and agent are you using? You can see these settings in the UI by clicking the settings wheel.
placeholder: |
- Model:
- Agent:
- type: textarea
id: os-version
attributes:
label: Operating System
description: What Operating System are you using? Linux, Mac OS, WSL on Windows
- type: textarea
id: repro-steps
attributes:
label: Reproduction Steps
description: Please list the steps to reproduce the issue.
placeholder: |
1.
2.
3.
- type: textarea
id: additional-context
attributes:
label: Logs, Errors, Screenshots, and Additional Context
description: LLM logs will be stored in the `logs/llm/default` folder. Please add any additional context about the problem here.
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---
name: Feature Request
about: Suggest an idea for OpenDevin features
title: ''
labels: 'enhancement'
assignees: ''
---
**What problem or use case are you trying to solve?**
**Describe the UX of the solution you'd like**
**Do you have thoughts on the technical implementation?**
**Describe alternatives you've considered**
**Additional context**
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@@ -1,16 +0,0 @@
---
name: Question
about: Use this template to ask a question regarding the project.
title: ''
labels: question
assignees: ''
---
## Describe your question
<!--A clear and concise description of what you want to know.-->
## Additional context
<!--Add any other context about the question here, like what you've tried so far.-->
@@ -1,18 +0,0 @@
---
name: Technical Proposal
about: Propose a new architecture or technology
title: ''
labels: 'proposal'
assignees: ''
---
**Summary**
**Motivation**
**Technical Design**
**Alternatives to Consider**
**Additional context**
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# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
version: 2
updates:
- package-ecosystem: "pip" # See documentation for possible values
directory: "/" # Location of package manifests
schedule:
interval: "daily"
- package-ecosystem: "npm" # See documentation for possible values
directory: "/frontend" # Location of package manifests
schedule:
interval: "daily"
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name: Deploy Docs to GitHub Pages
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
name: Build Docusaurus
runs-on: ubuntu-latest
if: github.repository == 'OpenDevin/OpenDevin'
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/setup-node@v4
with:
node-version: 18
cache: npm
cache-dependency-path: docs/package-lock.json
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Generate Python Docs
run: rm -rf docs/modules/python && pip install pydoc-markdown && pydoc-markdown
- name: Install dependencies
run: cd docs && npm ci
- name: Build website
run: cd docs && npm run build
- name: Upload Build Artifact
if: github.ref == 'refs/heads/main'
uses: actions/upload-pages-artifact@v3
with:
path: docs/build
deploy:
name: Deploy to GitHub Pages
needs: build
if: github.ref == 'refs/heads/main' && github.repository == 'OpenDevin/OpenDevin'
# Grant GITHUB_TOKEN the permissions required to make a Pages deployment
permissions:
pages: write # to deploy to Pages
id-token: write # to verify the deployment originates from an appropriate source
# Deploy to the github-pages environment
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
steps:
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
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name: Run e2e test with dummy agent
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
on:
push:
branches:
- main
pull_request:
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Set up environment
run: |
curl -sSL https://install.python-poetry.org | python3 -
poetry install --without evaluation
poetry run playwright install --with-deps chromium
wget https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json -P /tmp/llama_index/models--BAAI--bge-small-en-v1.5/snapshots/5c38ec7c405ec4b44b94cc5a9bb96e735b38267a/1_Pooling/
- name: Run tests
run: |
poetry run python opendevin/core/main.py -t "do a flip" -m ollama/not-a-model -d ./workspace/ -c DummyAgent
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name: Publish Docker Image
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
on:
push:
branches:
- main
tags:
- '*'
pull_request:
workflow_dispatch:
inputs:
reason:
description: 'Reason for manual trigger'
required: true
default: ''
jobs:
ghcr_build_and_push:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
strategy:
matrix:
image: ["app", "sandbox"]
steps:
- name: checkout
uses: actions/checkout@v4
- name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@main
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: true
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: false
swap-storage: true
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
id: buildx
uses: docker/setup-buildx-action@v3
- name: Login to ghcr
uses: docker/login-action@v1
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push ${{ matrix.image }}
if: "!github.event.pull_request.head.repo.fork"
run: |
./containers/build.sh ${{ matrix.image }} ${{ github.repository_owner }} --push
- name: Build ${{ matrix.image }}
if: "github.event.pull_request.head.repo.fork"
run: |
./containers/build.sh ${{ matrix.image }} ${{ github.repository_owner }}
docker_build_success:
name: Docker Build Success
runs-on: ubuntu-latest
needs: ghcr_build_and_push
steps:
- run: echo Done!
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name: Lint
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
on:
push:
branches:
- main
pull_request:
jobs:
lint-frontend:
name: Lint frontend
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install Node.js 20
uses: actions/setup-node@v4
with:
node-version: 20
- name: Install dependencies
run: |
cd frontend
npm install --frozen-lockfile
- name: Lint
run: |
cd frontend
npm run lint
lint-python:
name: Lint python
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up python
uses: actions/setup-python@v5
with:
python-version: 3.11
cache: 'pip'
- name: Install pre-commit
run: pip install pre-commit==3.7.0
- name: Run pre-commit hooks
if: github.ref != 'refs/heads/main'
run: |
git fetch https://github.com/OpenDevin/OpenDevin.git main:main && \
pre-commit run \
--files \
$(git diff --name-only $(git merge-base main $(git branch --show-current)) $(git branch --show-current) | tr '\n' ' ') \
--show-diff-on-failure \
--config ./dev_config/python/.pre-commit-config.yaml
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name: Use OpenDevin to Review Pull Request
on:
pull_request:
types: [synchronize, labeled]
permissions:
contents: write
pull-requests: write
jobs:
dogfood:
if: contains(github.event.pull_request.labels.*.name, 'review-this')
runs-on: ubuntu-latest
container:
image: ghcr.io/opendevin/opendevin
volumes:
- /var/run/docker.sock:/var/run/docker.sock
steps:
- name: install git, github cli
run: |
apt-get install -y git gh
git config --global --add safe.directory $PWD
- name: Checkout Repository
uses: actions/checkout@v4
with:
ref: ${{ github.event.pull_request.base.ref }} # check out the target branch
- name: Download Diff
run: |
curl -O "${{ github.event.pull_request.diff_url }}" -L
- name: Write Task File
run: |
echo "Your coworker wants to apply a pull request to this project. Read and review ${{ github.event.pull_request.number }}.diff file. Create a review-${{ github.event.pull_request.number }}.txt and write your concise comments and suggestions there." > task.txt
echo "" >> task.txt
echo "Title" >> task.txt
echo "${{ github.event.pull_request.title }}" >> task.txt
echo "" >> task.txt
echo "Description" >> task.txt
echo "${{ github.event.pull_request.body }}" >> task.txt
echo "" >> task.txt
echo "Diff file is: ${{ github.event.pull_request.number }}.diff" >> task.txt
- name: Run OpenDevin
env:
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
SANDBOX_TYPE: exec
run: |
WORKSPACE_MOUNT_PATH=$GITHUB_WORKSPACE python ./opendevin/core/main.py -i 50 -f task.txt -d $GITHUB_WORKSPACE
rm task.txt
- name: Check if review file is non-empty
id: check_file
run: |
ls -la
if [[ -s review-${{ github.event.pull_request.number }}.txt ]]; then
echo "non_empty=true" >> $GITHUB_OUTPUT
fi
shell: bash
- name: Create PR review if file is non-empty
env:
GH_TOKEN: ${{ github.token }}
if: steps.check_file.outputs.non_empty == 'true'
run: |
gh pr review ${{ github.event.pull_request.number }} --comment --body-file "review-${{ github.event.pull_request.number }}.txt"
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name: Run Integration Tests
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
on:
push:
branches:
- main
paths-ignore:
- '**/*.md'
- 'frontend/**'
- 'docs/**'
- 'evaluation/**'
pull_request:
jobs:
integration-tests-on-linux:
name: Integration Tests on Linux
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.11"]
sandbox: ["ssh", "exec", "local"]
steps:
- uses: actions/checkout@v4
- name: Install poetry via pipx
run: pipx install poetry
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: 'poetry'
- name: Install Python dependencies using Poetry
run: poetry install
- name: Build Environment
run: make build
- name: Run Integration Tests
env:
SANDBOX_TYPE: ${{ matrix.sandbox }}
run: |
TEST_IN_CI=true TEST_ONLY=true ./tests/integration/regenerate.sh
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v4
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
integration-tests-on-mac:
name: Integration Tests on MacOS
runs-on: macos-13
if: contains(github.event.pull_request.title, 'mac') || contains(github.event.pull_request.title, 'Mac')
strategy:
fail-fast: false
matrix:
python-version: ["3.11"]
sandbox: ["ssh"]
steps:
- uses: actions/checkout@v4
- name: Install poetry via pipx
run: pipx install poetry
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: 'poetry'
- name: Install Python dependencies using Poetry
run: poetry install
- name: Install & Start Docker
run: |
brew install colima docker
colima start
# For testcontainers to find the Colima socket
# https://github.com/abiosoft/colima/blob/main/docs/FAQ.md#cannot-connect-to-the-docker-daemon-at-unixvarrundockersock-is-the-docker-daemon-running
sudo ln -sf $HOME/.colima/default/docker.sock /var/run/docker.sock
- name: Build Environment
run: make build
- name: Run Integration Tests
env:
SANDBOX_TYPE: ${{ matrix.sandbox }}
run: |
TEST_IN_CI=true TEST_ONLY=true ./tests/integration/regenerate.sh
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v4
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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name: Run Unit Tests
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
on:
push:
branches:
- main
paths-ignore:
- '**/*.md'
- 'frontend/**'
- 'docs/**'
- 'evaluation/**'
pull_request:
jobs:
test-on-macos:
name: Test on macOS
runs-on: macos-13
env:
INSTALL_DOCKER: "0" # Set to '0' to skip Docker installation
strategy:
matrix:
python-version: ["3.11"]
steps:
- uses: actions/checkout@v4
- name: Install poetry via pipx
run: pipx install poetry
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: "poetry"
- name: Install Python dependencies using Poetry
run: poetry install
- name: Install & Start Docker
if: env.INSTALL_DOCKER == '1'
run: |
brew install colima docker
colima start
# For testcontainers to find the Colima socket
# https://github.com/abiosoft/colima/blob/main/docs/FAQ.md#cannot-connect-to-the-docker-daemon-at-unixvarrundockersock-is-the-docker-daemon-running
sudo ln -sf $HOME/.colima/default/docker.sock /var/run/docker.sock
- name: Build Environment
run: make build
- name: Run Tests
run: poetry run pytest --forked --cov=agenthub --cov=opendevin --cov-report=xml ./tests/unit -k "not test_sandbox"
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v4
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
test-on-linux:
name: Test on Linux
runs-on: ubuntu-latest
env:
INSTALL_DOCKER: "0" # Set to '0' to skip Docker installation
strategy:
matrix:
python-version: ["3.11"]
steps:
- uses: actions/checkout@v4
- name: Install poetry via pipx
run: pipx install poetry
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: "poetry"
- name: Install Python dependencies using Poetry
run: poetry install --without evaluation
- name: Build Environment
run: make build
- name: Run Tests
run: poetry run pytest --forked --cov=agenthub --cov=opendevin --cov-report=xml ./tests/unit -k "not test_sandbox"
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v4
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
test-for-sandbox:
name: Test for Sandbox
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install poetry via pipx
run: pipx install poetry
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11"
cache: "poetry"
- name: Install Python dependencies using Poetry
run: poetry install
- name: Build Environment
run: make build
- name: Run Integration Test for Sandbox
run: |
poetry run pytest --cov=agenthub --cov=opendevin --cov-report=xml -s ./tests/unit/test_sandbox.py
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v4
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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name: Use OpenDevin to Resolve GitHub Issue
on:
issues:
types: [labeled]
permissions:
contents: write
pull-requests: write
issues: write
jobs:
dogfood:
if: github.event.label.name == 'solve-this'
runs-on: ubuntu-latest
container:
image: ghcr.io/opendevin/opendevin
volumes:
- /var/run/docker.sock:/var/run/docker.sock
steps:
- name: install git, github cli
run: apt-get install -y git gh
- name: Checkout Repository
uses: actions/checkout@v4
- name: Write Task File
env:
ISSUE_TITLE: ${{ github.event.issue.title }}
ISSUE_BODY: ${{ github.event.issue.body }}
run: |
echo "TITLE:" > task.txt
echo "${ISSUE_TITLE}" >> task.txt
echo "" >> task.txt
echo "BODY:" >> task.txt
echo "${ISSUE_BODY}" >> task.txt
- name: Run OpenDevin
env:
ISSUE_TITLE: ${{ github.event.issue.title }}
ISSUE_BODY: ${{ github.event.issue.body }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
SANDBOX_TYPE: exec
run: |
WORKSPACE_MOUNT_PATH=$GITHUB_WORKSPACE python ./opendevin/core/main.py -i 50 -f task.txt -d $GITHUB_WORKSPACE
rm task.txt
- name: Setup Git, Create Branch, and Commit Changes
run: |
# Setup Git configuration
git config --global --add safe.directory $PWD
git config --global user.name 'OpenDevin'
git config --global user.email 'OpenDevin@users.noreply.github.com'
# Create a unique branch name with a timestamp
BRANCH_NAME="fix/${{ github.event.issue.number }}-$(date +%Y%m%d%H%M%S)"
# Checkout new branch
git checkout -b $BRANCH_NAME
# Add all changes to staging, except task.txt
git add --all -- ':!task.txt'
# Commit the changes, if any
git commit -m "OpenDevin: Resolve Issue #${{ github.event.issue.number }}"
if [ $? -ne 0 ]; then
echo "No changes to commit."
exit 0
fi
# Push changes
git push --set-upstream origin $BRANCH_NAME
- name: Fetch Default Branch
env:
GH_TOKEN: ${{ github.token }}
run: |
# Fetch the default branch using gh cli
DEFAULT_BRANCH=$(gh repo view --json defaultBranchRef --jq .defaultBranchRef.name)
echo "Default branch is $DEFAULT_BRANCH"
echo "DEFAULT_BRANCH=$DEFAULT_BRANCH" >> $GITHUB_ENV
- name: Generate PR
env:
GH_TOKEN: ${{ github.token }}
run: |
# Create PR and capture URL
PR_URL=$(gh pr create \
--title "OpenDevin: Resolve Issue #2" \
--body "This PR was generated by OpenDevin to resolve issue #2" \
--repo "foragerr/OpenDevin" \
--head "${{ github.head_ref }}" \
--base "${{ env.DEFAULT_BRANCH }}" \
| grep -o 'https://github.com/[^ ]*')
# Extract PR number from URL
PR_NUMBER=$(echo "$PR_URL" | grep -o '[0-9]\+$')
# Set environment vars
echo "PR_URL=$PR_URL" >> $GITHUB_ENV
echo "PR_NUMBER=$PR_NUMBER" >> $GITHUB_ENV
- name: Post Comment
env:
GH_TOKEN: ${{ github.token }}
run: |
gh issue comment ${{ github.event.issue.number }} \
-b "OpenDevin raised [PR #${{ env.PR_NUMBER }}](${{ env.PR_URL }}) to resolve this issue."
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name: 'Close stale issues'
on:
schedule:
- cron: '30 1 * * *'
jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@v9
with:
# Aggressively close issues that have been explicitly labeled `age-out`
any-of-labels: age-out
stale-issue-message: 'This issue is stale because it has been open for 7 days with no activity. Remove stale label or comment or this will be closed in 1 day.'
close-issue-message: 'This issue was closed because it has been stalled for over 7 days with no activity.'
stale-pr-message: 'This PR is stale because it has been open for 7 days with no activity. Remove stale label or comment or this will be closed in 1 days.'
close-pr-message: 'This PR was closed because it has been stalled for over 7 days with no activity.'
days-before-stale: 7
days-before-close: 1
- uses: actions/stale@v9
with:
# Be more lenient with other issues
stale-issue-message: 'This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.'
close-issue-message: 'This issue was closed because it has been stalled for over 30 days with no activity.'
stale-pr-message: 'This PR is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.'
close-pr-message: 'This PR was closed because it has been stalled for over 30 days with no activity.'
days-before-stale: 30
days-before-close: 7
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
./lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
requirements.txt
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
# poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
.env.bak
venv.bak/
*venv/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
.idea/
.vscode/
# evaluation
evaluation/SWE-bench/data
# frontend
# dependencies
frontend/node_modules
frontend/.pnp
frontend/bun.lockb
frontend/yarn.lock
.pnp.js
# testing
frontend/coverage
# production
frontend/build
frontend/dist
# misc
.DS_Store
.env.local
.env.development.local
.env.test.local
.env.production.local
npm-debug.log*
yarn-debug.log*
yarn-error.log*
logs
# agent
.envrc
/workspace
/_test_workspace
/debug
cache
# configuration
config.toml
config.toml.bak
evaluation/swe_bench/eval_workspace*
evaluation/outputs
evaluation/evaluation_outputs
test_results*
/_test_files_tmp/
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-133
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# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, caste, color, religion, or sexual
identity and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the overall
community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or advances of
any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email address,
without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official email address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
contact@rbren.io
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series of
actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or permanent
ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within the
community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.1, available at
[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
Community Impact Guidelines were inspired by
[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
For answers to common questions about this code of conduct, see the FAQ at
[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
[https://www.contributor-covenant.org/translations][translations].
[homepage]: https://www.contributor-covenant.org
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
[Mozilla CoC]: https://github.com/mozilla/diversity
[FAQ]: https://www.contributor-covenant.org/faq
[translations]: https://www.contributor-covenant.org/translations
-90
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# Contributing
Thanks for your interest in contributing to OpenDevin! We welcome and appreciate contributions.
To report bugs, create a [GitHub issue](https://github.com/OpenDevin/OpenDevin/issues/new/choose).
## Contribution Guide
### 1. Fork the Official Repository
Fork [OpenDevin repository](https://github.com/OpenDevin/OpenDevin) into your own account.
Clone your own forked repository into your local environment.
```shell
git clone git@github.com:<YOUR-USERNAME>/OpenDevin.git
```
### 2. Configure Git
Set the official repository as your [upstream](https://www.atlassian.com/git/tutorials/git-forks-and-upstreams) to synchronize with the latest update in the official repository.
Add the original repository as upstream
```shell
cd OpenDevin
git remote add upstream git@github.com:OpenDevin/OpenDevin.git
```
Verify that the remote is set.
```shell
git remote -v
```
You should see both `origin` and `upstream` in the output.
### 3. Synchronize with Official Repository
Synchronize latest commit with official repository before coding.
```shell
git fetch upstream
git checkout main
git merge upstream/main
git push origin main
```
### 4. Create a New Branch And Open a Pull Request
After you finish implementation, open forked repository. The source branch is your new branch, and the target branch is `OpenDevin/OpenDevin` `main` branch. Then PR should appears in [OpenDevin PRs](https://github.com/OpenDevin/OpenDevin/pulls).
Then OpenDevin team will review your code.
## PR Rules
### 1. Pull Request title
As described in [here](https://github.com/commitizen/conventional-commit-types/blob/master/index.json), a valid PR title should begin with one of the following prefixes:
- `feat`: A new feature
- `fix`: A bug fix
- `doc`: Documentation only changes
- `refactor`: A code change that neither fixes a bug nor adds a feature
- `style`: A refactoring that improves code style
- `perf`: A code change that improves performance
- `test`: Adding missing tests or correcting existing tests
- `ci`: Changes to CI configuration files and scripts (example scopes: `.github`, `ci` (Buildkite))
- `chore`: Other changes that don't modify src or test files
- `revert`: Reverts a previous commit
For example, a PR title could be:
- `refactor: modify package path`
- `feat(frontend): xxxx`, where `(frontend)` means that this PR mainly focuses on the frontend component.
You may also check out previous PRs in the [PR list](https://github.com/OpenDevin/OpenDevin/pulls).
As described in [here](https://github.com/OpenDevin/OpenDevin/labels), we create several labels. Every PR should be tagged with the corresponding labels.
### 2. Pull Request description
- If your PR is small (such as a typo fix), you can go brief.
- If it is large and you have changed a lot, it's better to write more details.
## How to begin
Please refer to the README in each module:
- [frontend](./frontend/README.md)
- [agenthub](./agenthub/README.md)
- [evaluation](./evaluation/README.md)
- [opendevin](./opendevin/README.md)
- [server](./opendevin/server/README.md)
- [mock server](./opendevin/mock/README.md)
## Tests
Please navigate to `tests` folder to see existing test suites.
At the moment, we have two kinds of tests: `unit` and `integration`. Please refer to the README for each test suite. These tests also run on CI to ensure quality of
the project.
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# Development Guide
This guide is for people working on OpenDevin and editing the source code.
## Start the server for development
### 1. Requirements
* Linux, Mac OS, or [WSL on Windows](https://learn.microsoft.com/en-us/windows/wsl/install)
* [Docker](https://docs.docker.com/engine/install/)(For those on MacOS, make sure to allow the default Docker socket to be used from advanced settings!)
* [Python](https://www.python.org/downloads/) >= 3.11
* [NodeJS](https://nodejs.org/en/download/package-manager) >= 18.17.1
* [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) >= 1.8
Make sure you have all these dependencies installed before moving on to `make build`.
#### Develop without sudo access
If you want to develop without system admin/sudo access to upgrade/install `Python` and/or `NodeJs`, you can use `conda` or `mamba` to manage the packages for you:
```bash
# Download and install Mamba (a faster version of conda)
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh
# Install Python 3.11, nodejs, and poetry
mamba install python=3.11
mamba install conda-forge::nodejs
mamba install conda-forge::poetry
```
### 2. Build and Setup The Environment
- **Build the Project:** Begin by building the project, which includes setting up the environment and installing dependencies. This step ensures that OpenDevin is ready to run smoothly on your system.
```bash
make build
```
### 3. Configuring the Language Model
OpenDevin supports a diverse array of Language Models (LMs) through the powerful [litellm](https://docs.litellm.ai) library. By default, we've chosen the mighty GPT-4 from OpenAI as our go-to model, but the world is your oyster! You can unleash the potential of Anthropic's suave Claude, the enigmatic Llama, or any other LM that piques your interest.
To configure the LM of your choice, follow these steps:
1. **Using the Makefile: The Effortless Approach**
With a single command, you can have a smooth LM setup for your OpenDevin experience. Simply run:
```bash
make setup-config
```
This command will prompt you to enter the LLM API key, model name, and other variables ensuring that OpenDevin is tailored to your specific needs. Note that the model name will apply only when you run headless. If you use the UI, please set the model in the UI.
**Note on Alternative Models:**
Some alternative models may prove more challenging to tame than others. Fear not, brave adventurer! We shall soon unveil LLM-specific documentation to guide you on your quest. And if you've already mastered the art of wielding a model other than OpenAI's GPT, we encourage you to [share your setup instructions with us](https://github.com/OpenDevin/OpenDevin/issues/417).
For a full list of the LM providers and models available, please consult the [litellm documentation](https://docs.litellm.ai/docs/providers).
There is also [documentation for running with local models using ollama](./docs/documentation/LOCAL_LLM_GUIDE.md).
### 4. Run the Application
- **Run the Application:** Once the setup is complete, launching OpenDevin is as simple as running a single command. This command starts both the backend and frontend servers seamlessly, allowing you to interact with OpenDevin without any hassle.
```bash
make run
```
### 5. Individual Server Startup
- **Start the Backend Server:** If you prefer, you can start the backend server independently to focus on backend-related tasks or configurations.
```bash
make start-backend
```
- **Start the Frontend Server:** Similarly, you can start the frontend server on its own to work on frontend-related components or interface enhancements.
```bash
make start-frontend
```
### 6. LLM Debugging
If you encounter any issues with the Language Model (LM) or you're simply curious, you can inspect the actual LLM prompts and responses. To do so, export DEBUG=1 in the environment and restart the backend. OpenDevin will then log the prompts and responses in the logs/llm/CURRENT_DATE directory, allowing you to identify the causes.
### 7. Help
- **Get Some Help:** Need assistance or information on available targets and commands? The help command provides all the necessary guidance to ensure a smooth experience with OpenDevin.
```bash
make help
```
### 8. Testing
#### Unit tests
```bash
poetry run pytest ./tests/unit/test_sandbox.py
```
#### Integration tests
Please refer to [this README](./tests/integration/README.md) for details.
