Agent skill

Deepagents Setup Configuration

by soba-labs in soba-labs/langchain-agent-skills

Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the deepagents package.

MITAuto-check passedAI & LLM Engineering

Install Deepagents Setup Configuration

skills CLI
$ npx skills add soba-labs/langchain-agent-skills --skill deepagents-setup-configuration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install soba-labs/langchain-agent-skills deepagents-setup-configuration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/soba-labs/langchain-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deepagents-setup-configuration .claude/skills/deepagents-setup-configuration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deepagents-setup-configuration
GitHub stars
107
Token cost
~1.9k tokens
SKILL.md length
548 words
Files
11 (incl. scripts, references, assets)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the deepagents package.

  • Works in 6 steps: Decide if Deep Agents is the right… → Scaffold with init_deep_agent_project.py… → Customize tools, prompt, backend,… → …
  • Users need to create agents with built-in planning/filesystem/subagents
  • SKILL.md covers Use This Skill When, Tooling In This Skill, Recommended Workflow and Choose The Right Abstraction, plus 10 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Deepagents Setup Configuration is an agent skill from soba-labs/langchain-agent-skills. Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the deepagents package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from createreactagent or createagent, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts, reference files and assets (for example `assets/examples/basic-deep-agent/README.md`, `assets/examples/basic-deep-agent/agent.py` and `assets/templates/deep-agent-simple/README.md`).

It sits in AI & LLM Engineering, covering Building AI agents. It works with Python, LangChain, LangGraph and React. The repository describes itself as: A collection of agent-optimized LangChain, LangGraph and LangSmith skills for AI coding assistants. The licence is MIT.

When your agent uses it

  • Users need to create agents with built-in planning/filesystem/subagents
  • Configure middleware/backends/checkpointing/HITL
  • Migrate from createreactagent
  • Scaffold projects with repo scripts

Example prompts

  • “/deepagents-setup-configuration”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Decide if Deep Agents is the right abstraction.
  2. Scaffold with init_deep_agent_project.py (Python or JS).
  3. Customize tools, prompt, backend, subagents, and persistence.
  4. Run validate_deep_agent_config.py.
  5. Use references/deep-agents-reference.md for advanced configuration.
  6. Run the generated project and verify traces/behavior.

What it can do on your machine

Read from SKILL.md and the folder at commit a2d4a10. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.langchain.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deepagents Setup Configuration loads about 1.9k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 548 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from soba-labs/langchain-agent-skills at commit a2d4a10, republished under its MIT licence (© soba-labs). 548 words, ~1,884 tokens.

Download SKILL.mdSave it as .claude/skills/deepagents-setup-configuration/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
deepagents-setup-configuration
description
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.

Deep Agents Setup and Configuration

Deep Agents are an agent harness on top of LangChain + LangGraph with built-in planning, filesystem context management, and subagent delegation.

Use This Skill When

  • You need a Deep Agent quickly (Python or JavaScript).
  • You need subagents, filesystem-backed context, planning (write_todos), or long-term memory patterns.
  • You need migration guidance from older create_react_agent flows.
  • You need to scaffold a starter project with repository scripts.
  • You need to statically validate an agent.py / agent.js / agent.ts config.
  • You need safety checks before open-sourcing Deep Agents examples/templates.

Tooling In This Skill

  • scripts/init_deep_agent_project.py: scaffolds Python/JS projects with templates.
  • scripts/validate_deep_agent_config.py: static checks for Deep Agent config quality.
  • references/deep-agents-reference.md: detailed API, middleware, backends, migration, troubleshooting.
  • assets/templates/deep-agent-simple/: minimal Python starter template.
  • assets/examples/basic-deep-agent/: richer Python example.
  1. Decide if Deep Agents is the right abstraction.
  2. Scaffold with init_deep_agent_project.py (Python or JS).
  3. Customize tools, prompt, backend, subagents, and persistence.
  4. Run validate_deep_agent_config.py.
  5. Use references/deep-agents-reference.md for advanced configuration.
  6. Run the generated project and verify traces/behavior.

