8 Best RestGPT Alternatives in 2026 (Open Source)
RestGPT — An LLM-based autonomous agent controlling real-world applications via RESTful APIs. 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
These 8 open-source tools do the same job. They are ordered by how closely they match RestGPT, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| RestGPT(original) | 1.4k | +2 | 2023-09-28 |
| workgpt | 731 | +-0 | 2023-06-23 |
| Composio | 30.4k | +454 | 2026-09-30 |
| Agentflow | 321 | +0 | 2023-08-11 |
| Open Assistant API | 367 | +1 | 2024-12-14 |
| FastAgency | 548 | +3 | 2025-12-09 |
| TaskWeaver | 6.2k | +6 | 2026-03-23 |
| XAgent | 8.6k | +5 | 2026-07-31 |
| AutoAct | 239 | +0 | 2025-01-13 |
1. workgpt
A GPT agent framework for invoking APIs
What sets it apart: vs LangChain / AutoGPT: TypeScript-native agent framework with first-class OpenAPI integration — any API with an OpenAPI spec becomes an LLM tool automatically, with built-in web browsing and structured output extraction
Best for: Automating multi-API workflows from natural language directives; Web scraping and structured data extraction with LLM intelligence; TypeScript developers wanting an agent framework with OpenAPI-first design
2. Composio
Composio powers 1000+ toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action.
What sets it apart: 500+ pre-built app integrations with managed auth — vs LangChain tools which require manual API setup per service
Best for: Building AI agents that need to interact with SaaS tools; Rapid prototyping of agent workflows with external integrations
3. Agentflow
Complex LLM Workflows from Simple JSON.
What sets it apart: vs AutoGPT / LangChain agents: deterministic step-by-step workflow execution from JSON definitions — balanced between chat flexibility and autonomous agent unpredictability, with custom function support
Best for: Developers wanting structured, repeatable LLM workflows vs. freeform chat; Multi-step content generation pipelines (e.g., market research → analysis → report); Teams needing predictable LLM execution with human-readable workflow definitions
4. Open Assistant API
The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities. It
What sets it apart: Open-source OpenAI Assistant API compatible service supporting multiple LLMs via One API, with RAG, web search, and local deployment
Best for: self-hosted-openai-assistant-alternative; multi-llm-assistant-apps; enterprise-local-deployment
5. 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
6. 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
7. XAgent
An Autonomous LLM Agent for Complex Task Solving
What sets it apart: vs AutoGPT: dual-loop mechanism with human-agent collaboration and active help-seeking — demonstrated superiority over AutoGPT in human preference evaluation across 50+ real-world tasks
Best for: Complex multi-step tasks: data analysis, coding, research, reports; Tasks requiring human-AI collaboration with approval gates; Autonomous problem-solving with tool-use capabilities
8. 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