langchain-agents-workflow
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- Author updated Live
- Author repo skills-registry
- Domain
- DevOps
- Compatible agents
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- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Manual integration
- External API key
- Required · OpenAI / Anthropic
- Operating systems
- Docker
- Runtime requirements
- Python · Docker
- Permissions
-
- Read-only
- Write / modify
- Env read
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: langchain-agents-workflow
description: Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the de…
category: devops
runtime: Python / Docker
---
# langchain-agents-workflow output preview
## PART A: Task fit
- Use case: Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the develop -> middleware -> evaluate -> deploy lifecycle, mapping each step to the right official tool..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Version floors this bundle assumes / You have two complementary tools / When to load which skill” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the develop -> middleware -> evaluate -> deploy lifecycle, mapping each step to the right official tool.”.
- **02** When the source has headings, the agent prioritizes “Version floors this bundle assumes / You have two complementary tools / When to load which skill” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, read environment variables; may access external network resources; requires OpenAI / Anthropic API keys.
## Running Rules
- read files, write/modify files, read environment variables; may access external network resources; requires OpenAI / Anthropic API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, read environment variables.
Validate with a small run
Start with a small task and check whether the result follows “Version floors this bundle assumes / You have two complementary tools / When to load which skill”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: langchain-agents-workflow
description: Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the de…
category: devops
source: tomevault-io/skills-registry
---
# langchain-agents-workflow
## When to use
- Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the develop -> middleware ->…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Version floors this bundle assumes / You have two complementary tools / When to load which skill” and keep inference separate from source facts.
- read files, write/modify files, read environment variables; may access external network resources; requires OpenAI / Anthropic API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "langchain-agents-workflow" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Version floors this bundle assumes / You have two complementary tools / When to load which skill
rules -> SKILL.md triggers / order / output contract
runtime -> Python / Docker | read files, write/modify files, read environment variables | may access external network resources
guardrails -> requires OpenAI / Anthropic API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}LangChain Agents Workflow
This skill is the entry point — read it first, then load the more specific skill for the step you're on.
Version floors this bundle assumes
langchain >= 1.2(v1 series — middleware-firstcreate_agent)langgraph >= 1.1,langgraph-cli >= 0.4deepagents >= 0.5.3(async sub-agents, structured sub-agent responses, filesystem permissions;model=Nonedeprecated)langsmith >= 0.7(pytest plugin + new evaluator API)langchain-anthropic >= 1.4,langchain-openai >= 1.0
If a project pins anything below these floors, suggest the bump before writing code — the API shapes in this bundle assume the v1+ surface.
You have two complementary tools
This skill bundle pairs with the mcpdoc MCP server. The two have distinct roles; use both.
mcpdoc (MCP server) |
This skill bundle | |
|---|---|---|
| Purpose | Live API reference | Opinionated playbook |
| Content | Whatever's on docs.langchain.com right now |
How to think about LangChain projects |
| When to use | "What's the signature of SummarizationMiddleware?" / "What kwargs does create_agent take?" / "What import path for X?" |
"What's the production middleware stack?" / "How do I wire Cloud Run + Secret Manager + Postgres checkpointer together?" / "Which mistakes does an agent typically make here?" |
| How to use | Call fetch_docs(url) or list_doc_sources() |
Load skills based on description triggers |
| Drift risk | Zero — always live | Owner updates as ecosystem evolves; some rot tolerated |
Rule of thumb: when you need an exact API detail, fetch from mcpdoc. When you need to make a design decision, load a skill. When in doubt, do both.
When to load which skill
| Goal | Skill |
|---|---|
| Start a new agent project | langchain-agents-scaffold |
| Build a modern agent (most cases) | langchain-agents-middleware ← first; uses create_agent(...) with middleware |
| Add nodes/edges/tools to a custom LangGraph | langchain-agents-langgraph-code |
| Customize a DeepAgent | langchain-agents-deepagents-code |
| Build a non-agentic LCEL pipeline (chains, RAG) | langchain-agents-langchain-code |
| Write or run evals; unit/integration test agents | langchain-agents-langsmith-evals |
| Deploy + productionise | langchain-agents-deploy |
| Debug / read traces / OTEL | langchain-agents-observability |
Mental model
Three layers in the modern stack:
create_agent(model, tools, middleware=...)— the v1 default for building agents. Middleware is how you add retries, fallbacks, summarization, HITL, PII handling, call limits. Read the middleware skill for the production stack.- Raw LangGraph (
StateGraph) — drop down whencreate_agentisn't enough (multi-graph workflows, custom state, parallel branches). Read langgraph-code. - DeepAgents —
create_deep_agent(...)iscreate_agent(...)pre-loaded withFilesystemMiddleware+SubAgentMiddleware+TodoListMiddleware. Read deepagents-code.
For non-agentic flows (RAG, classification), use plain LCEL chains — middleware does not apply to chains.
Common commands by lifecycle stage
| Stage | Command(s) |
|---|---|
| Scaffold a LangGraph project | langgraph new my-agent --template react-agent |
| Scaffold a DeepAgent / chain | No scaffolder — write a small agent.py (see scaffold skill) |
| Install deps | pip install -e . or uv sync |
| Iterate on a graph | langgraph dev |
| Run an agent ad hoc | python -c "from agent.agent import agent; print(agent.invoke({'messages': [...] }))" |
| Run evals | python evals/run.py |
| Unit-test agents (no API calls) | pytest with LLMToolEmulator middleware |
| Deploy to LangSmith Cloud | langgraph build -t my-agent && langgraph deploy |
| Deploy to Cloud Run | gcloud run deploy my-agent --source . |
| Deploy as a Docker image | docker build && docker run --env-file .env |
Hard rules
- Look up exact APIs via
mcpdoc, don't guess. Ifmcpdocisn't configured, ask the user to set it up (see this repo's README) before you write LangChain code. - Always check what's already installed before suggesting
pip install—pip show langchain langgraph deepagents langsmith. - Never print
.envcontents — refer to keys by name only. - For ANY production agent, add the production middleware stack (call limits, retries, fallback, summarization). Copy-paste-ready in the middleware skill.
- Run smoke evals before any deploy. Not enforced — you must do it.
- Read the project structure first (
ls,tree -L 2) before assuming layout.
Required environment variables (most projects)
LANGSMITH_API_KEY— for tracing and evals.LANGSMITH_TRACING=true— enables trace capture.LANGSMITH_PROJECT— trace bucket name.- One of
OPENAI_API_KEY,ANTHROPIC_API_KEY, etc.
If any are missing when needed, fail fast with a clear message that names the missing variable.
Source: cwijayasundara/agent_cli_langchain — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review