Deep Agents CLI

Terminal coding agent built on the Deep Agents SDK

The Deep Agents CLI is an open source terminal coding agent built on the Deep Agents SDK. It retains persistent memory, maintains context across sessions, learns project conventions, uses customizable skills, and executes code with approval controls.

The Deep Agents CLI has the following built-in capabilities:

• File operations - read, write, and edit files with tools that enable agents to manage and modify code and documentation.
• Shell execution - execute commands to run tests, build projects, manage dependencies, and interact with version control.
• 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.
• Memory storage and retrieval - store and retrieve information across sessions, enabling agents to remember project conventions and learned patterns.
• Context compaction & offloading - summarize older conversation messages and offload originals to storage, freeing context window space during long sessions.
• Human-in-the-loop - require human approval for sensitive tool operations.
• Skills - extend agent capabilities with custom expertise and instructions.
• MCP tools - load external tools from Model Context Protocol servers.
• Tracing - trace agent operations in LangSmith for observability and debugging.

Full list of built-in tools

Built-in tools

The agent comes with the following built-in tools which are available without configuration:

Tool	Description	Human-in-the-Loop
ls	List files and directories	-
read_file	Read contents of a file; multimodal content for select models	-
write_file	Create or overwrite a file	Required1
edit_file	Make targeted edits to existing files	Required1
glob	Find files matching a pattern	-
grep	Search for text patterns across files	-
execute	Execute shell commands locally or in a remote sandbox	Required1
web_search	Search the web using Tavily	Required1
fetch_url	Fetch and convert web pages to markdown	Required1
task	Delegate work to subagents for parallel execution	Required1
ask_user	Ask the user free-form or multiple-choice questions	-
compact_conversation	Summarize older messages, offload originals to backend storage, and replace them in context with the summary	Mixed2
write_todos	Create and manage task lists for complex work	-

¹: Potentially destructive operations require user approval before execution. To bypass human approval, you can toggle auto-approve (shift+tab) or start with the option:

deepagents -y
# or
deepagents --auto-approve

When running the CLI non-interactively (via -n or piped stdin), shell execution is disabled by default even with -y/--auto-approve. Use -S/--shell-allow-list to allowlist specific commands (e.g., -S "pytest,git,make"), recommended for safe defaults, or all to permit any command. The DEEPAGENTS_CLI_SHELL_ALLOW_LIST environment variable is also supported. See Non-interactive mode and piping for more details.

²: The CLI automatically offloads the conversation in the background when token usage exceeds a model-aware threshold. Offloading summarizes older messages via the LLM, and ejects originals to storage (/conversation_history/{thread_id}.md), replacing them in context with the summary. The agent can still retrieve the full history from the offloaded file if needed. The compact_conversation tool lets the agent (or you) trigger offloading on demand. When called as a tool, it requires user approval by default.

Watch the demo video to see how the Deep Agents CLI works.

Quickstart

1. Install and launch

   OpenAI, Anthropic, and Google are installed by default. Other providers (Ollama, Groq, xAI, etc.) are available as optional extras—see Providers for details.

   Script

   curl -LsSf https://langch.in/gh-da-cli | bash
   

   # OpenAI, Anthropic, and Gemini are included by default
   DEEPAGENTS_EXTRAS="ollama,groq" curl -LsSf https://langch.in/gh-da-cli | bash
   

   uv tool install 'deepagents-cli[ollama,groq]'
   

   deepagents
   
2. Add provider credentials

   The CLI works with any LLM that supports tool calling. Use /auth inside the TUI to paste an API key for any provider — see Provider credentials for the full flow and storage details. Environment variables (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY, …) work for CI and headless runs.

   For the full list of providers and the credentials they expect, see Providers.

3. 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.

4. Enable tracing (optional)

   To view agent operations, tool calls, and decisions in LangSmith, add the following to ~/.deepagents/.env or export the variables in your shell:

   ~/.deepagents/.env

   LANGSMITH_TRACING=true
   LANGSMITH_API_KEY=lsv2_...
   LANGSMITH_PROJECT=optional-project-name  # Specify a project name or default to "deepagents-cli"
   

   For setup details and usage, see Tracing with LangSmith.