Choose The Right Abstraction

NeedDeep AgentsLangChain create_agentLangGraph
Built-in planning/filesystem/subagents✅ Best fit⚠️ Manual middleware setup❌ Manual graph design
Fast path for complex multi-step tasks✅⚠️⚠️
Fully custom graph topology❌❌✅ Best fit
Minimal/simple agent (1-3 steps)⚠️ Overhead✅ Best fit⚠️

Initialize A Project

Use repo-local scripts and prefer uv run.

bash
# Python simple template
uv run skills/deepagents-setup-configuration/scripts/init_deep_agent_project.py my-agent --language python --template simple --path skills/

# Python with subagents
uv run skills/deepagents-setup-configuration/scripts/init_deep_agent_project.py my-agent --language python --template with-subagents --path skills/

# Python CLI-config template (memory/checkpointer toggles)
uv run skills/deepagents-setup-configuration/scripts/init_deep_agent_project.py my-agent --language python --template cli-config --path skills/

# JavaScript template
uv run skills/deepagents-setup-configuration/scripts/init_deep_agent_project.py my-agent --language javascript --template simple --path skills/

Templates currently supported by the script:

  • simple
  • with-subagents
  • cli-config

Generated outputs include:

  • agent.py or agent.js
  • tools/example_tools.py or tools/example_tools.js
  • .env.example
  • README.md
  • .gitignore
  • pyproject.toml (Python) or package.json (JavaScript)

Validate Agent Configuration

Run static validation before shipping examples/templates:

bash
uv run skills/deepagents-setup-configuration/scripts/validate_deep_agent_config.py path/to/agent.py
uv run skills/deepagents-setup-configuration/scripts/validate_deep_agent_config.py path/to/agent.js
uv run skills/deepagents-setup-configuration/scripts/validate_deep_agent_config.py path/to/agent.ts

Validator behavior:

  • Errors on missing agent calls or invalid file types.
  • Warns on risky/weak configs (missing prompt, odd backend usage, deprecated models).
  • Supports dynamic config patterns (create_deep_agent(**kwargs), createDeepAgent(config)), with warning that some static checks are skipped.
  • Validates HITL style: interrupt_on / interruptOn should be mapping/object, and requires checkpointer.

Current Deep Agents Defaults (Verified)

Default middleware includes:

  1. TodoListMiddleware
  2. FilesystemMiddleware
  3. SubAgentMiddleware
  4. SummarizationMiddleware
  5. AnthropicPromptCachingMiddleware
  6. PatchToolCallsMiddleware

Conditionally added middleware:

  • MemoryMiddleware when memory is set
  • SkillsMiddleware when skills is set
  • HumanInTheLoopMiddleware when interrupt_on / interruptOn is set
Show full SKILL.md (215 more words)Show less

Core Configuration Patterns

python
agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",  # string or model object
    tools=[...],
    system_prompt="...",
    subagents=[...],          # optional delegation specialists
    middleware=[...],         # optional custom middleware
    store=store,              # needed for StoreBackend patterns
    backend=backend_factory,  # State/Store/Filesystem/Composite
    checkpointer=checkpointer # required for HITL interrupts
)

Backend guidance:

  • StateBackend (default): thread-scoped, ephemeral.
  • StoreBackend: persistent files via LangGraph store (requires store=).
  • CompositeBackend: route prefixes (common /memories/ -> StoreBackend).
  • FilesystemBackend: direct disk access; use carefully, prefer virtual_mode=True with root_dir.

HITL And Persistence

If using human approval interrupts:

  • Python: use interrupt_on={...}
  • JavaScript: use interruptOn={...}
  • Always provide a checkpointer (InMemorySaver, MemorySaver, Sqlite/Postgres saver, etc.)

Migration Guidance

  • langgraph.prebuilt.create_react_agent is deprecated in LangGraph v1.
  • For standard agents, prefer langchain.agents.create_agent.
  • For harness capabilities (planning/filesystem/subagents), use deepagents.create_deep_agent / createDeepAgent.

Versioning Note

  • deepagents is currently a pre-1.0 package, so minor-version upgrades may include API changes.
  • Re-validate generated templates and examples when bumping deepagents versions.

