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LangChain Framework

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LangChain is a Python/JS framework for composing LLM applications. It abstracts prompt templates, memory, chains, and tool integration. Developers use it to build chatbots, question-answering systems, and autonomous agents. Mastery takes 4-6 weeks. Senior practitioners command 25-30% premiums because they can architect multi-stage LLM workflows. This is the most-used LLM framework in industry (2026).

LangChain Framework maali?

LangChain is a Python and JavaScript framework for building applications with large language models. It provides abstractions for common patterns: prompts (templates + input variables), chains (sequences of LLM calls), memory (conversation history), agents (LLMs that decide which tools to use), and tool integration (web search, calculators, databases). Without LangChain, you'd manually manage prompts, parse LLM outputs, handle errors, and orchestrate workflows. LangChain standardizes these patterns. Most LLM applications built in 2024-2026 use LangChain or its competitors (LlamaIndex, Haystack).

🔧 MEESHAALEE & SIRNA NAANNOO
LangChain PythonLangChain JSOpenAI APIAnthropic Claude APIVector databasesPrompt engineeringMemory systemsTool integration

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PythonAi Prompt Engineering

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NaannooJalqabaaGiddu-galeessaAngafa
USA$80k$135k$200k
UK£50k£82k£125k
EU€55k€90k€135k
CANADAC$85kC$145kC$215k

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When should I use LangChain vs CrewAI?
LangChain: foundational primitives (chains, memory, tools). Use if you're building custom orchestration. CrewAI: higher-level agents with roles and goals. Use if you want agents to think independently. Most: LangChain for building blocks, CrewAI for agent systems. CrewAI uses LangChain under the hood.
How do I add memory to a chain?
LangChain provides ConversationMemory, SummaryMemory, etc. Wrap your chain with memory: `memory = ConversationBufferMemory()`, then pass to chain init. Chain auto-retrieves conversation history, includes in prompt. Memory limits can be set (max tokens, conversation turns).
What's the difference between a chain and a tool?
Chain: sequence of LLM prompts (A asks B which asks C). Tool: external function (calculator, web search, database query) that LLM can call. Chains compose chains. Tools give LLMs agency (can invoke external compute). Both are primitives.
How do I handle token limits in long conversations?
Use SummaryMemory (summarize old conversation) or buffer with sliding window (keep last N turns). LangChain's built-in memory managers handle this. Monitor token count with `get_token_count()` to avoid surprises.
Can I use LangChain with open-source models?
Yes. LangChain supports Llama, Mistral, Falcon via Hugging Face, Ollama, Together AI. Same API surface. Trade-off: open models are cheaper but lower quality than GPT-4/Claude.

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