Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add frank-luongt/faos-skills-marketplace --skill langchain-architecturegit clone --depth 1 https://github.com/frank-luongt/faos-skills-marketplaceWrote 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/frank-luongt/faos-skills-marketplace/langchain-architecture)<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/langchain-architecture"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/langchain-architecture/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/frank-luongt/faos-skills-marketplace/langchain-architecture"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/langchain-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02385 |
| Opus 5.5 | $0.00000 | $0.00954 |
| Sonnet 5.5 | $0.00000 | $0.00477 |
| Haiku 4.5 | $0.00000 | $0.00238 |
Grade A, and why
langchain-architecture 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 1mo 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.
This is a copy
92% identical to langchain-architecture — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: langchain-architecture description: Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows. tags: [ai, langchain]
LangChain Architecture
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
Do not use this skill when
- The task is unrelated to langchain architecture
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Use this skill when
- 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
Core Concepts
1. Agents
Autonomous systems that use LLMs to decide which actions to take.
Agent Types:
- ReAct: Reasoning + Acting in interleaved manner
- OpenAI Functions: Leverages function calling API
- Structured Chat: Handles multi-input tools
- Conversational: Optimized for chat interfaces
- Self-Ask with Search: Decomposes complex queries
2. Chains
Sequences of calls to LLMs or other utilities.
Chain Types:
- LLMChain: Basic prompt + LLM combination
- SequentialChain: Multiple chains in sequence
- RouterChain: Routes inputs to specialized chains
- TransformChain: Data transformations between steps
- MapReduceChain: Parallel processing with aggregation
3. Memory
Systems for maintaining context across interactions.
Memory Types:
- ConversationBufferMemory: Stores all messages
- ConversationSummaryMemory: Summarizes older messages
- ConversationBufferWindowMemory: Keeps last N messages
- EntityMemory: Tracks information about entities
- VectorStoreMemory: Semantic similarity retrieval
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.
- 1mo ago First seen · 354 lines · 0 tokens per session scan A 953039179e4d
langchain-architecture is a skill published in the GitHub repository frank-luongt/faos-skills-marketplace (37 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,385 tokens. A static security scan graded it A with 0 findings. It is 92% identical to langchain-architecture, differing in 16 lines, and is treated as a copy.
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