Open-Source Safety Checklist

Before publishing this skill:

  • Ensure no real secrets are committed (.env.example must stay placeholder-only).
  • Remove generated artifacts like __pycache__/ and *.pyc from skill folders.
  • Avoid absolute local paths in code/examples.
  • Keep provider credentials in environment variables only.
  • Re-run validator on all shipped agent.py / agent.js templates.

Troubleshooting Quick Hits

  • Model/tool-call errors: verify tool-calling model and provider credentials.
  • Files not persisting: confirm StoreBackend route + store= wiring.
  • HITL not interrupting: verify interrupt mapping/object and checkpointer.
  • Too much overhead for simple tasks: use create_agent or plain LangGraph.

Resources

© soba-labs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references, assets) in skills/deepagents-setup-configuration of soba-labs/langchain-agent-skills.

  • SKILL.md
  • assets/examples/basic-deep-agent/README.md
  • assets/examples/basic-deep-agent/agent.py
  • assets/templates/deep-agent-simple/.env.example
  • assets/templates/deep-agent-simple/README.md
  • assets/templates/deep-agent-simple/agent.py
  • assets/templates/deep-agent-simple/pyproject.toml
  • references/deep-agents-reference.md
  • scripts/.gitignore
  • scripts/init_deep_agent_project.py
  • scripts/validate_deep_agent_config.py

Open the folder on GitHubat commit a2d4a10

Compare with similar skills

Deepagents Setup Configuration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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LangSmith Trace DebuggingComposioHQ/awesome-claude-skills77k9 repos~2.7kAutomated safety check: PassNone
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Questions about Deepagents Setup Configuration

What does Deepagents Setup Configuration do?

Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the deepagents package. Deepagents Setup Configuration is an agent skill from soba-labs/langchain-agent-skills. Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the deepagents package.

When should I use Deepagents Setup Configuration?

Deepagents Setup Configuration fits situations like: users need to create agents with built-in planning/filesystem/subagents; configure middleware/backends/checkpointing/HITL; migrate from createreactagent; scaffold projects with repo scripts.

How do I install Deepagents Setup Configuration in Claude Code?

Run `npx skills add soba-labs/langchain-agent-skills --skill deepagents-setup-configuration -a claude-code`. Or copy the skill folder (skills/deepagents-setup-configuration in soba-labs/langchain-agent-skills) into .claude/skills/deepagents-setup-configuration in your project. Claude Code loads it when a task matches its description.

How do I install Deepagents Setup Configuration in Codex?

Run `npx skills add soba-labs/langchain-agent-skills --skill deepagents-setup-configuration -a codex`. Or copy the skill folder (skills/deepagents-setup-configuration in soba-labs/langchain-agent-skills) into .agents/skills/deepagents-setup-configuration in your project. Codex loads it when a task matches its description.

Can I use Deepagents Setup Configuration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add soba-labs/langchain-agent-skills --skill deepagents-setup-configuration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepagents-setup-configuration, .gemini/skills/deepagents-setup-configuration, .github/skills/deepagents-setup-configuration and .opencode/skills/deepagents-setup-configuration in your project.

What does Deepagents Setup Configuration need to run?

Going by SKILL.md and its folder, Deepagents Setup Configuration needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Deepagents Setup Configuration access the network?

SKILL.md names 1 domain. As links in the text: docs.langchain.com. This is read from the text; nothing was executed.

Is Deepagents Setup Configuration safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Deepagents Setup Configuration use?

Deepagents Setup Configuration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deepagents Setup Configuration use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Deepagents Setup Configuration?

Skills that share tags, products or a category with Deepagents Setup Configuration: LangGraph Decision Models (langchain-ai/langchain-skills, 1.3k stars), Langchain Dependencies (langchain-ai/langchain-skills, 1.3k stars), Langgraph Python Quickstart (langchain-ai/langchain-skills, 1.3k stars) and LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepagents Setup Configuration?

soba-labs (a GitHub organization) maintains it in soba-labs/langchain-agent-skills, which has 107 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on August 17, 2026.

Source: soba-labs/langchain-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.