master
Kye Gomez
1 day ago
parent
1ab3eeea55
commit
e3ef675fc1
@ -0,0 +1,73 @@
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name: Docker Build and Publish
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on:
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push:
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branches: [ "master" ]
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# Publish semver tags as releases
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tags: [ 'v*.*.*' ]
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pull_request:
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branches: [ "master" ]
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env:
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# Use docker.io for Docker Hub if empty
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REGISTRY: docker.io
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# github.repository as <account>/<repo>
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IMAGE_NAME: ${{ github.repository }}
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jobs:
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build:
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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# This is used to complete the identity challenge
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# with sigstore/fulcio when running outside of PRs.
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id-token: write
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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# Setup QEMU for multi-platform builds
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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# Setup Docker BuildX
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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# Login to Docker Hub
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- name: Log into registry ${{ env.REGISTRY }}
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if: github.event_name != 'pull_request'
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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# Extract metadata (tags, labels) for Docker
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- name: Extract Docker metadata
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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tags: |
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type=ref,event=branch
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type=ref,event=pr
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type=semver,pattern={{version}}
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type=semver,pattern={{major}}.{{minor}}
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type=semver,pattern={{major}}
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type=sha
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# Build and push Docker image
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- name: Build and push Docker image
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id: build-and-push
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uses: docker/build-push-action@v5
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with:
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context: .
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push: ${{ github.event_name != 'pull_request' }}
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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platforms: linux/amd64,linux/arm64
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cache-from: type=gha
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cache-to: type=gha,mode=max
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@ -0,0 +1,65 @@
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from swarms import Agent
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from swarms.structs.concurrent_workflow import ConcurrentWorkflow
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# Initialize market research agent
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market_researcher = Agent(
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agent_name="Market-Researcher",
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system_prompt="""You are a market research specialist. Your tasks include:
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1. Analyzing market trends and patterns
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2. Identifying market opportunities and threats
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3. Evaluating competitor strategies
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4. Assessing customer needs and preferences
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5. Providing actionable market insights""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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temperature=0.7,
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# streaming_on=True,
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)
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# Initialize financial analyst agent
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financial_analyst = Agent(
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agent_name="Financial-Analyst",
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system_prompt="""You are a financial analysis expert. Your responsibilities include:
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1. Analyzing financial statements
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2. Evaluating investment opportunities
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3. Assessing risk factors
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4. Providing financial forecasts
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5. Recommending financial strategies""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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# streaming_on=True,
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temperature=0.7,
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)
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# Initialize technical analyst agent
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technical_analyst = Agent(
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agent_name="Technical-Analyst",
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system_prompt="""You are a technical analysis specialist. Your focus areas include:
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1. Analyzing price patterns and trends
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2. Evaluating technical indicators
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3. Identifying support and resistance levels
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4. Assessing market momentum
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5. Providing trading recommendations""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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temperature=0.7,
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# streaming_on=True,
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)
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# Create list of agents
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agents = [market_researcher, financial_analyst, technical_analyst]
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router = ConcurrentWorkflow(
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name="market-analysis-router",
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agents=agents,
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max_loops=1,
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# output_type="all",
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show_dashboard=True,
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)
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result = router.run(
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"Analyze Tesla (TSLA) stock from market, financial, and technical perspectives"
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)
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print(result)
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@ -0,0 +1,55 @@
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from swarms import Agent
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from swarms.structs.ma_utils import create_agent_map
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# Initialize market research agent
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market_researcher = Agent(
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agent_name="Market-Researcher",
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system_prompt="""You are a market research specialist. Your tasks include:
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1. Analyzing market trends and patterns
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2. Identifying market opportunities and threats
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3. Evaluating competitor strategies
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4. Assessing customer needs and preferences
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5. Providing actionable market insights""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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temperature=0.7,
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# streaming_on=True,
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)
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# Initialize financial analyst agent
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financial_analyst = Agent(
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agent_name="Financial-Analyst",
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system_prompt="""You are a financial analysis expert. Your responsibilities include:
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1. Analyzing financial statements
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2. Evaluating investment opportunities
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3. Assessing risk factors
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4. Providing financial forecasts
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5. Recommending financial strategies""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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# streaming_on=True,
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temperature=0.7,
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)
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# Initialize technical analyst agent
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technical_analyst = Agent(
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agent_name="Technical-Analyst",
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system_prompt="""You are a technical analysis specialist. Your focus areas include:
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1. Analyzing price patterns and trends
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2. Evaluating technical indicators
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3. Identifying support and resistance levels
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4. Assessing market momentum
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5. Providing trading recommendations""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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temperature=0.7,
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# streaming_on=True,
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)
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# Create list of agents
