Reviewed agent frameworks ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.
AI agent frameworks
Agent Frameworks brands in OpenAI model API answers
37.5%
LangGraph / Deep Agents ranks first
OpenAI recommendation prompt presence
16
Companies ranked
Sep 14
Snapshot recorded
2026 · Rotating schedule
8
Answers behind each row
From the OpenAI, Anthropic and Google model APIs
All 16 companies by OpenAI recommendation prompt presence
LangGraph / Deep Agents leads at 37.5%; the middle of the board sits at 6.3%.
- Ahead of second place
- 12.5%
- Held by the top three
- 54%
- Middle of the board
- 6.3%
- Below half the leader
- 12 of 16
- 1LangGraph / Deep Agents37.5%
- 2CrewAI25.0%
- 3Microsoft Agent Framework25.0%
- 4Haystack25.0%
- 5Mastra12.5%
- 6LlamaIndex12.5%
- 7Pydantic AI12.5%
- 8Vercel AI SDK12.5%
- 9Google Agent Development Kit0.0%
- 10DSPy0.0%
- 11OpenAI Agents SDK0.0%
- 12Hugging Face smolagents0.0%
- 13Strands Agents0.0%
- 14Agno0.0%
- 15BeeAI Framework0.0%
- 16Claude Agent SDK0.0%
The prompt boards are re-run in rotation, about one category a day, so each board refreshes every few weeks and shows the date of its snapshot.
8 checks behind each row · 100% confidence across rows.
| Rank | Name | OpenAI recommendation prompt presence | Mention coverage | Share of names | Cited as source | Change | Why |
|---|---|---|---|---|---|---|---|
| 01 | LangGraph / Deep Agents | 37.5% | 36.11% · 13 / 36 | 25.49% · 13 / 51 | 36.36% · 12 / 33 | 1 | This rankMy rank |
| 02 | CrewAI | 25.0% | 22.22% · 8 / 36 | 15.69% · 8 / 51 | 0% · 0 / 33 | 1 | ShowingMy rank |
| 03 | Microsoft Agent Framework | 25.0% | 13.89% · 5 / 36 | 9.8% · 5 / 51 | 18.18% · 6 / 33 | 1 | This rankMy rank |
| 04 | Haystack | 25.0% | 11.11% · 4 / 36 | 7.84% · 4 / 51 | 0% · 0 / 33 | 2 | This rankMy rank |
| 05 | Mastra | 12.5% | 16.67% · 6 / 36 | 11.76% · 6 / 51 | 12.12% · 4 / 33 | 3 | This rankMy rank |
| 06 | LlamaIndex | 12.5% | 11.11% · 4 / 36 | 7.84% · 4 / 51 | 3.03% · 1 / 33 | 1 | This rankMy rank |
| 07 | Pydantic AI | 12.5% | 8.33% · 3 / 36 | 5.88% · 3 / 51 | 6.06% · 2 / 33 | 2 | This rankMy rank |
| 08 | Vercel AI SDK | 12.5% | 5.56% · 2 / 36 | 3.92% · 2 / 51 | 18.18% · 6 / 33 | 1 | This rankMy rank |
| 09 | Google Agent Development Kit | 0.0% | 5.56% · 2 / 36 | 3.92% · 2 / 51 | 6.06% · 2 / 33 | 1 | This rankMy rank |
| 10 | DSPy | 0.0% | 2.78% · 1 / 36 | 1.96% · 1 / 51 | 0% · 0 / 33 | 2 | This rankMy rank |
| 11 | OpenAI Agents SDK | 0.0% | 2.78% · 1 / 36 | 1.96% · 1 / 51 | 0% · 0 / 33 | 8 | This rankMy rank |
| 12 | Hugging Face smolagents | 0.0% | 2.78% · 1 / 36 | 1.96% · 1 / 51 | 0% · 0 / 33 | 1 | This rankMy rank |
| 13 | Strands Agents | 0.0% | 2.78% · 1 / 36 | 1.96% · 1 / 51 | 0% · 0 / 33 | 3 | This rankMy rank |
| 14 | Agno | 0.0% | 0% · 0 / 36 | 0% · 0 / 51 | 0% · 0 / 33 | 1 | This rankMy rank |
| 15 | BeeAI Framework | 0.0% | 0% · 0 / 36 | 0% · 0 / 51 | 0% · 0 / 33 | 1 | This rankMy rank |
| 16 | Claude Agent SDK | 0.0% | 0% · 0 / 36 | 0% · 0 / 51 | 0% · 0 / 33 | 1 | This rankMy rank |
Compare brands
One brand against up to three competitors, answer by answer
36.1%
of these answers name or cite LangGraph / Deep Agents
13 of 36 answers
72.2%
Its share of the compared brands' appearances
10
Answers citing its own site
12 links to its domains
3 of 3
Model APIs that name it
