LLM MovieAgent leverages LangChain and TheMovieDB API to answer movie-related queries, provide plot summaries, cast & crew info, streaming availability, ratings, and deliver personalized recommendations via natural language interaction. It supports context-aware follow-up questions and integrates seamlessly into Python applications for scalable deployment. Developers can customize prompts, extend data sources, and deploy with Docker for cross-platform compatibility.
LLM MovieAgent leverages LangChain and TheMovieDB API to answer movie-related queries, provide plot summaries, cast & crew info, streaming availability, ratings, and deliver personalized recommendations via natural language interaction. It supports context-aware follow-up questions and integrates seamlessly into Python applications for scalable deployment. Developers can customize prompts, extend data sources, and deploy with Docker for cross-platform compatibility.
LLM MovieAgent is an AI-powered movie assistant built on LangChain and OpenAI that integrates with TheMovieDB API to provide comprehensive movie information. Users can ask about plot summaries, actor biographies, release dates, ratings, and streaming platform availability. The agent maintains conversational context to handle follow-up questions and refine recommendations based on preferences such as genre, mood, and user viewing history. It offers an interactive interface for developers through a Python SDK, enabling seamless integration into chatbots, web apps, and voice assistants. With customizable prompts, multi-turn conversation support, and Docker deployment, LLM MovieAgent simplifies building intelligent movie recommendation and information services.
Who will use LLM MovieAgent?
Movie enthusiasts
Developers building movie chatbots
Film critics and writers
Streaming platform users
How to use the LLM MovieAgent?
Step1: Clone the llm-movieagent repository from GitHub.
Step2: Install dependencies via pip install -r requirements.txt.
Step3: Set OpenAI and TMDB API keys in environment variables.
Step4: Run the agent script using python movie_agent.py.
Step5: Interact by typing natural language movie queries.
Step6: Review the agent’s responses with movie details and recommendations.