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⚡ Langchain apps in production using Jina & FastAPI
The main features of jina-ai/langchain-serve are: Application Services, Serving Frameworks.
Projects with overlapping indexed features include: amosjyng/langchain-visualizer — Visualization and debugging tool for LangChain workflows. andrewnguonly/chatabstractions — LangChain chat model abstractions for dynamic failover, load balancing, chaos engineering, and more! bentoml/bentoml — BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package,… blackhc/llm-strategy — Directly Connecting Python to LLMs via Strongly-Typed Functions, Dataclasses, Interfaces & Generic Types. chainlit/chainlit — Chainlit is a Python framework designed for building and deploying interactive, stateful conversational AI interfaces.… ajndkr/lanarky — The web framework for building LLM microservices [deprecated].
Visualization and debugging tool for LangChain workflows
LangChain chat model abstractions for dynamic failover, load balancing, chaos engineering, and more!
BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It
