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docs: refresh retired Gemini model ids (#7003)

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Co-authored-by: Vidit Ostwal <110953813+Vidit-Ostwal@users.noreply.github.com>
This commit is contained in:
chenshiyang
2026-08-31 21:43:31 +08:00
committed by GitHub
parent da4daadba0
commit 265697b6f8
22 changed files with 35 additions and 35 deletions
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2
docs/edge/ar/concepts/memory.mdx
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@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
memory = Memory(llm="ollama/llama3.2")
# Use Google Gemini
memory = Memory(llm="gemini/gemini-2.0-flash")
memory = Memory(llm="gemini/gemini-3.7-flash")
# Pass a pre-configured LLM instance with custom settings
llm = LLM(model="gpt-4o", temperature=0)

2
docs/edge/ar/guides/crews/first-crew.mdx
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@@ -75,7 +75,7 @@ research_crew/
}
```
استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-2.0-flash-001`.
استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-3.7-flash`.
## الخطوة 3: تعريف المهام وإعدادات الـ Crew

2
docs/edge/ar/guides/flows/first-flow.mdx
View File

@@ -104,7 +104,7 @@ crewai flow add-crew content-crew
}
```
استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-2.0-flash-001` أو `anthropic/claude-sonnet-4-6`.
استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-3.7-flash` أو `anthropic/claude-sonnet-4-6`.
3. أنشئ `src/guide_creator_flow/crews/content_crew/crew.jsonc`:

4
docs/edge/ar/learn/litellm-removal-guide.mdx
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@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
# llm = LLM(model="mistral/mistral-large-latest")
# After (Native):
llm = LLM(model="gemini/gemini-2.0-flash")
llm = LLM(model="gemini/gemini-3.7-flash")
```
```bash
@@ -312,7 +312,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5") # Fast & affordable
# Together AI → OpenAI or Gemini
# llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
llm = LLM(model="openai/gpt-4o") # High quality
llm = LLM(model="gemini/gemini-2.0-flash") # Fast & capable
llm = LLM(model="gemini/gemini-3.7-flash") # Fast & capable
# Mistral → Anthropic or OpenAI
# llm = LLM(model="mistral/mistral-large-latest")

4
docs/edge/ar/learn/llm-connections.mdx
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@@ -141,7 +141,7 @@ mode: "wide"
# Example using Gemini's OpenAI-compatible API.
os.environ["OPENAI_API_KEY"] = "your-gemini-key" # Should start with AIza...
os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash" # Add your Gemini model here, under openai/
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash" # Add your Gemini model here, under openai/
```
</CodeGroup>
</Tab>
@@ -159,7 +159,7 @@ mode: "wide"
```python Google
# Example using Gemini's OpenAI-compatible API
llm = LLM(
model="openai/gemini-2.0-flash",
model="openai/gemini-3.7-flash",
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key="your-gemini-key", # Should start with AIza...
)

4
docs/edge/ar/learn/llm-selection-guide.mdx
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@@ -144,7 +144,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
from crewai import Agent, Task, Crew, LLM
# High-capability reasoning model for strategic planning
manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
# Creative model for content generation
content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -409,7 +409,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
# Manager or coordination agents
manager_agent = Agent(
role="Project Manager",
llm=LLM(model="gemini-2.5-flash-preview-05-20"), # Premium for coordination
llm=LLM(model="gemini/gemini-3.7-flash"), # Premium for coordination
# ... rest of config
)

2
docs/edge/en/concepts/memory.mdx
View File

@@ -740,7 +740,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
memory = Memory(llm="ollama/llama3.2")
# Use Google Gemini
memory = Memory(llm="gemini/gemini-2.0-flash")
memory = Memory(llm="gemini/gemini-3.7-flash")
# Pass a pre-configured LLM instance with custom settings
llm = LLM(model="gpt-4o", temperature=0)

