8 Best Lumos Alternatives in 2026 (Open Source)
Lumos — Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs". vs GPT-4 agents: unified modular framework achieving competitive performance with 7B-13B models — planning + grounding + execution separation enables task-agnostic agent architecture from Allen AI
Short answer
- Closest match to Lumos: AutoAct.
- Most actively developed: LangChain (542 commits in the last 90 days).
- Fastest growing: LangChain (+23,097 GitHub stars in the last 30 days).
- No commit in 6+ months: AutoAct, TaskWeaver, RestGPT and Maestro and 1 more.
These 8 open-source tools do the same job. They are ordered by how closely they match Lumos, with live GitHub data so you can see which projects are actively maintained.
By package downloads LangChain is the most used here (169.4M in the last 30 days), and it also has the most GitHub stars. See all agent tools by downloads.
| Tool | GitHub stars | Stars / 30d | Last commit | Downloads / 30d |
|---|---|---|---|---|
| Lumos(original) | 477 | 0 | 2024-03-19 | — |
| AutoAct | 239 | 0 | 2025-01-13 | — |
| TaskWeaver | 6.2k | +6 | 2026-03-23 | — |
| loopgpt | 1.4k | -1 | 2026-07-03 | — |
| Griptape | 2.6k | +12 | 2026-09-24 | 44.2K |
| RestGPT | 1.4k | +1 | 2023-09-28 | — |
| Maestro | 4.4k | +5 | 2024-07-01 | — |
| OpenAgents | 4.9k | +20 | 2024-11-18 | — |
| LangChain | 147.4k | +23,097 | 2026-10-02 | 169.4M |
1. AutoAct
[ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
What sets it apart: vs ReAct/Reflexion/BOLAA: division-of-labor strategy automatically creates specialized Plan/Tool/Reflect sub-agents from self-synthesized trajectories — zero dependency on closed-source model data or human annotations
Best for: Research on automatic agent learning without GPT-4 dependency; Multi-hop QA requiring complex question decomposition; Teams wanting to train specialized sub-agents from self-generated data
2. TaskWeaver
The first "code-first" agent framework for seamlessly planning and executing data analytics tasks.
What sets it apart: Unlike text-only agent frameworks like AutoGen, TaskWeaver preserves full code execution state and in-memory data across turns, enabling seamless multi-step data analytics that manipulate DataFrames and complex structures directly
Best for: Data scientists needing automated multi-step analytics pipelines with code generation; Teams building AI agents that must handle complex data structures like DataFrames natively
3. loopgpt
Modular Auto-GPT Framework
What sets it apart: vs Auto-GPT: proper Python package with full state serialization and GPT-3.5 optimization — save and resume agent sessions without external databases, works well without GPT-4
Best for: Developers wanting a modular, Pythonic alternative to Auto-GPT; GPT-3.5 users wanting autonomous agent capabilities without GPT-4; Teams needing agent state persistence (save/resume sessions)
4. Griptape
Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
What sets it apart: vs LangChain: More structured and opinionated framework with first-class Pipeline/Workflow primitives, clear driver abstraction for provider-swapping, and a companion visual no-code desktop app (Griptape Nodes)
Best for: Building enterprise AI applications with modular, swappable components; Complex multi-step workflows with parallel task execution; Teams wanting strong abstraction layers for provider independence
5. RestGPT
An LLM-based autonomous agent controlling real-world applications via RESTful APIs
What sets it apart: vs basic API wrappers: iterative coarse-to-fine planning combining high-level task decomposition with fine-grained API selection — addresses practical challenges of multi-step API orchestration
Best for: Automating complex multi-step REST API workflows; Research into LLM planning for API orchestration; Testing LLM capabilities against realistic API integration tasks
6. Maestro
A framework for Claude Opus to intelligently orchestrate subagents.
What sets it apart: vs single-model agents (AutoGPT, BabyAGI): separates orchestration/execution/refinement across different models via LiteLLM — enables using Claude for planning + GPT-4o for coding + Llama for review in one workflow
Best for: Complex projects requiring iterative task decomposition; Cost-optimized workflows using different models per stage; Teams wanting to mix cloud and local models in one pipeline
7. OpenAgents
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild
What sets it apart: vs agent frameworks (LangChain/AutoGen): complete full-stack platform with web UI for general users, not just developers — three specialized agents (Data/Plugins/Web) ready to use
Best for: Data analysis and visualization workflows for non-technical users; Research on real-world agent evaluation and benchmarking
8. LangChain
The agent engineering platform
What sets it apart: vs other frameworks: Largest ecosystem with 100+ integrations, dual Python/JS support, backed by LangGraph for agent orchestration and LangSmith for production observability - the most widely adopted LLM framework
Best for: Building complex LLM applications with many integrations; Teams needing model interoperability and quick provider switching; Production AI applications requiring observability via LangSmith
FAQ
- What are the best alternatives to Lumos?
- The closest open-source alternatives to Lumos are AutoAct, TaskWeaver and loopgpt, followed by Griptape, RestGPT and Maestro. They are ranked by how closely they match what Lumos does.
- Which Lumos alternative is the most popular?
- LangChain has the most GitHub stars among Lumos alternatives, with 147,399 stars.
- Which Lumos alternative is the most actively maintained?
- By recent activity, LangChain (542 commits in the last 90 days) is the most actively developed alternative.
Maintain Lumos or one of these alternatives?
Each tool page has a maintainer box: a README badge with your live rank and stars, or a homepage feature for $49 / 7 days.