langchain-agents-anton-abyzov-specweave
LangChain & AI Agent Frameworks expert. Covers LCEL chains, ReAct/Plan-and-Execute/Multi-Agent architectures, tool calling, memory systems, LangGraph, CrewAI, prompt engineering, streaming, LangSmith tracing, RAG integration, and production deployment patterns.
npx skills add majiayu000/claude-skill-registry --skill langchain-agents-anton-abyzov-specweave --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain & AI Agent Frameworks Expert guidance for building LLM-powered applications using LangChain, LangGraph, CrewAI, and related agent frameworks. Covers chain composition, agent architectures, tool integration, memory management, and production deployment. ## LCEL (LangChain Expression Language) ### Chain Composition with Runnables LCEL is the declarative way to compose LangChain components. Every component implements the Runnable interface. ```python from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI # Basic chain: prompt | model | parser prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant specialized in {domain}."), ("human", "{question}") ]) model = ChatOpenAI(model="gpt-4o", temperature=0) parser = StrOutputParser() chain = prompt | model | parser # Invoke synchronously result = chain.invoke({"domain": "Python", "question": "Explain decorators"}) # Async invoke result = await chain.ainvoke({"domain": "Python", "question": "Explain decorators"}) # Batch processing results = chain.batch([ {"domain": "Python", "question": "Explain decorators"
- LCEL (LangChain Expression Language)
- Chain Composition with Runnables
- Parallel and Branching Chains
- Fallback Chains
- Routing
- Agent Architectures
- ReAct Agent (Reasoning + Acting)
- Tool Calling Agent (Preferred for Modern Models)
- Plan-and-Execute Agent
- Tool Calling and Function Calling
- Defining Tools
- Binding Tools to Models
- Memory Systems
- Conversation Memory
What does the langchain-agents-anton-abyzov-specweave skill do?
LangChain & AI Agent Frameworks expert. Covers LCEL chains, ReAct/Plan-and-Execute/Multi-Agent architectures, tool calling, memory systems, LangGraph, CrewAI, prompt engineering, streaming, LangSmith tracing, RAG integration, and production deployment patterns.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langchain-agents-anton-abyzov-specweave --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.