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LangChain & LangGraph Architecture · wshobson | DecimalAI

agent toolingby wshobson

LangChain & LangGraph Architecture

wshobson/langchain-architecture

Source wshobson/agents ↗License MIT

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration.

SkillScore100Maintained

Automatic

AI Engineer pack

Last verified 8/7/2026

What it changes

Passed 23 of 23 test cases with the skill, and 21 of 23 without it.

Without skill91%

With skill100%

Checks it passed that the model alone missed

subclasses MessagesState
mocks the model ainvoke method using unittest.mock

2 fixed21 passed either way0 still failing0 broke

Pass-rate lift: +9 pp
Tokens: +107%
Agent turns: 0%
Response time: -10%

Model gemini-3.6-flashMeasured 8/7/2026v123 of 23 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/wshobson-langchain-architecture/SKILL.md https://app.decimal.ai/s/wshobson-langchain-architecture/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add wshobson-langchain-architecture, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/wshobson-langchain-architecture/ and pins it in .decimal/skills.lock

decimalai skills add wshobson-langchain-architecture

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/wshobson-langchain-architecture/SKILL.md https://app.decimal.ai/s/wshobson-langchain-architecture/SKILL.md

Raw file: /s/wshobson-langchain-architecture/SKILL.md ↗ · machine-readable summary at /s/wshobson-langchain-architecture.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 2

Prompt

I am defining the state structure for a multi-step workflow using modern LangGraph. I want to extend standard chat message history with a custom `context` attribute holding retrieved documents. What base structure and field annotation pattern should I use for this state class?

What was checked, without and with the skill
Checked	Without skill	With skill
subclasses MessagesState	Missed	Passed
uses TypedDict field annotations with Annotated	Passed	Passed

case-02 · gemini-3.6-flash · 8/7/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md268 lines2 files

LangChain & LangGraph Architecture

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration.

When to Use This Skill

Building autonomous AI agents with tool access
Implementing complex multi-step LLM workflows
Managing conversation memory and state
Integrating LLMs with external data sources and APIs
Creating modular, reusable LLM application components
Implementing document processing pipelines
Building production-grade LLM applications

Package Structure (LangChain 1.x)

langchain (1.2.x)         # High-level orchestration
langchain-core (1.2.x)    # Core abstractions (messages, prompts, tools)
langchain-community       # Third-party integrations
langgraph                 # Agent orchestration and state management
langchain-openai          # OpenAI integrations
langchain-anthropic       # Anthropic/Claude integrations
langchain-voyageai        # Voyage AI embeddings
langchain-pinecone        # Pinecone vector store

Core Concepts

1. LangGraph Agents

LangGraph is the standard for building agents in 2026. It provides:

Key Features:

StateGraph: Explicit state management with typed state
Durable Execution: Agents persist through failures
Human-in-the-Loop: Inspect and modify state at any point
Memory: Short-term and long-term memory across sessions
Checkpointing: Save and resume agent state

Track record

Benchmark

23 / 23

test cases passed, vs 21 / 23 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

wshobson / agents

plugins/llm-application-dev/skills/langchain-architecture/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 1 file

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See it in action

What's inside

LangChain & LangGraph Architecture

When to Use This Skill

Package Structure (LangChain 1.x)

Core Concepts

1. LangGraph Agents

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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