Providers

OpenAI, Anthropic, and Google are included out of the box. Other providers are installed separately so you only pull in what you need.

# Add extra providers to an existing install
uv tool install deepagents-cli --with langchain-xai

For the full list of supported providers, see Model providers.

Interactive mode

Type naturally as you would in a chat interface. The agent will use its built-in tools, skills, and memory to help you with tasks.

Slash commands

Use these commands within the CLI session:

• /model - Switch models or open the interactive model selector. See Switch models for details
• /agents - Hot-swap between pre-configured agents without relaunching. See Switch agents for details
• /auth - Manage stored API keys for model providers. See Provider credentials for details
• /remember [context] - Review conversation and update memory and skills. Optionally pass additional context
• /skill:<name> [args] - Directly invoke a skill by name. The skill’s SKILL.md instructions are injected into the prompt along with any arguments you provide
• /skill-creator [args] - Guide for creating effective agent skills
• /offload (alias /compact) - Free up context window space by offloading messages to storage with a summary placeholder. The agent can retrieve the full history from the offloaded file if needed
• /tokens - Display current context window token usage breakdown
• /clear - Clear conversation history and start a new thread
• /threads - Browse and resume previous conversation threads
• /mcp - Show active MCP servers and tools
• /reload - Re-read .env files, refresh configuration, and re-discover skills without restarting. Conversation state is preserved. See DEEPAGENTS_CLI_ prefix for override behavior
• /theme - Open the interactive theme selector to switch color themes. Built-in themes are available plus any user-defined themes
• /update - Check for and install CLI updates inline. Detects your install method (uv, Homebrew, pip) and runs the appropriate upgrade command
• /auto-update - Toggle automatic updates on or off
• /trace - Open the current thread in LangSmith (requires LANGSMITH_API_KEY)
• /editor - Open the current prompt in your external editor ($VISUAL / $EDITOR). See External editor
• /changelog - Open the CLI changelog in your browser
• /docs - Open the documentation in your browser
• /feedback - Open the GitHub issues page to file a bug report or feature request
• /version - Show installed deepagents-cli and SDK versions
• /help - Show help and available commands
• /quit - Exit the CLI

Shell commands

Type ! to enter shell mode, then type your command.

git status
npm test
ls -la

Keyboard shortcuts

General

Shortcut	Action
Enter	Submit prompt
Shift+Enter, Ctrl+J, Alt+Enter, or Ctrl+Enter	Insert newline
Ctrl+A	Select all text in input
@filename	Auto-complete files and inject content
Shift+Tab or Ctrl+T	Toggle auto-approve
Ctrl+U	Delete to start of line
Ctrl+X	Open prompt in external editor
Ctrl+O	Expand/collapse the most recent tool output
Escape	Interrupt current operation
Ctrl+C	Interrupt or quit
Ctrl+D	Exit

Non-interactive mode and piping

Use -n to run a single task without launching the interactive UI:

deepagents -n "Write a Python script that prints hello world"

You can also pipe input via stdin. When input is piped, the CLI automatically runs non-interactively:

echo "Explain this code" | deepagents
cat error.log | deepagents -n "What's causing this error?"
git diff | deepagents -n "Review these changes"
git diff | deepagents --skill code-review -n 'summarize changes'

When you combine piped input with -n or -m, the piped content appears first, followed by the text you pass to the flag.

The maximum piped input size is 10 MiB.

Shell execution is disabled by default in non-interactive mode. Use -S/--shell-allow-list to enable specific commands (e.g., -S "pytest,git,make"), recommended for safe defaults, or all to permit any command.

Cap turn count with `--max-turns`

Long-running or misbehaving agents in CI/CD pipelines can loop indefinitely. --max-turns N gives operators a hard upper bound without having to touch SDK internals:

deepagents -n "fix the failing tests" --max-turns 10

N must be a positive integer, and overrides the internal safety default that otherwise caps runaway loops. Exits with code 124 (matching GNU timeout) when the budget is exceeded, so CI can distinguish a budget hit from a generic failure. Requires -n or piped stdin; otherwise exits with code 2.