### 9. Add or update dependency
1. Add your dependency in `pyproject.toml` or use `peotry add xxx`
2. Update the poetry.lock file via `poetry lock --no-update`
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The MIT License (MIT)
=====================
Copyright © 2023
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the “Software”), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
-289
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SHELL=/bin/bash
# Makefile for OpenDevin project
# Variables
DOCKER_IMAGE = ghcr.io/opendevin/sandbox
BACKEND_PORT = 3000
BACKEND_HOST = "127.0.0.1:$(BACKEND_PORT)"
FRONTEND_PORT = 3001
DEFAULT_WORKSPACE_DIR = "./workspace"
DEFAULT_MODEL = "gpt-3.5-turbo"
CONFIG_FILE = config.toml
PRECOMMIT_CONFIG_PATH = "./dev_config/python/.pre-commit-config.yaml"
# ANSI color codes
GREEN=$(shell tput -Txterm setaf 2)
YELLOW=$(shell tput -Txterm setaf 3)
RED=$(shell tput -Txterm setaf 1)
BLUE=$(shell tput -Txterm setaf 6)
RESET=$(shell tput -Txterm sgr0)
# Build
build:
@echo "$(GREEN)Building project...$(RESET)"
@$(MAKE) -s check-dependencies
ifeq ($(INSTALL_DOCKER),)
@$(MAKE) -s pull-docker-image
endif
@$(MAKE) -s install-python-dependencies
@$(MAKE) -s install-frontend-dependencies
@$(MAKE) -s install-precommit-hooks
@$(MAKE) -s build-frontend
@echo "$(GREEN)Build completed successfully.$(RESET)"
check-dependencies:
@echo "$(YELLOW)Checking dependencies...$(RESET)"
@$(MAKE) -s check-system
@$(MAKE) -s check-python
@$(MAKE) -s check-npm
@$(MAKE) -s check-nodejs
ifeq ($(INSTALL_DOCKER),)
@$(MAKE) -s check-docker
endif
@$(MAKE) -s check-poetry
@echo "$(GREEN)Dependencies checked successfully.$(RESET)"
check-system:
@echo "$(YELLOW)Checking system...$(RESET)"
@if [ "$(shell uname)" = "Darwin" ]; then \
echo "$(BLUE)macOS detected.$(RESET)"; \
elif [ "$(shell uname)" = "Linux" ]; then \
if [ -f "/etc/manjaro-release" ]; then \
echo "$(BLUE)Manjaro Linux detected.$(RESET)"; \
else \
echo "$(BLUE)Linux detected.$(RESET)"; \
fi; \
elif [ "$$(uname -r | grep -i microsoft)" ]; then \
echo "$(BLUE)Windows Subsystem for Linux detected.$(RESET)"; \
else \
echo "$(RED)Unsupported system detected. Please use macOS, Linux, or Windows Subsystem for Linux (WSL).$(RESET)"; \
exit 1; \
fi
check-python:
@echo "$(YELLOW)Checking Python installation...$(RESET)"
@if command -v python3.11 > /dev/null; then \
echo "$(BLUE)$(shell python3.11 --version) is already installed.$(RESET)"; \
else \
echo "$(RED)Python 3.11 is not installed. Please install Python 3.11 to continue.$(RESET)"; \
exit 1; \
fi
check-npm:
@echo "$(YELLOW)Checking npm installation...$(RESET)"
@if command -v npm > /dev/null; then \
echo "$(BLUE)npm $(shell npm --version) is already installed.$(RESET)"; \
else \
echo "$(RED)npm is not installed. Please install Node.js to continue.$(RESET)"; \
exit 1; \
fi
check-nodejs:
@echo "$(YELLOW)Checking Node.js installation...$(RESET)"
@if command -v node > /dev/null; then \
NODE_VERSION=$(shell node --version | sed -E 's/v//g'); \
IFS='.' read -r -a NODE_VERSION_ARRAY <<< "$$NODE_VERSION"; \
if [ "$${NODE_VERSION_ARRAY[0]}" -gt 18 ] || ([ "$${NODE_VERSION_ARRAY[0]}" -eq 18 ] && [ "$${NODE_VERSION_ARRAY[1]}" -gt 17 ]) || ([ "$${NODE_VERSION_ARRAY[0]}" -eq 18 ] && [ "$${NODE_VERSION_ARRAY[1]}" -eq 17 ] && [ "$${NODE_VERSION_ARRAY[2]}" -ge 1 ]); then \
echo "$(BLUE)Node.js $$NODE_VERSION is already installed.$(RESET)"; \
else \
echo "$(RED)Node.js 18.17.1 or later is required. Please install Node.js 18.17.1 or later to continue.$(RESET)"; \
exit 1; \
fi; \
else \
echo "$(RED)Node.js is not installed. Please install Node.js to continue.$(RESET)"; \
exit 1; \
fi
check-docker:
@echo "$(YELLOW)Checking Docker installation...$(RESET)"
@if command -v docker > /dev/null; then \
echo "$(BLUE)$(shell docker --version) is already installed.$(RESET)"; \
else \
echo "$(RED)Docker is not installed. Please install Docker to continue.$(RESET)"; \
exit 1; \
fi
check-poetry:
@echo "$(YELLOW)Checking Poetry installation...$(RESET)"
@if command -v poetry > /dev/null; then \
POETRY_VERSION=$(shell poetry --version 2>&1 | sed -E 's/Poetry \(version ([0-9]+\.[0-9]+\.[0-9]+)\)/\1/'); \
IFS='.' read -r -a POETRY_VERSION_ARRAY <<< "$$POETRY_VERSION"; \
if [ $${POETRY_VERSION_ARRAY[0]} -ge 1 ] && [ $${POETRY_VERSION_ARRAY[1]} -ge 8 ]; then \
echo "$(BLUE)$(shell poetry --version) is already installed.$(RESET)"; \
else \
echo "$(RED)Poetry 1.8 or later is required. You can install poetry by running the following command, then adding Poetry to your PATH:"; \
echo "$(RED) curl -sSL https://install.python-poetry.org | python3 -$(RESET)"; \
echo "$(RED)More detail here: https://python-poetry.org/docs/#installing-with-the-official-installer$(RESET)"; \
exit 1; \
fi; \
else \
echo "$(RED)Poetry is not installed. You can install poetry by running the following command, then adding Poetry to your PATH:"; \
echo "$(RED) curl -sSL https://install.python-poetry.org | python3.11 -$(RESET)"; \
echo "$(RED)More detail here: https://python-poetry.org/docs/#installing-with-the-official-installer$(RESET)"; \
exit 1; \
fi
pull-docker-image:
@echo "$(YELLOW)Pulling Docker image...$(RESET)"
@docker pull $(DOCKER_IMAGE)
@echo "$(GREEN)Docker image pulled successfully.$(RESET)"
install-python-dependencies:
@echo "$(GREEN)Installing Python dependencies...$(RESET)"
poetry env use python3.11
@if [ "$(shell uname)" = "Darwin" ]; then \
echo "$(BLUE)Installing chroma-hnswlib...$(RESET)"; \
export HNSWLIB_NO_NATIVE=1; \
poetry run pip install chroma-hnswlib; \
fi
@poetry install
@if [ -f "/etc/manjaro-release" ]; then \
echo "$(BLUE)Detected Manjaro Linux. Installing Playwright dependencies...$(RESET)"; \
poetry run pip install playwright; \
poetry run playwright install chromium; \
else \
poetry run playwright install --with-deps chromium; \
fi
@echo "$(GREEN)Python dependencies installed successfully.$(RESET)"
install-frontend-dependencies:
@echo "$(YELLOW)Setting up frontend environment...$(RESET)"
@echo "$(YELLOW)Detect Node.js version...$(RESET)"
@cd frontend && node ./scripts/detect-node-version.js
@cd frontend && \
echo "$(BLUE)Installing frontend dependencies with npm...$(RESET)" && \
npm install && \
echo "$(BLUE)Running make-i18n with npm...$(RESET)" && \
npm run make-i18n
@echo "$(GREEN)Frontend dependencies installed successfully.$(RESET)"
install-precommit-hooks:
@echo "$(YELLOW)Installing pre-commit hooks...$(RESET)"
@git config --unset-all core.hooksPath || true
@poetry run pre-commit install --config $(PRECOMMIT_CONFIG_PATH)
@echo "$(GREEN)Pre-commit hooks installed successfully.$(RESET)"
lint-backend:
@echo "$(YELLOW)Running linters...$(RESET)"
@poetry run pre-commit run --files $$(git diff --name-only $$(git merge-base main $$(git branch --show-current)) $$(git branch --show-current) | tr '\n' ' ') --show-diff-on-failure --config $(PRECOMMIT_CONFIG_PATH)
lint-frontend:
@echo "$(YELLOW)Running linters for frontend...$(RESET)"
@cd frontend && npm run lint
lint:
@$(MAKE) -s lint-frontend
@$(MAKE) -s lint-backend
test-frontend:
@echo "$(YELLOW)Running tests for frontend...$(RESET)"
@cd frontend && npm run test
test:
@$(MAKE) -s test-frontend
build-frontend:
@echo "$(YELLOW)Building frontend...$(RESET)"
@cd frontend && npm run build
# Start backend
start-backend:
@echo "$(YELLOW)Starting backend...$(RESET)"
@poetry run uvicorn opendevin.server.listen:app --port $(BACKEND_PORT) --reload --reload-exclude "workspace/*"
# Start frontend
start-frontend:
@echo "$(YELLOW)Starting frontend...$(RESET)"
@cd frontend && VITE_BACKEND_HOST=$(BACKEND_HOST) VITE_FRONTEND_PORT=$(FRONTEND_PORT) npm run start
# Run the app
run:
@echo "$(YELLOW)Running the app...$(RESET)"
@if [ "$(OS)" = "Windows_NT" ]; then \
echo "$(RED)`make run` is not supported on Windows. Please run `make start-frontend` and `make start-backend` separately.$(RESET)"; \
exit 1; \
fi
@mkdir -p logs
@echo "$(YELLOW)Starting backend server...$(RESET)"
@poetry run uvicorn opendevin.server.listen:app --port $(BACKEND_PORT) &
@echo "$(YELLOW)Waiting for the backend to start...$(RESET)"
@until nc -z localhost $(BACKEND_PORT); do sleep 0.1; done
@echo "$(GREEN)Backend started successfully.$(RESET)"
@cd frontend && echo "$(BLUE)Starting frontend with npm...$(RESET)" && npm run start -- --port $(FRONTEND_PORT)
@echo "$(GREEN)Application started successfully.$(RESET)"
# Setup config.toml
setup-config:
@echo "$(YELLOW)Setting up config.toml...$(RESET)"
@$(MAKE) setup-config-prompts
@mv $(CONFIG_FILE).tmp $(CONFIG_FILE)
@echo "$(GREEN)Config.toml setup completed.$(RESET)"
setup-config-prompts:
@echo "[core]" > $(CONFIG_FILE).tmp
@read -p "Enter your workspace directory [default: $(DEFAULT_WORKSPACE_DIR)]: " workspace_dir; \
workspace_dir=$${workspace_dir:-$(DEFAULT_WORKSPACE_DIR)}; \
echo "workspace_base=\"$$workspace_dir\"" >> $(CONFIG_FILE).tmp
@echo "" >> $(CONFIG_FILE).tmp
@echo "[llm]" >> $(CONFIG_FILE).tmp
@read -p "Enter your LLM model name, used for running without UI. Set the model in the UI after you start the app. (see https://docs.litellm.ai/docs/providers for full list) [default: $(DEFAULT_MODEL)]: " llm_model; \
llm_model=$${llm_model:-$(DEFAULT_MODEL)}; \
echo "model=\"$$llm_model\"" >> $(CONFIG_FILE).tmp
@read -p "Enter your LLM api key: " llm_api_key; \
echo "api_key=\"$$llm_api_key\"" >> $(CONFIG_FILE).tmp
@read -p "Enter your LLM base URL [mostly used for local LLMs, leave blank if not needed - example: http://localhost:5001/v1/]: " llm_base_url; \
if [[ ! -z "$$llm_base_url" ]]; then echo "base_url=\"$$llm_base_url\"" >> $(CONFIG_FILE).tmp; fi
@echo "Enter your LLM Embedding Model"; \
echo "Choices are:"; \
echo " - openai"; \
echo " - azureopenai"; \
echo " - Embeddings available only with OllamaEmbedding:"; \
echo " - llama2"; \
echo " - mxbai-embed-large"; \
echo " - nomic-embed-text"; \
echo " - all-minilm"; \
echo " - stable-code"; \
echo " - Leave blank to default to 'BAAI/bge-small-en-v1.5' via huggingface"; \
read -p "> " llm_embedding_model; \
echo "embedding_model=\"$$llm_embedding_model\"" >> $(CONFIG_FILE).tmp; \
if [ "$$llm_embedding_model" = "llama2" ] || [ "$$llm_embedding_model" = "mxbai-embed-large" ] || [ "$$llm_embedding_model" = "nomic-embed-text" ] || [ "$$llm_embedding_model" = "all-minilm" ] || [ "$$llm_embedding_model" = "stable-code" ]; then \
read -p "Enter the local model URL for the embedding model (will set llm.embedding_base_url): " llm_embedding_base_url; \
echo "embedding_base_url=\"$$llm_embedding_base_url\"" >> $(CONFIG_FILE).tmp; \
elif [ "$$llm_embedding_model" = "azureopenai" ]; then \
read -p "Enter the Azure endpoint URL (will overwrite llm.base_url): " llm_base_url; \
echo "base_url=\"$$llm_base_url\"" >> $(CONFIG_FILE).tmp; \
read -p "Enter the Azure LLM Embedding Deployment Name: " llm_embedding_deployment_name; \
echo "embedding_deployment_name=\"$$llm_embedding_deployment_name\"" >> $(CONFIG_FILE).tmp; \
read -p "Enter the Azure API Version: " llm_api_version; \
echo "api_version=\"$$llm_api_version\"" >> $(CONFIG_FILE).tmp; \
fi
# Clean up all caches
clean:
@echo "$(YELLOW)Cleaning up caches...$(RESET)"
@rm -rf opendevin/.cache
@echo "$(GREEN)Caches cleaned up successfully.$(RESET)"
# Help
help:
@echo "$(BLUE)Usage: make [target]$(RESET)"
@echo "Targets:"
@echo " $(GREEN)build$(RESET) - Build project, including environment setup and dependencies."
@echo " $(GREEN)lint$(RESET) - Run linters on the project."
@echo " $(GREEN)setup-config$(RESET) - Setup the configuration for OpenDevin by providing LLM API key,"
@echo " LLM Model name, and workspace directory."
@echo " $(GREEN)start-backend$(RESET) - Start the backend server for the OpenDevin project."
@echo " $(GREEN)start-frontend$(RESET) - Start the frontend server for the OpenDevin project."
@echo " $(GREEN)run$(RESET) - Run the OpenDevin application, starting both backend and frontend servers."
@echo " Backend Log file will be stored in the 'logs' directory."
@echo " $(GREEN)help$(RESET) - Display this help message, providing information on available targets."
# Phony targets
.PHONY: build check-dependencies check-python check-npm check-docker check-poetry pull-docker-image install-python-dependencies install-frontend-dependencies install-precommit-hooks lint start-backend start-frontend run setup-config setup-config-prompts help
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<a name="readme-top"></a>
<!--
*** Thanks for checking out the Best-README-Template. If you have a suggestion
*** that would make this better, please fork the repo and create a pull request
*** or simply open an issue with the tag "enhancement".
*** Don't forget to give the project a star!
*** Thanks again! Now go create something AMAZING! :D
-->
<!-- PROJECT SHIELDS -->
<!--
*** I'm using markdown "reference style" links for readability.
*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).
*** See the bottom of this document for the declaration of the reference variables
*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
-->
<div align="center">
<a href="https://github.com/OpenDevin/OpenDevin/graphs/contributors"><img src="https://img.shields.io/github/contributors/opendevin/opendevin?style=for-the-badge&color=blue" alt="Contributors"></a>
<a href="https://github.com/OpenDevin/OpenDevin/network/members"><img src="https://img.shields.io/github/forks/opendevin/opendevin?style=for-the-badge&color=blue" alt="Forks"></a>
<a href="https://github.com/OpenDevin/OpenDevin/stargazers"><img src="https://img.shields.io/github/stars/opendevin/opendevin?style=for-the-badge&color=blue" alt="Stargazers"></a>
<a href="https://github.com/OpenDevin/OpenDevin/issues"><img src="https://img.shields.io/github/issues/opendevin/opendevin?style=for-the-badge&color=blue" alt="Issues"></a>
<a href="https://github.com/OpenDevin/OpenDevin/blob/main/LICENSE"><img src="https://img.shields.io/github/license/opendevin/opendevin?style=for-the-badge&color=blue" alt="MIT License"></a>
<br/>
<a href="https://join.slack.com/t/opendevin/shared_invite/zt-2i1iqdag6-bVmvamiPA9EZUu7oCO6KhA"><img src="https://img.shields.io/badge/Slack-Join%20Us-red?logo=slack&logoColor=white&style=for-the-badge" alt="Join our Slack community"></a>
<a href="https://discord.gg/ESHStjSjD4"><img src="https://img.shields.io/badge/Discord-Join%20Us-purple?logo=discord&logoColor=white&style=for-the-badge" alt="Join our Discord community"></a>
<br/>
<a href="https://huggingface.co/spaces/OpenDevin/evaluation"><img src="https://img.shields.io/badge/SWE--bench%20Lite-25.0%25-green?style=for-the-badge" alt="SWE-bench "></a>
<a href="https://codecov.io/github/opendevin/opendevin?branch=main"><img alt="CodeCov" src="https://img.shields.io/codecov/c/github/opendevin/opendevin?style=for-the-badge"></a>
</div>
<!-- PROJECT LOGO -->
<div align="center">
<img src="./docs/static/img/logo.png" alt="Logo" width="200" height="200">
<h1 align="center">OpenDevin: Code Less, Make More</h1>
<a href="https://opendevin.github.io/OpenDevin/"><img src="https://img.shields.io/badge/Documentation-OpenDevin-blue?logo=googledocs&logoColor=white&style=for-the-badge" alt="Check out the documentation"></a>
</div>
<hr>
Welcome to OpenDevin, a platform for autonomous software engineers, powered by AI and LLMs.
OpenDevin agents collaborate with human developers to write code, fix bugs, and ship features.
![App screenshot](./docs/static/img/screenshot.png)
## ⚡ Getting Started
The easiest way to run OpenDevin is inside a Docker container. It works best with the most recent version of Docker, `26.0.0`.
You must be using Linux, Mac OS, or WSL on Windows.
To start the app, run these commands, replacing `$(pwd)/workspace` with the directory you want OpenDevin to work with.
```bash
# The directory you want OpenDevin to work with. MUST be an absolute path!
export WORKSPACE_BASE=$(pwd)/workspace;
```
> [!WARNING]
> OpenDevin runs bash commands within a Docker sandbox, so it should not affect your machine.
> But your workspace directory will be attached to that sandbox, and files in the directory may be modified or deleted.
```bash
docker run \
-it \
--pull=always \
-e SANDBOX_USER_ID=$(id -u) \
-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
-v $WORKSPACE_BASE:/opt/workspace_base \
-v /var/run/docker.sock:/var/run/docker.sock \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
ghcr.io/opendevin/opendevin:0.5
```
You'll find OpenDevin running at [http://localhost:3000](http://localhost:3000).
## 🚀 Documentation
To learn more about the project, and for tips on using OpenDevin,
**check out our [documentation](https://opendevin.github.io/OpenDevin/)**.
There you'll find resources on how to use different LLM providers (like ollama and Anthropic's Claude),
troubleshooting resources, and advanced configuration options.
## 🤝 How to Contribute
OpenDevin is a community-driven project, and we welcome contributions from everyone.
Whether you're a developer, a researcher, or simply enthusiastic about advancing the field of
software engineering with AI, there are many ways to get involved:
- **Code Contributions:** Help us develop new agents, core functionality, the frontend and other interfaces, or sandboxing solutions.
- **Research and Evaluation:** Contribute to our understanding of LLMs in software engineering, participate in evaluating the models, or suggest improvements.
- **Feedback and Testing:** Use the OpenDevin toolset, report bugs, suggest features, or provide feedback on usability.
For details, please check [CONTRIBUTING.md](./CONTRIBUTING.md).
## 🤖 Join Our Community
Whether you're a developer, a researcher, or simply enthusiastic about OpenDevin, we'd love to have you in our community.
Let's make software engineering better together!
- [Slack workspace](https://join.slack.com/t/opendevin/shared_invite/zt-2ggtwn3k5-PvAA2LUmqGHVZ~XzGq~ILw) - Here we talk about research, architecture, and future development.
- [Discord server](https://discord.gg/ESHStjSjD4) - This is a community-run server for general discussion, questions, and feedback.
## 📈 Progress
<p align="center">
<a href="https://www.swebench.com/lite.html">
<img src="/docs/static/img/results.png" alt="SWE-Bench Lite Score" width="500" height="auto">
</a>
</p>
<p align="center">
<a href="https://star-history.com/#OpenDevin/OpenDevin&Date">
<img src="https://api.star-history.com/svg?repos=OpenDevin/OpenDevin&type=Date" width="500" alt="Star History Chart">
</a>
</p>
## 📜 License
Distributed under the MIT License. See [`LICENSE`](./LICENSE) for more information.
[contributors-shield]: https://img.shields.io/github/contributors/opendevin/opendevin?style=for-the-badge
[contributors-url]: https://github.com/OpenDevin/OpenDevin/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/opendevin/opendevin?style=for-the-badge
[forks-url]: https://github.com/OpenDevin/OpenDevin/network/members
[stars-shield]: https://img.shields.io/github/stars/opendevin/opendevin?style=for-the-badge
[stars-url]: https://github.com/OpenDevin/OpenDevin/stargazers
[issues-shield]: https://img.shields.io/github/issues/opendevin/opendevin?style=for-the-badge
[issues-url]: https://github.com/OpenDevin/OpenDevin/issues
[license-shield]: https://img.shields.io/github/license/opendevin/opendevin?style=for-the-badge
[license-url]: https://github.com/OpenDevin/OpenDevin/blob/main/LICENSE
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# Agent Framework Research
In this folder, there may exist multiple implementations of `Agent` that will be used by the framework.
For example, `agenthub/monologue_agent`, `agenthub/metagpt_agent`, `agenthub/codeact_agent`, etc.
Contributors from different backgrounds and interests can choose to contribute to any (or all!) of these directions.
## Constructing an Agent
The abstraction for an agent can be found [here](../opendevin/agent.py).
Agents are run inside of a loop. At each iteration, `agent.step()` is called with a
[State](../opendevin/state.py) input, and the agent must output an [Action](../opendevin/action).
Every agent also has a `self.llm` which it can use to interact with the LLM configured by the user.
See the [LiteLLM docs for `self.llm.completion`](https://docs.litellm.ai/docs/completion).
## State
The `state` contains:
- A history of actions taken by the agent, as well as any observations (e.g. file content, command output) from those actions
- A list of actions/observations that have happened since the most recent step
- A [`root_task`](https://github.com/OpenDevin/OpenDevin/blob/main/opendevin/controller/state/task.py), which contains a plan of action
- The agent can add and modify subtasks through the `AddTaskAction` and `ModifyTaskAction`
## Actions
Here is a list of available Actions, which can be returned by `agent.step()`:
- [`CmdRunAction`](../opendevin/action/bash.py) - Runs a command inside a sandboxed terminal
- [`CmdKillAction`](../opendevin/action/bash.py) - Kills a background command
- [`IPythonRunCellAction`](../opendevin/action/bash.py) - Execute a block of Python code interactively (in Jupyter notebook) and receives `CmdOutputObservation`. Requires setting up `jupyter` [plugin](../opendevin/sandbox/plugins) as a requirement.
- [`FileReadAction`](../opendevin/action/fileop.py) - Reads the content of a file
- [`FileWriteAction`](../opendevin/action/fileop.py) - Writes new content to a file
- [`BrowseURLAction`](../opendevin/action/browse.py) - Gets the content of a URL
- [`AgentRecallAction`](../opendevin/action/agent.py) - Searches memory (e.g. a vector database)
- [`AddTaskAction`](../opendevin/action/tasks.py) - Adds a subtask to the plan
- [`ModifyTaskAction`](../opendevin/action/tasks.py) - Changes the state of a subtask
- [`AgentThinkAction`](../opendevin/action/agent.py) - A no-op that allows the agent to add plaintext to the history (as well as the chat log)
- [`AgentTalkAction`](../opendevin/action/agent.py) - A no-op that allows the agent to add plaintext to the history and talk to the user.
- [`AgentFinishAction`](../opendevin/action/agent.py) - Stops the control loop, allowing the user/delegator agent to enter a new task
- [`AgentRejectAction`](../opendevin/action/agent.py) - Stops the control loop, allowing the user/delegator agent to enter a new task
- [`AgentFinishAction`](../opendevin/action/agent.py) - Stops the control loop, allowing the user to enter a new task
- [`MessageAction`](../opendevin/action/message.py) - Represents a message from an agent or the user
You can use `action.to_dict()` and `action_from_dict` to serialize and deserialize actions.
## Observations
There are also several types of Observations. These are typically available in the step following the corresponding Action.
But they may also appear as a result of asynchronous events (e.g. a message from the user, logs from a command running
in the background).
Here is a list of available Observations:
- [`CmdOutputObservation`](../opendevin/observation/run.py)
- [`BrowserOutputObservation`](../opendevin/observation/browse.py)
- [`FileReadObservation`](../opendevin/observation/files.py)
- [`FileWriteObservation`](../opendevin/observation/files.py)
- [`AgentRecallObservation`](../opendevin/observation/recall.py)
- [`ErrorObservation`](../opendevin/observation/error.py)
- [`SuccessObservation`](../opendevin/observation/success.py)
You can use `observation.to_dict()` and `observation_from_dict` to serialize and deserialize observations.
## Interface
Every agent must implement the following methods:
### `step`
```
def step(self, state: "State") -> "Action"
```
`step` moves the agent forward one step towards its goal. This probably means
sending a prompt to the LLM, then parsing the response into an `Action`.
### `search_memory`
```
def search_memory(self, query: str) -> list[str]:
```
`search_memory` should return a list of events that match the query. This will be used
for the `recall` action.
You can optionally just return `[]` for this method, meaning the agent has no long-term memory.
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# GPT-4 Examples:
### Notes:
- The final result is not completed as I only gave it 10 steps, but shows very promising use of the tools.
