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 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.
• Context compaction & offloading - summarize older conversation messages and offload originals to backend 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 stored in skill directories.
• MCP tools - load external tools from Model Context Protocol servers via auto-discovered or explicit config files.
• Tracing - trace agent operations, tool calls, and decisions 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; supports images (.png, .jpg, .jpeg, .gif, .webp) as multimodal content	-
write_file	Create or overwrite a file	Required1
edit_file	Make targeted edits to existing files	Required1
glob	Find files matching a pattern (e.g., **/*.py)	-
grep	Search for text patterns across files	-
execute	Execute shell commands locally or in a remote sandbox	Required1
http_request	Make HTTP requests to APIs and web services (GET, POST, PUT, DELETE, etc.)	-
web_search	Search the web using Tavily API	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 when clarification is needed	-
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 or start the deep agent with the auto-approve option:

deepagents --auto-approve

When running the CLI non-interactively (via -n or piped stdin), shell execution is disabled by default even with --auto-approve. Use --shell-allow-list to allowlist specific commands (e.g., --shell-allow-list "pytest,git,make"), recommended for safe defaults, or all to permit any command. The DEEPAGENTS_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 backend 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. Set model credentials

   Export your provider’s API key as an environment variable. For example:

   export OPENAI_API_KEY="your-api-key"
   

   The CLI works with any LLM that supports tool calling — OpenAI, Anthropic, Google, Ollama, and many more. See Providers for setup details.

2. Install and run

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

   Script

   curl -LsSf https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/scripts/install.sh | bash
   

   DEEPAGENTS_EXTRAS="ollama,groq" curl -LsSf https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/scripts/install.sh | bash
   

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

   deepagents
   
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)

   See agent operations, tool calls, and decisions in LangSmith:

   export LANGCHAIN_TRACING=true
   export LANGCHAIN_API_KEY="your-api-key"
   

Providers

The CLI is intentionally lightweight—it ships with OpenAI, Anthropic, and Google support out of the box. Each additional model provider is a separate dependency, so you only pull in what you need.

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

To use a specific provider, pass --model at launch or switch mid-session with /model.

deepagents --model anthropic:claude-opus-4-5

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
• /remember [context] - Review conversation and update memory and skills. Optionally pass additional context
• /offload - Free up context window space by offloading messages to backend storage with a summary placeholder in the conversation. 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
• /reload - Refresh runtime configuration (API keys, .env changes, shell allow-list) without restarting. Conversation state is preserved
• /trace - Open the current thread in LangSmith (requires LANGSMITH_API_KEY)
• /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+E	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"

When piped input is combined with -n or -m, the piped content is prepended to the flag’s value.

The maximum piped input size is 10 MiB.

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

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" --shell-allow-list "pytest,git,make"

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

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

--shell-allow-list all lets the agent execute arbitrary shell commands with no human confirmation. Use with caution.

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-4-5
> /model openai:gpt-4o

deepagents --model openai:gpt-4o

Run /model with no arguments to open an interactive model selector that displays available models grouped by provider.

For full details on switching models, setting a default, and configuring custom providers, see Model providers. For config.toml reference and lifecycle hooks, see Configuration.

Interactive model selector

The selector shows a detail footer for the highlighted model with context window size, input modalities (text, image, audio, PDF, video), and capabilities (reasoning, tool calling, structured output). Values overridden by --profile-override or config.toml are marked with a yellow * prefix.

Model parameters

Pass extra model constructor parameters when switching mid-session using --model-params:

> /model --model-params '{"temperature": 0.7}' anthropic:claude-sonnet-4-5
> /model --model-params '{"temperature": 0.7}'  # opens selector, applies params to chosen model

These are session-only overrides and take the highest priority, overriding values from config file params. --model-params cannot be combined with --default.

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 defaults, provider settings, constructor params, profile overrides, MCP trust store
~/.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 list

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

Teach your agent project conventions

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:

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

Customize your deep agent

There are two primary ways to customize any agent:

• Memory: Global and project-specific AGENTS.md files which are loaded in full at session start. Use memory for general coding style and preferences.

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

Provide project or user context

AGENTS.md files provide persistent memory 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. Use /remember to explicitly prompt the agent to update its memory and skills from the current conversation.

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/

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/`.

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.

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

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. See the MCP tools guide for configuration format, 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.

1. Install provider dependency

   Install the LangChain integration package for your sandbox provider:

   Daytona

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

   Runloop

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

   Modal

   uv tool upgrade deepagents-cli --with langchain-modal
   
2. Set provider credentials

   Daytona

   export DAYTONA_API_KEY="your-key"
   

   Runloop

   export RUNLOOP_API_KEY="your-key"
   

   Modal

   modal setup
   
3. Run the CLI with a sandbox

   Daytona

   deepagents --sandbox daytona
   

   Runloop

   deepagents --sandbox runloop
   

   Modal

   deepagents --sandbox modal
   

Sandbox flags and examples

Flag	Description
--sandbox TYPE	Sandbox provider to use: 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 in a LangSmith project:

1. Enable LangSmith tracing:

   export LANGCHAIN_TRACING=true
   export LANGCHAIN_API_KEY="your-api-key"
   

2. Configure agent tracing for deep agent operations such as tool calls and agent decisions:

   export DEEPAGENTS_LANGSMITH_PROJECT="my-deep-agent-execution"
   

3. If you are building a LangChain app with deep agents, and want to separate agent traces from your app’s traces, also configure LANGSMITH_PROJECT:

   export LANGSMITH_PROJECT="my-app-calls-to-langchain"
   

When configured, the CLI displays:

✓ 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-sonnet-4-5
deepagents --model gpt-4o

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

Command-line options

Option	Description
-a, --agent NAME	Use named agent with separate memory (default: agent)
-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)
-n, --non-interactive TEXT	Run a single task non-interactively and exit. Shell is disabled unless --shell-allow-list is set
-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
--auto-approve	Auto-approve all tool calls without prompting (disables human-in-the-loop). Toggle with Shift+Tab during an interactive session
--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 (list, reset, threads, skills). Output envelope: {"schema_version": 1, "command": "...", "data": ...}
--sandbox TYPE	Remote sandbox for code execution: none (default), modal, daytona, runloop, langsmith
--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)
-v, --version	Display version
-h, --help	Show help

CLI commands

Command	Description
deepagents help	Show help
deepagents list	List all agents
deepagents reset --agent NAME	Clear agent memory and reset to default
deepagents reset --agent NAME --target SOURCE	Copy memory from another agent
deepagents skills list [--project]	List all skills (alias: ls)
deepagents skills create NAME [--project]	Create a new skill with template SKILL.md
deepagents skills info NAME [--project]	Show detailed information about a skill
deepagents skills delete NAME [--project] [-f]	Delete a skill and its contents
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

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

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