Everything you need to deploy, manage, and scale AI agents with StackAgent.
Install the StackAgent CLI using npm, pip, or download the binary directly.
# Using npm npm install -g @stackagent/cli # Using pip pip install stackagent-cli # Using Homebrew (macOS) brew install stackagent/tap/stackagent
Set up your AWS credentials and configure the StackAgent CLI with your API key.
# Configure AWS credentials aws configure # Initialize StackAgent stackagent init --api-key YOUR_API_KEY
Create and deploy an AI agent with a simple configuration file or natural language.
# Using natural language stackagent deploy "Create a RAG agent with GPT-4 that can answer questions about my PDF documents" # Using config file stackagent deploy -f agent.yaml
Send requests to your deployed agent and verify it's working correctly.
# Send a test request stackagent invoke my-agent "What is the main topic of my documents?" # View logs stackagent logs my-agent --tail
RESTful API for programmatic access to StackAgent
Create a new AI agent with specified configuration
Retrieve agent details and status
Update agent configuration
Delete an agent and associated resources
Invoke agent with a prompt and get response
Stream agent responses in real-time
Retrieve conversation history
Submit feedback for response quality
Official client libraries for your favorite language
from stackagent import StackAgent # Initialize client client = StackAgent(api_key="your-api-key") # Deploy an agent with natural language agent = client.agents.create( name="customer-support-agent", description="Answer customer questions about products", model="anthropic.claude-3-sonnet", knowledge_base={ "type": "s3", "bucket": "my-product-docs" } ) # Invoke the agent response = client.agents.invoke( agent_id=agent.id, prompt="What is the return policy?" ) print(response.content)
Ready-to-use templates for common use cases
Build a conversational AI that remembers context across sessions.
View Example →Guides, tutorials, and reference materials