Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsnpx agentmods add skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestrationWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestration)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestration"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestration"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/langchain-and-langgraph-agent-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.04483 |
| Opus 5 | $0.00069 | $0.02242 |
| Sonnet 5 | $0.00028 | $0.00897 |
| Haiku 4.5 | $0.00014 | $0.00448 |
Grade A, and why
langchain-and-langgraph-agent-orchestration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain and LangGraph Agent Orchestration
Purpose
LangChain provides two things that are easy to conflate: a set of composable
building blocks (prompt templates, model wrappers, retrievers, output
parsers, chained via LangChain Expression Language / Runnable), and a
higher-level AgentExecutor that wraps those blocks into a single-loop,
tool-calling agent. LangGraph is a separate, lower-level runtime built by the
same project specifically for agents whose control flow is not a single
linear loop — it models the agent as an explicit graph of nodes and edges,
supports cycles (a node can route back to an earlier node), and adds two
capabilities AgentExecutor does not have out of the box: durable
checkpointed state (so a run can pause, crash, and resume from its last
checkpoint) and first-class human-in-the-loop interrupts (a graph can pause
at a named node until a human approves, edits, or rejects the pending
state). This skill covers choosing between plain LangChain composition,
AgentExecutor, and LangGraph, and operating LangGraph's persistence and
interrupt features correctly. It is a framework-specific complement to
agent-architecture-design, which
covers the underlying control-flow patterns (ReAct loop, plan-and-execute,
finite-state/graph) in a vendor-neutral way — LangGraph is one concrete
runtime that implements the finite-state/graph pattern described there. For
tool access, LangChain/LangGraph agents can call tools defined directly in
Python or exposed via an MCP server; see
mcp-server-development for building
the tool-serving side, which this skill treats as an external dependency
rather than repeating.
When to use
- Deciding whether a task needs plain LangChain chain composition (a fixed
pipeline, no branching),
AgentExecutor(a single ReAct-style tool-calling loop), or a LangGraph graph (multi-step, branching, cyclical, or needing persistence/human approval). - Building an agent whose steps depend on prior results in ways a single linear chain can't express — retries, conditional branches, or loops back to an earlier step.
- Adding durable state to a LangChain/LangGraph agent so a long-running or multi-session workflow survives a process restart or crash mid-run.
- Adding a human-in-the-loop approval checkpoint before an irreversible tool call in an existing LangGraph graph.
- An agent built with
AgentExecutorloses context, re-does completed work, or can't be paused/resumed, and the team is evaluating migrating it to LangGraph. - Debugging a LangGraph graph that loops indefinitely, gets stuck at an interrupt, or fails to restore state correctly from a checkpoint.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 384 lines · 139 tokens per session scan A 3a2c7c038019
langchain-and-langgraph-agent-orchestration is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (39 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 139 tokens to every session and 4,483 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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