Orchestrate LLMs and external tools to create Python agents for multi-step tasks.
npx skills add https://github.com/belos-street/skill-kit --skill langchain-belos-street
Please help me install this Agent Skill. Skill: langchain Source: https://github.com/belos-street/skill-kit/tree/main/skills/langchain Command: npx skills add https://github.com/belos-street/skill-kit --skill langchain-belos-street
LangChain simplifies the complex work of composing language models, tools, memory, and streaming into reliable agents so developers can build multi-step, tool-enabled AI workflows without reinventing orchestration and integration patterns.
Create a Python agent using LangChain to integrate a weather lookup tool, enable streaming to the client, and produce structured JSON output.
High-intent search queries and answers about installing and using this skill.
To build LLM agents in Python, use a unified interface to compose models, tools, and middleware. This enables multi-step task execution, short-term memory management, and retrieval-augmented generation without manually coding orchestration logic.
Yes, you can stream LLM responses to client interfaces for real-time user experiences. Short-term memory management and summarization middleware track conversation context, allowing agents to maintain state across multiple interactions.
Yes, LangChain supports integration with OpenAI, Anthropic, Google, and Azure providers. It offers configuration and fallback patterns for model integration, allowing developers to connect multiple language models within a single agent workflow.
To add human-in-the-loop approvals and error handling, apply middleware to your AI agents. This includes retry and circuit-breaker patterns, logging, and approval gates to ensure production robustness during multi-step tool execution.
The best way to implement retrieval-augmented generation pipelines is by defining deterministic tools and runtime tool registration. This creates knowledge-backed answers by connecting external data sources directly to your language model workflows.
You can get structured JSON output from an LLM agent by applying structured output schemas. This enforces validated data formats returned by the agent, ensuring reliable integration with downstream systems and APIs.
IbIFACE-TechConfigure and switch between multiple LLM providers like OpenAI, Anthropic, Azure, and Ollama.
ldmrepoBuild real-time streaming AI agent applications using the AG-UI protocol.
JhonHanderConstruct RAG pipelines with OpenAI embeddings and FAISS, Chroma, or Pinecone vector stores.
managedcodeIntegrate provider-agnostic AI abstractions into .NET applications.