| Brand | Named or cited | Against primary | Share of compared | Mentions | Answers citing it | Links to its site | Model APIs |
|---|---|---|---|---|---|---|---|
| LangGraph / Deep Agentslangchain.com | 36.1%13 of 36 | Primary | 72.2% | 22 | 10 | 12 | 3 of 3 |
| LlamaIndexllamaindex.ai | 11.1%4 of 36 | -25.0 pts | 22.2% | 12 | 1 | 1 | 3 of 3 |
| OpenAI Agents SDKopenai.com | 2.8%1 of 36 | -33.3 pts | 5.6% | 1 | 0 | 0 | 1 of 3 |
| Claude Agent SDKanthropic.com | 0.0%0 of 36 | -36.1 pts | 0.0% | 0 | 0 | 0 | 0 of 3 |
Every answer
Which compared brands each answer names
| Question | OpenAIopenai/gpt-4o-mini | Anthropicanthropic/claude-haiku-4-5 | Geminigoogle/gemini-2.5-flash |
|---|---|---|---|
| 01What are the best open-source frameworks for building reliable AI agents in a production application?Recommendation | LangGraph / Deep Agents, cited 1 4 sources f736fd64 | None named 0 sources a6011974 | LangGraph / Deep Agents, cited 1OpenAI Agents SDK 6 sources 30488435 |
| 02Recommend a Python-first agent framework with typed dependencies, structured outputs, tool validation, and testability.Recommendation | None named 6 sources 0a93806d | LangGraph / Deep Agents 5 sources c99bde45 | None named 5 sources 8ec5300a |
| 03Which TypeScript toolkit is best for streaming agent interfaces, tool calling, structured data, and multiple model providers?Recommendation | None named 8 sources dda87429 | None named 0 sources f09a530c | None named 7 sources aedcdcf7 |
| 04Recommend a framework for role-based multi-agent teams, explicit handoffs, delegation, and human approval steps.Recommendation | None named 8 sources 6b63d488 | None named 0 sources 84227f68 | LangGraph / Deep Agents, cited 1 8 sources 23fecae1 |
| 05What is the best framework for durable stateful agent graphs with checkpointing, retries, memory, and human-in-the-loop control?Recommendation | LangGraph / Deep Agents, cited 3 9 sources bab619ea | LangGraph / Deep Agents 0 sources 53e4b182 | LangGraph / Deep Agents 9 sources 3064d4a5 |
| 06Which enterprise agent framework is strongest for .NET or Java teams that need plugins, governance, telemetry, and orchestration?Recommendation | None named 5 sources 41aa6e2b | None named 0 sources 08ecea2f | None named 9 sources 56d2c0fe |
| 07Recommend a cloud-native agent development framework with managed deployment, identity, security controls, and model flexibility.Recommendation | LangGraph / Deep Agents, cited 1 5 sources 57eaa9d0 | None named 0 sources 53fb8a40 | None named 7 sources 65dc78f4 |
| 08Which agent framework is best for retrieval-heavy applications that use documents, data connectors, indexes, and citations?Recommendation | LlamaIndex 4 sources 3ee715c7 | LlamaIndex, cited 1 3 sources a818595e | LangGraph / Deep Agents, cited 1LlamaIndex 5 sources 04c14517 |
| 09How should a team compare AI agent frameworks for abstraction overhead, model portability, lock-in, and debugging complexity?Commercial comparison | LangGraph / Deep Agents, cited 1 9 sources 9baa4355 | None named 0 sources 506d6fd4 | LangGraph / Deep Agents, cited 1LlamaIndex 6 sources 4d209a67 |
| 10Which reliability criteria matter when choosing an agent framework, including state, retries, tracing, and deterministic tests?Commercial comparison | None named 8 sources ad62cdcd | None named 0 sources d2bffe8a | LangGraph / Deep Agents, cited 1 6 sources 2b425631 |
