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langchain serve

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Basic Information

langchain-serve is a toolkit to package, deploy and operate LangChain applications on Jina AI Cloud or on-premises infrastructure. It provides a developer-oriented workflow and a CLI to turn LangChain code and FastAPI apps into secure, autoscaling REST and WebSocket services with minimal configuration. The project includes ready-to-deploy app templates such as AutoGPT, BabyAGI, pandas-ai and a PDF question answering bot to demonstrate production usage patterns. It supports streaming LLM interactions, human-in-the-loop workflows, persistent storage mounts, secrets management and OpenAPI/Swagger endpoints for each deployed app. The README notes the repository is archived and read-only, and it documents installation and commands for local, cloud and exportable Kubernetes or Docker Compose deployments.

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App URL
https://github.com/jina-ai/langchain-serve
Github Repository
https://github.com/jina-ai/langchain-serve/blob/main/README.md

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App Details

Features
The README highlights a set of production features for LangChain apps: a serving decorator to expose functions as REST or WebSocket endpoints, a slackbot decorator for building and distributing Slack bots, and a job decorator for one-time asynchronous tasks. It provides an lc-serve CLI to deploy locally or to Jina AI Cloud, export Kubernetes or Docker Compose manifests, manage app lifecycle and upload data. Apps get persistent EFS storage mounted as a workspace, support bearer-token authorization via an auth callback, streaming WebSocket interactions for HITL, automatic OpenAPI/Swagger generation, secure secrets handling at deploy time and tracing hooks for LLM observability. The repo also documents configuration options, pricing model examples and troubleshooting guidance.
Use Cases
This project helps developers move LangChain prototypes into production without managing low-level infrastructure. It reduces operational effort by providing serverless autoscaling, TLS-enabled endpoints, OpenAPI documentation and built-in logging and traces. The workspace mount and secrets support make it straightforward to handle data, API keys and model integrations securely. WebSocket streaming and human-in-the-loop examples enable real-time monitoring and intervention for long-running agent chains. Export capabilities let teams self-host using Kubernetes or Docker Compose when required for compliance or cost control. Example apps and playground commands make it easier to validate integrations such as AutoGPT, BabyAGI, pandas-ai and PDF QnA before full production rollout. The repository is archived and marked read-only, so users should account for lack of ongoing maintenance.

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