LLM application / agent orchestration framework
Haystack is an open-source Python framework for building production-ready LLM applications with modular pipelines for RAG, semantic search, and agent workflows.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
similar Python all-in-one framework with pipeline abstraction for RAG and semantic search; choose for simpler embedded vector indexing without external search services.
π‘ All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
graph-based state machine design is more flexible than pipelines for complex agent state; choose when you need fine-grained control over agent control flow.
Build resilient agents.
official OpenAI multi-agent framework, lightweight but tightly coupled to OpenAI APIs; choose for projects exclusively on OpenAI models.
A lightweight, powerful framework for multi-agent workflows
batteries-included harness for agentic coding, narrower than general LLM orchestration; choose for code-generation agents.
The batteries-included agent harness.
Google's code-first agent toolkit with built-in evaluation; choose if you are in the Google Cloud ecosystem.
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
focused on production multi-agent harness patterns; choose for large-scale agent fleets.
Multi-Agent Harness for Production AI
TypeScript agent framework; choose when your stack is JavaScript/TypeScript.
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
meta-harness for orchestrating existing CLI agents like Claude Code/Codex; choose to unify multiple external agent tools.
Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents β swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.
Go version of ADK; choose for Go-based agent services.
An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
MCP-first agent framework; choose if you want agent tool integrations standardised on Model Context Protocol.
Build effective agents using Model Context Protocol and simple workflow patterns
low-level SDK to build custom agent harnesses; choose when you need end-to-end control over agent execution.
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
Java distributed agent framework; choose for Java backend agent systems.
Build distributed, production-grade, long-running agents.
event-driven multi-agent framework on Solace; choose for event streaming and broker integration.
An event-driven framework designed to build and orchestrate multi-agent AI systems. It enables seamless integration of AI agents with real-world data sources and systems, facilitating complex, multi-step workflows.
agent harness runtime turning an LLM into a working agent; choose if you want a minimal, separate runtime layer.
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
Rust-core agent framework with Python wrapper; choose for high-performance multi-agent workloads.
GraphBit is the worldβs first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.
Go, event-driven agent framework with MCP discovery; choose for Go-native agent deployment.
Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade observability.
cross-language SDK for integrating LLMs into apps; choose if you need C#/.NET support.
Integrate cutting-edge LLM technology quickly and easily into your apps
Microsoft's agent orchestration framework for .NET/Python; choose for Microsoft-centric agent solutions.
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
Java agent framework with Spring Boot integration; choose for Java/Spring enterprise stacks.
Agentic AI Framework for Java Developers
Java enterprise agentic workflow platform; choose if you need a commercial-friendly Java agent platform.
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
Rust library for LLM applications; choose for Rust performance and memory safety.
βοΈπ¦ Build modular and scalable LLM Applications in Rust
focus on multi-agent swarm orchestration; choose if your application needs structured swarm patterns.
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
formerly AutoGen, conversational multi-agent framework; choose for human-in-the-loop agent conversations.
AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3x
memory-first agent harness on EverOS; choose if persistent memory and self-improvement are key.
The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
deployable RAG system with REST API; choose if you want a turnkey server instead of a composable library.
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
These projects were analysed and named haystack among their alternatives. The relationship is not symmetric β how haystack rates them is a separate judgement, made when haystack is analysed in its own right.
calls haystack βChoose if you want a broader LLM orchestration framework with many integrations rather than llmware's bundled local model catalog.β
Unified framework for building enterprise RAG pipelines with small, specialized models
calls haystack βChoose it when you want to build custom RAG pipelines with explicit control over components in Python.β
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
calls haystack βwhen you need a modular Python framework for custom LLM pipelinesβ
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. π³Docker-friendly.β‘Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
calls haystack βChoose if you want modular, explicit control over pipeline components in an orchestration framework.β
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
calls haystack βChoose when you need a mature, modular LLM pipeline framework with explicit control over retrieval and routing beyond just RAG.β
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
calls haystack βChoose it when you need a modular production RAG/agent pipeline framework rather than graph-based indexing.β
A modular graph-based Retrieval-Augmented Generation (RAG) system
calls haystack βUse when you need a production orchestration framework with modular pipelines and many integrations; PageIndex is a self-contained document index, not a general pipeline framework.β
π PageIndex: Document Index for Vectorless, Reasoning-based RAG
calls haystack βCode-first orchestration framework for LLM applications when you want to avoid a visual platform.β
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.
calls haystack βchoose for a pipeline-centric LLM framework with strong retrieval and modular components.β
Integrate cutting-edge LLM technology quickly and easily into your apps
calls haystack βModular LLM orchestration framework; choose when you need fine-grained pipeline control for production applications.β
"RAG-Anything: All-in-One RAG Framework"
calls haystack βPipeline-based LLM orchestration with explicit retrieval control; choose it for production RAG workflows.β
ποΈ δΈηε ι¨εΆ Β· OpenClaw Multi-Agent Orchestration System β 9 specialized AI agents with real-time dashboard, model config, and full audit trails
calls haystack βChoose when you need a full AI orchestration framework with composable pipelines and many integrations, not a single-file memory layer.β
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
calls haystack βchoose if you want pipeline-based orchestration with explicit retrieval/context control.β
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
calls haystack βChoose when you need a mature, production-ready pipeline framework with extensive integrations.β
π‘ All-in-one AI framework for semantic search, LLM orchestration and language model workflows
calls haystack βChoose when you want a mature NLP framework with extensive pipeline control and retrieval components, accepting higher overhead.β
[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
calls haystack βchoose when you need a modular production LLM/RAG orchestration framework for arbitrary use cases.β
High accuracy RAG for answering questions from scientific documents with citations
calls haystack βchoose when you need a Python pipeline framework with explicit control over retrieval and agents.β
βοΈπ¦ Build modular and scalable LLM Applications in Rust
calls haystack βChoose when you need a broader LLM orchestration framework with more modular pipeline control than R2R's agentic approach.β
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
calls haystack βWhen you need production LLM pipelines with deep retrieval and RAG features rather than an autonomous-agent-centric framework.β
Build autonomous AI agents in Python.
calls haystack βwhen you need retrieval-centric, pipeline-driven LLM orchestration with deterministic control rather than standalone multi-agent patternsβ
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai