diff --git a/docs/edge/ar/concepts/knowledge.mdx b/docs/edge/ar/concepts/knowledge.mdx index 807e0801e9..0d61ffa88c 100644 --- a/docs/edge/ar/concepts/knowledge.mdx +++ b/docs/edge/ar/concepts/knowledge.mdx @@ -577,7 +577,7 @@ agent = Agent( role="Researcher", goal="Research topics", backstory="Expert researcher", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + llm=LLM(provider="anthropic", model="claude-sonnet-4-6") # Using Claude ) # CrewAI will still use OpenAI embeddings by default for knowledge diff --git a/docs/edge/ar/concepts/memory.mdx b/docs/edge/ar/concepts/memory.mdx index 655b5957d4..12b8371bc4 100644 --- a/docs/edge/ar/concepts/memory.mdx +++ b/docs/edge/ar/concepts/memory.mdx @@ -730,7 +730,7 @@ memory = Memory() memory = Memory(llm="gpt-4o") # Use Anthropic -memory = Memory(llm="anthropic/claude-3-haiku-20240307") +memory = Memory(llm="anthropic/claude-haiku-4-5") # Use Ollama for fully local/private analysis memory = Memory(llm="ollama/llama3.2") diff --git a/docs/edge/ar/guides/agents/crafting-effective-agents.mdx b/docs/edge/ar/guides/agents/crafting-effective-agents.mdx index c1c6b1db35..dd700314ca 100644 --- a/docs/edge/ar/guides/agents/crafting-effective-agents.mdx +++ b/docs/edge/ar/guides/agents/crafting-effective-agents.mdx @@ -423,7 +423,7 @@ writer: role: "Creative Content Writer" goal: "..." backstory: "..." - llm: anthropic/claude-3-opus + llm: anthropic/claude-opus-4-6 ``` ## اختبار تصميم الـ Agent والتكرار عليه diff --git a/docs/edge/ar/learn/llm-selection-guide.mdx b/docs/edge/ar/learn/llm-selection-guide.mdx index 7e900aa51f..100aef9700 100644 --- a/docs/edge/ar/learn/llm-selection-guide.mdx +++ b/docs/edge/ar/learn/llm-selection-guide.mdx @@ -147,7 +147,7 @@ from crewai import Agent, Task, Crew, LLM 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) +content_llm = LLM(model="claude-sonnet-4-6", temperature=0.7) # Efficient model for data processing processing_llm = LLM(model="gpt-4o-mini", temperature=0) @@ -320,7 +320,7 @@ domain_expert = Agent( You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving you perspective on both established and disruptive go-to-market strategies. """, - llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Balanced creativity with domain knowledge + llm=LLM(model="claude-sonnet-4-6", temperature=0.3) # Balanced creativity with domain knowledge ) # This context enables Claude to perform like a domain expert @@ -352,7 +352,7 @@ tech_writer = Agent( and practical use cases. You prioritize accuracy and usability over marketing fluff. """, llm=LLM( - model="claude-3-5-sonnet", # Excellent for technical writing + model="claude-sonnet-4-6", # Excellent for technical writing temperature=0.1 # Low temperature for accuracy ), tools=[code_analyzer_tool, api_scanner_tool], @@ -416,7 +416,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i # Creative or customer-facing agents content_agent = Agent( role="Content Creator", - llm=LLM(model="claude-3-5-sonnet"), # Best for writing + llm=LLM(model="claude-sonnet-4-6"), # Best for writing # ... rest of config ) ``` @@ -511,7 +511,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i ) # Agents inherit crew LLM unless specifically overridden - agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) # Override for specific needs + agent1 = Agent(llm=LLM(model="claude-sonnet-4-6")) # Override for specific needs ``` @@ -529,7 +529,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i tools=[search_tool, api_tool, data_tool], llm=LLM(model="gpt-4o"), # Excellent function calling # OR - llm=LLM(model="claude-3-5-sonnet") # Also strong with tools + llm=LLM(model="claude-sonnet-4-6") # Also strong with tools ) ``` diff --git a/docs/edge/en/concepts/knowledge.mdx b/docs/edge/en/concepts/knowledge.mdx index 937cca1fda..6c9c9bebcf 100644 --- a/docs/edge/en/concepts/knowledge.mdx +++ b/docs/edge/en/concepts/knowledge.mdx @@ -577,7 +577,7 @@ agent = Agent( role="Researcher", goal="Research topics", backstory="Expert researcher", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + llm=LLM(provider="anthropic", model="claude-sonnet-4-6") # Using Claude ) # CrewAI will still use OpenAI embeddings by default for knowledge diff --git a/docs/edge/en/concepts/memory.mdx b/docs/edge/en/concepts/memory.mdx index d23b478429..9c07aeebfe 100644 --- a/docs/edge/en/concepts/memory.mdx +++ b/docs/edge/en/concepts/memory.mdx @@ -734,7 +734,7 @@ memory = Memory() memory = Memory(llm="gpt-4o") # Use Anthropic -memory = Memory(llm="anthropic/claude-3-haiku-20240307") +memory = Memory(llm="anthropic/claude-haiku-4-5") # Use Ollama for fully local/private analysis memory = Memory(llm="ollama/llama3.2") diff --git a/docs/edge/en/guides/agents/crafting-effective-agents.mdx b/docs/edge/en/guides/agents/crafting-effective-agents.mdx index c0141ddac3..c88de407f2 100644 --- a/docs/edge/en/guides/agents/crafting-effective-agents.mdx +++ b/docs/edge/en/guides/agents/crafting-effective-agents.mdx @@ -423,7 +423,7 @@ writer: role: "Creative Content Writer" goal: "..." backstory: "..." - llm: anthropic/claude-3-opus + llm: anthropic/claude-opus-4-6 ``` ## Testing and Iterating on Agent Design diff --git a/docs/edge/en/learn/llm-selection-guide.mdx b/docs/edge/en/learn/llm-selection-guide.mdx index f8ebb05e53..9e63c1b19d 100644 --- a/docs/edge/en/learn/llm-selection-guide.mdx +++ b/docs/edge/en/learn/llm-selection-guide.mdx @@ -150,7 +150,7 @@ from crewai import Agent, Task, Crew, LLM 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) +content_llm = LLM(model="claude-sonnet-4-6", temperature=0.7) # Efficient model for data processing processing_llm = LLM(model="gpt-4o-mini", temperature=0) @@ -323,7 +323,7 @@ domain_expert = Agent( You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving you perspective on both established and disruptive go-to-market strategies. """, - llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Balanced creativity with domain knowledge + llm=LLM(model="claude-sonnet-4-6", temperature=0.3) # Balanced creativity with domain knowledge ) # This context enables Claude to perform like a domain expert @@ -355,7 +355,7 @@ tech_writer = Agent( and practical use cases. You prioritize accuracy and usability over marketing fluff. """, llm=LLM( - model="claude-3-5-sonnet", # Excellent for technical writing + model="claude-sonnet-4-6", # Excellent for technical writing temperature=0.1 # Low temperature for accuracy ), tools=[code_analyzer_tool, api_scanner_tool], @@ -419,7 +419,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i # Creative or customer-facing agents content_agent = Agent( role="Content Creator", - llm=LLM(model="claude-3-5-sonnet"), # Best for writing + llm=LLM(model="claude-sonnet-4-6"), # Best for writing # ... rest of config ) ``` @@ -514,7 +514,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i ) # Agents inherit crew LLM unless specifically overridden - agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) # Override for specific needs + agent1 = Agent(llm=LLM(model="claude-sonnet-4-6")) # Override for specific needs ``` @@ -532,7 +532,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i