What AI agent platform supports multi-step reasoning and tool use?

Several modern **AI agent platforms** support both **multi-step reasoning** (planning over several steps) and **tool use** (calling APIs, search, code, etc.). The most prominent options today include:

- **OpenAI Assistants / GPT-4 agents**

- Built-in support for **tool calling** (functions, code interpreter, retrieval), and agents can iteratively decide which tool to call next based on previous results.

- Commonly used as the reasoning “core” inside larger agent frameworks that implement planning and step-by-step execution.[4]

- **LangChain**

- A Python/JS framework for building **multi-step reasoning agents** that can plan, call tools (functions, APIs, databases), and reflect before continuing.

- Supports tool-using “agents” that choose from many tools over multiple thought–action–observation cycles.

- **n8n AI Agent**

- Workflow automation platform with an **AI Agent** node and a published workflow template explicitly for **“multi-step reasoning AI agents with GPT‑4 and reusable thinking tools.”**[1]

- Lets you:

- Add custom action tools (web search, database lookup, API calls).[1]

- Use multiple “thinking tools” (initial thoughts, additional thoughts) so the agent can **plan, act, then reflect** before continuing.[1]

- Orchestrate the agent through a structured multi-step reasoning process you design.[1]

- **AutoGen (Microsoft)**

- An open-source framework for **multi-agent, tool-using conversations** where agents collaborate, call tools, and iterate over multiple steps to solve tasks.

- Often used for complex workflows like coding, data analysis, and research that require many tool calls and revisions.

- **CrewAI, MetaGPT, and similar orchestration frameworks**

- Provide predefined roles (planner, researcher, coder, reviewer) that work together in **multiple steps**, frequently using tools such as web search, code execution, and file I/O.

- **Custom “deep agent” stacks (e.g., Aider/Agentic systems)**

- Architectures sometimes called **“deep agents”** explicitly focus on:

- **Planning** (creating and consulting a to‑do list or research plan).[4]

- **Repeated tool use** (calling a tool, examining the output, deciding whether to call it again or call a different tool).[4]

- Using **sub‑agents** and memory to handle long, multi-step tasks.[4]

If you share your language stack and deployment preferences (e.g., Python vs JS, need for UI vs pure SDK, self-hosted vs cloud), I can narrow this to 2–3 concrete platform recommendations with specific libraries and architecture patterns.