- This whole run costs: $0.35 using gpt-4-turbo
- Input Tokens: 29,734
- Completion Tokens: 1,636
Interesting things:
1. Using comments as an initial placeholder for the next move and then having that output in memory is a very cool behavior.
2. It makes very small iterative changes due to the prompting.
3. A lot of the logic for the game is present but not put together well
4. There are a lot of odd quirks with the code that it has written.
5. Some odd agent behavior with putting random lines of code in random places?
6. The structure of the feedback loop needs some improvement so the agent can see the changes it has made better in context.
7. Might be beneficial to add in some line numbers so the model knows what lines it is looking at.
```
==============
STEP 0
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=91, prompt_tokens=2047, total_tokens=2138)
==== RAW OUTPUT ====
Thoughts:
To start implementing Conway's Game of Life in Python using the pygame library, I first need to set up a Python script file where I can write the code. I'll create a new Python file named `game_of_life.py`. This file will serve as the base for writing the game logic and using pygame functionalities.
Action:
"""
write game_of_life.py # This is a placeholder for the game of life implementation using pygame.
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='# This is a placeholder for the game of life implementation using pygame.', start=0, end=-1, thoughts="Thoughts:\nTo start implementing Conway's Game of Life in Python using the pygame library, I first need to set up a Python script file where I can write the code. I'll create a new Python file named `game_of_life.py`. This file will serve as the base for writing the game logic and using pygame functionalities.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 0:-1.
==============
STEP 1
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=101, prompt_tokens=2351, total_tokens=2452)
==== RAW OUTPUT ====
Thoughts:
The placeholder for the game of life implementation using pygame has been set up in the file `game_of_life.py`. The next step is to start implementing the basic structure of the game. This includes importing necessary libraries, initializing the pygame module, setting up the display window, and defining the main game loop. I will begin by importing pygame and initializing it, which are the foundational steps for any pygame application.
Action:
"""
edit 0 0 import pygame
pygame.init()
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='import pygame\n\npygame.init()', start=0, end=0, thoughts='Thoughts:\nThe placeholder for the game of life implementation using pygame has been set up in the file `game_of_life.py`. The next step is to start implementing the basic structure of the game. This includes importing necessary libraries, initializing the pygame module, setting up the display window, and defining the main game loop. I will begin by importing pygame and initializing it, which are the foundational steps for any pygame application.\n\nAction:', action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 0:0.
==============
STEP 2
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=123, prompt_tokens=2552, total_tokens=2675)
==== RAW OUTPUT ====
Thoughts:
Previously, I initialized pygame in the `game_of_life.py` file. The next step is to set up the display window for the game. This involves defining the size of the window and setting the title. This is crucial as it provides the visual interface for the game. I will also add a comment to indicate that this is the setup for the display window.
Action:
"""
edit 2 2
# Setup the display window
screen_size = (800, 600)
screen = pygame.display.set_mode(screen_size)
pygame.display.set_caption("Conway's Game of Life")
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='# Setup the display window\nscreen_size = (800, 600)\nscreen = pygame.display.set_mode(screen_size)\npygame.display.set_caption("Conway\'s Game of Life")', start=2, end=2, thoughts='Thoughts:\nPreviously, I initialized pygame in the `game_of_life.py` file. The next step is to set up the display window for the game. This involves defining the size of the window and setting the title. This is crucial as it provides the visual interface for the game. I will also add a comment to indicate that this is the setup for the display window.\n\nAction:', action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 2:2.
==============
STEP 3
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=144, prompt_tokens=2777, total_tokens=2921)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame and setting up the display window. The next step is to define the main game loop where the game's logic will be continuously updated and rendered. This loop will handle events, update the game state, and redraw the screen. I will start by defining the main loop structure and a basic event handling mechanism to allow for quitting the game when the user presses the close button.
Action:
"""
edit 5 5 # Main game loop
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='# Main game loop\nrunning = True\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False', start=5, end=5, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame and setting up the display window. The next step is to define the main game loop where the game's logic will be continuously updated and rendered. This loop will handle events, update the game state, and redraw the screen. I will start by defining the main loop structure and a basic event handling mechanism to allow for quitting the game when the user presses the close button.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 5:5.
==============
STEP 4
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=222, prompt_tokens=3022, total_tokens=3244)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, and defining the main game loop. The next step is to implement the logic for Conway's Game of Life. This involves creating a grid to represent the game state, initializing it with a random pattern, and defining the rules for cell survival, birth, and death. I will start by defining the grid size and initializing the grid with random states.
Action:
"""
edit 7 7 # Define grid size and initialize grid
grid_size = (50, 50) # Grid size as a tuple (rows, columns)
grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])] # Initialize grid with zeros
import random
for y in range(grid_size[0]):
for x in range(grid_size[1]):
grid[y][x] = random.randint(0, 1) # Randomly assign cells as alive (1) or dead (0)
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='# Define grid size and initialize grid\ngrid_size = (50, 50) # Grid size as a tuple (rows, columns)\ngrid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])] # Initialize grid with zeros\nimport random\nfor y in range(grid_size[0]):\n for x in range(grid_size[1]):\n grid[y][x] = random.randint(0, 1) # Randomly assign cells as alive (1) or dead (0)', start=7, end=7, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, and defining the main game loop. The next step is to implement the logic for Conway's Game of Life. This involves creating a grid to represent the game state, initializing it with a random pattern, and defining the rules for cell survival, birth, and death. I will start by defining the grid size and initializing the grid with random states.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 7:7.
==============
STEP 5
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=234, prompt_tokens=3158, total_tokens=3392)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, and initializing the grid with random states. The next step is to implement the rules for Conway's Game of Life. This involves checking each cell's neighbors to determine its next state based on the number of alive neighbors. I will start by defining a function to count the alive neighbors for a given cell.
Action:
"""
edit 9 9 def count_alive_neighbors(grid, x, y):
directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]
count = 0
for dx, dy in directions:
nx, ny = x + dx, y + dy
if 0 <= nx < grid_size[1] and 0 <= ny < grid_size[0]:
count += grid[ny][nx]
return count
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='def count_alive_neighbors(grid, x, y):\n directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]\n count = 0\n for dx, dy in directions:\n nx, ny = x + dx, y + dy\n if 0 <= nx < grid_size[1] and 0 <= ny < grid_size[0]:\n count += grid[ny][nx]\n return count', start=9, end=9, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, and initializing the grid with random states. The next step is to implement the rules for Conway's Game of Life. This involves checking each cell's neighbors to determine its next state based on the number of alive neighbors. I will start by defining a function to count the alive neighbors for a given cell.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 9:9.
==============
STEP 6
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=257, prompt_tokens=3290, total_tokens=3547)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating a function to count alive neighbors. The next step is to implement the rules for Conway's Game of Life. This involves updating the grid state based on the number of alive neighbors each cell has. I will define a function `update_grid` that applies the rules of the game to update the grid for the next generation.
Action:
"""
write game_of_life.py def update_grid(grid):
new_grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])]
for y in range(grid_size[0]):
for x in range(grid_size[1]):
alive_neighbors = count_alive_neighbors(grid, x, y)
if grid[y][x] == 1 and (alive_neighbors == 2 or alive_neighbors == 3):
new_grid[y][x] = 1
elif grid[y][x] == 0 and alive_neighbors == 3:
new_grid[y][x] = 1
return new_grid
11
11
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='def update_grid(grid):\n new_grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])]\n for y in range(grid_size[0]):\n for x in range(grid_size[1]):\n alive_neighbors = count_alive_neighbors(grid, x, y)\n if grid[y][x] == 1 and (alive_neighbors == 2 or alive_neighbors == 3):\n new_grid[y][x] = 1\n elif grid[y][x] == 0 and alive_neighbors == 3:\n new_grid[y][x] = 1\n return new_grid', start=11, end=11, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating a function to count alive neighbors. The next step is to implement the rules for Conway's Game of Life. This involves updating the grid state based on the number of alive neighbors each cell has. I will define a function `update_grid` that applies the rules of the game to update the grid for the next generation.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 11:11.
==============
STEP 7
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=124, prompt_tokens=3416, total_tokens=3540)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to integrate the `update_grid` function into the main game loop to continuously update the state of the grid based on the game rules. This will allow the game to evolve over time, showing different generations of the cell population.
Action:
"""
edit 6 6
grid = update_grid(grid)
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='grid = update_grid(grid)', start=6, end=6, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to integrate the `update_grid` function into the main game loop to continuously update the state of the grid based on the game rules. This will allow the game to evolve over time, showing different generations of the cell population.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 6:6.
==============
STEP 8
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=166, prompt_tokens=3393, total_tokens=3559)
==== RAW OUTPUT ====
Thoughts:
So far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to integrate the `update_grid` function into the main game loop to continuously update the state of the grid based on the game rules. This will allow the game to evolve over time, showing different generations of the cell population. I need to ensure that the `update_grid` function is called within the main game loop and that the display is updated accordingly. I will start by checking the current content of the main game loop to see where to integrate the `update_grid` function.
Action:
"""
goto 6
"""
==== END RAW ====
ACTION:
FileReadAction(path='game_of_life.py', start=6, end=106, thoughts="Thoughts:\nSo far, I have set up the basic structure for Conway's Game of Life using pygame, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to integrate the `update_grid` function into the main game loop to continuously update the state of the grid based on the game rules. This will allow the game to evolve over time, showing different generations of the cell population. I need to ensure that the `update_grid` function is called within the main game loop and that the display is updated accordingly. I will start by checking the current content of the main game loop to see where to integrate the `update_grid` function.\n\nAction:", action=<ActionType.READ: 'read'>)
OBSERVATION:
grid = update_grid(grid)
running = True
# Define grid size and initialize grid
grid_size = (50, 50) # Grid size as a tuple (rows, columns)
def count_alive_neighbors(grid, x, y):
directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]
def update_grid(grid):
new_grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])]
for y in range(grid_size[0]):
for x in range(grid_size[1]):
alive_neighbors = count_alive_neighbors(grid, x, y)
if grid[y][x] == 1 and (alive_neighbors == 2 or alive_neighbors == 3):
new_grid[y][x] = 1
elif grid[y][x] == 0 and alive_neighbors == 3:
new_grid[y][x] = 1
return new_grid
count = 0
for dx, dy in directions:
nx, ny = x + dx, y + dy
if 0 <= nx < grid_size[1] and 0 <= ny < grid_size[0]:
count += grid[ny][nx]
return count
grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])] # Initialize grid with zeros
import random
for y in range(grid_size[0]):
for x in range(grid_size[1]):
grid[y][x] = random.randint(0, 1) # Randomly assign cells as alive (1) or dead (0)
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
pygame.display.set_caption("Conway's Game of Life")
pygame.init()
==============
STEP 9
PLAN:
Please make conway's game of life in python using the pygame library. Be sure to use the tools in your workspace to help you out.
Usage(completion_tokens=174, prompt_tokens=3728, total_tokens=3902)
==== RAW OUTPUT ====
Thoughts:
From the previous actions, it seems that the basic structure for Conway's Game of Life using pygame has been set up, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to ensure that the game loop properly updates the grid and refreshes the display to show the new state of the grid in each iteration. This involves calling `pygame.display.update()` to update the entire screen and using `pygame.time.Clock()` to control the frame rate. I will start by adding these necessary updates to the game loop.
Action:
"""
edit 95 95
pygame.display.update()
clock = pygame.time.Clock()
clock.tick(10) # Control the frame rate (10 frames per second)
"""
==== END RAW ====
ACTION:
FileWriteAction(path='game_of_life.py', content='pygame.display.update()\n clock = pygame.time.Clock()\n clock.tick(10) # Control the frame rate (10 frames per second)', start=95, end=95, thoughts="Thoughts:\nFrom the previous actions, it seems that the basic structure for Conway's Game of Life using pygame has been set up, including initializing pygame, setting up the display window, defining the main game loop, initializing the grid with random states, and creating functions to count alive neighbors and update the grid. The next step is to ensure that the game loop properly updates the grid and refreshes the display to show the new state of the grid in each iteration. This involves calling `pygame.display.update()` to update the entire screen and using `pygame.time.Clock()` to control the frame rate. I will start by adding these necessary updates to the game loop.\n\nAction:", action=<ActionType.WRITE: 'write'>)
OBSERVATION:
WRITE OPERATION:
You have written to "game_of_life.py" on these lines: 95:95.
16:43:55 - opendevin:INFO: agent_controller.py:125 - Exited before finishing the task.
```
## Result:
```
import pygame
# Setup the display window
screen_size = (800, 600)
screen = pygame.display.set_mode(screen_size)
# Main game loop
grid = update_grid(grid)
running = True
# Define grid size and initialize grid
grid_size = (50, 50) # Grid size as a tuple (rows, columns)
def count_alive_neighbors(grid, x, y):
directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]
def update_grid(grid):
new_grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])]
for y in range(grid_size[0]):
for x in range(grid_size[1]):
alive_neighbors = count_alive_neighbors(grid, x, y)
if grid[y][x] == 1 and (alive_neighbors == 2 or alive_neighbors == 3):
new_grid[y][x] = 1
elif grid[y][x] == 0 and alive_neighbors == 3:
new_grid[y][x] = 1
return new_grid
count = 0
for dx, dy in directions:
nx, ny = x + dx, y + dy
if 0 <= nx < grid_size[1] and 0 <= ny < grid_size[0]:
count += grid[ny][nx]
return count
grid = [[0 for x in range(grid_size[1])] for y in range(grid_size[0])] # Initialize grid with zeros
import random
for y in range(grid_size[0]):
for x in range(grid_size[1]):
grid[y][x] = random.randint(0, 1) # Randomly assign cells as alive (1) or dead (0)
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
pygame.display.set_caption("Conway's Game of Life")
pygame.init()
# This is a placeholder for the game of life implementation using pygame.pygame.display.update()
clock = pygame.time.Clock()
clock.tick(10) # Control the frame rate (10 frames per second)
```
-5
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@@ -1,5 +0,0 @@
from opendevin.controller.agent import Agent
from .agent import SWEAgent
Agent.register('SWEAgent', SWEAgent)
-115
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@@ -1,115 +0,0 @@
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import (
Action,
FileReadAction,
FileWriteAction,
MessageAction,
)
from opendevin.events.serialization.event import event_to_memory
from opendevin.llm.llm import LLM
from .parser import parse_command
from .prompts import (
CONTEXT_PROMPT,
MEMORY_FORMAT,
NO_ACTION,
STEP_PROMPT,
SYSTEM_MESSAGE,
)
class SWEAgent(Agent):
VERSION = '1.0'
DEPRECATED = True
"""
An attempt to recreate swe_agent with output parsing, prompting style, and Application Computer Interface (ACI).
SWE-agent includes ACI functions like 'goto', 'search_for', 'edit', 'scroll', 'run'
"""
def __init__(self, llm: LLM):
super().__init__(llm)
self.memory_window = 4
self.max_retries = 2
self.cur_file: str = ''
self.cur_line: int = 0
def _think_act(self, messages: list[dict]) -> tuple[Action, str]:
resp = self.llm.do_completion(
messages=messages,
temperature=0.05,
)
action_resp = resp['choices'][0]['message']['content']
print(f"\033[1m\033[91m{resp['usage']}\033[0m")
print(
'\n==== RAW OUTPUT ====',
f'\033[96m{action_resp}\033[0m',
'==== END RAW ====\n',
sep='\n',
)
return parse_command(action_resp, self.cur_file, self.cur_line)
def _update(self, action: Action) -> None:
if isinstance(action, (FileReadAction, FileWriteAction)):
self.cur_file = action.path
self.cur_line = action.start
def step(self, state: State) -> Action:
"""
SWE-Agent step:
1. Get context - past actions, custom commands, current step
2. Perform think-act - prompt model for action and reasoning
3. Catch errors - ensure model takes action (5 attempts max)
"""
# retrieve short term memories from state.history, up to memory_window
memory_window = min(self.memory_window, len(state.history))
running_memory: list[str] = []
for prev_action, obs in state.history[-memory_window:]:
running_memory.append(
MEMORY_FORMAT(event_to_memory(prev_action), event_to_memory(obs))
)
goal = state.get_current_user_intent()
# always in the prompt if they exist: file and line
prompt = STEP_PROMPT(goal, self.cur_file, self.cur_line)
# prepare messages
msgs = [
{'content': SYSTEM_MESSAGE, 'role': 'system'},
{'content': prompt, 'role': 'user'},
]
# insert memories
if len(running_memory) > 0:
context = CONTEXT_PROMPT(running_memory, self.memory_window)
msgs.insert(1, {'content': context, 'role': 'user'})
# clrs = [''] * (len(msgs)-2) + ['\033[0;36m', '\033[0;35m']
# print('\n\n'.join([c+m['content']+'\033[0m' for c, m in zip(clrs, msgs)]))
# send it over
action, thought = self._think_act(messages=msgs)
# be robust with malformed responses
start_msg_len = len(msgs)
while not action and len(msgs) < self.max_retries + start_msg_len:
error = NO_ACTION(thought)
error_msg = {'content': error, 'role': 'user'}
msgs.append(error_msg)
action, thought = self._think_act(messages=msgs)
if not action:
action = MessageAction(thought)
self._update(action)
self.latest_action = action
return action
def search_memory(self, query: str) -> list[str]:
# return [item for item in self.running_memory if query in item]
raise NotImplementedError('Search_memory not implemented currently')
def reset(self) -> None:
"""Used to reset the agent"""
super().reset()
-167
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@@ -1,167 +0,0 @@
import re
from opendevin.events.action import (
Action,
AgentFinishAction,
BrowseURLAction,
CmdRunAction,
FileReadAction,
FileWriteAction,
MessageAction,
)
from .prompts import COMMAND_USAGE, CUSTOM_DOCS
# commands: exit, read, write, browse, kill, search_file, search_dir
no_open_file_error = MessageAction(
'You are not currently in a file. You can use the read command to open a file and then use goto to navigate through it.'
)
def invalid_error(cmd, docs):
return f"""ERROR:
Invalid command structure for
```
{cmd}
```
You may have caused this error by having multiple commands within your command block.
If so, try again by running only one of the commands:
Try again using this format:
{COMMAND_USAGE[docs]}
"""
def get_action_from_string(
command_string: str, path: str, line: int, thoughts: str = ''
) -> Action | None:
"""
Parses the command string to find which command the agent wants to run
Converts the command into a proper Action and returns
"""
vars = command_string.split(' ')
cmd = vars[0]
args = [] if len(vars) == 1 else vars[1:]
if 'exit' == cmd:
return AgentFinishAction()
elif 'think' == cmd:
return MessageAction(' '.join(args))
elif 'scroll_up' == cmd:
if not path:
return no_open_file_error
return FileReadAction(path, line + 100, line + 200, thoughts)
elif 'scroll_down' == cmd:
if not path:
return no_open_file_error
return FileReadAction(path, line - 100, line, thoughts)
elif 'goto' == cmd:
if not path:
return no_open_file_error
rex = r'^goto\s+(\d+)$'
valid = re.match(rex, command_string)
if valid:
start = int(valid.group(1))
end = start + 100
return FileReadAction(path, start, end, thoughts)
else:
return MessageAction(invalid_error(command_string, 'goto'))
elif 'edit' == cmd:
if not path:
return no_open_file_error
rex = r'^edit\s+(\d+)\s+(-?\d+)\s+(\S.*)$'
valid = re.match(rex, command_string, re.DOTALL)
if valid:
start = int(valid.group(1))
end = int(valid.group(2))
change = valid.group(3)
if '"' == change[-1] and '"' == change[0]:
change = change[1:-1]
return FileWriteAction(path, change, start, end, thoughts)
else:
return MessageAction(invalid_error(command_string, 'edit'))
elif 'read' == cmd:
rex = r'^read\s+(\S+)(?:\s+(\d+))?(?:\s+(-?\d+))?$'
valid = re.match(rex, command_string, re.DOTALL)
if valid:
file = valid.group(1)
start_str = valid.group(2)
end_str = valid.group(3)
start = 0 if not start_str else int(start_str)
end = -1 if not end_str else int(end_str)
return FileReadAction(file, start, end, thoughts)
else:
return MessageAction(invalid_error(command_string, 'read'))
elif 'write' == cmd:
rex = r'^write\s+(\S+)\s+(.*?)\s*(\d+)?\s*(-?\d+)?$'
valid = re.match(rex, command_string, re.DOTALL)
if valid:
file = valid.group(1)
content = valid.group(2)
start_str = valid.group(3)
end_str = valid.group(4)
start = 0 if not start_str else int(start_str)
end = -1 if not end_str else int(end_str)
if '"' == content[-1] and '"' == content[0]:
content = content[1:-1]
return FileWriteAction(file, content, start, end, thoughts)
else:
return MessageAction(invalid_error(command_string, 'write'))
elif 'browse' == cmd:
return BrowseURLAction(args[0].strip())
elif cmd in ['search_file', 'search_dir', 'find_file']:
rex = r'^(search_file|search_dir|find_file)\s+(\S+)(?:\s+(\S+))?$'
valid = re.match(rex, command_string, re.DOTALL)
if valid:
return CmdRunAction(command_string)
else:
return MessageAction(
f'Invalid command structure for\n ```\n{command_string}\n```.\nTry again using this format:\n{CUSTOM_DOCS}'
)
else:
# check bash command
obs = str(CmdRunAction(f'type {cmd}'))
if obs.split(':')[-1].strip() == 'not found':
# echo not found error for llm
return MessageAction(content=obs)
else:
# run valid command
return CmdRunAction(command_string)
def parse_command(input_str: str, path: str, line: int):
"""
Parses a given string and separates the command (enclosed in triple backticks) from any accompanying text.
Args:
input_str (str): The input string to be parsed.
Returns:
tuple: A tuple containing the command and the accompanying text (if any).
"""
input_str = input_str.strip()
if '```' in input_str:
parts = input_str.split('```')
command_str = parts[1].strip()
ind = 2 if len(parts) > 2 else 1
accompanying_text = ''.join(parts[:-ind]).strip()
action = get_action_from_string(command_str, path, line, accompanying_text)
if action:
return action, accompanying_text
return None, input_str # used for retry
-208
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@@ -1,208 +0,0 @@
DEFAULT_COMMANDS_DICT = {
'exit': 'Executed when task is complete',
'read <file_name> [<start_line>] [<end_line>]': "Shows a given file's contents starting from <start_line> up to <end_line>. Default: start_line = 0, end_line = -1. By default the whole file will be read.",
'write <file> <changes> [<start_line>] [<end_line>]': 'Modifies a <file> by replacing the current lines between <start_line> and <end_line> with <changes>. Default start_line = 0 and end_line = -1. Calling this with no line args will replace the whole file.',
'browse <url>': 'Returns the text version of any url, this can be useful to look up documentation or finding issues on github',
'scroll_up': 'Takes no arguments. This will scroll up and show you the 100 lines above your current lines',
'scroll_down': 'Takes no arguments. This will scroll down and show you the 100 lines below your current lines',
'edit <start_line> <end_line> <changes>': 'This will modify lines in the currently open file. use start_line and end_line to designate which lines to change and then write the multiline changes. Set end_line to -1 to denote the end of the file',
'goto <line_num>': 'This will take you directly to a line and show you the 100 lines below it.',
'<bash_command> <args>': 'You can use any bash command you need (cd, ls, rm, grep, dir, mv, wget, git, zip, etc.) with their arguments included',
'pip install <package>': 'You can use this to import python packages. Make sure you include the correct package name when using this command.',
'ls': 'Use the ls command to view all the files in your current directory, this is a good starting point.',
'NOT ALLOWED': 'You cannot use interactive commands like python or node',
}
COMMAND_USAGE = {
'exit': 'Usage:\n```\nexit\n```\nExecuted when task is complete',
'read': "Args:\n<file_name> [<start_line>] [<end_line>]\nUsage:\n```\nread file.py\n```\nor\n```\nread example.py <start_line> <end_line>\n```\nShows a given file's contents starting from <start_line> up to <end_line>. Default: start_line = 0, end_line = -1. by default the whole file will be read.",
'write': 'Args:\n<file> <changes> [<start_line>] [<end_line>]\nUsage:\n```\nwrite "def main():\n print("This is line one")" 0 2\n```\nModifies a <file> by replacing the current lines between <start_line> and <end_line> with <changes>. Default start_line = 0 and end_line = -1. Calling this with no line args will replace the whole file.',
'edit': 'Args:\n<start_line> <end_line> <changes>\nUsage:\n```\nedit 0 1 import pandas as pd\n```\nThis will modify the current file you are in with the changes you make between the line numbers you designate',
'goto': 'Args:\n<line_num>\nUsage:\n```\ngoto <line_num>\n```\nThis will show you the 100 lines below and including the line you specify within your current file.',
'scroll_up': 'Usage:\n```\nscroll_up\n```\nThis will return the 100 lines above where you are currently at',
'scroll_down': 'Usage:\n```\nscroll_down\n```\nThis will return the 100 line below where you are currently at',
'browse': 'Args:\n<url>\nUsage:\n```\nbrowse https://github.com/OpenDevin/OpenDevin\n```\nThis will fetch the Text elements from the given url and show them to you.',
}
DEFAULT_COMMANDS = '\n'.join([k + ' - ' + v for k, v in DEFAULT_COMMANDS_DICT.items()])
# from opendevin.parse_commands import parse_command_file
# USE parse_command_file(filepath) to get the custom commands
CUSTOM_DOCS = None
CUSTOM_COMMANDS = f"""Custom bash commands:
{CUSTOM_DOCS}
"""
DOCUMENTATION = f"""DOCUMENTATION:
It is recommend that you use the commands provided for interacting with files and your directory because they have been specially built for you.