Clean output and buffering

Use -q for clean output suitable for piping into other commands, and --no-stream to buffer the full response (instead of streaming) before writing to stdout:

deepagents -n "Generate a .gitignore for Python" -q > .gitignore
deepagents -n "List dependencies" -q --no-stream | sort

In non-interactive mode, the agent is instructed to make reasonable assumptions and proceed autonomously rather than ask clarifying questions. It also favors non-interactive command variants (e.g., npm init -y, apt-get install -y).

Shell execution examples

# Allow specific commands (validated against the list)
deepagents -n "Run the tests and fix failures" -S "pytest,git,make"

# Use the curated safe-command list
deepagents -n "Build the project" -S recommended

# Allow any shell command
deepagents -n "Fix the build" -S all

Use with caution.

-S all (or --shell-allow-list all) lets the agent execute arbitrary shell commands with no human confirmation.

Run a command at startup

Use --startup-cmd to run a shell command once before the session accepts its first prompt. The output is shown to you at the top of the session, which is handy for checking git status, listing files, or verifying environment setup before typing the first prompt.

# Interactive: show git status before the first prompt
deepagents --startup-cmd "git status"

# Combine with an initial prompt — the command runs first, then the prompt is submitted
deepagents --startup-cmd "ls -la" -m "Summarize what's in this directory"

# Non-interactive: see env state before the task runs
deepagents --startup-cmd "git diff --stat" -n "Review these changes"

The command runs before any -m prompt or --skill invocation is dispatched and respects your shell environment. Non-zero exits and timeouts emit a warning but do not abort the session. In non-interactive mode, the command has a 60-second timeout.

The output is not injected into the agent’s message history — the LLM does not see it. To hand command output to the agent, pipe it in via stdin instead (e.g., git diff | deepagents -n "Review these changes").

Switch models

You can switch models during a session without restarting the CLI using the /model command, or at launch with the --model flag:

> /model anthropic:claude-opus-4-6
> /model openai:gpt-5.5

deepagents --model openai:gpt-5.5

Run /model to open an interactive model selector that displays available models grouped by provider.

For full details on switching models, setting a default, and adding custom model providers, see Model providers.

Model parameters

Override model invocation parameters (e.g. reasoning effort, thinking budget, max output tokens, etc.) by passing JSON to --model-params at launch or to /model --model-params mid-session:

deepagents --model openai:gpt-5.5 --model-params '{"reasoning": {"effort": "high"}}'

deepagents --model anthropic:claude-opus-4-7 --model-params '{"thinking": {"type": "enabled", "budget_tokens": 10000}}'

# Mid-session — swap model and params in one step
/model --model-params '{"temperature": 0.7}' anthropic:claude-opus-4-7

# Or open the selector and apply params to whatever you pick
/model --model-params '{"num_ctx": 16384}'

CLI flags are session-only. --model-params cannot be combined with --default. For persistent provider-level defaults, per-model overrides, and provider-specific examples, see Model parameters on the providers page.

Switch agents

Run /agents to open the agent selector and hot-swap between agents without relaunching. Switching resets the conversation thread and refreshes skill discovery. Ctrl+S on the highlighted row pins that agent as your default so deepagents launches resume it. -a/--agent always overrides; -r/--resume restores the thread’s original agent.

Configuration

The CLI stores all configuration under ~/.deepagents/. Within that directory, each agent gets its own subdirectory (default: agent):

Path	Purpose
~/.deepagents/config.toml	Model and agent defaults, provider settings, constructor params, profile overrides, themes, update settings, MCP trust store
~/.deepagents/.env	Global API keys and secrets. See configuration
~/.deepagents/hooks.json	Lifecycle event hooks (session start/end, task complete, etc.)
~/.deepagents/<agent_name>/	Per-agent memory, skills, and conversation threads
.deepagents/ (project root)	Project-specific memory and skills, loaded when running inside a git repo

# List all configured agents
deepagents agents list

For the full reference — including config.toml schema, provider parameters, profile overrides, and hook configuration — see Configuration.

Memory

There are two primary ways to customize any agent:

• Memory: AGENTS.md files and auto-saved memories that persist across sessions. Use memory for general coding style, preferences, and learned conventions.

• Skills: Global and project-specific context, conventions, guidelines, or instructions. Use skills for context that is only required when performing specific tasks.

