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[LangChain/LangGraph] Enhance native API compatibility for private/custom LLMs

Users need improved native support within LangChain and LangGraph to seamlessly integrate private or custom LLM models. This includes full compatibility with advanced API features such as OpenAI-style tool/function calls, non-blocking asynchronous performance, callback hooks, and graph state/context management, which are currently difficult to achieve without custom implementations.

Original Source

@Phu Tran, PhD | 427 pts

LangChain and LangGraph are loved by AI engineers. But what if your projects must use private LLMs that aren't in the LangChain ecosystem, and you still want the beautiful rapid development and orchestration those libraries provide? I'm sharing working code (under MIT license) that shows how to make any private LLM models fully and natively compatible with LangChain and LangGraph APIs. The custom chat model supports OpenAI‑style tool/function calls with automatic tool invocation, non-blocking asynchronous performance, callback hooks, graph state and context management, and example graphs to get you started. Github repo: https://lnkd.in/e9fvWnPP Tutorial: https://lnkd.in/eZEKNX9B If you find it helpful, please consider starring ⭐ the repo and sharing it. And feedback is very much welcomed and appreciated 😇 #HealthcareAI #AIEngineering #LangChain #LangGraph #LLM #PrivateLLM #AI #MLOps #OpenSource #DeveloperTools #Orchestration #OpenAI #API

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