tools=[search_tool, api_tool, data_tool], llm=LLM(model="gpt-4o"), # Excellent function calling # OR - llm=LLM(model="claude-3-5-sonnet") # Also strong with tools + llm=LLM(model="claude-sonnet-4-6") # Also strong with tools ) ``` diff --git a/docs/edge/ko/concepts/knowledge.mdx b/docs/edge/ko/concepts/knowledge.mdx index 92f688392e..207ee80bf5 100644 --- a/docs/edge/ko/concepts/knowledge.mdx +++ b/docs/edge/ko/concepts/knowledge.mdx @@ -544,7 +544,7 @@ agent = Agent( role="Researcher", goal="Research topics", backstory="Expert researcher", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + llm=LLM(provider="anthropic", model="claude-sonnet-4-6") # Using Claude ) # CrewAI will still use OpenAI embeddings by default for knowledge diff --git a/docs/edge/ko/concepts/memory.mdx b/docs/edge/ko/concepts/memory.mdx index d6b3d5f61a..f8f7d6d685 100644 --- a/docs/edge/ko/concepts/memory.mdx +++ b/docs/edge/ko/concepts/memory.mdx @@ -730,7 +730,7 @@ memory = Memory() memory = Memory(llm="gpt-4o") # Anthropic 사용 -memory = Memory(llm="anthropic/claude-3-haiku-20240307") +memory = Memory(llm="anthropic/claude-haiku-4-5") # 완전한 로컬/비공개 분석을 위해 Ollama 사용 memory = Memory(llm="ollama/llama3.2") diff --git a/docs/edge/ko/guides/agents/crafting-effective-agents.mdx b/docs/edge/ko/guides/agents/crafting-effective-agents.mdx index b7ec97a7c7..6ddf291b16 100644 --- a/docs/edge/ko/guides/agents/crafting-effective-agents.mdx +++ b/docs/edge/ko/guides/agents/crafting-effective-agents.mdx @@ -424,7 +424,7 @@ writer: role: "Creative Content Writer" goal: "..." backstory: "..." - llm: anthropic/claude-3-opus + llm: anthropic/claude-opus-4-6 ``` ## 에이전트 설계 테스트 및 반복 diff --git a/docs/edge/ko/learn/llm-selection-guide.mdx b/docs/edge/ko/learn/llm-selection-guide.mdx index 3cfcb9a631..7b1e6fa678 100644 --- a/docs/edge/ko/learn/llm-selection-guide.mdx +++ b/docs/edge/ko/learn/llm-selection-guide.mdx @@ -148,7 +148,7 @@ from crewai import Agent, Task, Crew, LLM 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) +content_llm = LLM(model="claude-sonnet-4-6", temperature=0.7) # Efficient model for data processing processing_llm = LLM(model="gpt-4o-mini", temperature=0) @@ -322,7 +322,7 @@ domain_expert = Agent( You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving you perspective on both established and disruptive go-to-market strategies. """, - llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Balanced creativity with domain knowledge + llm=LLM(model="claude-sonnet-4-6", temperature=0.3) # Balanced creativity with domain knowledge ) # This context enables Claude to perform like a domain expert @@ -354,7 +354,7 @@ tech_writer = Agent( and practical use cases. You prioritize accuracy and usability over marketing fluff. """, llm=LLM( - model="claude-3-5-sonnet", # Excellent for technical writing + model="claude-sonnet-4-6", # Excellent for technical writing temperature=0.1 # Low temperature for accuracy ), tools=[code_analyzer_tool, api_scanner_tool], @@ -418,7 +418,7 @@ tech_writer = Agent( # Creative 또는 고객 대응 agent content_agent = Agent( role="Content Creator", - llm=LLM(model="claude-3-5-sonnet"), # 글쓰기에 최적 + llm=LLM(model="claude-sonnet-4-6"), # 글쓰기에 최적 # ... 