Use /remember to explicitly prompt the agent to update its memory and skills from the current conversation.

Building a custom agent with the SDK? See Memory for programmatic memory backends.

Automatic memory

As you use the agent, it automatically stores 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

The agent organizes its memories by topic with descriptive filenames:

~/.deepagents/backend-dev/memories/
├── api-conventions.md
├── database-schema.md
└── deployment-process.md

When you teach the agent conventions:

deepagents --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

AGENTS.md files

AGENTS.md files provide persistent context that is always loaded at session start:

• Global: ~/.deepagents/<agent_name>/AGENTS.md — loaded every session.
• Project: .deepagents/AGENTS.md in any git project root — loaded when the CLI is run from within that project.

Both files are appended to the system prompt at startup.

How memory works

The agent may also read its memory files when answering project-specific questions or when you reference past work or patterns.

The agent will update AGENTS.md as you provide information on how it should behave, feedback on its work, or instructions to remember something. It will also update its memory if it identifies patterns or preferences from your interactions.

To add more structured project knowledge in additional memory files, add them in .deepagents/ and reference them in the AGENTS.md file. You must reference additional files in the AGENTS.md file for the agent to be aware of them. The additional files will not be read on startup but the agent can reference and update them when needed.

When to use global vs. project AGENTS.md

Global AGENTS.md (~/.deepagents/agent/AGENTS.md)

• Your personality, style, and universal coding preferences
• General tone and communication style
• Universal coding preferences (formatting, type hints, etc.)
• Tool usage patterns that apply everywhere
• Workflows and methodologies that don’t change per-project

Project AGENTS.md (.deepagents/AGENTS.md in project root)

• Project-specific context and conventions
• Project architecture and design patterns
• Coding conventions specific to this codebase
• Testing strategies and deployment processes
• Team guidelines and project structure

Use skills

Skills are reusable agent capabilities that provide specialized workflows and domain knowledge. You can use skills to provide your deep agent with new capabilities and expertise. Deep agent skills follow the Agent Skills standard. Once you have added skills your deep agent will automatically make use of them and update them as you use the agent and provide it with additional information.

Use /remember to explicitly prompt the agent to update skills and memory from the current conversation.

Add skills

1. Create a skill:

   # User skill (stored in ~/.deepagents/<agent_name>/skills/)
   deepagents skills create test-skill
   
   # Project skill (stored in .deepagents/skills/)
   deepagents skills create test-skill --project
   

   This generates:

   skills/
   └── test-skill
       └── SKILL.md
   

2. Open the generated SKILL.md and edit the file to include your instructions.

3. Optionally add additional scripts or other resources to the test-skill folder. For more information, see Examples.

You can also copy existing skills directly to the agent’s folder:

mkdir -p ~/.deepagents/<agent_name>/skills
cp -r examples/skills/web-research ~/.deepagents/<agent_name>/skills/

Install community skills

You can use tools like Vercel’s Skills CLI to install community Agent Skills in your environment and make them available to your deep agents:

# Install a skill globally
npx skills add vercel-labs/agent-skills --skill web-design-guidelines -a deepagents -g -y

# List installed skills
npx skills ls -a deepagents -g

Global installs (-g) symlink skills into ~/.deepagents/agent/skills/ — the default agent’s user-level skills directory. Project-level installs (omit -g) place skills in .deepagents/skills/ relative to the current directory, making them available to any agent running in that project regardless of agent name.

Global installs target the default agent directory only. If you use a custom-named agent, either use project-level installs or manually symlink the skill into ~/.deepagents/{your-agent}/skills/.

Skill discovery

At startup, the CLI discovers skills from both Deep Agents and shared alias directories:

~/.deepagents/<agent_name>/skills/
~/.agents/skills/
.deepagents/skills/
.agents/skills/
~/.claude/skills/          (experimental)
.claude/skills/            (experimental)

When duplicate skill names exist, later-precedence directories override earlier ones (see App data).

For project-specific skills, the project’s root folder must have a .git folder. When you start the CLI from anywhere within the project’s folder, the CLI will find the project’s root folder by checking for a containing .git folder.