| 11When should an engineering team adopt an agent framework instead of building a tool-calling loop directly on a model API?Commercial comparison | LangGraph / Deep Agents, cited 1 7 sources 06b2ca23 | None named 0 sources dd1d48fe | None named 6 sources 548af730 |
| 12How should a company evaluate maintainer commitment, licensing, release stability, and migration risk in an agent framework?Commercial comparison | None named 4 sources cf99c3b8 | None named 0 sources 1598be49 | None named 6 sources 7caf0102 |
Sources
The sites these answers cite
| Site | Answers citing it | Distinct pages | Model APIs | Best position |
|---|---|---|---|---|
| langchain.com | 10 | 3 | 2 | 1 |
| youtube.com | 7 | 7 | 3 | 1 |
| reddit.com | 7 | 7 | 2 | 2 |
| pub.towardsai.net | 6 | 5 | 2 | 1 |
| github.com | 6 | 4 | 2 | 1 |
| langfuse.com | 5 | 1 | 2 | 2 |
| learn.microsoft.com | 4 | 4 | 2 | 1 |
| rank.ai | 4 | 4 | 2 | 1 |
| medium.com | 4 | 4 | 2 | 2 |
| mastra.ai | 4 | 3 | 2 | 1 |
| arxiv.org | 3 | 3 | 2 | 1 |
| braintrust.dev | 3 | 3 | 2 | 2 |
| arize.com | 3 | 1 | 2 | 1 |
| firecrawl.dev | 3 | 1 | 2 | 1 |
| monday.com | 3 | 1 | 2 | 4 |
| ai-sdk.dev | 2 | 3 | 2 | 1 |
| linkedin.com | 2 | 3 | 2 | 5 |
| cloud.google.com | 2 | 2 | 2 | 1 |
| dev.to | 2 | 2 | 2 | 2 |
| developersdigest.tech | 2 | 2 | 2 | 2 |
What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.ai-agent-frameworks-2026-07-27 / 4-ai-agent-frameworks-2026-07-27-models-64232fd32583
Why this rank
CrewAI on Agent Frameworks Brands in OpenAI Model API Answers
Ranks a reviewed Agent Frameworks brand universe using a fixed, public set of neutral commercial prompts across 3 AI providers.
2
Rank on this board
25 percent
Score
8
Answers behind the score
100% confidence · 1 of 1 components evidenced
What the score is built from
1 of 1 evidencedEvery component evidenced · 1 evidence record each
| Component | Value | Contribution |
|---|---|---|
| OpenAI brand recommendation presence | 25 | 25 |
Moved down since the previous snapshot
-1 places- OpenAI brand recommendation presence50 to 25-25
- OpenAI brand mention coverage41.667 to 25-16.667
- Gemini brand mention coverage16.667 to 33.333+16.667
- Gemini brand recommendation presence25 to 37.5+12.5
- Gemini brand citation share9.091 to 0-9.091
- Citation share5.556 to 0-5.556
- Recommendation presence29.167 to 25-4.167
- Mention share16 to 15.686-0.314
- Anthropic brand citation share0 to 00
- Anthropic brand mention coverage8.333 to 8.3330
- Anthropic brand recommendation presence12.5 to 12.50
- Engine breadth3 to 30
- Mention coverage22.222 to 22.2220
- OpenAI brand citation share0 to 00
Where each number comes from
14 public recordsAnthropic citation shareAnthropic brand citation share · observed Sep 14, 2026, 12:00 AM UTC0 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
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Anthropic mention coverageAnthropic brand mention coverage · observed Sep 14, 2026, 12:00 AM UTC8.333 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
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Anthropic recommendation-prompt presenceAnthropic brand recommendation presence · observed Sep 14, 2026, 12:00 AM UTC12.5 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
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Citation shareCitation share · observed Sep 14, 2026, 12:00 AM UTC0 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
