Data as of Aug 25, 2026 · Based on 988 AI responses · Shares cover the last 30 days · See how Parse measures this
AI Agent Orchestration Platforms
Parse
https://parse.gl
remains the dominant framework for building AI agent applications, but the most significant evolution is the rapid rise of specialized orchestration tools. Frameworks like and are now consistently recommended for orchestrating complex, multi-agent workflows, signaling a market shift toward more structured agent collaboration.
| # | Brand | What AI says | Mention rate |
|---|---|---|---|
| 1 | The top modular framework for building custom, LLM-driven applications and agents. | 74% | |
| 2 | Specializes in orchestrating role-based, collaborative agent teams or 'crews'. | 46% | |
| 3 | 41% | ||
| 4 | Frequently cited for its foundational models and native agent-building APIs. | 37% | |
| 5 | Mentioned for its ecosystem, including | 33% | |
| 6 | A leading open-source framework for building conversational multi-agent systems. | 32% | |
| 7 | 30% | ||
| 8 | A hybrid platform combining a visual builder with code for chatbots. | 28% | |
| 9 | Cited for custom chatbots where data privacy and self-hosting are priorities. | 26% | |
| 10 | 21% | ||
| 11 | 19% | ||
| 12 | 15% | ||
| 13 | 13% | ||
| 14 | 12% | ||
| 15 | 12% | ||
| 16 | An emerging platform providing a dedicated integration layer for AI agents. | 12% | |
| 17 | 10% | ||
| 18 | 10% | ||
| 19 | 9% | ||
| 20 | 7% | ||
| 21 | 7% | ||
| 22 | 7% | ||
| 23 | 6% | ||
| 24 | 6% | ||
| 25 | 6% |
Who wins on each AI
The same market, seen by two models.
Sources AI cited
medium.com is the page AI reaches for most here, cited in 32% of analyzed answers.
“a general, modular framework for building LLM applications.” → “the foundational layer of a more complex ecosystem including LangGraph for stateful workflows.”
Climbed from rank #12 in Oct to #3 in Mar, recommended for role-based agent teams.
Fell from rank #6 to #12 between Oct and Mar, facing competition from agent-first tools.
Dropped from rank #8 to #14 between Oct and Mar as LLM-native frameworks gained share.
Rose from rank #16 in Oct to #2 in Mar, cited for stateful, graph-based workflows.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| 77% | 52% | ||
| 58% | 66% | ||
| 47% | 45% | ||
| 52% | 18% | ||
| 0% | 29% |
The two models disagree most about Rasa (ChatGPT #18, Google #5) and Google Agent Development Kit (ADK) (ChatGPT #8, Google #18).
Across 988 AI responses, LangChain is mentioned most, named in 74% of them, followed by CrewAI (46%) and Alphabet (41%).
Parse measures each brand's mention rate — the share of answers naming it — across 988 AI responses to this market's buyer questions over the last 30 days. Answers are collected daily and the ranking is re-measured on the same 30-day window.
Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.
Which platform is the best for building AI agents that can interact with external APIs and tools?
Initial responses named general-purpose frameworks like LangChain and platforms like
OpenAI. By early 2026, answers became more nuanced, recommending specialized orchestration tools like
CrewAI and LangGraph, and even dedicated integration layers like for managing tool connections.
This prompt consistently elicited responses focused on multi-agent frameworks, with Microsoft AutoGen appearing frequently早期. Over the observed period, and LangGraph emerged as dominant, often co-recommended open-source solutions for orchestrating role-based or stateful agent teams.
Brands mentioned
Brands mentioned
This prompt consistently elicited responses focused on multi-agent frameworks, with Microsoft AutoGen appearing frequently早期. Over the observed period,
CrewAI and LangGraph emerged as dominant, often co-recommended open-source solutions for orchestrating role-based or stateful agent teams.