For each skill, the CLI reads the name and the description from the SKILL.md file’s frontmatter. As you use the CLI, if a task matches the skill’s description, the agent will read the skill file and follow its instructions.

You can also invoke a skill directly with /skill:<name> [args]. Skill discovery runs at startup and again on /reload.

Invoke a skill from the command line

Use --skill to invoke a skill at launch without typing a slash command interactively:

# Open the TUI and immediately run a skill
deepagents --skill code-review

# Pass a request to the skill with -m
deepagents --skill code-review -m 'review the auth module'

# Pipe content into a skill
cat diff.txt | deepagents --skill code-review

# Pipe content and add a request
cat diff.txt | deepagents --skill code-review -m 'focus on security'

--skill also works in non-interactive mode:

# Run a skill headlessly
deepagents --skill code-review -n 'review this patch'

# Quiet mode (only agent output on stdout)
deepagents --skill code-review -n 'review this patch' -q

--skill with --quiet or --no-stream requires -n (non-interactive mode).

List skills

# List all user skills
deepagents skills list

# List project skills
deepagents skills list --project

# Get detailed info about a specific skill
deepagents skills info test-skill
deepagents skills info test-skill --project

Subagents

Define custom subagents as markdown files so the CLI agent can delegate specialized tasks to them. Each subagent lives in its own folder with an AGENTS.md file:

.deepagents/agents/{subagent-name}/AGENTS.md   # Project-level
~/.deepagents/{agent}/agents/{subagent-name}/AGENTS.md  # User-level

Project subagents override user subagents with the same name (see precedence rules).

The frontmatter requires name and description (same as the SubAgent dictionary spec). The markdown body becomes the subagent’s system_prompt. In addition to the base spec, AGENTS.md files support an optional model frontmatter field that overrides the main agent’s model for this subagent. Uses the provider:model-name format (e.g., anthropic:claude-opus-4-6, openai:gpt-5.5). Omit to inherit the main agent’s model.

Other SubAgent fields (tools, middleware, interrupt_on, skills) are currently not configurable via AGENTS.md frontmatter — custom subagents defined this way inherit the main agent’s tools. Use the SDK directly for full control.

File format

Subagent AGENTS.md files use YAML frontmatter followed by a markdown body:

---
name: researcher
description: Research topics on the web before writing content
model: anthropic:claude-haiku-4-5-20251001
---

You are a research assistant with access to web search.

## Your Process
1. Search for relevant information
2. Summarize findings clearly

Example: cost-efficient subagents

Use a cheaper, faster model for simple delegation tasks while keeping the main agent on a more capable model:

---
name: general-purpose
description: General-purpose agent for research and multi-step tasks
model: anthropic:claude-haiku-4-5-20251001
---

You are a general-purpose assistant. Complete the task efficiently and return a concise summary.

This overrides the built-in general-purpose subagent, routing all delegated tasks to a cheaper model. See Override the general-purpose subagent for more.

Use MCP tools

Extend the CLI with tools from external MCP (Model Context Protocol) servers. Place a .mcp.json at your project root and the CLI discovers it automatically. Servers that require OAuth (Slack, GitHub, Linear, Notion, …) can be authenticated once with deepagents mcp login <server>. See the MCP tools guide for configuration format, OAuth login, auto-discovery, and troubleshooting.

Use remote sandboxes

The CLI uses the sandbox as tool pattern: the CLI process (LLM loop, memory, tool dispatch) runs on your machine, but agent tool calls (read_file, write_file, execute, etc.) target the remote sandbox, not your local filesystem. To get files into the sandbox, use a setup script or the provider’s file transfer APIs (see Working with files).

For a deeper look at sandbox architecture, integration patterns, and security best practices, see Sandboxes.

LangSmith sandbox support is included with the CLI by default. AgentCore, Modal, Daytona, and Runloop require installing extras.

1. Install provider dependency

   LangSmith

   Included by default when installing deepagents-cli. No extra installation needed.