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agents = [market_researcher, financial_analyst, technical_analyst]
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out = create_agent_map(agents)
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print(out)
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print(out.keys())
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@ -1,57 +1,130 @@
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from concurrent.futures import Future
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from unittest.mock import Mock, create_autospec, patch
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from swarms.structs import Agent, ConcurrentWorkflow, Task
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def test_add():
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workflow = ConcurrentWorkflow(max_workers=2)
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task = Mock(spec=Task)
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workflow.add(task)
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assert task in workflow.tasks
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def test_run():
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workflow = ConcurrentWorkflow(max_workers=2)
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task1 = create_autospec(Task)
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task2 = create_autospec(Task)
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workflow.add(task1)
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workflow.add(task2)
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with patch(
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"concurrent.futures.ThreadPoolExecutor"
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) as mock_executor:
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future1 = Future()
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future1.set_result(None)
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future2 = Future()
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future2.set_result(None)
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mock_executor.return_value.__enter__.return_value.submit.side_effect = [
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future1,
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future2,
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]
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mock_executor.return_value.__enter__.return_value.as_completed.return_value = [
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future1,
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future2,
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]
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workflow.run()
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task1.execute.assert_called_once()
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task2.execute.assert_called_once()
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def test_execute_task():
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workflow = ConcurrentWorkflow(max_workers=2)
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task = create_autospec(Task)
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workflow._execute_task(task)
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task.execute.assert_called_once()
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def test_agent_execution():
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workflow = ConcurrentWorkflow(max_workers=2)
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agent = create_autospec(Agent)
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task = Task(agent)
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workflow.add(task)
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workflow._execute_task(task)
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agent.execute.assert_called_once()
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from swarms import Agent
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from swarms.structs.concurrent_workflow import ConcurrentWorkflow
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def test_basic_workflow():
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"""Test basic workflow initialization and execution"""
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# Create test agents
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agent1 = Agent(
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agent_name="Test-Agent-1",
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system_prompt="You are a test agent 1",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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)
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agent2 = Agent(
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agent_name="Test-Agent-2",
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system_prompt="You are a test agent 2",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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)
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# Create workflow
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workflow = ConcurrentWorkflow(
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name="test-workflow", agents=[agent1, agent2], max_loops=1
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)
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# Run workflow
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result = workflow.run("Test task")
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# Verify results
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assert len(result) == 2
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assert all(isinstance(r, dict) for r in result)
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assert all("agent" in r and "output" in r for r in result)
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def test_dashboard_workflow():
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"""Test workflow with dashboard enabled"""
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agent = Agent(
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agent_name="Dashboard-Test-Agent",
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system_prompt="You are a test agent",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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)
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workflow = ConcurrentWorkflow(
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name="dashboard-test",
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agents=[agent],
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max_loops=1,
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show_dashboard=True,
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)
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result = workflow.run("Test task")
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assert len(result) == 1
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assert isinstance(result[0], dict)
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assert "agent" in result[0]
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assert "output" in result[0]
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def test_multiple_agents():
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"""Test workflow with multiple agents"""
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agents = [
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Agent(
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agent_name=f"Agent-{i}",
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system_prompt=f"You are test agent {i}",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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)
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for i in range(3)
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]
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workflow = ConcurrentWorkflow(
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name="multi-agent-test", agents=agents, max_loops=1
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)
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result = workflow.run("Multi-agent test task")
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assert len(result) == 3
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assert all(isinstance(r, dict) for r in result)
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assert all("agent" in r and "output" in r for r in result)
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def test_error_handling():
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"""Test workflow error handling"""
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# Create an agent that will raise an exception
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agent = Agent(
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agent_name="Error-Agent",
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system_prompt="You are a test agent that will raise an error",
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model_name="invalid-model", # This will cause an error
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max_loops=1,
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)
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workflow = ConcurrentWorkflow(
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name="error-test", agents=[agent], max_loops=1
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)
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try:
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workflow.run("Test task")
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assert False, "Expected an error but none was raised"
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except Exception as e:
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assert str(e) != "" # Verify we got an error message
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def test_max_loops():
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"""Test workflow respects max_loops setting"""
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agent = Agent(
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agent_name="Loop-Test-Agent",
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system_prompt="You are a test agent",
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model_name="claude-3-sonnet-20240229",
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max_loops=2,
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)
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workflow = ConcurrentWorkflow(
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name="loop-test",
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agents=[agent],
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max_loops=1, # This should override agent's max_loops
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)
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result = workflow.run("Test task")
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assert len(result) == 1
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assert isinstance(result[0], dict)
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if __name__ == "__main__":
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test_basic_workflow()
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test_dashboard_workflow()
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test_multiple_agents()
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test_error_handling()
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test_max_loops()
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