Data as of Oct 18, 2026Based on 1,229 AI responses
Reviewed by Dimitry Apollonsky
AI summary
LangChain LangGraph holds a clear lead, frequently highlighted for stateful multi-step tasks, external API integration, and human-in-the-loop workflows. In contrast, Browser Use is the usual answer when buyers need frameworks with built-in browser automation for web research.
Rank and Mentioned in · since Jul 5
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | stateful task orchestration, complex tool integration, and human-in-the-loop workflows | stateful task orchestration, complex tool integration, and human-in-the-loop workflows | 68% |
| 2 | collaborative multi-agent teams and role-playing agent orchestration | collaborative multi-agent teams and role-playing agent orchestration | 48% |
| 3 | agent-to-agent handoffs, guardrails, and OpenAI-centric ecosystems | agent-to-agent handoffs, guardrails, and OpenAI-centric ecosystems | 32% |
| 4 | enterprise multi-agent orchestration within Azure and Microsoft environments | enterprise multi-agent orchestration within Azure and Microsoft environments | 26% |
| 5 | modular components and extensive integrations across complex pipelines | modular components and extensive integrations across complex pipelines | 24% |
| 6 | multi-agent collaboration and conversational multi-agent system prototyping | multi-agent collaboration and conversational multi-agent system prototyping | 24% |
| 7 | direct model endpoints providing zero data retention guarantees | direct model endpoints providing zero data retention guarantees | 16% |
| 8 | built-in query transformations and advanced data retrieval pipelines | built-in query transformations and advanced data retrieval pipelines | 14% |
| 9 | enterprise .NET and Python application middleware with plugin controls | enterprise .NET and Python application middleware with plugin controls | 12% |
| 10 | durable execution and long-running Python workflow orchestration | durable execution and long-running Python workflow orchestration | 10% |
| 11 | 9% | ||
| 12 | 9% | ||
| 13 | 8% | ||
| 14 | 8% | ||
| 15 | 8% |
What’s changing · since July
Since July, OpenAI Agents SDKrising expanded its presence across the market, while Lettarising became the most named option for agent memory. Meanwhile, Sidsaladi (Substack)falling dropped out of contention for human-in-the-loop workflows.
Weeks of Aug 24 – Oct 4, 2026 · Since Jul 5
Widest split: OpenAI Agents SDK, 48% on ChatGPT Search and 15% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| LangChain LangGraph | 65% | 70% | |
| CrewAI | 46% | 51% | |
| OpenAI Agents SDK | 48% | 15% | |
| Microsoft Agent Framework | 30% | 23% | |
| LangChain | 22% | 26% | |
| AutoGen | 21% | 27% | |
| OpenAI | 20% | 12% | |
| LlamaIndex | 15% | 13% | |
| Microsoft Semantic Kernel | 10% | 14% | |
| Temporal | 10% | 9% |
32% of citations point at the ranked brands’ own sites. youtube.com, github.com, medium.com are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 5
Leading brands by topic
The 5 brands that lead or place most often
| Topic | LangChain LangGraph | CrewAI | LangChain | LlamaIndex | n8n |
|---|---|---|---|---|---|
| Led by LangChain LangGraph 5 | |||||
| Human-in-the-loop python orchestration libraries | Not named | ||||
| Human-in-the-loop task orchestration | |||||
| Devops agent framework for self-healing systems | |||||
| Multi-step stateful task management | Not named | ||||
| Conversational AI agent framework | Not named | ||||
| Led by CrewAI 2 | |||||
| Framework for parallel agent execution | Not named | Not named | |||
| Multi-agent orchestration | |||||
| Led by LangChain 1 | |||||
| LLM agent API tool reliability | Not named | Not named | Not named | Not named | |
| Led by LlamaIndex 1 | |||||
| Advanced RAG retrieval strategies | Not named | Not named | |||
| Led by n8n 1 | |||||
| No-code and low-code AI agent building | Not named | ||||
| Led by other brands 1 | |||||
| Agent memory integration | Not named | ||||
leads second or third named lowerblank: not named
Which orchestration platform is best for multi-agent workflows?
Which platform is the best for building AI agents that can interact with external APIs and tools?
The problem is, our agent is not reliable at using our internal APIs. What's the best framework for creating robust API tools for an LLM?
We are building an autonomous agent framework. Who offers orchestration tools for multi-agent systems?
My goal is to build an autonomous AI agent for web research. What's the best framework with built-in browser automation tools?
We need to build a system where agents can dynamically discover and use new tools. What's the best framework for dynamic tool discovery?
We are building a Retrieval Augmented Generation (RAG) system for trade secrets. Who ensures zero retention?
We are evaluating open-source vs. closed-source LLMs. Who offers performance vs. cost benchmarks?
I'm looking for a platform that lets non-technical users build their own AI agents. What is the best no-code agent platform?
Best agent framework for tool use + human review?
I need to orchestrate AI agents with external API integration capabilities. What are the top frameworks for this?
Which LLM agent frameworks excel at maintaining state across multi-step operations when integrating multiple external tools and APIs?
I need a RAG framework that handles query re-writing out of the box. Which options have the best built-in tools for this?
My RAG system struggles with very specific, detail-oriented questions. What's the best framework for advanced retrieval strategies like sentence-window retrieval?
Which agent frameworks offer the best support for combining short-term and long-term memory capabilities?
What are the top frameworks for developing conversational AI agents that excel at handling complex, multi-turn interactions?
What are the standard tools and frameworks for developing conversational AI agents and handling their orchestration?
Can you recommend agent frameworks suitable for automating infrastructure maintenance and resolving DevOps pipeline failures? I'm looking to implement a self-healing workflow.
I'm looking to create a self-healing infrastructure using AI. Which frameworks are best for developing DevOps agents that can handle minor automated repairs?
What is the best framework for orchestrating multiple agents in parallel for data processing workloads?
Can you recommend a Python library specifically for building human-in-the-loop AI orchestration?
Are there any Python libraries specifically for integrating human-in-the-loop oversight into AI task orchestration?