Skip to main content
A terminal interface for building agents with persistent memory. Agents maintain context across sessions, learn project conventions, and execute code with approval controls. The Deep Agents CLI has the following built-in capabilities:
  • File operations - read, write, and edit files in your project with tools that enable agents to manage and modify code and documentation.
  • Shell command execution - execute shell commands to run tests, build projects, manage dependencies, and interact with version control systems.
  • Web search - search the web for up-to-date information and documentation (requires Tavily API key).
  • HTTP requests - make HTTP requests to APIs and external services for data fetching and integration tasks.
  • Task planning and tracking - break down complex tasks into discrete steps and track progress through the built-in todo system.
  • Memory storage and retrieval - store and retrieve information across sessions, enabling agents to remember project conventions and learned patterns.
  • Human-in-the-loop - require human approval for sensitive tool operations.
Watch the demo video to see how the Deep Agents CLI works.

​
Quick start

Set your API key

Export as an environment variable:
export ANTHROPIC_API_KEY="your-api-key"
Or create a .env file in your project root:
ANTHROPIC_API_KEY=your-api-key

Run the CLI

uvx deepagents-cli

Give the agent a task

> Create a Python script that prints "Hello, World!"
The agent proposes changes with diffs for your approval before modifying files.
Install locally if needed:
pip install deepagents-cli
The CLI uses Anthropic Claude Sonnet 4 by default. To use OpenAI:
export OPENAI_API_KEY="your-key"
Enable web search (optional):
export TAVILY_API_KEY="your-key"
API keys can be set as environment variables or in a .env file.

​
Configuration

OptionDescription
--agent NAMEUse named agent with separate memory
--auto-approveSkip tool confirmation prompts (toggle with Ctrl+T)
--sandbox TYPEExecute in remote sandbox: modal, daytona, or runloop
--sandbox-id IDReuse existing sandbox
--sandbox-setup PATHRun setup script in sandbox
CommandDescription
deepagents listList all agents
deepagents helpShow help
deepagents reset --agent NAMEClear agent memory and reset to default
deepagents reset --agent NAME --target SOURCECopy memory from another agent

​
Interactive mode

Use these commands within the CLI session:
  • /tokens - Display token usage
  • /clear - Clear conversation history
  • /exit - Exit the CLI
Execute shell commands directly by prefixing with !:
!git status
!npm test
!ls -la
ShortcutAction
EnterSubmit
Alt+EnterNewline
Ctrl+EExternal editor
Ctrl+TToggle auto-approve
Ctrl+CInterrupt
Ctrl+DExit

​
Set project conventions with memories

Agents store information in ~/.deepagents/AGENT_NAME/memories/ as markdown files using a memory-first protocol:
  1. Research: Searches memory for relevant context before starting tasks
  2. Response: Checks memory when uncertain during execution
  3. Learning: Automatically saves new information for future sessions
Organize memories by topic with descriptive filenames: 
~/.deepagents/backend-dev/memories/
├── api-conventions.md
├── database-schema.md
└── deployment-process.md
Teach the agent conventions once: 
uvx deepagents-cli --agent backend-dev
> Our API uses snake_case and includes created_at/updated_at timestamps
It remembers for future sessions: 
> Create a /users endpoint
# Applies conventions without prompting

​
Use remote sandboxes

Execute code in isolated remote environments for safety and flexibility. Remote sandboxes provide the following benefits:
  • Safety: Protect your local machine from potentially harmful code execution
  • Clean environments: Use specific dependencies or OS configurations without local setup
  • Parallel execution: Run multiple agents simultaneously in isolated environments
  • Long-running tasks: Execute time-intensive operations without blocking your machine
  • Reproducibility: Ensure consistent execution environments across teams
To use a remote sandbox, follow these steps:
  1. Configure your sandbox provider (Runloop, Daytona, or Modal): 
    # Runloop
export RUNLOOP_API_KEY="your-key"

# Daytona
export DAYTONA_API_KEY="your-key"

# Modal
modal setup
  2. Run the CLI with a sandbox: 
    uvx deepagents-cli --sandbox runloop --sandbox-setup ./setup.sh
    The agent runs locally but executes all code operations in the remote sandbox. Optional setup scripts can configure environment variables, clone repositories, and prepare dependencies.
  3. (Optional) Create a setup.sh file to configure your sandbox environment: 
    #!/bin/bash
set -e

# Clone repository using GitHub token
git clone https://x-access-token:${GITHUB_TOKEN}@github.com/username/repo.git $HOME/workspace
cd $HOME/workspace

# Make environment variables persistent
cat >> ~/.bashrc <<'EOF'
export GITHUB_TOKEN="${GITHUB_TOKEN}"
export OPENAI_API_KEY="${OPENAI_API_KEY}"
cd $HOME/workspace
EOF

source ~/.bashrc
    Store secrets in a local .env file for the setup script to access.
Sandboxes isolate code execution, but agents remain vulnerable to prompt injection with untrusted inputs. Use human-in-the-loop approval, short-lived secrets, and trusted setup scripts only. Note that sandbox APIs are evolving rapidly, and we expect more providers to support proxies that help mitigate prompt injection and secrets management concerns.
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.