나머지 설정 ) ``` @@ -513,7 +513,7 @@ tech_writer = Agent( ) # agent는 특별히 지정하지 않으면 crew LLM을 상속받음 - agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) # 특정 요구에 따라 오버라이드 + agent1 = Agent(llm=LLM(model="claude-sonnet-4-6")) # 특정 요구에 따라 오버라이드 ``` @@ -531,7 +531,7 @@ tech_writer = Agent( tools=[search_tool, api_tool, data_tool], llm=LLM(model="gpt-4o"), # 함수 호출에 우수 # OR - llm=LLM(model="claude-3-5-sonnet") # 도구 사용에 강점 + llm=LLM(model="claude-sonnet-4-6") # 도구 사용에 강점 ) ``` diff --git a/docs/edge/pt-BR/concepts/knowledge.mdx b/docs/edge/pt-BR/concepts/knowledge.mdx index eabe22fabd..92bf64c2f8 100644 --- a/docs/edge/pt-BR/concepts/knowledge.mdx +++ b/docs/edge/pt-BR/concepts/knowledge.mdx @@ -542,7 +542,7 @@ agent = Agent( role="Researcher", goal="Research topics", backstory="Expert researcher", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + llm=LLM(provider="anthropic", model="claude-sonnet-4-6") # Using Claude ) # CrewAI will still use OpenAI embeddings by default for knowledge diff --git a/docs/edge/pt-BR/concepts/memory.mdx b/docs/edge/pt-BR/concepts/memory.mdx index 6d57ce1304..e80b568c82 100644 --- a/docs/edge/pt-BR/concepts/memory.mdx +++ b/docs/edge/pt-BR/concepts/memory.mdx @@ -730,7 +730,7 @@ memory = Memory() memory = Memory(llm="gpt-4o") # Usar Anthropic -memory = Memory(llm="anthropic/claude-3-haiku-20240307") +memory = Memory(llm="anthropic/claude-haiku-4-5") # Usar Ollama para análise totalmente local/privada memory = Memory(llm="ollama/llama3.2") diff --git a/docs/edge/pt-BR/guides/agents/crafting-effective-agents.mdx b/docs/edge/pt-BR/guides/agents/crafting-effective-agents.mdx index b80fd6fe52..90ddc315e7 100644 --- a/docs/edge/pt-BR/guides/agents/crafting-effective-agents.mdx +++ b/docs/edge/pt-BR/guides/agents/crafting-effective-agents.mdx @@ -423,7 +423,7 @@ writer: role: "Creative Content Writer" goal: "..." backstory: "..." - llm: anthropic/claude-3-opus + llm: anthropic/claude-opus-4-6 ``` ## Testando e Iterando no Design de Agentes diff --git a/docs/edge/pt-BR/learn/llm-selection-guide.mdx b/docs/edge/pt-BR/learn/llm-selection-guide.mdx index e296665d32..6b993f1051 100644 --- a/docs/edge/pt-BR/learn/llm-selection-guide.mdx +++ b/docs/edge/pt-BR/learn/llm-selection-guide.mdx @@ -151,7 +151,7 @@ from crewai import Agent, Task, Crew, LLM 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) +content_llm = LLM(model="claude-sonnet-4-6", temperature=0.7) # Modelo eficiente para processamento de dados processing_llm = LLM(model="gpt-4o-mini", temperature=0) @@ -324,7 +324,7 @@ domain_expert = Agent( You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving you perspective on both established and disruptive go-to-market strategies. """, - llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Criatividade balanceada com conhecimento de domínio + llm=LLM(model="claude-sonnet-4-6", temperature=0.3) # Criatividade balanceada com conhecimento de domínio ) # Esse contexto faz o Claude agir como especialista do setor @@ -356,7 +356,7 @@ tech_writer = Agent( and practical use cases. You prioritize accuracy and usability over marketing fluff. """, llm=LLM( - model="claude-3-5-sonnet", + model="claude-sonnet-4-6", temperature=0.1 ), tools=[code_analyzer_tool, api_scanner_tool], @@ -420,7 +420,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl # Agentes criativos ou customer-facing content_agent = Agent( role="Content Creator", - llm=LLM(model="claude-3-5-sonnet"), + llm=LLM(model="claude-sonnet-4-6"), # ... demais configs ) ``` @@ -515,7 +515,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl ) # Agentes herdam o LLM da crew, salvo sobrescrita - agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) + agent1 = Agent(llm=LLM(model="claude-sonnet-4-6")) ``` @@ -533,7 +533,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl tools=[search_tool, api_tool, data_tool], llm=LLM(model="gpt-4o"), # OU - llm=LLM(model="claude-3-5-sonnet") + llm=LLM(model="claude-sonnet-4-6") ) ```