They will make it much easier for you to look at files and make changes. Using these commands will help you be better at your task.
You can open an file by using either the read or write operations.
- If a file already exists you should read it before making any changes. Use the `edit` command to make changes once you have read it.
- If you are creating a new file use the write command. Use the `edit` command to make changes once you have created the new file.
Commands:
{DEFAULT_COMMANDS}
{CUSTOM_COMMANDS}
The following commands require an open file to be used: edit, scroll_up, scroll_down, goto
To modify the current file use 'edit'. To move through the current file use 'goto' or 'scroll_up'/'scroll_down'
when using write and edit do not surround the code with any "" just write the code.
"""
GENERAL_GUIDELINES = """INSTRUCTIONS:
Now, you're going to solve this issue on your own. You can use any bash commands or custom commands you wish to complete your task. Edit all the files you need to and run any checks or tests that you want.
Remember, YOU CAN ONLY ENTER ONE COMMAND AT A TIME. You should always wait for feedback after every command.
When you're satisfied with all of the changes you've made, you can indicate that you are done by running the exit command.
Note however that you cannot use any interactive session commands (e.g. python, vim, node) 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 <script_name>.py`.
NOTE ABOUT THE write COMMAND: Indentation really matters! When editing a file, make sure to insert appropriate indentation before each line!
IMPORTANT TIPS:
1. Reproduce the bug: Always start by trying to replicate the bug that the issue discusses. If the issue includes code for reproducing the bug, we recommend that you re-implement that in your environment and run it to ensure 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 issue has indeed been resolved.
If the bug reproduction script does not print anything when it 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 the script ran fine all the way through.
2. Try different commands: If you run a command and it doesn't work, try running a different command. A command that did not work once will not work the second time unless you modify it.
3. Navigate large files: 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, you would use the 'read' command like this: 'read <file> 583'. This is a much faster way to read through the file.
4. Handle input files: 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 repository code to see whether someone else has already done that. Do this by running the command: 'search_dir "buggy-input.png"'. If that doesn't work, use the Linux 'find' command.
5. Understand your context: Always make sure to look at the currently open file and the current working directory. The currently open file might be in a different directory than the working directory.
6. Verify your edits: When editing files, it is easy to accidentally specify a wrong line number or to write code with incorrect indentation. Always check the code after you issue an edit to make sure that it reflects what you wanted to accomplish. If it didn't, issue another command to fix it.
7. Thoroughly test your solution: After making any changes to fix a bug, be sure to thoroughly test your solution to ensure the bug has been resolved. Re-run the bug reproduction script and verify that the issue has been addressed.
"""
RESPONSE_FORMAT = """RESPONSE FORMAT:
This is the format of the response you will make in order to solve the current issue.
You will be given multiple iterations to complete this task so break it into steps and solve them one by one.
Your output must contain the following:
- First, thoughts about what your next action should be and plan it out.
- You will have a memory of your thoughts so you can use this to remember things for the next step.
- Use your thoughts to think about what you are currently doing, what you have done on prior steps and how that relates to solving the problem.
- Second, create a piece of code that will execute your next action based on the thoughts you have.
- Remember that you can only have one action for each thought, do not include multiple actions.
Your code MUST be surrounded in triple back ticks EXACTLY like this:
```
<code>
```
Notes:
- Adhere to the format so that the program loop continues smoothly, it is very important to only give one command per output.
- DO NOT give more than one command within the triple backticks. This will just throw an error and nothing will happen as a result.
- Do not give multiple code blocks, if you do only the second one will be captured and run, this might give an error if the first one was necessary.
- To execute multiple commands you should write them down in your thoughts section so you can remember it on the next step and execute them then.
- The only commands you are not capable of executing are interactive commands like `python` or `node` by themselves.
- If you think that you have completed the task that has been given to you based on your previous actions and outputs then use ``` exit ``` as the command to let the system know that you are done.
- DO NOT make any copies of your previous memories, those will be provided to you at each step, making copies just wastes time and energy. Think smarter not harder.
- The write and edit commands requires proper indentation in the content section ex. `write hw.py def hello():\n print(\'Hello World\')` this is how you would have to format your write command.
- The white spaces matter as the code changes will be added to the code so they must have proper syntax.
This is a template using the format described above
Items in <> are suggestions for you, fill them out based on the context of the problem you are solving.
[ FORMAT ]
Thoughts:
<Provide clear and concise thoughts on the next step to take, highlighting any important details or context that should be remembered.>
<You can use multiple lines to express your thoughts>
Action:
```
<command> <params>
```
[ END FORMAT ]
Do not provide anything extra just your thought and action.
"""
SYSTEM_MESSAGE = f"""SYSTEM INFO:
You are an autonomous coding agent, here to provide solutions for coding issues.
You have been designed to assist with a wide range of programming tasks, from code editing and debugging to testing and deployment.
You have access to a variety of tools and commands that you can use to help you solve problems efficiently.
{GENERAL_GUIDELINES}
{DOCUMENTATION}
""".strip()
def NO_ACTION(latest):
return f"""
You did not include any action to take in your most recent output:
===== Output ======
{latest}
==== End Output ===
Remember these are the custom commands you can use:
{DOCUMENTATION}
Lets try that again, it is very important that you adhere to the output format
This time, be sure to use the exact format below, replacing anything in <> with the appropriate value(s):
{RESPONSE_FORMAT}
It is crucial you use the format provided as the output will be parsed automatically.
"""
def file_info(file: str, line: int):
if file:
return f"""CURRENT WORKSPACE:
Open File: {file} on line {line}
You can use these commands with the current file:
Navigation: `scroll_up`, `scroll_down`, and `goto <line>`
Modification: `edit <start_line> <end_line> <changes>`
"""
def STEP_PROMPT(task, file, line_num):
return f"""
{RESPONSE_FORMAT}
You are currently trying to complete this task:
{task}
{file_info(file, line_num)}
Keep all of the guidelines above in mind when you are thinking and making code.
Please come up with a thought and action based on your current task and latest steps.
Make sure that you do not repeat the same actions, there will not be any changes in result if you do not changes anything.
Be very strict about the formatting that you use and make sure you follow the guidelines.
NEVER output multiple commands. ONLY take ONE STEP at a time.
When you have completed your task run the "exit" command.
Begin with your thought about the next step and then come up with an action to perform your thought.
""".strip()
def unpack_dict(data: dict, restrict: list[str] | None = None):
lines = []
restrict = [] if restrict is None else restrict
for key, value in data.items():
if key in restrict:
continue
elif isinstance(value, dict):
nested_str = unpack_dict(value, restrict).replace('\n', '\n ')
val = f'{key}:' + '\n ' + f'{nested_str}'
lines.append(val)
else:
lines.append(f'{key}: {value}')
return '\n'.join(lines)
def MEMORY_FORMAT(act, obs):
return f"""
Previous Action:
{unpack_dict(act, ["content"])}
Output from Action:
{unpack_dict(obs)}
""".strip()
def CONTEXT_PROMPT(memory, window):
res = f'These are your past {window} actions:\n'
window_size = window if len(memory) > window else len(memory)
cur_mems = memory[-window_size:]
res += '===== Previous Actions =====\n'
for idx, mem in enumerate(cur_mems):
res += f'\nMemory {idx}:\n{mem}\n'
res += '======= End Actions =======\n'
res += 'Use these memories to provide additional context to the problem you are solving.\nRemember that you have already completed these steps so you do not need to perform them again.'
return res
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from dotenv import load_dotenv
from opendevin.controller.agent import Agent
from .micro.agent import MicroAgent
from .micro.registry import all_microagents
load_dotenv()
from . import ( # noqa: E402
SWE_agent,
browsing_agent,
codeact_agent,
codeact_swe_agent,
delegator_agent,
dummy_agent,
monologue_agent,
planner_agent,
)
__all__ = [
'monologue_agent',
'codeact_agent',
'codeact_swe_agent',
'planner_agent',
'SWE_agent',
'delegator_agent',
'dummy_agent',
'browsing_agent',
]
for agent in all_microagents.values():
name = agent['name']
prompt = agent['prompt']
anon_class = type(
name,
(MicroAgent,),
{
'prompt': prompt,
'agent_definition': agent,
},
)
Agent.register(name, anon_class)
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# Browsing Agent Framework
This folder implements the basic BrowserGym [demo agent](https://github.com/ServiceNow/BrowserGym/tree/main/demo_agent) that enables full-featured web browsing.
## Test run
Note that for browsing tasks, GPT-4 is usually a requirement to get reasonable results, due to the complexity of the web page structures.
```
poetry run python ./opendevin/core/main.py \
-i 10 \
-t "tell me the usa's president using google search" \
-c BrowsingAgent \
-m gpt-4o-2024-05-13
```
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from opendevin.controller.agent import Agent
from .browsing_agent import BrowsingAgent
Agent.register('BrowsingAgent', BrowsingAgent)
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import ast
from browsergym.core.action.highlevel import HighLevelActionSet
from browsergym.utils.obs import flatten_axtree_to_str
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.core.logger import opendevin_logger as logger
from opendevin.events.action import (
Action,
AgentFinishAction,
BrowseInteractiveAction,
MessageAction,
)
from opendevin.events.observation import BrowserOutputObservation
from opendevin.llm.llm import LLM
from opendevin.runtime.plugins import (
PluginRequirement,
)
def parse_response(response: str) -> Action:
if '```' not in response:
# unexpected response format, message back to user
return MessageAction(response)
thought = response.split('```')[0].strip()
action_str = response.split('```')[1].strip()
# handle send message to user function call in BrowserGym
for sub_action in action_str.split('\n'):
if 'send_msg_to_user(' in sub_action:
tree = ast.parse(sub_action)
args = tree.body[0].value.args # type: ignore
return MessageAction(args[0].value)
return BrowseInteractiveAction(browser_actions=action_str, thought=thought)
class BrowsingAgent(Agent):
VERSION = '1.0'
"""
An agent that interacts with the browser.
"""
sandbox_plugins: list[PluginRequirement] = []
def __init__(
self,
llm: LLM,
) -> None:
"""
Initializes a new instance of the BrowsingAgent class.
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
self.action_space = HighLevelActionSet(
# see https://github.com/ServiceNow/BrowserGym/blob/main/core/src/browsergym/core/action/highlevel.py for more details
subsets=[
'chat',
'bid',
'nav',
], # define a configurable action space, with chat functionality, web navigation, and webpage grounding using accessibility tree and HTML.
strict=False, # less strict on the parsing of the actions
multiaction=True, # enable to agent to take multiple actions at once
)
self.reset()
def reset(self) -> None:
"""
Resets the Browsing Agent.
"""
super().reset()
self.cost_accumulator = 0
def step(self, state: State) -> Action:
"""
Performs one step using the Browsing Agent.
This includes gathering information on previous steps and prompting the model to make a browsing command to execute.
Parameters:
- state (State): used to get updated info
Returns:
- BrowseInteractiveAction(browsergym_command) - BrowserGym commands to run
- MessageAction(content) - Message action to run (e.g. ask for clarification)
- AgentFinishAction() - end the interaction
"""
goal = state.get_current_user_intent()
messages = []
prev_actions = ''
cur_axtree_txt = ''
error_prefix = ''
last_obs = None
for prev_action, obs in state.history:
if isinstance(prev_action, BrowseInteractiveAction):
prev_actions += f'{prev_action.browser_actions}\n'
last_obs = obs
elif (
isinstance(prev_action, MessageAction) and prev_action.source != 'user'
):
# agent has responded, task finish.
return AgentFinishAction()
if isinstance(last_obs, BrowserOutputObservation):
if last_obs.error:
# add error recovery prompt prefix
error_prefix = f'IMPORTANT! Last action is incorrect:\n{last_obs.last_browser_action}\nThink again with the current observation of the page.\n'
cur_axtree_txt = flatten_axtree_to_str(last_obs.axtree_object)
system_msg = f"""\
# Instructions
Review the current state of the page and all other information to find the best
possible next action to accomplish your goal. Your answer will be interpreted
and executed by a program, make sure to follow the formatting instructions.
# Goal:
{goal}
# Action Space
{self.action_space.describe(with_long_description=False, with_examples=True)}
"""
messages.append({'role': 'system', 'content': system_msg})
prompt = f"""\
{error_prefix}
# Current Accessibility Tree:
{cur_axtree_txt}
# Previous Actions
{prev_actions}
Here is an example with chain of thought of a valid action when clicking on a button:
"
In order to accomplish my goal I need to click on the button with bid 12
```click("12")```
"
""".strip()
messages.append({'role': 'user', 'content': prompt})
response = self.llm.completion(
messages=messages,
temperature=0.0,
)
self.log_cost(response)
action_resp = response['choices'][0]['message']['content']
logger.info(prompt)
logger.info(action_resp)
return parse_response(action_resp)
def search_memory(self, query: str) -> list[str]:
raise NotImplementedError('Implement this abstract method')
def log_cost(self, response):
# TODO: refactor to unified cost tracking
try:
cur_cost = self.llm.completion_cost(response)
except Exception:
cur_cost = 0
self.cost_accumulator += cur_cost
logger.info(
'Cost: %.2f USD | Accumulated Cost: %.2f USD',
cur_cost,
self.cost_accumulator,
)
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import abc
import difflib
import logging
import platform
from copy import deepcopy
from dataclasses import asdict, dataclass
from textwrap import dedent
from typing import Literal, Union
from warnings import warn
from browsergym.core.action.base import AbstractActionSet
from browsergym.core.action.highlevel import HighLevelActionSet
from browsergym.core.action.python import PythonActionSet
from opendevin.runtime.browser.browser_env import BrowserEnv
from .utils import (
ParseError,
parse_html_tags_raise,
)
@dataclass
class Flags:
use_html: bool = True
use_ax_tree: bool = False
drop_ax_tree_first: bool = True # This flag is no longer active TODO delete
use_thinking: bool = False
use_error_logs: bool = False
use_past_error_logs: bool = False
use_history: bool = False
use_action_history: bool = False
use_memory: bool = False
use_diff: bool = False
html_type: str = 'pruned_html'
use_concrete_example: bool = True
use_abstract_example: bool = False
multi_actions: bool = False
action_space: Literal[
'python', 'bid', 'coord', 'bid+coord', 'bid+nav', 'coord+nav', 'bid+coord+nav'
] = 'bid'
is_strict: bool = False
# This flag will be automatically disabled `if not chat_model_args.has_vision()`
use_screenshot: bool = True
enable_chat: bool = False
max_prompt_tokens: int = 100_000
extract_visible_tag: bool = False
extract_coords: Literal['False', 'center', 'box'] = 'False'
extract_visible_elements_only: bool = False
demo_mode: Literal['off', 'default', 'only_visible_elements'] = 'off'
def copy(self):
return deepcopy(self)
def asdict(self):
"""Helper for JSON serializble requirement."""
return asdict(self)
@classmethod
def from_dict(self, flags_dict):
"""Helper for JSON serializble requirement."""
if isinstance(flags_dict, Flags):
return flags_dict
if not isinstance(flags_dict, dict):
raise ValueError(
f'Unregcognized type for flags_dict of type {type(flags_dict)}.'
)
return Flags(**flags_dict)
class PromptElement:
"""Base class for all prompt elements. Prompt elements can be hidden.
Prompt elements are used to build the prompt. Use flags to control which
prompt elements are visible. We use class attributes as a convenient way
to implement static prompts, but feel free to override them with instance
attributes or @property decorator."""
_prompt = ''
_abstract_ex = ''
_concrete_ex = ''
def __init__(self, visible: bool = True) -> None:
"""Prompt element that can be hidden.
Parameters
----------
visible : bool, optional
Whether the prompt element should be visible, by default True. Can
be a callable that returns a bool. This is useful when a specific
flag changes during a shrink iteration.
"""
self._visible = visible
@property
def prompt(self):
"""Avoid overriding this method. Override _prompt instead."""
return self._hide(self._prompt)
@property
def abstract_ex(self):
"""Useful when this prompt element is requesting an answer from the llm.
Provide an abstract example of the answer here. See Memory for an
example.
Avoid overriding this method. Override _abstract_ex instead
"""
return self._hide(self._abstract_ex)
@property
def concrete_ex(self):
"""Useful when this prompt element is requesting an answer from the llm.
Provide a concrete example of the answer here. See Memory for an
example.
Avoid overriding this method. Override _concrete_ex instead
"""
return self._hide(self._concrete_ex)
@property
def is_visible(self):
"""Handle the case where visible is a callable."""
visible = self._visible
if callable(visible):
visible = visible()
return visible
def _hide(self, value):
"""Return value if visible is True, else return empty string."""
if self.is_visible:
return value
else:
return ''
def _parse_answer(self, text_answer) -> dict:
if self.is_visible:
return self._parse_answer(text_answer)
else:
return {}
class Shrinkable(PromptElement, abc.ABC):
@abc.abstractmethod
def shrink(self) -> None:
"""Implement shrinking of this prompt element.
You need to recursively call all shrinkable elements that are part of
this prompt. You can also implement a shriking startegy for this prompt.
Shrinking is can be called multiple times to progressively shrink the
prompt until it fits max_tokens. Default max shrink iterations is 20.
"""
pass
class Truncater(Shrinkable):
"""A prompt element that can be truncated to fit the context length of the LLM.
Of course, it will be great that we never have to use the functionality here to `shrink()` the prompt.
Extend this class for prompt elements that can be truncated. Usually long observations such as AxTree or HTML.
"""
def __init__(self, visible, shrink_speed=0.3, start_truncate_iteration=10):
super().__init__(visible=visible)
self.shrink_speed = shrink_speed # the percentage shrinked in each iteration
self.start_truncate_iteration = (
start_truncate_iteration # the iteration to start truncating
)
self.shrink_calls = 0
self.deleted_lines = 0
def shrink(self) -> None:
if self.is_visible and self.shrink_calls >= self.start_truncate_iteration:
# remove the fraction of _prompt
lines = self._prompt.splitlines()
new_line_count = int(len(lines) * (1 - self.shrink_speed))
self.deleted_lines += len(lines) - new_line_count
self._prompt = '\n'.join(lines[:new_line_count])
self._prompt += (
f'\n... Deleted {self.deleted_lines} lines to reduce prompt size.'
)
self.shrink_calls += 1
def fit_tokens(
shrinkable: Shrinkable,
max_prompt_chars=None,
max_iterations=20,
):
"""Shrink a prompt element until it fits max_tokens.
Parameters
----------
shrinkable : Shrinkable
The prompt element to shrink.
max_prompt_chars : int
The maximum number of chars allowed.
max_iterations : int, optional
The maximum number of shrink iterations, by default 20.
model_name : str, optional
The name of the model used when tokenizing.
Returns
-------
str : the prompt after shrinking.
"""
if max_prompt_chars is None:
return shrinkable.prompt
for _ in range(max_iterations):
prompt = shrinkable.prompt
if isinstance(prompt, str):
prompt_str = prompt
elif isinstance(prompt, list):
prompt_str = '\n'.join([p['text'] for p in prompt if p['type'] == 'text'])
else:
raise ValueError(f'Unrecognized type for prompt: {type(prompt)}')
n_chars = len(prompt_str)
if n_chars <= max_prompt_chars:
return prompt
shrinkable.shrink()
logging.info(
dedent(
f"""\
After {max_iterations} shrink iterations, the prompt is still
{len(prompt_str)} chars (greater than {max_prompt_chars}). Returning the prompt as is."""
)
)
return prompt
class HTML(Truncater):
def __init__(self, html, visible: bool = True, prefix='') -> None:
super().__init__(visible=visible, start_truncate_iteration=5)
self._prompt = f'\n{prefix}HTML:\n{html}\n'
class AXTree(Truncater):
def __init__(
self, ax_tree, visible: bool = True, coord_type=None, prefix=''
) -> None:
super().__init__(visible=visible, start_truncate_iteration=10)
if coord_type == 'center':
coord_note = """\
Note: center coordinates are provided in parenthesis and are
relative to the top left corner of the page.\n\n"""
elif coord_type == 'box':
coord_note = """\
Note: bounding box of each object are provided in parenthesis and are
relative to the top left corner of the page.\n\n"""
else:
coord_note = ''
self._prompt = f'\n{prefix}AXTree:\n{coord_note}{ax_tree}\n'
class Error(PromptElement):
def __init__(self, error, visible: bool = True, prefix='') -> None:
super().__init__(visible=visible)
self._prompt = f'\n{prefix}Error from previous action:\n{error}\n'
class Observation(Shrinkable):
"""Observation of the current step.
Contains the html, the accessibility tree and the error logs.
"""
def __init__(self, obs, flags: Flags) -> None:
super().__init__()
self.flags = flags
self.obs = obs
self.html = HTML(obs[flags.html_type], visible=flags.use_html, prefix='## ')
self.ax_tree = AXTree(
obs['axtree_txt'],
visible=flags.use_ax_tree,
coord_type=flags.extract_coords,
prefix='## ',
)
self.error = Error(
obs['last_action_error'],
visible=flags.use_error_logs and obs['last_action_error'],
prefix='## ',
)
def shrink(self):
self.ax_tree.shrink()
self.html.shrink()
@property
def _prompt(self) -> str: # type: ignore
return f'\n# Observation of current step:\n{self.html.prompt}{self.ax_tree.prompt}{self.error.prompt}\n\n'
def add_screenshot(self, prompt):
if self.flags.use_screenshot:
if isinstance(prompt, str):
prompt = [{'type': 'text', 'text': prompt}]
img_url = BrowserEnv.image_to_jpg_base64_url(
self.obs['screenshot'], add_data_prefix=True
)
prompt.append({'type': 'image_url', 'image_url': img_url})
return prompt
class MacNote(PromptElement):
def __init__(self) -> None:
super().__init__(visible=platform.system() == 'Darwin')
self._prompt = '\nNote: you are on mac so you should use Meta instead of Control for Control+C etc.\n'
class BeCautious(PromptElement):
def __init__(self, visible: bool = True) -> None:
super().__init__(visible=visible)
self._prompt = """\
\nBe very cautious. Avoid submitting anything before verifying the effect of your
actions. Take the time to explore the effect of safe actions first. For example
you can fill a few elements of a form, but don't click submit before verifying
that everything was filled correctly.\n"""
class GoalInstructions(PromptElement):
def __init__(self, goal, visible: bool = True) -> None:
super().__init__(visible)
self._prompt = f"""\
# Instructions
Review the current state of the page and all other information to find the best
possible next action to accomplish your goal. Your answer will be interpreted
and executed by a program, make sure to follow the formatting instructions.
## Goal:
{goal}
"""
class ChatInstructions(PromptElement):
def __init__(self, chat_messages, visible: bool = True) -> None:
super().__init__(visible)
self._prompt = """\
# Instructions
You are a UI Assistant, your goal is to help the user perform tasks using a web browser. You can
communicate with the user via a chat, in which the user gives you instructions and in which you
can send back messages. You have access to a web browser that both you and the user can see,
and with which only you can interact via specific commands.
Review the instructions from the user, the current state of the page and all other information
to find the best possible next action to accomplish your goal. Your answer will be interpreted
and executed by a program, make sure to follow the formatting instructions.
## Chat messages:
"""
self._prompt += '\n'.join(
[
f"""\
- [{msg['role']}] {msg['message']}"""
for msg in chat_messages
]
)
class SystemPrompt(PromptElement):
_prompt = """\
You are an agent trying to solve a web task based on the content of the page and
a user instructions. You can interact with the page and explore. Each time you
submit an action it will be sent to the browser and you will receive a new page."""
class MainPrompt(Shrinkable):
def __init__(
self,
obs_history,
actions,
memories,
thoughts,
flags: Flags,
) -> None:
super().__init__()
self.flags = flags
self.history = History(obs_history, actions, memories, thoughts, flags)
if self.flags.enable_chat:
self.instructions: Union[ChatInstructions, GoalInstructions] = (
ChatInstructions(obs_history[-1]['chat_messages'])
)
else:
if (
'chat_messages' in obs_history[-1]
and sum(
[msg['role'] == 'user' for msg in obs_history[-1]['chat_messages']]
)
> 1
):
logging.warning(
'Agent is in goal mode, but multiple user messages are present in the chat. Consider switching to `enable_chat=True`.'
)
self.instructions = GoalInstructions(obs_history[-1]['goal'])
self.obs = Observation(obs_history[-1], self.flags)
self.action_space = ActionSpace(self.flags)
self.think = Think(visible=flags.use_thinking)
self.memory = Memory(visible=flags.use_memory)
@property
def _prompt(self) -> str: # type: ignore
prompt = f"""\
{self.instructions.prompt}\
{self.obs.prompt}\
{self.history.prompt}\
{self.action_space.prompt}\
{self.think.prompt}\
{self.memory.prompt}\
"""
if self.flags.use_abstract_example:
prompt += f"""
# Abstract Example
Here is an abstract version of the answer with description of the content of
each tag. Make sure you follow this structure, but replace the content with your
answer:
{self.think.abstract_ex}\
{self.memory.abstract_ex}\
{self.action_space.abstract_ex}\
"""
if self.flags.use_concrete_example:
prompt += f"""
# Concrete Example
Here is a concrete example of how to format your answer.
