langchain production starter

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Basic Information

This repository is a starter project that provides scaffolding to build, run, and deploy LangChain-based agents in production with memory and Telegram integration. It is focused on getting a multimodal conversational agent online quickly by offering example code and deployment steps rather than a finished consumer product. The README highlights adding an agent implementation in src/api.py, installing Python dependencies from requirements.txt, running locally with python src/api.py, and deploying with the Steamship CLI using ship deploy and ship use. It also points to documentation for registering a Telegram bot and configuring a payment provider for monetization. The project supports development in local or VS Code dev containers and is licensed under MIT.

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App Details

Features
The project documents and includes key capabilities to accelerate creation of LangChain agents. It lists support for OpenAI GPT-4 and GPT-3.5, built-in memory support for agents, an embeddable chat window example, and connectors to Telegram for message delivery. It mentions options to give the agent a voice and to monetize the bot via payment provider configuration. The repo provides quick-start instructions, example API placement at src/api.py, a requirements.txt for dependencies, deployment commands using the Steamship CLI, and developer workflows for local and containerized VS Code development. Documentation files cover Telegram registration and payment setup.
Use Cases
This starter makes it easier for developers to prototype and deploy conversational agents by providing ready-made scaffolding and clear next steps. It reduces setup time by centralizing example agent code, dependency lists, and deployment commands so developers can focus on agent logic rather than infra. Telegram integration and guides mean teams can quickly connect a bot to a messaging channel and configure monetization. Support for OpenAI models and agent memory accelerates building interactive, stateful assistants. The inclusion of dev container workflows and local run instructions helps teams test and iterate prior to deployment, making the project useful for productionizing LangChain-based chatbots.

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