# Design Reliable LangChain Applications

Complex LLM applications need clear patterns for agents, retrieval, memory, and monitoring. This skill provides practical LangChain architecture guidance and production checks.

## Install

```bash
npx skillstore add sickn33/langchain-architecture
```

## Metadata

- Status: approved
- Slug: langchain-architecture
- Skillstore revision: r2
- Version status: missing
- Tree hash: 2e482af5aae6bc3389a1a3bb572bd397ec3121a3e14de4862cbf94fa25881e6b
- Author: sickn33
- GitHub username: sickn33
- License: MIT
- Repository: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/langchain-architecture
- Ref: ebdfe608f5de2b66ff37ab4af12af8ac4f5e8006
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/langchain-architecture
- Manifest: https://skillstore.pages.dev/api/skills/langchain-architecture/manifest

## Capabilities

- Explains agents, chains, memory, document processing, and callbacks.
- Provides example patterns for retrieval, custom tools, and sequential chains.
- Compares memory approaches for short, long, windowed, entity, and vector contexts.
- Shows callback patterns for logging, monitoring, and error handling.
- Outlines testing, caching, batch processing, streaming, and production checks.

## Use Cases

- Plan a retrieval application: Define document loading, chunking, embeddings, storage, retrieval, and source-return requirements.
- Review an agent design: Assess tool descriptions, memory selection, error handling, timeouts, validation, and observability.
- Prepare production safeguards: Create a checklist for testing, token monitoring, rate limits, fallbacks, prompt versions, and callbacks.

## Prompt Templates

### Choose core components

```
Recommend LangChain components for [application], considering inputs, outputs, data sources, conversation length, and deployment constraints.
```

### Design a retrieval flow

```
Design a LangChain retrieval workflow for [documents] and [queries], including chunking, embeddings, storage, retrieval, citations, and evaluation.
```

### Review an agent

```
Review this LangChain agent design: [design]. Identify tool, memory, validation, timeout, error-handling, logging, and testing gaps.
```

### Create a production architecture

```
Produce a production LangChain architecture for [requirements], covering orchestration, retrieval, state, observability, scaling, security boundaries, failure recovery, and verification.
```

## Limitations

- Examples use older LangChain APIs and may require migration for current releases.
- The skill provides guidance and examples but does not execute or deploy applications.
- Referenced resource and asset files are not included in this package.
- Environment-specific security, costs, credentials, and service limits still require validation.

## Best Practices

- Clarify inputs, tools, permissions, state, constraints, and success measures before selecting components.
- Validate retrieval, prompts, tool calls, and memory behavior with representative tests.
- Add timeouts, rate limits, safe logging, error handling, monitoring, and fallback paths before production.

## Anti Patterns

- Do not give agents broad tool access without validation and permission boundaries.
- Do not retain unlimited conversation history or sensitive data without a retention policy.
- Do not deploy examples unchanged without checking current LangChain APIs and environment requirements.

## Security Audit

- Audited at: 2026-07-23T15:40:20.856\+00:00
- Summary: All 20 external-command findings are false positives caused by Markdown backticks and fenced Python examples. The reconnaissance finding is generic agent scope text, not network probing. No prompt injection or malicious intent was found.

## Stats

- Views: 121
- Downloads: 10
- Favorites: 0
- Popularity score: 0