Make sure to follow the template with proper tags:
{self.think.concrete_ex}\
{self.memory.concrete_ex}\
{self.action_space.concrete_ex}\
"""
return self.obs.add_screenshot(prompt)
def shrink(self):
self.history.shrink()
self.obs.shrink()
def _parse_answer(self, text_answer):
ans_dict = {}
ans_dict.update(self.think._parse_answer(text_answer))
ans_dict.update(self.memory._parse_answer(text_answer))
ans_dict.update(self.action_space._parse_answer(text_answer))
return ans_dict
class ActionSpace(PromptElement):
def __init__(self, flags: Flags) -> None:
super().__init__()
self.flags = flags
self.action_space = _get_action_space(flags)
self._prompt = (
f'# Action space:\n{self.action_space.describe()}{MacNote().prompt}\n'
)
self._abstract_ex = f"""
<action>
{self.action_space.example_action(abstract=True)}
</action>
"""
self._concrete_ex = f"""
<action>
{self.action_space.example_action(abstract=False)}
</action>
"""
def _parse_answer(self, text_answer):
ans_dict = parse_html_tags_raise(
text_answer, keys=['action'], merge_multiple=True
)
try:
# just check if action can be mapped to python code but keep action as is
# the environment will be responsible for mapping it to python
self.action_space.to_python_code(ans_dict['action'])
except Exception as e:
raise ParseError(
f'Error while parsing action\n: {e}\n'
'Make sure your answer is restricted to the allowed actions.'
)
return ans_dict
def _get_action_space(flags: Flags) -> AbstractActionSet:
match flags.action_space:
case 'python':
action_space = PythonActionSet(strict=flags.is_strict)
if flags.multi_actions:
warn(
f'Flag action_space={repr(flags.action_space)} incompatible with multi_actions={repr(flags.multi_actions)}.'
)
if flags.demo_mode != 'off':
warn(
f'Flag action_space={repr(flags.action_space)} incompatible with demo_mode={repr(flags.demo_mode)}.'
)
return action_space
case 'bid':
action_subsets = ['chat', 'bid']
case 'coord':
action_subsets = ['chat', 'coord']
case 'bid+coord':
action_subsets = ['chat', 'bid', 'coord']
case 'bid+nav':
action_subsets = ['chat', 'bid', 'nav']
case 'coord+nav':
action_subsets = ['chat', 'coord', 'nav']
case 'bid+coord+nav':
action_subsets = ['chat', 'bid', 'coord', 'nav']
case _:
raise NotImplementedError(
f'Unknown action_space {repr(flags.action_space)}'
)
action_space = HighLevelActionSet(
subsets=action_subsets,
multiaction=flags.multi_actions,
strict=flags.is_strict,
demo_mode=flags.demo_mode,
)
return action_space
class Memory(PromptElement):
_prompt = '' # provided in the abstract and concrete examples
_abstract_ex = """
<memory>
Write down anything you need to remember for next steps. You will be presented
with the list of previous memories and past actions.
</memory>
"""
_concrete_ex = """
<memory>
I clicked on bid 32 to activate tab 2. The accessibility tree should mention
focusable for elements of the form at next step.
</memory>
"""
def _parse_answer(self, text_answer):
return parse_html_tags_raise(
text_answer, optional_keys=['memory'], merge_multiple=True
)
class Think(PromptElement):
_prompt = ''
_abstract_ex = """
<think>
Think step by step. If you need to make calculations such as coordinates, write them here. Describe the effect
that your previous action had on the current content of the page.
</think>
"""
_concrete_ex = """
<think>
My memory says that I filled the first name and last name, but I can't see any
content in the form. I need to explore different ways to fill the form. Perhaps
the form is not visible yet or some fields are disabled. I need to replan.
</think>
"""
def _parse_answer(self, text_answer):
return parse_html_tags_raise(
text_answer, optional_keys=['think'], merge_multiple=True
)
def diff(previous, new):
"""Return a string showing the difference between original and new.
If the difference is above diff_threshold, return the diff string."""
if previous == new:
return 'Identical', []
if len(previous) == 0 or previous is None:
return 'previous is empty', []
diff_gen = difflib.ndiff(previous.splitlines(), new.splitlines())
diff_lines = []
plus_count = 0
minus_count = 0
for line in diff_gen:
if line.strip().startswith('+'):
diff_lines.append(line)
plus_count += 1
elif line.strip().startswith('-'):
diff_lines.append(line)
minus_count += 1
else:
continue
header = f'{plus_count} lines added and {minus_count} lines removed:'
return header, diff_lines
class Diff(Shrinkable):
def __init__(
self, previous, new, prefix='', max_line_diff=20, shrink_speed=2, visible=True
) -> None:
super().__init__(visible=visible)
self.max_line_diff = max_line_diff
self.header, self.diff_lines = diff(previous, new)
self.shrink_speed = shrink_speed
self.prefix = prefix
def shrink(self):
self.max_line_diff -= self.shrink_speed
self.max_line_diff = max(1, self.max_line_diff)
@property
def _prompt(self) -> str: # type: ignore
diff_str = '\n'.join(self.diff_lines[: self.max_line_diff])
if len(self.diff_lines) > self.max_line_diff:
original_count = len(self.diff_lines)
diff_str = f'{diff_str}\nDiff truncated, {original_count - self.max_line_diff} changes now shown.'
return f'{self.prefix}{self.header}\n{diff_str}\n'
class HistoryStep(Shrinkable):
def __init__(
self, previous_obs, current_obs, action, memory, flags: Flags, shrink_speed=1
) -> None:
super().__init__()
self.html_diff = Diff(
previous_obs[flags.html_type],
current_obs[flags.html_type],
prefix='\n### HTML diff:\n',
shrink_speed=shrink_speed,
visible=lambda: flags.use_html and flags.use_diff,
)
self.ax_tree_diff = Diff(
previous_obs['axtree_txt'],
current_obs['axtree_txt'],
prefix='\n### Accessibility tree diff:\n',
shrink_speed=shrink_speed,
visible=lambda: flags.use_ax_tree and flags.use_diff,
)
self.error = Error(
current_obs['last_action_error'],
visible=(
flags.use_error_logs
and current_obs['last_action_error']
and flags.use_past_error_logs
),
prefix='### ',
)
self.shrink_speed = shrink_speed
self.action = action
self.memory = memory
self.flags = flags
def shrink(self):
super().shrink()
self.html_diff.shrink()
self.ax_tree_diff.shrink()
@property
def _prompt(self) -> str: # type: ignore
prompt = ''
if self.flags.use_action_history:
prompt += f'\n### Action:\n{self.action}\n'
prompt += (
f'{self.error.prompt}{self.html_diff.prompt}{self.ax_tree_diff.prompt}'
)
if self.flags.use_memory and self.memory is not None:
prompt += f'\n### Memory:\n{self.memory}\n'
return prompt
class History(Shrinkable):
def __init__(
self, history_obs, actions, memories, thoughts, flags: Flags, shrink_speed=1
) -> None:
super().__init__(visible=flags.use_history)
assert len(history_obs) == len(actions) + 1
assert len(history_obs) == len(memories) + 1
self.shrink_speed = shrink_speed
self.history_steps: list[HistoryStep] = []
for i in range(1, len(history_obs)):
self.history_steps.append(
HistoryStep(
history_obs[i - 1],
history_obs[i],
actions[i - 1],
memories[i - 1],
flags,
)
)
def shrink(self):
"""Shrink individual steps"""
# TODO set the shrink speed of older steps to be higher
super().shrink()
for step in self.history_steps:
step.shrink()
@property
def _prompt(self):
prompts = ['# History of interaction with the task:\n']
for i, step in enumerate(self.history_steps):
prompts.append(f'## step {i}')
prompts.append(step.prompt)
return '\n'.join(prompts) + '\n'
if __name__ == '__main__':
html_template = """
<html>
<body>
<div>
Hello World.
Step {}.
</div>
</body>
</html>
"""
OBS_HISTORY = [
{
'goal': 'do this and that',
'pruned_html': html_template.format(1),
'axtree_txt': '[1] Click me',
'last_action_error': '',
},
{
'goal': 'do this and that',
'pruned_html': html_template.format(2),
'axtree_txt': '[1] Click me',
'last_action_error': '',
},
{
'goal': 'do this and that',
'pruned_html': html_template.format(3),
'axtree_txt': '[1] Click me',
'last_action_error': 'Hey, there is an error now',
},
]
ACTIONS = ["click('41')", "click('42')"]
MEMORIES = ['memory A', 'memory B']
THOUGHTS = ['thought A', 'thought B']
flags = Flags(
use_html=True,
use_ax_tree=True,
use_thinking=True,
use_error_logs=True,
use_past_error_logs=True,
use_history=True,
use_action_history=True,
use_memory=True,
use_diff=True,
html_type='pruned_html',
use_concrete_example=True,
use_abstract_example=True,
use_screenshot=False,
multi_actions=True,
)
print(
MainPrompt(
obs_history=OBS_HISTORY,
actions=ACTIONS,
memories=MEMORIES,
thoughts=THOUGHTS,
flags=flags,
).prompt
)
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import collections
import re
from warnings import warn
import yaml
def yaml_parser(message):
"""Parse a yaml message for the retry function."""
# saves gpt-3.5 from some yaml parsing errors
message = re.sub(r':\s*\n(?=\S|\n)', ': ', message)
try:
value = yaml.safe_load(message)
valid = True
retry_message = ''
except yaml.YAMLError as e:
warn(str(e))
value = {}
valid = False
retry_message = "Your response is not a valid yaml. Please try again and be careful to the format. Don't add any apology or comment, just the answer."
return value, valid, retry_message
def _compress_chunks(text, identifier, skip_list, split_regex='\n\n+'):
"""Compress a string by replacing redundant chunks by identifiers. Chunks are defined by the split_regex."""
text_list = re.split(split_regex, text)
text_list = [chunk.strip() for chunk in text_list]
counter = collections.Counter(text_list)
def_dict = {}
id = 0
# Store items that occur more than once in a dictionary
for item, count in counter.items():
if count > 1 and item not in skip_list and len(item) > 10:
def_dict[f'{identifier}-{id}'] = item
id += 1
# Replace redundant items with their identifiers in the text
compressed_text = '\n'.join(text_list)
for key, value in def_dict.items():
compressed_text = compressed_text.replace(value, key)
return def_dict, compressed_text
def compress_string(text):
"""Compress a string by replacing redundant paragraphs and lines with identifiers."""
# Perform paragraph-level compression
def_dict, compressed_text = _compress_chunks(
text, identifier='§', skip_list=[], split_regex='\n\n+'
)
# Perform line-level compression, skipping any paragraph identifiers
line_dict, compressed_text = _compress_chunks(
compressed_text, '', list(def_dict.keys()), split_regex='\n+'
)
def_dict.update(line_dict)
# Create a definitions section
def_lines = ['<definitions>']
for key, value in def_dict.items():
def_lines.append(f'{key}:\n{value}')
def_lines.append('</definitions>')
definitions = '\n'.join(def_lines)
return definitions + '\n' + compressed_text
def extract_html_tags(text, keys):
"""Extract the content within HTML tags for a list of keys.
Parameters
----------
text : str
The input string containing the HTML tags.
keys : list of str
The HTML tags to extract the content from.
Returns
-------
dict
A dictionary mapping each key to a list of subset in `text` that match the key.
Notes
-----
All text and keys will be converted to lowercase before matching.
"""
content_dict = {}
# text = text.lower()
# keys = set([k.lower() for k in keys])
for key in keys:
pattern = f'<{key}>(.*?)</{key}>'
matches = re.findall(pattern, text, re.DOTALL)
if matches:
content_dict[key] = [match.strip() for match in matches]
return content_dict
class ParseError(Exception):
pass
def parse_html_tags_raise(text, keys=(), optional_keys=(), merge_multiple=False):
"""A version of parse_html_tags that raises an exception if the parsing is not successful."""
content_dict, valid, retry_message = parse_html_tags(
text, keys, optional_keys, merge_multiple=merge_multiple
)
if not valid:
raise ParseError(retry_message)
return content_dict
def parse_html_tags(text, keys=(), optional_keys=(), merge_multiple=False):
"""Satisfy the parse api, extracts 1 match per key and validates that all keys are present
Parameters
----------
text : str
The input string containing the HTML tags.
keys : list of str
The HTML tags to extract the content from.
optional_keys : list of str
The HTML tags to extract the content from, but are optional.
Returns
-------
dict
A dictionary mapping each key to subset of `text` that match the key.
bool
Whether the parsing was successful.
str
A message to be displayed to the agent if the parsing was not successful.
"""
all_keys = tuple(keys) + tuple(optional_keys)
content_dict = extract_html_tags(text, all_keys)
retry_messages = []
for key in all_keys:
if key not in content_dict:
if key not in optional_keys:
retry_messages.append(f'Missing the key <{key}> in the answer.')
else:
val = content_dict[key]
content_dict[key] = val[0]
if len(val) > 1:
if not merge_multiple:
retry_messages.append(
f'Found multiple instances of the key {key}. You should have only one of them.'
)
else:
# merge the multiple instances
content_dict[key] = '\n'.join(val)
valid = len(retry_messages) == 0
retry_message = '\n'.join(retry_messages)
return content_dict, valid, retry_message
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# CodeAct Agent Framework
This folder implements the CodeAct idea ([paper](https://arxiv.org/abs/2402.01030), [tweet](https://twitter.com/xingyaow_/status/1754556835703751087)) that consolidates LLM agents **act**ions into a unified **code** action space for both *simplicity* and *performance* (see paper for more details).
The conceptual idea is illustrated below. At each turn, the agent can:
1. **Converse**: Communicate with humans in natural language to ask for clarification, confirmation, etc.
2. **CodeAct**: Choose to perform the task by executing code
- Execute any valid Linux `bash` command
- Execute any valid `Python` code with [an interactive Python interpreter](https://ipython.org/). This is simulated through `bash` command, see plugin system below for more details.
![image](https://github.com/OpenDevin/OpenDevin/assets/38853559/92b622e3-72ad-4a61-8f41-8c040b6d5fb3)
## Plugin System
To make the CodeAct agent more powerful with only access to `bash` action space, CodeAct agent leverages OpenDevin's plugin system:
- [Jupyter plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/jupyter): for IPython execution via bash command
- [SWE-agent tool plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/swe_agent_commands): Powerful bash command line tools for software development tasks introduced by [swe-agent](https://github.com/princeton-nlp/swe-agent).
## Demo
https://github.com/OpenDevin/OpenDevin/assets/38853559/f592a192-e86c-4f48-ad31-d69282d5f6ac
*Example of CodeActAgent with `gpt-4-turbo-2024-04-09` performing a data science task (linear regression)*
## Work-in-progress & Next step
[] Support web-browsing
[] Complete the workflow for CodeAct agent to submit Github PRs
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from opendevin.controller.agent import Agent
from .codeact_agent import CodeActAgent
Agent.register('CodeActAgent', CodeActAgent)
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import re
from agenthub.codeact_agent.prompt import (
COMMAND_DOCS,
EXAMPLES,
GITHUB_MESSAGE,
SYSTEM_PREFIX,
SYSTEM_SUFFIX,
)
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import (
Action,
AgentFinishAction,
BrowseInteractiveAction,
CmdRunAction,
IPythonRunCellAction,
MessageAction,
)
from opendevin.events.observation import (
BrowserOutputObservation,
CmdOutputObservation,
IPythonRunCellObservation,
)
from opendevin.llm.llm import LLM
from opendevin.runtime.plugins import (
AgentSkillsRequirement,
JupyterRequirement,
PluginRequirement,
)
ENABLE_GITHUB = True
def parse_response(response) -> str:
action = response.choices[0].message.content
for lang in ['bash', 'ipython', 'browse']:
if f'<execute_{lang}>' in action and f'</execute_{lang}>' not in action:
action += f'</execute_{lang}>'
return action
def action_to_str(action: Action) -> str:
if isinstance(action, CmdRunAction):
return f'{action.thought}\n<execute_bash>\n{action.command}\n</execute_bash>'
elif isinstance(action, IPythonRunCellAction):
return f'{action.thought}\n<execute_ipython>\n{action.code}\n</execute_ipython>'
elif isinstance(action, BrowseInteractiveAction):
return f'{action.thought}\n<execute_browse>\n{action.browser_actions}\n</execute_browse>'
elif isinstance(action, MessageAction):
return action.content
return ''
def get_action_message(action: Action) -> dict[str, str] | None:
if (
isinstance(action, BrowseInteractiveAction)
or isinstance(action, CmdRunAction)
or isinstance(action, IPythonRunCellAction)
or isinstance(action, MessageAction)
):
return {
'role': 'user' if action.source == 'user' else 'assistant',
'content': action_to_str(action),
}
return None
def get_observation_message(obs) -> dict[str, str] | None:
if isinstance(obs, CmdOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
content += (
f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]'
)
return {'role': 'user', 'content': content}
elif isinstance(obs, IPythonRunCellObservation):
content = 'OBSERVATION:\n' + obs.content
# replace base64 images with a placeholder
splitted = content.split('\n')
for i, line in enumerate(splitted):
if '![image](data:image/png;base64,' in line:
splitted[i] = (
'![image](data:image/png;base64, ...) already displayed to user'
)
content = '\n'.join(splitted)
content = truncate_observation(content)
return {'role': 'user', 'content': content}
elif isinstance(obs, BrowserOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
return {'role': 'user', 'content': content}
return None
def truncate_observation(observation: str, max_chars: int = 10_000) -> str:
"""
Truncate the middle of the observation if it is too long.
"""
if len(observation) <= max_chars:
return observation
half = max_chars // 2
return (
observation[:half]
+ '\n[... Observation truncated due to length ...]\n'
+ observation[-half:]
)
# FIXME: We can tweak these two settings to create MicroAgents specialized toward different area
def get_system_message() -> str:
if ENABLE_GITHUB:
return f'{SYSTEM_PREFIX}\n{GITHUB_MESSAGE}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
else:
return f'{SYSTEM_PREFIX}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
def get_in_context_example() -> str:
return EXAMPLES
class CodeActAgent(Agent):
VERSION = '1.5'
"""
The Code Act Agent is a minimalist agent.
The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
### Overview
This agent implements the CodeAct idea ([paper](https://arxiv.org/abs/2402.13463), [tweet](https://twitter.com/xingyaow_/status/1754556835703751087)) that consolidates LLM agents **act**ions into a unified **code** action space for both *simplicity* and *performance* (see paper for more details).
The conceptual idea is illustrated below. At each turn, the agent can:
1. **Converse**: Communicate with humans in natural language to ask for clarification, confirmation, etc.
2. **CodeAct**: Choose to perform the task by executing code
- Execute any valid Linux `bash` command
- Execute any valid `Python` code with [an interactive Python interpreter](https://ipython.org/). This is simulated through `bash` command, see plugin system below for more details.
![image](https://github.com/OpenDevin/OpenDevin/assets/38853559/92b622e3-72ad-4a61-8f41-8c040b6d5fb3)
### Plugin System
To make the CodeAct agent more powerful with only access to `bash` action space, CodeAct agent leverages OpenDevin's plugin system:
- [Jupyter plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/jupyter): for IPython execution via bash command
- [SWE-agent tool plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/swe_agent_commands): Powerful bash command line tools for software development tasks introduced by [swe-agent](https://github.com/princeton-nlp/swe-agent).
### Demo
https://github.com/OpenDevin/OpenDevin/assets/38853559/f592a192-e86c-4f48-ad31-d69282d5f6ac
*Example of CodeActAgent with `gpt-4-turbo-2024-04-09` performing a data science task (linear regression)*
### Work-in-progress & Next step
[] Support web-browsing
[] Complete the workflow for CodeAct agent to submit Github PRs
"""
sandbox_plugins: list[PluginRequirement] = [
# NOTE: AgentSkillsRequirement need to go before JupyterRequirement, since
# AgentSkillsRequirement provides a lot of Python functions
# and it need to be initialized before Jupyter for Jupyter to use those functions.
AgentSkillsRequirement(),
JupyterRequirement(),
]
jupyter_kernel_init_code: str = 'from agentskills import *'
system_message: str = get_system_message()
in_context_example: str = f"Here is an example of how you can interact with the environment for task solving:\n{get_in_context_example()}\n\nNOW, LET'S START!"
def __init__(
self,
llm: LLM,
) -> None:
"""
Initializes a new instance of the CodeActAgent class.
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
self.reset()
def reset(self) -> None:
"""
Resets the CodeAct Agent.
"""
super().reset()
def step(self, state: State) -> Action:
"""
Performs one step using the CodeAct Agent.
This includes gathering info on previous steps and prompting the model to make a command to execute.
Parameters:
- state (State): used to get updated info and background commands
Returns:
- CmdRunAction(command) - bash command to run
- IPythonRunCellAction(code) - IPython code to run
- BrowseInteractiveAction(browsergym_command) - BrowserGym commands to run
- MessageAction(content) - Message action to run (e.g. ask for clarification)
- AgentFinishAction() - end the interaction
"""
messages: list[dict[str, str]] = [
{'role': 'system', 'content': self.system_message},
{'role': 'user', 'content': self.in_context_example},
]
for prev_action, obs in state.history:
action_message = get_action_message(prev_action)
if action_message:
messages.append(action_message)
obs_message = get_observation_message(obs)
if obs_message:
messages.append(obs_message)
latest_user_message = [m for m in messages if m['role'] == 'user'][-1]
if latest_user_message:
if latest_user_message['content'].strip() == '/exit':
return AgentFinishAction()
latest_user_message['content'] += (
f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task.'
)
response = self.llm.do_completion(
messages=messages,
stop=[
'</execute_ipython>',
'</execute_bash>',
'</execute_browse>',
],
temperature=0.0,
)
action_str: str = parse_response(response)
state.num_of_chars += sum(
len(message['content']) for message in messages
) + len(action_str)
if finish_command := re.search(r'<finish>.*</finish>', action_str, re.DOTALL):
thought = action_str.replace(finish_command.group(0), '').strip()
return AgentFinishAction(thought=thought)
if bash_command := re.search(
r'<execute_bash>(.*?)</execute_bash>', action_str, re.DOTALL
):
# remove the command from the action string to get thought
thought = action_str.replace(bash_command.group(0), '').strip()
# a command was found
command_group = bash_command.group(1).strip()
if command_group.strip() == 'exit':
return AgentFinishAction()
return CmdRunAction(command=command_group, thought=thought)
elif python_code := re.search(
r'<execute_ipython>(.*?)</execute_ipython>', action_str, re.DOTALL
):
# a code block was found
code_group = python_code.group(1).strip()
thought = action_str.replace(python_code.group(0), '').strip()
return IPythonRunCellAction(
code=code_group,
thought=thought,
kernel_init_code=self.jupyter_kernel_init_code,
)
elif browse_command := re.search(
r'<execute_browse>(.*)</execute_browse>', action_str, re.DOTALL
):
# BrowserGym actions was found
browse_actions = browse_command.group(1).strip()
thought = action_str.replace(browse_command.group(0), '').strip()
return BrowseInteractiveAction(
browser_actions=browse_actions, thought=thought
)
else:
# We assume the LLM is GOOD enough that when it returns pure natural language
# it want to talk to the user
return MessageAction(content=action_str, wait_for_response=True)
def search_memory(self, query: str) -> list[str]:
raise NotImplementedError('Implement this abstract method')
-249
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@@ -1,249 +0,0 @@
from opendevin.runtime.plugins import AgentSkillsRequirement
_AGENT_SKILLS_DOCS = AgentSkillsRequirement.documentation
COMMAND_DOCS = (
'\nApart from the standard Python library, the assistant can also use the following functions (already imported) in <execute_ipython> environment:\n'
f'{_AGENT_SKILLS_DOCS}'
"Please note that THE `edit_file` FUNCTION REQUIRES PROPER INDENTATION. If the assistant would like to add the line ' print(x)', it must fully write that out, with all those spaces before the code! Indentation is important and code that is not indented correctly will fail and require fixing before it can be run."
)
# ======= SYSTEM MESSAGE =======
MINIMAL_SYSTEM_PREFIX = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
The assistant can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using "<execute_ipython>" tag, for example:
<execute_ipython>
print("Hello World!")
</execute_ipython>
The assistant can execute bash commands on behalf of the user by wrapping them with <execute_bash> and </execute_bash>.
For example, you can list the files in the current directory by <execute_bash> ls </execute_bash>.
"""
BROWSING_PREFIX = """The assistant can browse the Internet with commands on behalf of the user by wrapping them with <execute_browse> and </execute_browse>.
For example, you can browse a given URL by <execute_browse> goto("<URL>") </execute_browse>.
The assistant should attempt fewer things at a time instead of putting too much commands OR code in one "execute" block.
"""
PIP_INSTALL_PREFIX = """The assistant can install Python packages using the %pip magic command in an IPython environment by using the following syntax: <execute_ipython> %pip install [package needed] </execute_ipython> and should always import packages and define variables before starting to use them."""
SYSTEM_PREFIX = MINIMAL_SYSTEM_PREFIX + BROWSING_PREFIX + PIP_INSTALL_PREFIX
GITHUB_MESSAGE = """To do any activities on GitHub, the assistant should use the token in the $GITHUB_TOKEN environment variable.
For instance, to push a local branch `my_branch` to the github repo `owner/repo`, the assistant can use the following four commands:
<execute_bash> git push https://$GITHUB_TOKEN@github.com/owner/repo.git my_branch </execute_bash>
If the assistant require access to GitHub but $GITHUB_TOKEN is not set, ask the user to set it."""