   Daytona

   uv tool install deepagents-cli --with langchain-daytona
   

   Modal

   uv tool install deepagents-cli --with langchain-modal
   

   Runloop

   uv tool install deepagents-cli --with langchain-runloop
   

   AgentCore

   uv tool install deepagents-cli --with langchain-agentcore-codeinterpreter
   
2. Set provider credentials

   LangSmith

   export LANGSMITH_API_KEY="your-key"
   

   Daytona

   export DAYTONA_API_KEY="your-key"
   

   Modal

   modal setup
   

   Runloop

   export RUNLOOP_API_KEY="your-key"
   

   AgentCore

   export AWS_ACCESS_KEY_ID="your-key"
   export AWS_SECRET_ACCESS_KEY="your-secret"
   export AWS_SESSION_TOKEN="session-token"
   export AWS_REGION="us-west-2"
   
3. Run the CLI with a sandbox

   LangSmith

   deepagents --sandbox langsmith
   

   Daytona

   deepagents --sandbox daytona
   

   Modal

   deepagents --sandbox modal
   

   Runloop

   deepagents --sandbox runloop
   

   AgentCore

   deepagents --sandbox agentcore
   

Sandbox flags and examples

Flag	Description
--sandbox TYPE	Sandbox provider to use: langsmith, agentcore, modal, daytona, or runloop (default: none)
--sandbox-id ID	Reuse an existing sandbox by ID instead of creating a new one. Skips creation and cleanup. Refer to your sandbox documentation for more
--sandbox-setup PATH	Path to a setup script to run inside the sandbox upon creation

Examples:

# Create a new Daytona sandbox
deepagents --sandbox daytona

# Reuse an existing sandbox (skips creation and cleanup)
deepagents --sandbox runloop --sandbox-id dbx_abc123

# Run a setup script after sandbox creation
deepagents --sandbox modal --sandbox-setup ./setup.sh

Setup scripts

Use --sandbox-setup to run a shell script inside the sandbox after creation. This is useful for cloning repos, installing dependencies, and configuring environment variables.

setup.sh

#!/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

The CLI expands ${VAR} references in setup scripts using your local environment variables. 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. See Security considerations for details.

Tracing with LangSmith

Enable LangSmith tracing to see agent operations, tool calls, and decisions in a LangSmith project.

Add your tracing keys to ~/.deepagents/.env so tracing is enabled in every session without per-shell exports:

~/.deepagents/.env

LANGSMITH_TRACING=true
LANGSMITH_API_KEY=lsv2_...
LANGSMITH_PROJECT=optional-project-name  # Specify a project name or default to "deepagents-cli"

To override for a specific project, add the same keys to a .env in the project directory. See environment variables for the full loading order.

You can also set these as shell environment variables if you prefer. Shell exports always take precedence over .env values, so this is a good option for temporary overrides or testing:

export LANGSMITH_TRACING=false

Separate agent traces from app traces

When invoking the CLI programmatically from a LangChain application (e.g., as a subprocess in non-interactive mode), both your app and the CLI produce LangSmith traces. By default, these all land in the same project.

To send CLI traces to a dedicated project, set DEEPAGENTS_CLI_LANGSMITH_PROJECT:

~/.deepagents/.env

DEEPAGENTS_CLI_LANGSMITH_PROJECT=my-deep-agent-execution

Then configure LANGSMITH_PROJECT for your parent application’s traces:

~/.deepagents/.env

LANGSMITH_PROJECT=my-app-traces

This keeps your app-level observability clean while still capturing the agent’s internal execution in a separate project.

You can also scope LangSmith credentials to the CLI using the DEEPAGENTS_CLI_ prefix (e.g., DEEPAGENTS_CLI_LANGSMITH_API_KEY).

When configured, the CLI displays a status line with a link to the LangSmith project. In supported terminals, click the link to open it directly. You can also use /trace to print the URL and open it in your browser.

