8 Best LlamaDeploy Alternatives in 2026 (Open Source)
LlamaDeploy — Deploy your agentic worfklows to production. vs Ray Serve / BentoML: LlamaIndex-native deployment framework with llamactl CLI — zero-code-change transition from notebook workflows to production multi-service systems
These 8 open-source tools do the same job. They are ordered by how closely they match LlamaDeploy, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| LlamaDeploy(original) | 453 | +-260 | 2026-09-25 |
| BentoML | 8.9k | +52 | 2026-09-07 |
| Jina-Serve | 21.9k | +2 | 2025-03-24 |
| Ray | 44.0k | +332 | 2026-09-30 |
| Langchain-serve | 1.6k | +0 | 2023-09-20 |
| Agno | 42.4k | +551 | 2026-09-30 |
| AgentScope | 32.6k | +1,846 | 2026-09-30 |
| FastAgency | 548 | +3 | 2025-12-09 |
| Eidolon | 492 | +1 | 2024-12-19 |
1. BentoML
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
What sets it apart: Unified model serving framework with Bento packaging — turn any model into a production API with automatic Docker, adaptive batching, and multi-model orchestration
Best for: Teams deploying ML/AI models as production APIs; Applications needing dynamic batching and GPU optimization; Multi-model inference pipelines (LLM + embedding + reranker)
2. Jina-Serve
☁️ Build multimodal AI applications with cloud-native stack
What sets it apart: vs FastAPI/Flask: built-in containerization, gRPC-first architecture, dynamic batching, and one-command Kubernetes/cloud deployment specifically designed for ML serving
Best for: Deploying ML models as scalable microservices; LLM inference with streaming and dynamic batching requirements
3. Ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
What sets it apart: vs Spark: Python-native with actor model and ML-specific libraries (Train/Tune/Serve); vs Dask: broader AI/ML ecosystem with RLlib, serving, and managed Anyscale platform
Best for: Scaling ML training and serving across clusters; Distributed hyperparameter tuning; Building scalable AI inference pipelines
4. Langchain-serve
⚡ Langchain apps in production using Jina & FastAPI
What sets it apart: vs manual FastAPI setup: decorator-based syntax (@serving, @slackbot, @job) for instant LangChain deployment — zero Docker/infrastructure knowledge needed with pre-built agent templates
Best for: Rapid prototyping of LangChain agents for production APIs; Deploying autonomous agents without infrastructure management; Building Slack-integrated AI assistants
5. Agno
Build, run, manage agentic software at scale.
What sets it apart: Production-first agent runtime with built-in session isolation, approval workflows, and scalable FastAPI serving — unlike LangChain which is framework-first
Best for: Production multi-agent systems with session isolation; Enterprise agentic applications needing approval workflows and audit trails
6. AgentScope
Build and run agents you can see, understand and trust.
What sets it apart: Unlike LangGraph (stateful graph orchestration) and CrewAI (role-based crews), AgentScope uniquely combines realtime voice agents, A2A protocol, agentic RL fine-tuning, and Kubernetes-native deployment — designed for the rising capability of agentic LLMs
Best for: Teams building production multi-agent systems with realtime voice and A2A interoperability; Chinese-market developers wanting first-class DashScope/Qwen integration
7. FastAgency
The fastest way to bring multi-agent workflows to production.
What sets it apart: vs raw AutoGen/AG2: production deployment framework with unified interface, built-in testing, and FastAPI/NATS.io adapters for scaling agent workflows
Best for: Teams deploying AG2/AutoGen workflows to production; Projects needing unified console + web interfaces for agent workflows
8. Eidolon
The first AI Agent Server, Eidolon is a pluggable Agent SDK and enterprise ready, deployment server for Agentic applications
What sets it apart: vs LangChain/CrewAI: agents are deployed as HTTP services with built-in server, enabling true microservice agent architectures with dynamic inter-agent tool discovery
Best for: Deploying agents as production HTTP services; Multi-agent systems needing inter-agent communication