SYSTEM_SUFFIX = """The assistant's response should be concise.
The assistant should include ONLY ONE <execute_ipython> or <execute_bash> or <execute_browse> in every one of the responses, unless the assistant is finished with the task or need more input or action from the user in order to proceed.
IMPORTANT: Whenever possible, execute the code for the user using <execute_ipython> or <execute_bash> or <execute_browse> instead of providing it.
"""
# ======= EXAMPLE MESSAGE =======
EXAMPLES = """
--- START OF EXAMPLE ---
USER: Can you create a list of numbers from 1 to 10, and create a web page to display them at port 5000?
ASSISTANT:
Sure! Let me create a file first:
<execute_ipython>
create_file('app.py')
</execute_ipython>
USER:
OBSERVATION:
[File: /workspace/app.py (1 lines total)]
1|
[File app.py created.]
ASSISTANT:
Now I will write the Python code for starting a web server and save it to the file `app.py`:
<execute_ipython>
EDITED_CODE=\"\"\"from flask import Flask
app = Flask(__name__)
@app.route('/')
def index():
numbers = list(range(1, 11))
return str(numbers)
if __name__ == '__main__':
app.run(port=5000)\"\"\"
edit_file(start=1, end=1, content=EDITED_CODE)
</execute_ipython>
USER:
OBSERVATION:
1|from flask import Flask
2|app = Flask(__name__)
3|
4|@app.route('/')
5|def index():
6| numbers = list(range(1, 11))
7| return str(numbers)
8|
9|if __name__ == '__main__':
10| app.run(port=5000)
[File updated. Please review the changes and make sure they are correct (correct indentation, no duplicate lines, etc). Edit the file again if necessary.]
ASSISTANT:
I have created a Python file `app.py` that will display a list of numbers from 1 to 10 when you run it. Let me run the Python file for you:
<execute_bash>
python3 app.py > server.log 2>&1 &
</execute_bash>
USER:
OBSERVATION:
[1] 121[1]+ Exit 1 python3 app.py > server.log 2>&1
ASSISTANT: Looks like the server is running with PID 121 then crashed. Let me check the server log:
<execute_bash>
cat server.log
</execute_bash>
USER:
OBSERVATION:
Traceback (most recent call last):
File "/workspace/app.py", line 2, in <module>
from flask import Flask
ModuleNotFoundError: No module named 'flask'
ASSISTANT:
It seems that Flask is not installed. Let me install Flask for you:
<execute_bash>
pip install flask
</execute_bash>
USER:
OBSERVATION:
Defaulting to user installation because normal site-packages is not writeable
Collecting flask
Using cached flask-3.0.3-py3-none-any.whl (101 kB)
Collecting blinker>=1.6.2
Using cached blinker-1.7.0-py3-none-any.whl (13 kB)
Collecting Werkzeug>=3.0.0
Using cached werkzeug-3.0.2-py3-none-any.whl (226 kB)
Collecting click>=8.1.3
Using cached click-8.1.7-py3-none-any.whl (97 kB)
Collecting itsdangerous>=2.1.2
Using cached itsdangerous-2.2.0-py3-none-any.whl (16 kB)
Requirement already satisfied: Jinja2>=3.1.2 in /home/opendevin/.local/lib/python3.10/site-packages (from flask) (3.1.3)
Requirement already satisfied: MarkupSafe>=2.0 in /home/opendevin/.local/lib/python3.10/site-packages (from Jinja2>=3.1.2->flask) (2.1.5)
Installing collected packages: Werkzeug, itsdangerous, click, blinker, flask
Successfully installed Werkzeug-3.0.2 blinker-1.7.0 click-8.1.7 flask-3.0.3 itsdangerous-2.2.0
ASSISTANT:
Now that Flask is installed, let me run the Python file again:
<execute_bash>
python3 app.py > server.log 2>&1 &
</execute_bash>
USER:
OBSERVATION:
[1] 124
ASSISTANT:
Let me check the server log again:
<execute_bash>
cat server.log
</execute_bash>
USER:
OBSERVATION:
* Serving Flask app 'app'
* Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on http://127.0.0.1:5000
Press CTRL+C to quit
ASSISTANT:
The server is running on port 5000 with PID 124. You can access the list of numbers by visiting http://127.0.0.1:5000. If you have any further questions, feel free to ask!
USER: Now browse the newly started server's homepage and show me the content.
ASSISTANT:
Sure! Let me browse the server's homepage at http://127.0.0.1:5000:
<execute_browse>
goto("http://127.0.0.1:5000")
</execute_browse>
USER:
OBSERVATION:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ASSISTANT:
The content of the server's homepage is "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]". If you have any further questions, feel free to ask!
USER: Now kill the server, make it display the numbers in a table format.
ASSISTANT:
Sure! Let me stop the server first:
<execute_bash>
kill 124
</execute_bash>
USER:
OBSERVATION:
[1]+ Terminated python3 app.py > server.log 2>&1
ASSISTANT:
The server has been stopped. Let me open the Python file and modify it.
<execute_ipython>
open_file('app.py')
</execute_ipython>
USER:
[File: /workspace/app.py (10 lines total)]
1|from flask import Flask
2|app = Flask(__name__)
3|
4|@app.route('/')
5|def index():
6| numbers = list(range(1, 11))
7| return str(numbers)
8|
9|if __name__ == '__main__':
10| app.run(port=5000)
ASSISTANT:
I should edit the file to display the numbers in a table format. I should include correct indentation. Let me update the file:
<execute_ipython>
edit_file(start=7, end=7, content=" return '<table>' + ''.join([f'<tr><td>{i}</td></tr>' for i in numbers]) + '</table>'")
</execute_ipython>
USER:
Observation:
[File: /workspace/app.py (10 lines total after edit)]
1|from flask import Flask
2|app = Flask(__name__)
3|
4|@app.route('/')
5|def index():
6| numbers = list(range(1, 11))
7| return '<table>' + ''.join([f'<tr><td>{i}</td></tr>' for i in numbers]) + '</table>'
8|
9|if __name__ == '__main__':
10| app.run(port=5000)
[File updated. Please review the changes and make sure they are correct (correct indentation, no duplicate lines, etc). Edit the file again if necessary.]
ASSISTANT:
The file has been updated. Let me run the Python file again with the new changes:
<execute_bash>
python3 app.py > server.log 2>&1 &
</execute_bash>
USER:
Observation:
[1] 126
ASSISTANT:
The server is running on port 5000 with PID 126. You can access the list of numbers in a table format by visiting http://127.0.0.1:5000. Free free to let me know if you have any further requests!
--- END OF EXAMPLE ---
"""
INVALID_INPUT_MESSAGE = (
"I don't understand your input. \n"
'If you want to execute a bash command, please use <execute_bash> YOUR_COMMAND_HERE </execute_bash>.\n'
'If you want to execute a block of Python code, please use <execute_ipython> YOUR_COMMAND_HERE </execute_ipython>.\n'
'If you want to browse the Internet, please use <execute_browse> YOUR_COMMAND_HERE </execute_browse>.\n'
)
-7
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@@ -1,7 +0,0 @@
# CodeAct (SWE Edit Specialized)
This agent is an adaptation of the original [SWE Agent](https://swe-agent.com/) based on CodeAct using the `agentskills` library of OpenDevin.
It is intended use is **solving Github issues**.
It removes web-browsing and Github capability from the original CodeAct agent to avoid confusion to the agent.
-5
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@@ -1,5 +0,0 @@
from opendevin.controller.agent import Agent
from .codeact_swe_agent import CodeActSWEAgent
Agent.register('CodeActSWEAgent', CodeActSWEAgent)
@@ -1,246 +0,0 @@
import re
from agenthub.codeact_swe_agent.prompt import (
COMMAND_DOCS,
MINIMAL_SYSTEM_PREFIX,
SWE_EXAMPLE,
SYSTEM_SUFFIX,
)
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import (
Action,
AgentFinishAction,
BrowseInteractiveAction,
CmdRunAction,
IPythonRunCellAction,
MessageAction,
)
from opendevin.events.observation import (
BrowserOutputObservation,
CmdOutputObservation,
IPythonRunCellObservation,
)
from opendevin.llm.llm import LLM
from opendevin.runtime.plugins import (
AgentSkillsRequirement,
JupyterRequirement,
PluginRequirement,
)
def parse_response(response) -> str:
action = response.choices[0].message.content
for lang in ['bash', 'ipython', 'browse']:
if f'<execute_{lang}>' in action and f'</execute_{lang}>' not in action:
action += f'</execute_{lang}>'
return action
def action_to_str(action: Action) -> str:
if isinstance(action, CmdRunAction):
return f'{action.thought}\n<execute_bash>\n{action.command}\n</execute_bash>'
elif isinstance(action, IPythonRunCellAction):
return f'{action.thought}\n<execute_ipython>\n{action.code}\n</execute_ipython>'
elif isinstance(action, BrowseInteractiveAction):
return f'{action.thought}\n<execute_browse>\n{action.browser_actions}\n</execute_browse>'
elif isinstance(action, MessageAction):
return action.content
return ''
def get_action_message(action: Action) -> dict[str, str] | None:
if (
isinstance(action, BrowseInteractiveAction)
or isinstance(action, CmdRunAction)
or isinstance(action, IPythonRunCellAction)
or isinstance(action, MessageAction)
):
return {
'role': 'user' if action.source == 'user' else 'assistant',
'content': action_to_str(action),
}
return None
def get_observation_message(obs) -> dict[str, str] | None:
if isinstance(obs, CmdOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
content += (
f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]'
)
return {'role': 'user', 'content': content}
elif isinstance(obs, IPythonRunCellObservation):
content = 'OBSERVATION:\n' + obs.content
# replace base64 images with a placeholder
splitted = content.split('\n')
for i, line in enumerate(splitted):
if '![image](data:image/png;base64,' in line:
splitted[i] = (
'![image](data:image/png;base64, ...) already displayed to user'
)
content = '\n'.join(splitted)
content = truncate_observation(content)
return {'role': 'user', 'content': content}
elif isinstance(obs, BrowserOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
return {'role': 'user', 'content': content}
return None
def truncate_observation(observation: str, max_chars: int = 10_000) -> str:
"""
Truncate the middle of the observation if it is too long.
"""
if len(observation) <= max_chars:
return observation
half = max_chars // 2
return (
observation[:half]
+ '\n[... Observation truncated due to length ...]\n'
+ observation[-half:]
)
def get_system_message() -> str:
return f'{MINIMAL_SYSTEM_PREFIX}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
def get_in_context_example() -> str:
return SWE_EXAMPLE
class CodeActSWEAgent(Agent):
VERSION = '1.5'
"""
This agent is an adaptation of the original [SWE Agent](https://swe-agent.com/) based on CodeAct 1.5 using the `agentskills` library of OpenDevin.
It is intended use is **solving Github issues**.
It removes web-browsing and Github capability from the original CodeAct agent to avoid confusion to the agent.
"""
sandbox_plugins: list[PluginRequirement] = [
# NOTE: AgentSkillsRequirement need to go before JupyterRequirement, since
# AgentSkillsRequirement provides a lot of Python functions
# and it need to be initialized before Jupyter for Jupyter to use those functions.
AgentSkillsRequirement(),
JupyterRequirement(),
]
jupyter_kernel_init_code: str = 'from agentskills import *'
system_message: str = get_system_message()
in_context_example: str = f"Here is an example of how you can interact with the environment for task solving:\n{get_in_context_example()}\n\nNOW, LET'S START!"
def __init__(
self,
llm: LLM,
) -> None:
"""
Initializes a new instance of the CodeActAgent class.
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
self.reset()
def reset(self) -> None:
"""
Resets the CodeAct Agent.
"""
super().reset()
def step(self, state: State) -> Action:
"""
Performs one step using the CodeAct Agent.
This includes gathering info on previous steps and prompting the model to make a command to execute.
Parameters:
- state (State): used to get updated info and background commands
Returns:
- CmdRunAction(command) - bash command to run
- IPythonRunCellAction(code) - IPython code to run
- BrowseInteractiveAction(browsergym_command) - BrowserGym commands to run
- MessageAction(content) - Message action to run (e.g. ask for clarification)
- AgentFinishAction() - end the interaction
"""
messages: list[dict[str, str]] = [
{'role': 'system', 'content': self.system_message},
{'role': 'user', 'content': self.in_context_example},
]
for prev_action, obs in state.history:
action_message = get_action_message(prev_action)
if action_message:
messages.append(action_message)
obs_message = get_observation_message(obs)
if obs_message:
messages.append(obs_message)
latest_user_message = [m for m in messages if m['role'] == 'user'][-1]
if latest_user_message:
if latest_user_message['content'].strip() == '/exit':
return AgentFinishAction()
latest_user_message['content'] += (
f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task.'
)
response = self.llm.do_completion(
messages=messages,
stop=[
'</execute_ipython>',
'</execute_bash>',
'</execute_browse>',
],
temperature=0.0,
)
action_str: str = parse_response(response)
state.num_of_chars += sum(
len(message['content']) for message in messages
) + len(action_str)
if finish_command := re.search(r'<finish>.*</finish>', action_str, re.DOTALL):
thought = action_str.replace(finish_command.group(0), '').strip()
return AgentFinishAction(thought=thought)
if bash_command := re.search(
r'<execute_bash>(.*?)</execute_bash>', action_str, re.DOTALL
):
# remove the command from the action string to get thought
thought = action_str.replace(bash_command.group(0), '').strip()
# a command was found
command_group = bash_command.group(1).strip()
if command_group.strip() == 'exit':
return AgentFinishAction()
return CmdRunAction(command=command_group, thought=thought)
elif python_code := re.search(
r'<execute_ipython>(.*?)</execute_ipython>', action_str, re.DOTALL
):
# a code block was found
code_group = python_code.group(1).strip()
thought = action_str.replace(python_code.group(0), '').strip()
return IPythonRunCellAction(
code=code_group,
thought=thought,
kernel_init_code=self.jupyter_kernel_init_code,
)
elif browse_command := re.search(
r'<execute_browse>(.*)</execute_browse>', action_str, re.DOTALL
):
# BrowserGym actions was found
browse_actions = browse_command.group(1).strip()
thought = action_str.replace(browse_command.group(0), '').strip()
return BrowseInteractiveAction(
browser_actions=browse_actions, thought=thought
)
else:
# We assume the LLM is GOOD enough that when it returns pure natural language
# it want to talk to the user
return MessageAction(content=action_str, wait_for_response=True)
def search_memory(self, query: str) -> list[str]:
raise NotImplementedError('Implement this abstract method')
-451
View File
@@ -1,451 +0,0 @@
from opendevin.runtime.plugins import AgentSkillsRequirement
_AGENT_SKILLS_DOCS = AgentSkillsRequirement.documentation
COMMAND_DOCS = (
'\nApart from the standard Python library, the assistant can also use the following functions (already imported) in <execute_ipython> environment:\n'
f'{_AGENT_SKILLS_DOCS}'
"Please note that THE `edit_file` FUNCTION REQUIRES PROPER INDENTATION. If the assistant would like to add the line ' print(x)', it must fully write that out, with all those spaces before the code! Indentation is important and code that is not indented correctly will fail and require fixing before it can be run."
)
# ======= SYSTEM MESSAGE =======
MINIMAL_SYSTEM_PREFIX = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
The assistant can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using "<execute_ipython>" tag, for example:
<execute_ipython>
print("Hello World!")
</execute_ipython>
The assistant can execute bash commands on behalf of the user by wrapping them with <execute_bash> and </execute_bash>.
For example, you can list the files in the current directory by <execute_bash> ls </execute_bash>.
"""
SYSTEM_SUFFIX = """The assistant's response should be concise.
The assistant should include ONLY ONE <execute_ipython> or <execute_bash> or <execute_browse> in every one of the responses, unless the assistant is finished with the task or need more input or action from the user in order to proceed.
IMPORTANT: Whenever possible, execute the code for the user using <execute_ipython> or <execute_bash> or <execute_browse> instead of providing it.
"""
SWE_EXAMPLE = """
--- START OF EXAMPLE ---
USER: We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
TimeDelta serialization precision
Hi there!
I just found quite strange behaviour of `TimeDelta` field serialization
```python3
from marshmallow.fields import TimeDelta
from datetime import timedelta
td_field = TimeDelta(precision="milliseconds")
obj = dict()
obj["td_field"] = timedelta(milliseconds=345)
print(td_field.serialize("td_field", obj))
```
Output of this snippet is `344`, but it seems that `345` is correct.
Looks like a rounding issue here: https://github.com/marshmallow-code/marshmallow/blob/dev/src/marshmallow/fields.py#L1474
--- END ISSUE ---
[Current directory: /workspace/marshmallow-code__marshmallow]
ASSISTANT:
Let's first start by reproducing the results of the issue. The issue includes some example code for reproduction, which we can use. We'll create a new file called `reproduce.py` and paste the example code into it.
<execute_ipython>
create_file('/workspace/marshmallow-code__marshmallow/reproduce.py')
</execute_ipython>
USER:
OBSERVATION:
[File: /workspace/marshmallow-code__marshmallow/reproduce.py (1 lines total)]
1|
[File /workspace/marshmallow-code__marshmallow/reproduce.py created.]
ASSISTANT:
Now let's paste in the example code from the issue.
<execute_ipython>
EDITED_CODE = \"\"\"from marshmallow.fields import TimeDelta
from datetime import timedelta
td_field = TimeDelta(precision="milliseconds")
obj = dict()
obj["td_field"] = timedelta(milliseconds=345)
print(td_field.serialize("td_field", obj))
\"\"\"
edit_file(start=1, end=1, content=EDITED_CODE)
</execute_ipython>
USER:
OBSERVATION:
[File: /workspace/marshmallow-code__marshmallow/reproduce.py (9 lines total)]
1|from marshmallow.fields import TimeDelta
2|from datetime import timedelta
3|
4|td_field = TimeDelta(precision="milliseconds")
5|
6|obj = dict()
7|obj["td_field"] = timedelta(milliseconds=345)
8|
9|print(td_field.serialize("td_field", obj))
[File updated. Please review the changes and make sure they are correct (correct indentation, no duplicate lines, etc). Edit the file again if necessary.]
ASSISTANT:
Now let's run the code to see if we see the same output as the issue.
<execute_bash>
python reproduce.py
</execute_bash>
USER:
OBSERVATION:
344
ASSISTANT:
We are indeed seeing the same output as the issue. The issue suggests that we should look at line 1474 of the `fields.py` file to see if there is a rounding issue. Let's open the `fields.py` file and navigate to line 1474 to see the relevant code. First we should find the `fields.py` file in the repository. It is likely to be in the `src/marshmallow` directory, but we should check to be sure.
<execute_bash>
ls -F
</execute_bash>
USER:
OBSERVATION:
AUTHORS.rst
CHANGELOG.rst
CODE_OF_CONDUCT.md
CONTRIBUTING.rst
LICENSE
MANIFEST.in
NOTICE
README.rst
RELEASING.md
azure-pipelines.yml
docs/
examples/
performance/
pyproject.toml
reproduce.py
setup.cfg
setup.py
src/
tests/
tox.ini
ASSISTANT:
It looks like the `src` directory is present, which suggests that the `fields.py` file is likely to be in the `src` directory. Let's use find_file to see where it is.
<execute_ipython>
find_file("fields.py", "/workspace/marshmallow-code__marshmallow/src")
</execute_ipython>
USER:
OBSERVATION:
[Found 1 matches for "fields.py" in /workspace/marshmallow-code__marshmallow/src]
/workspace/marshmallow-code__marshmallow/src/marshmallow/fields.py
ASSISTANT:
It looks like the `fields.py` file is present in the `./src/marshmallow/` directory. The issue also points to a specific URL with line number 1474. We should navigate to that line in fields.py to see the relevant code for the `TimeDelta` serialization.
<execute_ipython>
open_file("/workspace/marshmallow-code__marshmallow/src/marshmallow/fields.py", 1474)
</execute_ipython>
USER:
OBSERVATION:
[File: /workspace/marshmallow-code__marshmallow/src/marshmallow/fields.py (1997 lines total)]
(1458 more lines above)
1459| self.WEEKS,
1460| )
1461|
1462| if precision not in units:
1463| msg = 'The precision must be {} or "{}".'.format(
1464| ", ".join([f'"{each}"' for each in units[:-1]]), units[-1]
1465| )
1466| raise ValueError(msg)
1467|
1468| self.precision = precision
1469| super().__init__(**kwargs)
1470|
1471| def _serialize(self, value, attr, obj, **kwargs):
1472| if value is None:
1473| return None
1474| base_unit = dt.timedelta(**{self.precision: 1})
1475| return int(value.total_seconds() / base_unit.total_seconds())
1476|
1477| def _deserialize(self, value, attr, data, **kwargs):
1478| try:
1479| value = int(value)
1480| except (TypeError, ValueError) as error:
1481| raise self.make_error("invalid") from error
1482|
1483| kwargs = {self.precision: value}
1484|
1485| try:
1486| return dt.timedelta(**kwargs)
1487| except OverflowError as error:
1488| raise self.make_error("invalid") from error
1489|
1490|
1491|class Mapping(Field):
1492| \"\"\"An abstract class for objects with key-value pairs.
1493|
1494| :param keys: A field class or instance for dict keys.
1495| :param values: A field class or instance for dict values.
1496| :param kwargs: The same keyword arguments that :class:`Field` receives.
1497|
1498| .. note::
1499| When the structure of nested data is not known, you may omit the
1500| `keys` and `values` arguments to prevent content validation.
1501|
1502| .. versionadded:: 3.0.0rc4
1503| \"\"\"
1504|
1505| mapping_type = dict
1506|
1507| #: Default error messages.
1508| default_error_messages = {"invalid": "Not a valid mapping type."}
1509|
1510| def __init__(
1511| self,
1512| keys: typing.Optional[typing.Union[Field, type]] = None,
1513| values: typing.Optional[typing.Union[Field, type]] = None,
1514| **kwargs,
1515| ):
1516| super().__init__(**kwargs)
1517| if keys is None:
1518| self.key_field = None
1519| else:
1520| try:
1521| self.key_field = resolve_field_instance(keys)
1522| except FieldInstanceResolutionError as error:
1523| raise ValueError(
1524| '"keys" must be a subclass or instance of '
1525| "marshmallow.base.FieldABC."
1526| ) from error
1527|
1528| if values is None:
1529| self.value_field = None
1530| else:
1531| try:
1532| self.value_field = resolve_field_instance(values)
1533| except FieldInstanceResolutionError as error:
1534| raise ValueError(
1535| '"values" must be a subclass or instance of '
1536| "marshmallow.base.FieldABC."
1537| ) from error
1538| if isinstance(self.value_field, Nested):
1539| self.only = self.value_field.only
1540| self.exclude = self.value_field.exclude
1541|
1542| def _bind_to_schema(self, field_name, schema):
1543| super()._bind_to_schema(field_name, schema)
1544| if self.value_field:
1545| self.value_field = copy.deepcopy(self.value_field)
1546| self.value_field._bind_to_schema(field_name, self)
1547| if isinstance(self.value_field, Nested):
1548| self.value_field.only = self.only
1549| self.value_field.exclude = self.exclude
1550| if self.key_field:
1551| self.key_field = copy.deepcopy(self.key_field)
1552| self.key_field._bind_to_schema(field_name, self)
1553|
1554| def _serialize(self, value, attr, obj, **kwargs):
1555| if value is None:
1556| return None
1557| if not self.value_field and not self.key_field:
1558| return self.mapping_type(value)
(439 more lines below)
ASSISTANT:
We are now looking at the relevant section of the `fields.py` file where the `TimeDelta` serialization occurs. The issue suggests that there is a rounding problem near line 1474. The current implementation uses integer division, which truncates the decimal part and can lead to incorrect rounding on line 1475. To fix this issue, we should ensure that the division results in a float, and then round the result to the nearest integer before converting it to an int. This should preserve the milliseconds precision as expected. Let's make the necessary edit to the code.
<execute_ipython>
edit_file(start=1475, end=1475, content="return int(round(value.total_seconds() / base_unit.total_seconds())) # round to nearest int")
</execute_ipython>
USER:
OBSERVATION:
[Your proposed edit has introduced new syntax error(s). Please understand the errors and retry your edit command.]
ERRORS:
- E999 IndentationError: unexpected indent
[This is how your edit would have looked if applied]
-------------------------------------------------
[File: /marshmallow-code__marshmallow/src/marshmallow/fields.py (1997 lines total)]
(1468 more lines above)
1469| super().__init__(**kwargs)
1470|
1471| def _serialize(self, value, attr, obj, **kwargs):
1472| if value is None:
1473| return None
1474| base_unit = dt.timedelta(**{self.precision: 1})
1475|return int(round(value.total_seconds() / base_unit.total_seconds())) # round to nearest int
1476|
1477| def _deserialize(self, value, attr, data, **kwargs):
1478| try:
1479| value = int(value)
(518 more lines below)
-------------------------------------------------
[This is the original code before your edit]
-------------------------------------------------
[File: /marshmallow-code__marshmallow/src/marshmallow/fields.py (1997 lines total)]
(1469 more lines above)
1470|
1471| def _serialize(self, value, attr, obj, **kwargs):
1472| if value is None:
1473| return None
1474| base_unit = dt.timedelta(**{self.precision: 1})
1475| return int(value.total_seconds() / base_unit.total_seconds())
1476|
1477| def _deserialize(self, value, attr, data, **kwargs):
1478| try:
1479| value = int(value)
1480| except (TypeError, ValueError) as error:
(517 more lines below)
-------------------------------------------------
Your changes have NOT been applied. Please fix your edit command and try again.
