Individual skill
langchain-langgraph-checkpointing
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plugins/saas-packs/langchain-py-pack/skills/langchain-langgraph-checkpointing/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- No
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- Observed behavior
- Not checked yet
- Last checked
- Nov 12, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,916
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- langchain Declared skill tag
- langgraph Declared skill tag
- python Declared skill tag
- langchain-1.0 Declared skill tag
- checkpointing Declared skill tag
- persistence Declared skill tag
- memory Declared skill tag
- engineering Derived category
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Read excerpt
# LangGraph Checkpointing (Python)
## Overview
A chat agent that "keeps introducing itself" is almost always P16. The caller
invokes `graph.invoke(state)` without passing `config={"configurable":
{"thread_id": ...}}` — LangGraph's checkpointer silently spawns a fresh state per
call. No error, no warning, no log line. The user sees it; the code does not.
That is one of five separate checkpointing pitfalls this skill covers:
- **P16** — missing `thread_id` silently resets memory
- **P17** — `interrupt_before` raises `TypeError` when state holds non-JSON values
(`datetime`, `Decimal`, custom classes) — and it raises *at the interrupt
boundary*, not when the bad value was first assigned, so the traceback points