Strands CLI

When terminal meets intelligence: AI at your fingertips.

A minimalist Strands Agents-powered CLI assistant for your terminal. Strands brings the power of Claude 3.7 Sonnet to your command line with advanced tool integration and knowledge base capabilities.

Quick Start

# Install
pipx install strands-agents-builder

# Run interactive mode
strands

# One-off query
strands "What's the capital of France?"

# Pipe content
cat document.txt | strands "Summarize this"

# Use with knowledge base
strands --kb YOUR_KB_ID "Tell me about our project"

Features

• 💬 Interactive command-line interface with rich output
• 🔍 One-off queries via arguments or pipes
• 🛠️ Powerful integrated tools (12+ tools including shell, editor, HTTP, Python)
• 🧠 Knowledge base integration for memory and context
• 🎮 Customizable system prompt
• 🪄 Nested agent capabilities with tool delegation
• 🔧 Dynamic tool loading for extending functionality
• 🖥️ Environment variable management and customization

Integrated Tools

Strands comes with a comprehensive set of built-in tools:

• shell: Run command-line operations with interactive support
• editor: View and edit files with syntax highlighting
• http_request: Make API calls with authentication support
• python_repl: Execute Python code interactively
• calculator: Perform mathematical operations powered by SymPy
• retrieve: Query knowledge bases for relevant information
• store_in_kb: Save content to knowledge bases for future reference
• load_tool: Dynamically load additional tools at runtime
• environment: Manage environment variables
• strands: Create nested agent instances with specialized capabilities
• dialog: Create interactive dialog interfaces
• use_aws: Make AWS API calls through boto3

Knowledge Base Integration

Strands can leverage Amazon Bedrock Knowledge Bases to retrieve information and remember conversations.

# Specify knowledge base via command line
strands --kb YOUR_KB_ID "What does our API do?"

# Or set a default knowledge base via environment variable
export STRANDS_KNOWLEDGE_BASE_ID="YOUR_KB_ID"
strands "What were our key decisions last week?"

Features:

• 🔄 Automatic retrieval of relevant information before answering queries
• 💾 Conversation storage for building persistent memory
• 📝 Every exchange is stored with proper formatting for future reference
• 🔍 Contextual awareness across multiple conversations

Nested Agent Capabilities

Use the strands tool to create specialized sub-agents with their own tools and system prompts:

# Basic usage
agent.tool.strand(query="List files in the current directory")

# With specific tools
agent.tool.strand(
    query="Analyze this Python code",
    tool_names=["python_repl", "editor"],
    system_prompt="You are an expert Python developer specializing in code optimization."
)

Custom Model Provider

You can configure strands to use a different model provider with specific settings by passing in the following arguments:

strands --model-provider <NAME> --model-config <JSON|FILE>

As an example, if you wanted to use the packaged Ollama provider with a specific model id, you would run:

strands --model-provider ollama --model-config '{"model_id": <ID>}'

Strands is packaged with the following providers:

Name	Config
bedrock	reference
ollama	reference

If you have implemented a custom model provider (instructions) and would like to use it with strands, create a python module under the directory "$CWD/.models" and expose an instance function that returns an instance of your provider. As an example, assume you have:

$ cat ./.models/custom_model.py
from mymodels import CustomModel

def instance(**config):
    return CustomModel(**config)

You can then use it with strands by running:

$ strands --model-provider custom_model --model-config <JSON|FILE>

Custom Systm Prompts

# Via environment variable
export STRANDS_SYSTEM_PROMPT="You are a Python expert."

# Or local file
echo "You are a security expert." > .prompt

Contributing

git clone https://github.com/strands-agents/agent-builder.git ~/.agent-builder
cd ~/.agent-builder
python3 -m venv venv && source venv/bin/activate
pip3 install -e .
pip3 install -e ".[test]"

# Run tests
hatch run test         # Run all tests with verbose coverage output
hatch run test -k test_pattern  # Run specific tests matching a pattern

Testing is managed through hatch scripting in pyproject.toml.

Exit

Type exit, quit, or press Ctrl+C/Ctrl+D