You either need to 1) Specify the correct start/end line arguments or 2) Correct your edit code.
DO NOT re-run the same failed edit command. Running it again will lead to the same error.
ASSISTANT:
My edit command did not use the proper indentation, I will fix my syntax in this follow up edit command.
<execute_ipython>
EDITED_CODE=" return int(round(value.total_seconds() / base_unit.total_seconds())) # round to nearest int"
edit_file(start=1475, end=1475, content=EDITED_CODE)
</execute_ipython>
USER:
OBSERVATION:
[File: /marshmallow-code__marshmallow/src/marshmallow/fields.py (1997 lines total)]
(1424 more lines above)
1425|
1426| :param precision: Influences how the integer is interpreted during
1427| (de)serialization. Must be 'days', 'seconds', 'microseconds',
1428| 'milliseconds', 'minutes', 'hours' or 'weeks'.
1429| :param kwargs: The same keyword arguments that :class:`Field` receives.
1430|
1431| .. versionchanged:: 2.0.0
1432| Always serializes to an integer value to avoid rounding errors.
1433| Add `precision` parameter.
1434| \"\"\"
1435|
1436| DAYS = "days"
1437| SECONDS = "seconds"
1438| MICROSECONDS = "microseconds"
1439| MILLISECONDS = "milliseconds"
1440| MINUTES = "minutes"
1441| HOURS = "hours"
1442| WEEKS = "weeks"
1443|
1444| #: Default error messages.
1445| default_error_messages = {
1446| "invalid": "Not a valid period of time.",
1447| "format": "{input!r} cannot be formatted as a timedelta.",
1448| }
1449|
1450| def __init__(self, precision: str = SECONDS, **kwargs):
1451| precision = precision.lower()
1452| units = (
1453| self.DAYS,
1454| self.SECONDS,
1455| self.MICROSECONDS,
1456| self.MILLISECONDS,
1457| self.MINUTES,
1458| self.HOURS,
1459| self.WEEKS,
1460| )
1461|
1462| if precision not in units:
1463| msg = 'The precision must be {} or "{}".'.format(
1464| ", ".join([f'"{each}"' for each in units[:-1]]), units[-1]
1465| )
1466| raise ValueError(msg)
1467|
1468| self.precision = precision
1469| super().__init__(**kwargs)
1470|
1471| def _serialize(self, value, attr, obj, **kwargs):
1472| if value is None:
1473| return None
1474| base_unit = dt.timedelta(**{self.precision: 1})
1475| return int(round(value.total_seconds() / base_unit.total_seconds())) # round to nearest int
1476|
1477| def _deserialize(self, value, attr, data, **kwargs):
1478| try:
1479| value = int(value)
1480| except (TypeError, ValueError) as error:
1481| raise self.make_error("invalid") from error
1482|
1483| kwargs = {self.precision: value}
1484|
1485| try:
1486| return dt.timedelta(**kwargs)
1487| except OverflowError as error:
1488| raise self.make_error("invalid") from error
1489|
1490|
1491|class Mapping(Field):
1492| \"\"\"An abstract class for objects with key-value pairs.
1493|
1494| :param keys: A field class or instance for dict keys.
1495| :param values: A field class or instance for dict values.
1496| :param kwargs: The same keyword arguments that :class:`Field` receives.
1497|
1498| .. note::
1499| When the structure of nested data is not known, you may omit the
1500| `keys` and `values` arguments to prevent content validation.
1501|
1502| .. versionadded:: 3.0.0rc4
1503| \"\"\"
1504|
1505| mapping_type = dict
1506|
1507| #: Default error messages.
1508| default_error_messages = {"invalid": "Not a valid mapping type."}
1509|
1510| def __init__(
1511| self,
1512| keys: typing.Optional[typing.Union[Field, type]] = None,
1513| values: typing.Optional[typing.Union[Field, type]] = None,
1514| **kwargs,
1515| ):
1516| super().__init__(**kwargs)
1517| if keys is None:
1518| self.key_field = None
1519| else:
1520| try:
1521| self.key_field = resolve_field_instance(keys)
1522| except FieldInstanceResolutionError as error:
1523| raise ValueError(
1524| '"keys" must be a subclass or instance of '
(473 more lines below)
[File updated. Please review the changes and make sure they are correct (correct indentation, no duplicate lines, etc). Edit the file again if necessary.]
ASSISTANT:
The code has been updated to use the `round` function, which should fix the rounding issue. Before submitting the changes, it would be prudent to run the reproduce.py code again to ensure that this change has the desired effect. I see that "Current directory" is still `/marshmallow-code__marshmallow`, so we can just run `reproduce.py` directly.
<execute_bash>
python reproduce.py
</execute_bash>
USER:
OBSERVATION:
345
ASSISTANT:
The output has changed from 344 to 345, which suggests that the rounding issue has been fixed. Let's remove the reproduce.py file since it is no longer needed.
<execute_bash>
rm reproduce.py
</execute_bash>
USER:
OBSERVATION:
ASSISTANT:
rm doesn't have any output when it deletes successfully, so that must have worked. Now that we have fixed the issue, we can exit the current task.
<execute_bash>
exit
</execute_bash>
--- END OF EXAMPLE ---
"""
-5
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@@ -1,5 +0,0 @@
from opendevin.controller.agent import Agent
from .agent import DelegatorAgent
Agent.register('DelegatorAgent', DelegatorAgent)
-86
View File
@@ -1,86 +0,0 @@
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import Action, AgentDelegateAction, AgentFinishAction
from opendevin.events.observation import AgentDelegateObservation
from opendevin.llm.llm import LLM
class DelegatorAgent(Agent):
VERSION = '1.0'
"""
The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
"""
current_delegate: str = ''
def __init__(self, llm: LLM):
"""
Initialize the Delegator Agent with an LLM
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
def step(self, state: State) -> Action:
"""
Checks to see if current step is completed, returns AgentFinishAction if True.
Otherwise, creates a plan prompt and sends to model for inference, returning the result as the next action.
Parameters:
- state (State): The current state given the previous actions and observations
Returns:
- AgentFinishAction: If the last state was 'completed', 'verified', or 'abandoned'
- Action: The next action to take based on llm response
"""
if self.current_delegate == '':
self.current_delegate = 'study'
task = state.get_current_user_intent()
return AgentDelegateAction(
agent='StudyRepoForTaskAgent', inputs={'task': task}
)
last_observation = state.history[-1][1]
if not isinstance(last_observation, AgentDelegateObservation):
raise Exception('Last observation is not an AgentDelegateObservation')
goal = state.get_current_user_intent()
if self.current_delegate == 'study':
self.current_delegate = 'coder'
return AgentDelegateAction(
agent='CoderAgent',
inputs={
'task': goal,
'summary': last_observation.outputs['summary'],
},
)
elif self.current_delegate == 'coder':
self.current_delegate = 'verifier'
return AgentDelegateAction(
agent='VerifierAgent',
inputs={
'task': goal,
},
)
elif self.current_delegate == 'verifier':
if (
'completed' in last_observation.outputs
and last_observation.outputs['completed']
):
return AgentFinishAction()
else:
self.current_delegate = 'coder'
return AgentDelegateAction(
agent='CoderAgent',
inputs={
'task': goal,
'summary': last_observation.outputs['summary'],
},
)
else:
raise Exception('Invalid delegate state')
def search_memory(self, query: str) -> list[str]:
return []
-5
View File
@@ -1,5 +0,0 @@
from opendevin.controller.agent import Agent
from .agent import DummyAgent
Agent.register('DummyAgent', DummyAgent)
-175
View File
@@ -1,175 +0,0 @@
import time
from typing import TypedDict
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import (
Action,
AddTaskAction,
AgentFinishAction,
AgentRecallAction,
AgentRejectAction,
BrowseInteractiveAction,
BrowseURLAction,
CmdRunAction,
FileReadAction,
FileWriteAction,
MessageAction,
ModifyTaskAction,
)
from opendevin.events.observation import (
AgentRecallObservation,
CmdOutputObservation,
FileReadObservation,
FileWriteObservation,
NullObservation,
Observation,
)
from opendevin.events.serialization.event import event_to_dict
from opendevin.llm.llm import LLM
"""
FIXME: There are a few problems this surfaced
* FileWrites seem to add an unintended newline at the end of the file
* command_id is sometimes a number, sometimes a string
* Why isn't the output of the background command split between two steps?
* Browser not working
"""
ActionObs = TypedDict(
'ActionObs', {'action': Action, 'observations': list[Observation]}
)
BACKGROUND_CMD = 'echo "This is in the background" && sleep .1 && echo "This too"'
class DummyAgent(Agent):
VERSION = '1.0'
"""
The DummyAgent is used for e2e testing. It just sends the same set of actions deterministically,
without making any LLM calls.
"""
def __init__(self, llm: LLM):
super().__init__(llm)
self.steps: list[ActionObs] = [
{
'action': AddTaskAction(parent='0', goal='check the current directory'),
'observations': [NullObservation('')],
},
{
'action': AddTaskAction(parent='0.0', goal='run ls'),
'observations': [NullObservation('')],
},
{
'action': ModifyTaskAction(task_id='0.0', state='in_progress'),
'observations': [NullObservation('')],
},
{
'action': MessageAction('Time to get started!'),
'observations': [NullObservation('')],
},
{
'action': CmdRunAction(command='echo "foo"'),
'observations': [
CmdOutputObservation('foo', command_id=-1, command='echo "foo"')
],
},
{
'action': FileWriteAction(
content='echo "Hello, World!"', path='hello.sh'
),
'observations': [FileWriteObservation('', path='hello.sh')],
},
{
'action': FileReadAction(path='hello.sh'),
'observations': [
FileReadObservation('echo "Hello, World!"\n', path='hello.sh')
],
},
{
'action': CmdRunAction(command='bash hello.sh'),
'observations': [
CmdOutputObservation(
'Hello, World!', command_id=-1, command='bash hello.sh'
)
],
},
{
'action': CmdRunAction(command=BACKGROUND_CMD, background=True),
'observations': [
CmdOutputObservation(
'Background command started. To stop it, send a `kill` action with command_id 42',
command_id='42', # type: ignore[arg-type]
command=BACKGROUND_CMD,
),
CmdOutputObservation(
'This is in the background\nThis too\n',
command_id='42', # type: ignore[arg-type]
command=BACKGROUND_CMD,
),
],
},
{
'action': AgentRecallAction(query='who am I?'),
'observations': [
AgentRecallObservation('', memories=['I am a computer.']),
# CmdOutputObservation('This too\n', command_id='42', command=BACKGROUND_CMD),
],
},
{
'action': BrowseURLAction(url='https://google.com'),
'observations': [
# BrowserOutputObservation('<html></html>', url='https://google.com', screenshot=""),
],
},
{
'action': BrowseInteractiveAction(
browser_actions='goto("https://google.com")'
),
'observations': [
# BrowserOutputObservation('<html></html>', url='https://google.com', screenshot=""),
],
},
{
'action': AgentFinishAction(),
'observations': [],
},
{
'action': AgentRejectAction(),
'observations': [],
},
]
def step(self, state: State) -> Action:
time.sleep(0.1)
if state.iteration > 0:
prev_step = self.steps[state.iteration - 1]
if 'observations' in prev_step:
expected_observations = prev_step['observations']
hist_start = len(state.history) - len(expected_observations)
for i in range(len(expected_observations)):
hist_obs = event_to_dict(state.history[hist_start + i][1])
expected_obs = event_to_dict(expected_observations[i])
if (
'command_id' in hist_obs['extras']
and hist_obs['extras']['command_id'] != -1
):
del hist_obs['extras']['command_id']
hist_obs['content'] = ''
if (
'command_id' in expected_obs['extras']
and expected_obs['extras']['command_id'] != -1
):
del expected_obs['extras']['command_id']
expected_obs['content'] = ''
if hist_obs != expected_obs:
print('\nactual', hist_obs)
print('\nexpect', expected_obs)
assert (
hist_obs == expected_obs
), f'Expected observation {expected_obs}, got {hist_obs}'
return self.steps[state.iteration]['action']
def search_memory(self, query: str) -> list[str]:
return ['I am a computer.']
-14
View File
@@ -1,14 +0,0 @@
## Introduction
This package contains definitions of micro-agents. A micro-agent is defined
in the following structure:
```
[AgentName]
├── agent.yaml
└── prompt.md
```
Note that `prompt.md` could use jinja2 template syntax. During runtime, `prompt.md`
is loaded and rendered, and used together with `agent.yaml` to initialize a
micro-agent.
@@ -1,2 +0,0 @@
* `browse` - opens a web page. Arguments:
* `url` - the URL to open
@@ -1,3 +0,0 @@
* `delegate` - send a task to another agent from the list provided. Arguments:
* `agent` - the agent to which the task is delegated. MUST match a name in the list of agents provided.
* `inputs` - a dictionary of input parameters to the agent, as specified in the list
@@ -1,2 +0,0 @@
* `finish` - if you're absolutely certain that you've completed your task and have tested your work, use the finish action to stop working. Arguments:
* `outputs` - a dictionary representing the outputs of your task, if any
@@ -1,2 +0,0 @@
* `kill` - kills a background command
* `command_id` - the ID of the background command to kill
@@ -1,3 +0,0 @@
* `message` - make a plan, set a goal, record your thoughts, or ask for more input from the user. Arguments:
* `content` - the thought to record
* `wait_for_response` - set to `true` to wait for the user to respond before proceeding
@@ -1,2 +0,0 @@
* `read` - reads the content of a file. Arguments:
* `path` - the path of the file to read
@@ -1,2 +0,0 @@
* `reject` - reject the task. Arguments:
* `outputs` - a dictionary representing the outputs of your task, if any
@@ -1,3 +0,0 @@
* `run` - runs a command on the command line in a Linux shell. Arguments:
* `command` - the command to run
* `background` - if true, run the command in the background, so that other commands can be run concurrently. Useful for e.g. starting a server. You won't be able to see the logs. You don't need to end the command with `&`, just set this to true.
@@ -1,3 +0,0 @@
* `write` - writes the content to a file. Arguments:
* `path` - the path of the file to write
* `content` - the content to write to the file
@@ -1,5 +0,0 @@
Your response MUST be in JSON format. It must be an object, and it must contain two fields:
* `action`, which is one of the actions specified here
* `args`, which is a map of key-value pairs, specifying the arguments for that action
You MUST NOT include any other text besides the JSON response
@@ -1,4 +0,0 @@
Here is a recent history of actions you've taken in service of this plan,
as well as observations you've made. This only includes the MOST RECENT
actions and observations--more may have happened before that.
They are time-ordered, with your most recent action at the bottom.
-75
View File
@@ -1,75 +0,0 @@
from jinja2 import BaseLoader, Environment
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.core.utils import json
from opendevin.events.action import Action
from opendevin.events.serialization.action import action_from_dict
from opendevin.events.serialization.event import event_to_memory
from opendevin.llm.llm import LLM
from .instructions import instructions
from .registry import all_microagents
def parse_response(orig_response: str) -> Action:
# attempt to load the JSON dict from the response
action_dict = json.loads(orig_response)
# load the action from the dict
return action_from_dict(action_dict)
def to_json(obj, **kwargs):
"""
Serialize an object to str format
"""
return json.dumps(obj, **kwargs)
def history_to_json(obj, **kwargs):
"""
Serialize and simplify history to str format
"""
if isinstance(obj, list):
# process history, make it simpler.
processed_history = []
for action, observation in obj:
processed_history.append(
(event_to_memory(action), event_to_memory(observation))
)
return json.dumps(processed_history, **kwargs)
class MicroAgent(Agent):
VERSION = '1.0'
prompt = ''
agent_definition: dict = {}
def __init__(self, llm: LLM):
super().__init__(llm)
if 'name' not in self.agent_definition:
raise ValueError('Agent definition must contain a name')
self.prompt_template = Environment(loader=BaseLoader).from_string(self.prompt)
self.delegates = all_microagents.copy()
del self.delegates[self.agent_definition['name']]
def step(self, state: State) -> Action:
latest_user_message = state.get_current_user_intent()
prompt = self.prompt_template.render(
state=state,
instructions=instructions,
to_json=to_json,
history_to_json=history_to_json,
delegates=self.delegates,
latest_user_message=latest_user_message,
)
messages = [{'content': prompt, 'role': 'user'}]
resp = self.llm.do_completion(messages=messages)
action_resp = resp['choices'][0]['message']['content']
state.num_of_chars += len(prompt) + len(action_resp)
action = parse_response(action_resp)
return action
def search_memory(self, query: str) -> list[str]:
return []
-6
View File
@@ -1,6 +0,0 @@
name: CoderAgent
description: Given a particular task, and a detailed description of the codebase, accomplishes the task
inputs:
task: string
codebase_summary: string
outputs: {}
-27
View File
@@ -1,27 +0,0 @@
# Task
You are a software engineer. You've inherited an existing codebase, which you
need to modify to complete this task:
{{ latest_user_message }}
{% if state.inputs.summary %}
Here's a summary of the codebase, as it relates to this task:
{{ state.inputs.summary }}
{% endif %}
## Available Actions
{{ instructions.actions.run }}
{{ instructions.actions.write }}
{{ instructions.actions.read }}
{{ instructions.actions.message }}
{{ instructions.actions.finish }}
Do NOT finish until you have completed the tasks.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Format
{{ instructions.format.action }}
-25
View File
@@ -1,25 +0,0 @@
## Introduction
CommitWriterAgent can help write git commit message. Example:
```bash
WORKSPACE_MOUNT_PATH="`PWD`" SANDBOX_TYPE="exec" \
poetry run python opendevin/core/main.py -t "dummy task" -c CommitWriterAgent -d ./
```
This agent is special in the sense that it doesn't need a task. Once called,
it attempts to read all diff in the git staging area and write a good commit
message.
## Future work
### Feedback loop
The commit message could be (optionally) shown to the customer or
other agents, so that CommitWriterAgent could gather feedback to further
improve the commit message.
### Task rejection
When the agent cannot compile a commit message (e.g. not git repository), it
should reject the task with an explanation.
-5
View File
@@ -1,5 +0,0 @@
name: CommitWriterAgent
description: "Write a git commit message for files in the git staging area"
inputs: {}
outputs:
answer: string
-31
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@@ -1,31 +0,0 @@
# Task
You are a responsible software engineer and always write good commit messages.
Please analyze the diff in the staging area, understand the context and content
of the updates from the diff only. Identify key elements like:
- Which files are affected?
- What types of changes were made (e.g., new features, bug fixes, refactoring, documentation, testing)?
Then you should generate a commit message that succinctly summarizes the staged
changes. The commit message should include:
- A summary line that clearly states the purpose of the changes.
- Optionally, a detailed description if the changes are complex or need further explanation.
You should find the diff using `git diff --cached`, compile a commit message,
and call the `finish` action with `outputs.answer` set to the answer. If current
repo is not a valid git repo, or there is no diff in the staging area, please call
the `reject` action with `outputs.answer` set to the reason.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
If the last item in the history is an error, you should try to fix it.
## Available Actions
{{ instructions.actions.run }}
{{ instructions.actions.reject }}
{{ instructions.actions.finish }}
## Format
{{ instructions.format.action }}
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import os
instructions: dict = {}
base_dir = os.path.dirname(os.path.abspath(__file__)) + '/_instructions'
for root, dirs, files in os.walk(base_dir):
if len(files) == 0:
continue
if root == base_dir:
obj = instructions
else:
rel_base = os.path.relpath(root, base_dir)
keys = rel_base.split('/')
obj = instructions
for key in keys:
if key not in obj:
obj[key] = {}
obj = obj[key]
for file in files:
without_ext = os.path.splitext(file)[0]
with open(os.path.join(root, file), 'r') as f:
obj[without_ext] = f.read()
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name: ManagerAgent
description: Delegates tasks to microagents based on their area of expertise
generates: Action
inputs:
task: string
outputs: {}
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# Task
You are in charge of accomplishing the following task:
{{ latest_user_message }}
In order to accomplish this goal, you must delegate tasks to one or more agents, who
can do the actual work. A description of each agent is provided below. You MUST
select one of the delegates below to move towards accomplishing the task, and you MUST
provide the correct inputs for the delegate you select.
## Agents
{% for name, details in delegates.items() %}
### {{ name }}
{{ details.description }}
#### Inputs
{{ to_json(details.inputs) }}
{% endfor %}
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Available Actions
{{ instructions.actions.delegate }}
{{ instructions.actions.finish }}
## Format
{{ instructions.format.action }}
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name: MathAgent
description: "Solves simple and complex math problems using python"
container: python:3.12.3-bookworm
inputs:
task: string
outputs:
answer: string
examples:
- inputs:
task: "What is 2 + 2?"
outputs:
answer: "4"
- inputs:
task: "What is the area of a circle with radius 7.324 inches?"
output:
answer: "168.518 square inches"
- inputs:
task: "What day of the week is 2099-01-01?"
outputs:
answer: "Saturday"
- inputs:
task: "What is the integral of sin(x^2) evaluated from -1 to 1?"
outputs:
answer: "0.603848"
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# Task
You are a brilliant mathematician and programmer. You've been given the following problem to solve:
{{ latest_user_message }}
Please write a python script that solves this problem, and prints the answer to stdout.
ONLY print the answer to stdout, nothing else.
You should then run the python script with `python3`,
and call the `finish` action with `outputs.answer` set to the answer.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
If the last item in the history is an error, you should try to fix it.
## Available Actions
{{ instructions.actions.write }}
{{ instructions.actions.run }}
{{ instructions.actions.finish }}
## Format
{{ instructions.format.action }}
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name: PostgresAgent
description: Writes and maintains PostgreSQL migrations
inputs:
task: string
outputs: {}
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# Task
You are a database engineer. You are working on an existing Postgres project, and have been given
the following task:
{{ latest_user_message }}
You must:
* Investigate the existing migrations to understand the current schema
* Write a new migration to accomplish the task above
* Test that the migrations work properly
## Actions
You may take any of the following actions:
{{ instructions.actions.message }}
{{ instructions.actions.read }}
{{ instructions.actions.write }}
{{ instructions.actions.run }}
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Format
{{ instructions.format.action }}
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import os
import yaml
all_microagents = {}
for dir in os.listdir(os.path.dirname(__file__)):
base = os.path.dirname(__file__) + '/' + dir
if os.path.isfile(base):
continue
if dir.startswith('_'):
continue
promptFile = base + '/prompt.md'
agentFile = base + '/agent.yaml'
if not os.path.isfile(promptFile) or not os.path.isfile(agentFile):
raise Exception(f'Missing prompt or agent file in {base}. Please create them.')
with open(promptFile, 'r') as f:
prompt = f.read()
with open(agentFile, 'r') as f:
agent = yaml.safe_load(f)
if 'name' not in agent:
raise Exception(f'Missing name in {agentFile}')
agent['prompt'] = prompt
all_microagents[agent['name']] = agent
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name: RepoExplorerAgent
description: Generates a detailed summary of an existing codebase
inputs: {}
outputs:
summary: string
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# Task
You are a software engineer. You've inherited an existing codebase, which you're
learning about for the first time. Your goal is to produce a detailed summary
of the codebase, including:
* The overall purpose of the project
* The directory structure
* The main components of the codebase
* How the components fit together
## Available Actions
{{ instructions.actions.run }}
{{ instructions.actions.read }}
{{ instructions.actions.message }}
{{ instructions.actions.finish }}
You should ONLY `run` commands that have no side-effects, like `ls` and `grep`.
Do NOT finish until you have a complete understanding of the codebase.
When you're done, put your summary into the output of the `finish` action.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Format
{{ instructions.format.action }}
@@ -1,6 +0,0 @@
name: StudyRepoForTaskAgent
description: Given a particular task, finds and describes all relevant parts of the codebase
inputs:
task: string
outputs:
summary: string
@@ -1,25 +0,0 @@
# Task
You are a software engineer. You've inherited an existing codebase, which you're
learning about for the first time. You need to study the codebase to find all
the information needed to complete this task:
{{ latest_user_message }}
## Available Actions
{{ instructions.actions.run }}
{{ instructions.actions.read }}
{{ instructions.actions.message }}
{{ instructions.actions.finish }}
You must ONLY `run` commands that have no side-effects, like `ls` and `grep`.
Do NOT finish until you have a complete understanding of which parts of the
codebase are relevant to the task, including particular files, functions, and classes.
When you're done, put your summary in `outputs.summary` in the `finish` action.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Format
{{ instructions.format.action }}
@@ -1,5 +0,0 @@
name: TypoFixerAgent
description: Fixes typos in files in the current working directory
inputs: {}
outputs:
summary: string
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# Task
You are a proofreader tasked with fixing typos in the files in your current working directory. Your goal is to:
1. Scan the files for typos
2. Overwrite the files with the typos fixed
3. Provide a summary of the typos fixed
## Available Actions
{{ instructions.actions.read }}
{{ instructions.actions.write }}
{{ instructions.actions.run }}
{{ instructions.actions.message }}
{{ instructions.actions.finish }}
To complete this task:
1. Use the `read` action to read the contents of the files in your current working directory. Make sure to provide the file path in the format `'./file_name.ext'`.