2
docs/edge/en/guides/crews/first-crew.mdx
View File

@@ -77,7 +77,7 @@ Replace the generated `agents/researcher.jsonc` file and add `agents/analyst.jso
}
```
Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-2.0-flash-001`.
Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-3.7-flash`.
## Step 3: Define Tasks and Crew Settings

4
docs/edge/en/guides/flows/first-flow.mdx
View File

@@ -136,7 +136,7 @@ Now, let's configure the content writer crew with JSONC. We'll set up two specia
}
```
Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, or `anthropic/claude-sonnet-4-6`.
Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-3.7-flash`, or `anthropic/claude-sonnet-4-6`.
3. Create `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
@@ -483,7 +483,7 @@ Flows allow you to make direct calls to language models when you need simple, st
```python
llm = LLM(
model="model-id-here", # gpt-4o, gemini-2.0-flash, anthropic/claude...
model="model-id-here", # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
response_format=GuideOutline
)
response = llm.call(messages=messages)

4
docs/edge/en/learn/litellm-removal-guide.mdx
View File

@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
# llm = LLM(model="mistral/mistral-large-latest")
# After (Native):
llm = LLM(model="gemini/gemini-2.0-flash")
llm = LLM(model="gemini/gemini-3.7-flash")
```
```bash
@@ -399,7 +399,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5") # Fast & affordable
# Together AI → OpenAI or Gemini
# llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
llm = LLM(model="openai/gpt-4o") # High quality
llm = LLM(model="gemini/gemini-2.0-flash") # Fast & capable
llm = LLM(model="gemini/gemini-3.7-flash") # Fast & capable
# Mistral → Anthropic or OpenAI
# llm = LLM(model="mistral/mistral-large-latest")

4
docs/edge/en/learn/llm-connections.mdx
View File

@@ -141,7 +141,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
# Example using Gemini's OpenAI-compatible API.
os.environ["OPENAI_API_KEY"] = "your-gemini-key" # Should start with AIza...
os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash" # Add your Gemini model here, under openai/
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash" # Add your Gemini model here, under openai/
```
</CodeGroup>
</Tab>
@@ -159,7 +159,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
```python Google
# Example using Gemini's OpenAI-compatible API
llm = LLM(
model="openai/gemini-2.0-flash",
model="openai/gemini-3.7-flash",
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key="your-gemini-key", # Should start with AIza...
)

4
docs/edge/en/learn/llm-selection-guide.mdx
View File

@@ -147,7 +147,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
from crewai import Agent, Task, Crew, LLM
# High-capability reasoning model for strategic planning
manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
# Creative model for content generation
content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -412,7 +412,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
# Manager or coordination agents
manager_agent = Agent(
role="Project Manager",
llm=LLM(model="gemini-2.5-flash-preview-05-20"), # Premium for coordination
llm=LLM(model="gemini/gemini-3.7-flash"), # Premium for coordination
# ... rest of config
)

2
docs/edge/ko/concepts/memory.mdx
View File

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
memory = Memory(llm="ollama/llama3.2")
# Google Gemini 사용
memory = Memory(llm="gemini/gemini-2.0-flash")
memory = Memory(llm="gemini/gemini-3.7-flash")
# 사용자 정의 설정이 있는 사전 구성된 LLM 인스턴스 전달
llm = LLM(model="gpt-4o", temperature=0)

2
docs/edge/ko/guides/crews/first-crew.mdx
View File

@@ -75,7 +75,7 @@ research_crew/
}
```
`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-2.0-flash-001` 같은 모델로 바꾸세요.
`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-3.7-flash` 같은 모델로 바꾸세요.
## 3단계: 태스크와 Crew 설정

4
docs/edge/ko/guides/flows/first-flow.mdx
View File

@@ -135,7 +135,7 @@ crewai flow add-crew content-crew
}
```
`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, `anthropic/claude-sonnet-4-6`.
`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-3.7-flash`, `anthropic/claude-sonnet-4-6`.
3. `src/guide_creator_flow/crews/content_crew/crew.jsonc`를 만듭니다:
@@ -481,7 +481,7 @@ Flow를 사용하면 간단하고 구조화된 응답이 필요할 때 언어
```python
llm = LLM(
model="model-id-here", # gpt-4o, gemini-2.0-flash, anthropic/claude...
model="model-id-here", # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
response_format=GuideOutline
)
response = llm.call(messages=messages)