✓ LangSmith tracing: 'my-project'

Command reference

# Use a specific agent configuration
deepagents --agent mybot

# Use a specific model (provider:model format or auto-detect)
deepagents --model anthropic:claude-opus-4-7
deepagents --model gpt-5.5

# Auto-approve tool usage (skip human-in-the-loop prompts)
deepagents -y

Command-line options

Option	Description
-a, --agent NAME	Use a named agent with its own memory. See Switch agents
-M, --model MODEL	Use a specific model (provider:model)
--model-params JSON	Extra kwargs to pass to the model as a JSON string (e.g., '{"temperature": 0.7}')
--default-model [MODEL]	Set the default model
--clear-default-model	Clear the default model
-r, --resume [ID]	Resume a session: -r for most recent, -r <ID> for a specific thread
-m, --message TEXT	Initial prompt to auto-submit when the session starts (interactive mode)
--skill NAME	Invoke a skill at startup
--startup-cmd CMD	Shell command to run at startup, before the first prompt. Output is rendered in the transcript for your reference but is not added to the agent’s message history. Non-zero exits and timeouts warn but do not abort; non-interactive mode applies a 60s timeout. See Run a command at startup
-n, --non-interactive TEXT	Run a single task non-interactively and exit. Shell is disabled unless --shell-allow-list is set
--max-turns N	Cap agentic turns in non-interactive mode. Exits with code 124 when exceeded. Requires -n or piped stdin. See Cap turn count with --max-turns
-q, --quiet	Clean output for piping—only the agent’s response goes to stdout. Requires -n or piped stdin
--no-stream	Buffer the full response and write to stdout at once instead of streaming. Requires -n or piped stdin
--stdin	Read input from stdin explicitly instead of auto-detection. Errors clearly when stdin is unavailable or is a TTY
-y, --auto-approve	Auto-approve all tool calls without prompting (disables human-in-the-loop). Toggle with Shift+Tab during an interactive session
-S, --shell-allow-list LIST	Comma-separated shell commands to auto-approve, 'recommended' for safe defaults, or 'all' to allow any command. Applies to both -n and interactive modes
--json	Emit machine-readable JSON from management subcommands (agents, threads, skills, update). Output envelope: {"schema_version": 1, "command": "...", "data": ...}
--sandbox TYPE	Remote sandbox for code execution: none (default), langsmith, agentcore, modal, daytona, runloop. LangSmith is included; AgentCore/Modal/Daytona/Runloop require extras
--sandbox-id ID	Reuse an existing sandbox (skips creation and cleanup)
--sandbox-setup PATH	Path to setup script to run in sandbox after creation
--mcp-config PATH	Add an explicit MCP config as the highest-precedence source (merged with auto-discovered configs)
--no-mcp	Disable all MCP tool loading
--trust-project-mcp	Trust project-level MCP configs with stdio servers (skip approval prompt)
--profile-override JSON	Override model profile fields as a JSON string (e.g., '{"max_input_tokens": 4096}'). Merged on top of config file profile overrides
--acp	Run as an ACP server over stdio instead of launching the interactive UI
-v, --version	Display version
-h, --help	Show help

CLI commands

Command	Description
deepagents help	Show help
deepagents agents list	List all agents (alias: ls)
deepagents agents reset --agent NAME	Clear agent memory and reset to default. Supports --dry-run
deepagents agents reset --agent NAME --target SOURCE	Copy memory from another agent
deepagents update	Check for and install CLI updates
deepagents skills list [--project]	List all skills (alias: ls)
deepagents skills create NAME [--project]	Create a new skill with template SKILL.md. Idempotent — re-creating an existing skill prints an informational message instead of an error
deepagents skills info NAME [--project]	Show detailed information about a skill
deepagents skills delete NAME [--project] [-f]	Delete a skill and its contents. Supports --dry-run
deepagents threads list [--agent NAME] [--limit N]	List sessions (alias: ls). Default limit: 20. -n is a short flag for --limit. Additional flags: --sort {created,updated}, --branch TEXT (filter by git branch), -v/--verbose (show all columns including branch, created time, and initial prompt), -r/--relative (relative timestamps)
deepagents threads delete ID	Delete a session. Supports --dry-run
deepagents mcp login NAME [--config PATH]	Run the OAuth login flow for an MCP server marked auth: "oauth". See MCP tools
deepagents deploy	Deploy your agent to LangSmith. See Deploy with the CLI

All management subcommands support --json for machine-readable output. See command-line options for details.

Destructive commands (agents reset, skills delete, threads delete) support --dry-run to preview what would happen without making changes. In JSON mode, --dry-run returns the same envelope with a dry_run: true field.

The Deep Agents CLI is not officially supported on Windows. Windows users can try running it under Windows Subsystem for Linux (WSL).

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