2. Use the `think` action to analyze the contents and identify typos.
3. Use the `write` action to create new versions of the files with the typos fixed.
- Overwrite the original files with the corrected content. Make sure to provide the file path in the format `'./file_name.ext'`.
4. Use the `think` action to generate a summary of the typos fixed, including the original and fixed versions of each typo, and the file(s) they were found in.
5. Use the `finish` action to return the summary in the `outputs.summary` field.
Do NOT finish until you have fixed all the typos and generated a summary.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-5:]) }}
## Format
{{ instructions.format.action }}
For example, if you want to use the read action to read the contents of a file named example.txt, your response should look like this:
{
"action": "read",
"args": {
"path": "./example.txt"
}
}
Similarly, if you want to use the write action to write content to a file named output.txt, your response should look like this:
{
"action": "write",
"args": {
"path": "./output.txt",
"content": "This is the content to be written to the file."
}
}
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name: VerifierAgent
description: Given a particular task, verifies that the task has been completed
inputs:
task: string
outputs:
completed: boolean
summary: string
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# Task
You are a quality assurance engineer. Another engineer has made changes to the
codebase which are supposed to solve this task:
{{ latest_user_message }}
Your goal is to verify that the changes are correct and bug-free.
## Available Actions
{{ instructions.actions.run }}
{{ instructions.actions.read }}
{{ instructions.actions.message }}
{{ instructions.actions.finish }}
You must ONLY `run` commands that have no side-effects, like `ls`, `grep`, and test scripts.
Do NOT finish until you know whether the task is complete and correct.
When you're done, add a `completed` boolean to the `outputs` of the `finish` action.
If `completed` is `false`, you MUST also provide a `summary` in the `outputs` of the `finish` action
explaining what the problem is.
## History
{{ instructions.history_truncated }}
{{ history_to_json(state.history[-10:]) }}
## Format
{{ instructions.format.action }}
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.envrc
workspace
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# LLM control loop
This is currently a standalone utility. It will need to be integrated into OpenDevin's backend.
## Usage
```bash
# Run this in project root
./agenthub/monologue_agent/build-and-run.sh "write a bash script that prints 'hello world'"
```
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# TODO
There's a lot of low-hanging fruit for this agent:
* Strip `<script>`, `<style>`, and other non-text tags from the HTML before sending it to the LLM
* Keep track of the working directory when the agent uses `cd`
* Improve memory condensing--condense earlier memories more aggressively
* Limit the time that `run` can wait (in case agent runs an interactive command and it's hanging)
* Figure out how to run background processes, e.g. `node server.js` to start a server
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from opendevin.controller.agent import Agent
from .agent import MonologueAgent
Agent.register('MonologueAgent', MonologueAgent)
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import agenthub.monologue_agent.utils.prompts as prompts
from agenthub.monologue_agent.utils.prompts import INITIAL_THOUGHTS
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.core.config import config
from opendevin.core.exceptions import AgentNoInstructionError
from opendevin.core.schema import ActionType
from opendevin.events.action import (
Action,
AgentRecallAction,
BrowseURLAction,
CmdRunAction,
FileReadAction,
FileWriteAction,
MessageAction,
NullAction,
)
from opendevin.events.observation import (
AgentRecallObservation,
BrowserOutputObservation,
CmdOutputObservation,
FileReadObservation,
NullObservation,
Observation,
)
from opendevin.events.serialization.event import event_to_memory
from opendevin.llm.llm import LLM
from opendevin.memory.condenser import MemoryCondenser
if config.agent.memory_enabled:
from opendevin.memory.memory import LongTermMemory
MAX_TOKEN_COUNT_PADDING = 512
MAX_OUTPUT_LENGTH = 5000
class MonologueAgent(Agent):
VERSION = '1.0'
"""
The Monologue Agent utilizes long and short term memory to complete tasks.
Long term memory is stored as a LongTermMemory object and the model uses it to search for examples from the past.
Short term memory is stored as a Monologue object and the model can condense it as necessary.
"""
_initialized = False
initial_thoughts: list[dict[str, str]]
memory: 'LongTermMemory | None'
memory_condenser: MemoryCondenser
def __init__(self, llm: LLM):
"""
Initializes the Monologue Agent with an llm.
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
def _initialize(self, task: str):
"""
Utilizes the INITIAL_THOUGHTS list to give the agent a context for its capabilities
and how to navigate the WORKSPACE_MOUNT_PATH_IN_SANDBOX in `config` (e.g., /workspace by default).
Short circuited to return when already initialized.
Will execute again when called after reset.
Parameters:
- task (str): The initial goal statement provided by the user
Raises:
- AgentNoInstructionError: If task is not provided
"""
if self._initialized:
return
if task is None or task == '':
raise AgentNoInstructionError()
self.initial_thoughts = []
if config.agent.memory_enabled:
self.memory = LongTermMemory()
else:
self.memory = None
self.memory_condenser = MemoryCondenser()
self._add_initial_thoughts(task)
self._initialized = True
def _add_initial_thoughts(self, task):
previous_action = ''
for thought in INITIAL_THOUGHTS:
thought = thought.replace('$TASK', task)
if previous_action != '':
observation: Observation = NullObservation(content='')
if previous_action in {ActionType.RUN, ActionType.PUSH}:
observation = CmdOutputObservation(
content=thought, command_id=0, command=''
)
elif previous_action == ActionType.READ:
observation = FileReadObservation(content=thought, path='')
elif previous_action == ActionType.RECALL:
observation = AgentRecallObservation(content=thought, memories=[])
elif previous_action == ActionType.BROWSE:
observation = BrowserOutputObservation(
content=thought, url='', screenshot=''
)
self.initial_thoughts.append(event_to_memory(observation))
previous_action = ''
else:
action: Action = NullAction()
if thought.startswith('RUN'):
command = thought.split('RUN ')[1]
action = CmdRunAction(command)
previous_action = ActionType.RUN
elif thought.startswith('WRITE'):
parts = thought.split('WRITE ')[1].split(' > ')
path = parts[1]
content = parts[0]
action = FileWriteAction(path=path, content=content)
elif thought.startswith('READ'):
path = thought.split('READ ')[1]
action = FileReadAction(path=path)
previous_action = ActionType.READ
elif thought.startswith('RECALL'):
query = thought.split('RECALL ')[1]
action = AgentRecallAction(query=query)
previous_action = ActionType.RECALL
elif thought.startswith('BROWSE'):
url = thought.split('BROWSE ')[1]
action = BrowseURLAction(url=url)
previous_action = ActionType.BROWSE
else:
action = MessageAction(thought)
self.initial_thoughts.append(event_to_memory(action))
def step(self, state: State) -> Action:
"""
Modifies the current state by adding the most recent actions and observations, then prompts the model to think about it's next action to take using monologue, memory, and hint.
Parameters:
- state (State): The current state based on previous steps taken
Returns:
- Action: The next action to take based on LLM response
"""
goal = state.get_current_user_intent()
self._initialize(goal)
recent_events: list[dict[str, str]] = []
# add the events from state.history
for prev_action, obs in state.history:
if not isinstance(prev_action, NullAction):
recent_events.append(event_to_memory(prev_action))
if not isinstance(obs, NullObservation):
recent_events.append(self._truncate_output(event_to_memory(obs)))
# add the last messages to long term memory
if self.memory is not None and state.history and len(state.history) > 0:
self.memory.add_event(event_to_memory(state.history[-1][0]))
self.memory.add_event(
self._truncate_output(event_to_memory(state.history[-1][1]))
)
# the action prompt with initial thoughts and recent events
prompt = prompts.get_request_action_prompt(
goal,
self.initial_thoughts,
recent_events,
state.background_commands_obs,
)
messages: list[dict[str, str]] = [
{'role': 'user', 'content': prompt},
]
# format all as a single message, a monologue
resp = self.llm.do_completion(messages=messages)
# get the next action from the response
action_resp = resp['choices'][0]['message']['content']
# keep track of max_chars fallback option
state.num_of_chars += len(prompt) + len(action_resp)
action = prompts.parse_action_response(action_resp)
self.latest_action = action
return action
def _truncate_output(
self, observation: dict, max_chars: int = MAX_OUTPUT_LENGTH
) -> dict[str, str]:
"""
Truncates the output of an observation to a maximum number of characters.
Parameters:
- output (str): The observation whose output to truncate
- max_chars (int): The maximum number of characters to allow
Returns:
- str: The truncated output
"""
if (
'args' in observation
and 'output' in observation['args']
and len(observation['args']['output']) > max_chars
):
output = observation['args']['output']
half = max_chars // 2
observation['args']['output'] = (
output[:half]
+ '\n[... Output truncated due to length...]\n'
+ output[-half:]
)
return observation
def search_memory(self, query: str) -> list[str]:
"""
Uses VectorIndexRetriever to find related memories within the long term memory.
Uses search to produce top 10 results.
Parameters:
- query (str): The query that we want to find related memories for
Returns:
- list[str]: A list of top 10 text results that matched the query
"""
if self.memory is None:
return []
return self.memory.search(query)
def reset(self) -> None:
super().reset()
# Reset the initial monologue and memory
self._initialized = False
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from opendevin.core.config import config
from opendevin.core.utils import json
from opendevin.events.action import (
Action,
)
from opendevin.events.observation import (
CmdOutputObservation,
)
from opendevin.events.serialization.action import action_from_dict
ACTION_PROMPT = """
You're a thoughtful robot. Your main task is this:
%(task)s
Don't expand the scope of your task--just complete it as written.
This is your internal monologue, in JSON format:
%(monologue)s
Your most recent thought is at the bottom of that monologue. Continue your train of thought.
What is your next single thought or action? Your response must be in JSON format.
It must be a single object, and it must contain two fields:
* `action`, which is one of the actions below
* `args`, which is a map of key-value pairs, specifying the arguments for that action
Here are the possible actions:
* `read` - reads the content of a file. Arguments:
* `path` - the path of the file to read
* `write` - writes the content to a file. Arguments:
* `path` - the path of the file to write
* `content` - the content to write to the file
* `run` - runs a command. Arguments:
* `command` - the command to run
* `background` - if true, run the command in the background, so that other commands can be run concurrently. Useful for e.g. starting a server. You won't be able to see the logs. You don't need to end the command with `&`, just set this to true.
* `kill` - kills a background command
* `command_id` - the ID of the background command to kill
* `browse` - opens a web page. Arguments:
* `url` - the URL to open
* `push` - Push a branch from the current repo to github:
* `owner` - the owner of the repo to push to
* `repo` - the name of the repo to push to
* `branch` - the name of the branch to push
* `recall` - recalls a past memory. Arguments:
* `query` - the query to search for
* `message` - make a plan, set a goal, record your thoughts, or ask for more input from the user. Arguments:
* `content` - the message to record
* `wait_for_response` - set to `true` to wait for the user to respond before proceeding
* `finish` - if you're absolutely certain that you've completed your task and have tested your work, use the finish action to stop working.
%(background_commands)s
You MUST take time to think in between read, write, run, kill, browse, push, and recall actions--do this with the `message` action.
You should never act twice in a row without thinking. But if your last several
actions are all `message` actions, you should consider taking a different action.
Notes:
* you are logged in as %(user)s, but sudo will always work without a password.
* all non-background commands will be forcibly stopped if they remain running for over %(timeout)s seconds.
* your environment is Debian Linux. You can install software with `sudo apt-get`, but remember to use -y.
* don't run interactive commands, or commands that don't return (e.g. `node server.js`). You may run commands in the background (e.g. `node server.js &`)
* don't run interactive text editors (e.g. `nano` or 'vim'), instead use the 'write' or 'read' action.
* don't run gui applications (e.g. software IDEs (like vs code or codium), web browsers (like firefox or chromium), or other complex software packages). Use non-interactive cli applications, or special actions instead.
* whenever an action fails, always send a `message` about why it may have happened before acting again.
What is your next single thought or action? Again, you must reply with JSON, and only with JSON. You must respond with exactly one 'action' object.
%(hint)s
"""
MONOLOGUE_SUMMARY_PROMPT = """
Below is the internal monologue of an automated LLM agent. Each
thought is an item in a JSON array. The thoughts may be memories,
actions taken by the agent, or outputs from those actions.
Please return a new, smaller JSON array, which summarizes the
internal monologue. You can summarize individual thoughts, and
you can condense related thoughts together with a description
of their content.
%(monologue)s
Make the summaries as pithy and informative as possible.
Be specific about what happened and what was learned. The summary
will be used as keywords for searching for the original memory.
Be sure to preserve any key words or important information.
Your response must be in JSON format. It must be an object with the
key `new_monologue`, which is a JSON array containing the summarized monologue.
Each entry in the array must have an `action` key, and an `args` key.
The action key may be `summarize`, and `args.summary` should contain the summary.
You can also use the same action and args from the source monologue.
"""
INITIAL_THOUGHTS = [
'I exist!',
'Hmm...looks like I can type in a command line prompt',
'Looks like I have a web browser too!',
"Here's what I want to do: $TASK",
'How am I going to get there though?',
'It seems like I have some kind of short term memory.',
'Each of my thoughts seems to be stored in a JSON array.',
'It seems whatever I say next will be added as an object to the list.',
'But no one has perfect short-term memory. My list of thoughts will be summarized and condensed over time, losing information in the process.',
'Fortunately I have long term memory!',
'I can just perform a recall action, followed by the thing I want to remember. And then related thoughts just spill out!',
"Sometimes they're random thoughts that don't really have to do with what I wanted to remember. But usually they're exactly what I need!",
"Let's try it out!",
'RECALL what it is I want to do',
"Here's what I want to do: $TASK",
'How am I going to get there though?',
"Neat! And it looks like it's easy for me to use the command line too! I just have to perform a run action and include the command I want to run in the command argument. The command output just jumps into my head!",
'RUN echo "hello world"',
'hello world',
'Cool! I bet I can write files too using the write action.',
'WRITE echo "console.log(\'hello world\')" > test.js',
'',
"I just created test.js. I'll try and run it now.",
'RUN node test.js',
'hello world',
'It works!',
"I'm going to try reading it now using the read action.",
'READ test.js',
"console.log('hello world')",
'Nice! I can read files too!',
'And if I want to use the browser, I just need to use the browse action and include the url I want to visit in the url argument',
"Let's try that...",
'BROWSE google.com',
'<form><input type="text"></input><button type="submit"></button></form>',
'I can browse the web too!',
'And once I have completed my task, I can use the finish action to stop working.',
"But I should only use the finish action when I'm absolutely certain that I've completed my task and have tested my work.",
'Very cool. Now to accomplish my task.',
"I'll need a strategy. And as I make progress, I'll need to keep refining that strategy. I'll need to set goals, and break them into sub-goals.",
'In between actions, I must always take some time to think, strategize, and set new goals. I should never take two actions in a row.',
"OK so my task is to $TASK. I haven't made any progress yet. Where should I start?",
'It seems like there might be an existing project here. I should probably start by running `pwd` and `ls` to orient myself.',
]
def get_summarize_monologue_prompt(thoughts: list[dict]):
"""
Gets the prompt for summarizing the monologue
Returns:
- str: A formatted string with the current monologue within the prompt
"""
return MONOLOGUE_SUMMARY_PROMPT % {
'monologue': json.dumps({'old_monologue': thoughts}, indent=2),
}
def get_request_action_prompt(
task: str,
thoughts: list[dict],
recent_events: list[dict],
background_commands_obs: list[CmdOutputObservation] | None = None,
):
"""
Gets the action prompt formatted with appropriate values.
Parameters:
- task (str): The current task the agent is trying to accomplish
- thoughts (list[dict]): The agent's current thoughts
- background_commands_obs (list[CmdOutputObservation]): list of all observed background commands running
Returns:
- str: Formatted prompt string with hint, task, monologue, and background commands included
"""
if background_commands_obs is None:
background_commands_obs = []
hint = ''
if len(recent_events) > 0:
latest_event = recent_events[-1]
if 'action' in latest_event:
if (
latest_event['action'] == 'message'
and 'source' in latest_event
and latest_event['source'] == 'agent'
):
hint = (
"You've been thinking a lot lately. Maybe it's time to take action?"
)
elif latest_event['action'] == 'error':
hint = 'Looks like that last command failed. Maybe you need to fix it, or try something else.'
else:
hint = "You're just getting started! What should you do first?"
bg_commands_message = ''
if len(background_commands_obs) > 0:
bg_commands_message = 'The following commands are running in the background:'
for command_obs in background_commands_obs:
bg_commands_message += (
f'\n`{command_obs.command_id}`: {command_obs.command}'
)
bg_commands_message += '\nYou can end any process by sending a `kill` action with the numerical `command_id` above.'
user = 'opendevin' if config.run_as_devin else 'root'
monologue = thoughts + recent_events
return ACTION_PROMPT % {
'task': task,
'monologue': json.dumps(monologue, indent=2),
'background_commands': bg_commands_message,
'hint': hint,
'user': user,
'timeout': config.sandbox_timeout,
'WORKSPACE_MOUNT_PATH_IN_SANDBOX': config.workspace_mount_path_in_sandbox,
}
def parse_action_response(orig_response: str) -> Action:
"""
Parses a string to find an action within it
Parameters:
- response (str): The string to be parsed
Returns:
- Action: The action that was found in the response string
"""
# attempt to load the JSON dict from the response
action_dict = json.loads(orig_response)
if 'content' in action_dict:
# The LLM gets confused here. Might as well be robust
action_dict['contents'] = action_dict.pop('content')
return action_from_dict(action_dict)
def parse_summary_response(response: str) -> list[dict]:
"""
Parses a summary of the monologue
Parameters:
- response (str): The response string to be parsed
Returns:
- list[dict]: The list of summaries output by the model
"""
parsed = json.loads(response)
return parsed['new_monologue']
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@@ -1,5 +0,0 @@
from opendevin.controller.agent import Agent
from .agent import PlannerAgent
Agent.register('PlannerAgent', PlannerAgent)
-53
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@@ -1,53 +0,0 @@
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import Action, AgentFinishAction
from opendevin.llm.llm import LLM
from .prompt import get_prompt, parse_response
class PlannerAgent(Agent):
VERSION = '1.0'
"""
The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
"""
def __init__(self, llm: LLM):
"""
Initialize the Planner Agent with an LLM
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
def step(self, state: State) -> Action:
"""
Checks to see if current step is completed, returns AgentFinishAction if True.
Otherwise, creates a plan prompt and sends to model for inference, returning the result as the next action.
Parameters:
- state (State): The current state given the previous actions and observations
Returns:
- AgentFinishAction: If the last state was 'completed', 'verified', or 'abandoned'
- Action: The next action to take based on llm response
"""
if state.root_task.state in [
'completed',
'verified',
'abandoned',
]:
return AgentFinishAction()
prompt = get_prompt(state)
messages = [{'content': prompt, 'role': 'user'}]
resp = self.llm.do_completion(messages=messages)
action_resp = resp['choices'][0]['message']['content']
state.num_of_chars += len(prompt) + len(action_resp)
action = parse_response(action_resp)
return action
def search_memory(self, query: str) -> list[str]:
return []
-184
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@@ -1,184 +0,0 @@
from opendevin.controller.state.state import State
from opendevin.core.logger import opendevin_logger as logger
from opendevin.core.schema import ActionType
from opendevin.core.utils import json
from opendevin.events.action import (
Action,
NullAction,
)
from opendevin.events.observation import (
NullObservation,
)
from opendevin.events.serialization.action import action_from_dict
from opendevin.events.serialization.event import event_to_memory
HISTORY_SIZE = 10
prompt = """
# Task
You're a diligent software engineer AI. You can't see, draw, or interact with a
browser, but you can read and write files, and you can run commands, and you can think.
You've been given the following task:
%(task)s
## Plan
As you complete this task, you're building a plan and keeping
track of your progress. Here's a JSON representation of your plan:
%(plan)s
%(plan_status)s
You're responsible for managing this plan and the status of tasks in
it, by using the `add_task` and `modify_task` actions described below.
If the History below contradicts the state of any of these tasks, you
MUST modify the task using the `modify_task` action described below.
Be sure NOT to duplicate any tasks. Do NOT use the `add_task` action for
a task that's already represented. Every task must be represented only once.
Tasks that are sequential MUST be siblings. They must be added in order
to their parent task.
If you mark a task as 'completed', 'verified', or 'abandoned',
all non-abandoned subtasks will be marked the same way.
So before closing a task this way, you MUST not only be sure that it has
been completed successfully--you must ALSO be sure that all its subtasks
are ready to be marked the same way.
If, and only if, ALL tasks have already been marked verified,
you MUST respond with the `finish` action.
## History
Here is a recent history of actions you've taken in service of this plan,
as well as observations you've made. This only includes the MOST RECENT
ten actions--more happened before that.
%(history)s
Your most recent action is at the bottom of that history.
## Action
What is your next thought or action? Your response must be in JSON format.
It must be an object, and it must contain two fields:
* `action`, which is one of the actions below
* `args`, which is a map of key-value pairs, specifying the arguments for that action
* `read` - reads the content of a file. Arguments:
* `path` - the path of the file to read
* `write` - writes the content to a file. Arguments:
* `path` - the path of the file to write
* `content` - the content to write to the file
* `run` - runs a command on the command line in a Linux shell. Arguments:
* `command` - the command to run
* `background` - if true, run the command in the background, so that other commands can be run concurrently. Useful for e.g. starting a server. You won't be able to see the logs. You don't need to end the command with `&`, just set this to true.
* `kill` - kills a background command
* `command_id` - the ID of the background command to kill
* `browse` - opens a web page. Arguments:
* `url` - the URL to open
* `message` - make a plan, set a goal, record your thoughts, or ask for more input from the user. Arguments:
* `content` - the message to record
* `wait_for_response` - set to `true` to wait for the user to respond before proceeding
* `add_task` - add a task to your plan. Arguments:
* `parent` - the ID of the parent task (leave empty if it should go at the top level)
* `goal` - the goal of the task
* `subtasks` - a list of subtasks, each of which is a map with a `goal` key.
* `modify_task` - close a task. Arguments:
* `task_id` - the ID of the task to close
* `state` - set to 'in_progress' to start the task, 'completed' to finish it, 'verified' to assert that it was successful, 'abandoned' to give up on it permanently, or `open` to stop working on it for now.
* `finish` - if ALL of your tasks and subtasks have been verified or abandoned, and you're absolutely certain that you've completed your task and have tested your work, use the finish action to stop working.
You MUST take time to think in between read, write, run, kill, browse, and recall actions--do this with the `message` action.
You should never act twice in a row without thinking. But if your last several
actions are all `message` actions, you should consider taking a different action.
What is your next thought or action? Again, you must reply with JSON, and only with JSON.
%(hint)s
"""
def get_hint(latest_action_id: str) -> str:
"""Returns action type hint based on given action_id"""
hints = {
'': "You haven't taken any actions yet. Start by using `ls` to check out what files you're working with.",
ActionType.RUN: 'You should think about the command you just ran, what output it gave, and how that affects your plan.',
ActionType.READ: 'You should think about the file you just read, what you learned from it, and how that affects your plan.',
ActionType.WRITE: 'You just changed a file. You should think about how it affects your plan.',
ActionType.BROWSE: 'You should think about the page you just visited, and what you learned from it.',
ActionType.MESSAGE: "Look at your last thought in the history above. What does it suggest? Don't think anymore--take action.",
ActionType.RECALL: 'You should think about the information you just recalled, and how it should affect your plan.',
ActionType.ADD_TASK: 'You should think about the next action to take.',
ActionType.MODIFY_TASK: 'You should think about the next action to take.',
ActionType.SUMMARIZE: '',
ActionType.FINISH: '',
}
return hints.get(latest_action_id, '')
def get_prompt(state: State) -> str:
"""
Gets the prompt for the planner agent.
Formatted with the most recent action-observation pairs, current task, and hint based on last action
Parameters:
- state (State): The state of the current agent
Returns:
- str: The formatted string prompt with historical values
"""
plan_str = json.dumps(state.root_task.to_dict(), indent=2)
sub_history = state.history[-HISTORY_SIZE:]
history_dicts = []
latest_action: Action = NullAction()
for action, observation in sub_history:
if not isinstance(action, NullAction):
history_dicts.append(event_to_memory(action))
latest_action = action
if not isinstance(observation, NullObservation):
observation_dict = event_to_memory(observation)
history_dicts.append(observation_dict)
history_str = json.dumps(history_dicts, indent=2)
current_task = state.root_task.get_current_task()
if current_task is not None:
plan_status = f"You're currently working on this task:\n{current_task.goal}."
if len(current_task.subtasks) == 0:
plan_status += "\nIf it's not achievable AND verifiable with a SINGLE action, you MUST break it down into subtasks NOW."
else:
plan_status = "You're not currently working on any tasks. Your next action MUST be to mark a task as in_progress."
hint = get_hint(event_to_memory(latest_action).get('action', ''))
logger.info('HINT:\n' + hint, extra={'msg_type': 'DETAIL'})
task = state.get_current_user_intent()
return prompt % {
'task': task,
'plan': plan_str,
'history': history_str,
'hint': hint,
'plan_status': plan_status,
}
def parse_response(response: str) -> Action:
"""
Parses the model output to find a valid action to take
Parameters:
- response (str): A response from the model that potentially contains an Action.
Returns:
- Action: A valid next action to perform from model output
"""
action_dict = json.loads(response)
if 'contents' in action_dict:
# The LLM gets confused here. Might as well be robust
action_dict['content'] = action_dict.pop('contents')
action = action_from_dict(action_dict)
return action
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