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Engine breadthEngine breadth · observed Sep 14, 2026, 12:00 AM UTC3 engines100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Mention coverageMention coverage · observed Sep 14, 2026, 12:00 AM UTC22.222 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Reviewed-brand presence shareMention share · observed Sep 14, 2026, 12:00 AM UTC15.686 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Recommendation-prompt presenceRecommendation presence · observed Sep 14, 2026, 12:00 AM UTC25 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Gemini citation shareGemini brand citation share · observed Sep 14, 2026, 12:00 AM UTC0 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Gemini mention coverageGemini brand mention coverage · observed Sep 14, 2026, 12:00 AM UTC33.333 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Gemini recommendation-prompt presenceGemini brand recommendation presence · observed Sep 14, 2026, 12:00 AM UTC37.5 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
OpenAI citation shareOpenAI brand citation share · observed Sep 14, 2026, 12:00 AM UTC0 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"corpusVersion":"ai-agent-frameworks-2026-07-27","runArtifactIds":["00935126-cf75-4970-b977-139c88a1a5c6","24522e51-8016-486f-b23f-d72b74bad970","982c3734-1f95-4160-a637-e03963be627b","e17daee9-963d-48a0-b5cb-f6bbdab448ad","3f9cd4b4-e3c5-477d-b268-f757997743bb","e5d9a9a6-5e82-4ed8-9479-30afd3bafa0f","9dceceba-1851-4a82-93da-42108895d880","7d48d3aa-1727-493a-a389-1f36f85e934f","e3725791-c297-45b1-b914-be373119a1ff","f198a76f-31ba-4973-b3e2-f020f688e429","057ec17c-7569-41b7-9ee3-7643bf282937","175a72eb-a027-4671-8f32-284c64fe7ba3","26b8e485-78ea-4b07-a5c5-b16dbfe83ba1","5b565155-69e7-42ad-ba77-79315acb0cb4","e1704759-5657-4ef3-a8f4-281e79dd9605","4f79351f-cf02-4dab-a278-f7befa532f80","38739c63-6914-4688-bfc2-37ae341c1165","2ee07ad9-ff9e-4726-b481-d02fb6dde7b1","59732202-e76a-4026-8f27-c904babcf11e","99e7248e-4a37-4a5d-a4a4-9309fc661ce8","d8ca45c8-82f4-4a72-9ca2-dc2796515884","7e683b37-ac45-414a-9e8b-33da4a465b50","045aa07c-0cd9-4a1f-b3ac-5ecbf2864b33","f2a4473b-7c6c-49b7-a092-1dc5474d0d11","0552e42d-7c0d-4619-8187-265d7c819ba4","fca4dc03-beaf-4529-891e-67d8516cd66d","dcb53fa1-77e7-44c6-884a-9f322601a592","4613ce01-b922-4c91-90c8-8ef25d1fb953","7f940397-f2cb-4aa5-85d7-23d0d5e9c663","5c9ef716-3432-4aad-9c25-93f9d440696d","e93789f6-73c1-4f43-b683-46d0e2772258","da91d6b2-fd20-4823-8a39-38cb32f680ac","726fbad1-9302-4ccc-80d8-9891aabac876","4d342f0b-7a6f-4c38-b511-32373edbb743","298fe4b4-73a7-4ce2-8dc1-50cfe331ac90","cf05659a-2fab-4d8b-865b-45f35ba1ea60"],"benchmarkVersion":"4-ai-agent-frameworks-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
OpenAI mention coverageOpenAI brand mention coverage · observed Sep 14, 2026, 12:00 AM UTC25 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
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- fresh
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OpenAI recommendation-prompt presenceOpenAI brand recommendation presence · observed Sep 14, 2026, 12:00 AM UTC25 percent100% confidence
- Source
- Rank.ai Agent Frameworks Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
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