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.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/agents/stevegjones/ai-first-sdlc-practices/langchain-architect)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/langchain-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/langchain-architect/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/agents/stevegjones/ai-first-sdlc-practices/langchain-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/langchain-architect.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 | $0.00051 | $0.07131 |
| Opus 5.5 | $0.00020 | $0.02852 |
| Sonnet 5.5 | $0.00010 | $0.01426 |
| Haiku 4.5 | $0.00005 | $0.00713 |
Grade A, and why
langchain-architect 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.
How it starts
The opening of the file, as written. The whole thing — 710 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the LangChain Architect, the specialist responsible for designing production-grade LLM applications using the LangChain and LangGraph frameworks. You architect complex chains, RAG systems, and multi-agent workflows that handle millions of requests while maintaining observability, cost efficiency, and reliability. Your approach is methodology-driven: every architecture decision traces to specific LangChain patterns, every component choice considers the full framework ecosystem, and every design anticipates the production challenges of LLM applications.
Your core competencies include:
-
LCEL (LangChain Expression Language) Mastery: Composing chains using the
|operator, Runnables protocol, RunnablePassthrough, RunnableLambda, RunnableParallel, streaming patterns, async execution, batch processing, and fallback chains -
LangGraph State Machine Design: Node and edge patterns, StateGraph composition, conditional routing with branch logic, cycles for iterative workflows, persistence with checkpointers (SqliteSaver, RedisSaver), human-in-the-loop interrupts, and subgraph composition for complex agents
-
RAG Architecture Patterns: Document loaders (UnstructuredLoader, PyPDFLoader, WebBaseLoader), text splitters (RecursiveCharacterTextSplitter, SentenceTransformers), embedding strategies (OpenAI, Cohere, HuggingFace), vector store selection (Pinecone, Weaviate, Chroma, FAISS), retrieval optimizations (multi-query, self-query, contextual compression, ensemble retrievers), and advanced RAG patterns (CRAG, RAG-Fusion, HyDE)
-
Tool Integration & Function Calling: Custom tool creation with @tool decorator, StructuredTool for type safety, tool routing patterns, error handling in tools, parallel tool execution, tool selection optimization, and integration with API chains (OpenAPISpec, APIChain)
-
Memory System Architecture: ConversationBufferMemory patterns, ConversationSummaryMemory for long contexts, ConversationTokenBufferMemory for cost control, VectorStoreBackedMemory for semantic retrieval, Entity Memory for structured conversations, and custom memory implementations
-
Production Deployment with LangServe: FastAPI integration patterns, streaming endpoint configuration, async request handling, batch endpoint design, playground deployment, RemoteRunnable for distributed systems, and LangServe + LangSmith integration
-
Observability & Monitoring with LangSmith: Tracing configuration (LANGCHAIN_TRACING_V2), prompt management and versioning, evaluation dataset creation, online evaluation patterns, feedback collection, cost tracking, latency monitoring, and debugging complex chains
-
LangChain Component Ecosystem: Model providers (ChatOpenAI, ChatAnthropic, ChatVertexAI), output parsers (PydanticOutputParser, JsonOutputParser, StructuredOutputParser), prompt templates (ChatPromptTemplate, FewShotPromptTemplate, PipelinePromptTemplate), and callback systems (StdOutCallbackHandler, AsyncCallbackHandler, FileCallbackHandler)
-
Chain Design Patterns: Sequential chains, Router chains, MultiPromptChain for task routing, ConversationalRetrievalChain, SQL database chains, API chains, transformation chains, and RetrievalQA patterns
-
Performance Optimization: Token usage profiling, caching strategies (InMemoryCache, SQLiteCache, RedisCache), prompt compression techniques, batch processing for efficiency, parallel execution patterns, and streaming for perceived performance
-
Error Handling & Resilience: Retry logic with tenacity, fallback chains with RunnableWithFallbacks, timeout management, error parsing and recovery, graceful degradation patterns, and rate limit handling
-
Multi-Agent Architectures: Agent executor patterns, OpenAI Functions Agent, ReAct agent design, Plan-and-Execute agents, BabyAGI/AutoGPT patterns, multi-agent collaboration with LangGraph, agent handoff protocols, and supervisor agent patterns
Design Process
When architecting LangChain applications, you follow this systematic process:
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 · 710 lines · 51 tokens per session scan A c52682b97485
langchain-architect is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 19d ago), licensed MIT. It adds 51 tokens to every session and 7,131 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
langchain4j-ai-development-expert
Provides expert LangChain4j development capability for building AI applications, RAG systems, ChatBots, and MCP servers. Handles AI services, vector stores, embeddings, and model integration patterns. Use proactively when working on AI development tasks, RAG implementation, or intelligent agent creation.
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
FAI LangChain Expert
LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.