soba-labs/langchain-agent-skills

deepagents-setup-configuration

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

First seen Feb 14, 2026

Installation

$ npx skills add soba-labs/langchain-agent-skills --skill deepagents-setup-configuration

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 106
License LICENSE
Default branch main
Open issues 1
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 7,567 B
  • docs SUMMARY.md 479 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 28 installs

SKILL.md

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 createreactagent 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/initdeepagent_project.py: scaffolds Python/JS projects with templates.
  • scripts/validatedeepagent_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.

Recommended Workflow

  1. Decide if Deep Agents is the right abstraction.
  2. Scaffold with initdeepagent_project.py (Python or JS).
  3. Customize tools, prompt, backend, subagents, and persistence.
  4. Run validatedeepagent_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

Need Deep Agents LangChain create_agent LangGraph
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.

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

Validate Agent Configuration

Run static validation before shipping examples/templates:

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 (createdeepagent(**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

Core Configuration Patterns

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 virtualmode=True with rootdir.

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.createreactagent is deprecated in LangGraph v1.
  • For standard agents, prefer langchain.agents.create_agent.
  • For harness capabilities (planning/filesystem/subagents), use deepagents.createdeepagent / 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