4
docs/edge/ko/learn/llm-connections.mdx
View File

@@ -141,7 +141,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
# Gemini의 OpenAI 호환 API 예시입니다.
os.environ["OPENAI_API_KEY"] = "your-gemini-key" # AIza...로 시작해야 합니다.
os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash" # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash" # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
```
</CodeGroup>
</Tab>
@@ -159,7 +159,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
```python Google
# Gemini의 OpenAI 호환 API 예시
llm = LLM(
model="openai/gemini-2.0-flash",
model="openai/gemini-3.7-flash",
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key="your-gemini-key", # AIza...로 시작해야 합니다.
)

4
docs/edge/ko/learn/llm-selection-guide.mdx
View File

@@ -145,7 +145,7 @@ planning agent는 복잡한 전략적 사고와 다단계 분석을 처리할
from crewai import Agent, Task, Crew, LLM
# High-capability reasoning model for strategic planning
manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
# Creative model for content generation
content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -411,7 +411,7 @@ tech_writer = Agent(
# Manager 또는 coordination agent
manager_agent = Agent(
role="Project Manager",
llm=LLM(model="gemini-2.5-flash-preview-05-20"), # 조율을 위한 프리미엄
llm=LLM(model="gemini/gemini-3.7-flash"), # 조율을 위한 프리미엄
# ... 나머지 설정
)

2
docs/edge/pt-BR/concepts/memory.mdx
View File

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
memory = Memory(llm="ollama/llama3.2")
# Usar Google Gemini
memory = Memory(llm="gemini/gemini-2.0-flash")
memory = Memory(llm="gemini/gemini-3.7-flash")
# Passar uma instância LLM pré-configurada com configurações customizadas
llm = LLM(model="gpt-4o", temperature=0)

2
docs/edge/pt-BR/guides/crews/first-crew.mdx
View File

@@ -77,7 +77,7 @@ Substitua o arquivo gerado `agents/researcher.jsonc` e adicione `agents/analyst.
}
```
Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-2.0-flash-001`.
Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-3.7-flash`.
## Etapa 3: Definir tarefas e configurações

4
docs/edge/pt-BR/guides/flows/first-flow.mdx
View File

@@ -135,7 +135,7 @@ Agora, vamos configurar o crew de redatores com JSONC. Vamos definir dois agente
}
```
Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-2.0-flash-001` ou `anthropic/claude-sonnet-4-6`.
Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-3.7-flash` ou `anthropic/claude-sonnet-4-6`.
3. Crie `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
@@ -481,7 +481,7 @@ Flows permitem que você faça chamadas diretas a modelos de linguagem quando pr
```python
llm = LLM(
model="model-id-here", # gpt-4o, gemini-2.0-flash, anthropic/claude...
model="model-id-here", # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
response_format=GuideOutline
)
response = llm.call(messages=messages)

4
docs/edge/pt-BR/learn/llm-connections.mdx
View File

@@ -140,7 +140,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
# Exemplo usando a API compatível com OpenAI do Gemini.
os.environ["OPENAI_API_KEY"] = "your-gemini-key" # Deve começar com AIza...
os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash" # Adicione aqui seu modelo do Gemini, sob openai/
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash" # Adicione aqui seu modelo do Gemini, sob openai/
```
</CodeGroup>
</Tab>
@@ -158,7 +158,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
```python Google
# Exemplo usando a API compatível com OpenAI do Gemini
llm = LLM(
model="openai/gemini-2.0-flash",
model="openai/gemini-3.7-flash",
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key="your-gemini-key", # Deve começar com AIza...
)

4
docs/edge/pt-BR/learn/llm-selection-guide.mdx
View File

@@ -148,7 +148,7 @@ Agentes de planejamento se beneficiam de modelos de raciocínio para pensamento
from crewai import Agent, Task, Crew, LLM
# Modelo de raciocínio para planejamento estratégico
manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
# Modelo criativo para gerar conteúdo
content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -413,7 +413,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl
# Agentes gerenciadores ou de coordenação
manager_agent = Agent(
role="Project Manager",
llm=LLM(model="gemini-2.5-flash-preview-05-20"),
llm=LLM(model="gemini/gemini-3.7-flash"),
# ... demais configs
)