diff --git a/docs-website/docs/overview/migrating-from-langgraphlangchain-to-haystack.mdx b/docs-website/docs/overview/migrating-from-langgraphlangchain-to-haystack.mdx
index 5d9c67a6ec..9849df2ced 100644
--- a/docs-website/docs/overview/migrating-from-langgraphlangchain-to-haystack.mdx
+++ b/docs-website/docs/overview/migrating-from-langgraphlangchain-to-haystack.mdx
@@ -44,7 +44,7 @@ Here's a table of key concepts and their approximate equivalents between the two
| Model Context Protocol `load_mcp_tools` `MultiServerMCPClient` | Model Context Protocol - `MCPTool`, `MCPToolset`, `StdioServerInfo`, `StreamableHttpServerInfo` | Haystack provides [various MCP primitives](https://haystack.deepset.ai/integrations/mcp) for connecting multiple MCP servers and organizing MCP toolsets. |
| Memory (State, short-term, long-term) | Memory (Agent State, short-term, long-term) | Agent [State](../concepts/agents/state.mdx) provides a structured way to share data between tools and store intermediate results during agent execution. For short-term memory, Haystack offers a [ChatMessage Store](/reference/experimental-chatmessage-store-api) to persist chat history. More memory options are coming soon. |
| Time travel (Checkpoints) | Breakpoints (Breakpoint, AgentBreakpoint, ToolBreakpoint, Snapshot) | [Breakpoints](../concepts/pipelines/pipeline-breakpoints.mdx) let you pause, inspect, modify, and resume a pipeline, agent, or tool for debugging or iterative development. |
-| Human-in-the-Loop (Interrupts / Commands) | Human-in-the-loop ( ConfirmationStrategy / ConfirmationPolicy) | (Experimental) Haystack uses [confirmation strategies](https://haystack.deepset.ai/tutorials/47_human_in_the_loop_agent) to pause or block the execution to gather user feedback |
+| Human-in-the-Loop (Interrupts / Commands) | Human-in-the-loop ( ConfirmationStrategy / ConfirmationPolicy) | Haystack uses [confirmation strategies](https://haystack.deepset.ai/tutorials/47_human_in_the_loop_agent) to pause or block the execution to gather user feedback |
## Ecosystem and Tooling Mapping: LangChain → Haystack
@@ -373,6 +373,282 @@ for m in messages["messages"]:
+### Creating Agents
+
+The [Agentic Flows](#agentic-flows-with-haystack-vs-langgraph) walkthrough above showed how to assemble an agent loop manually from pipeline primitives. Haystack also provides a high-level `Agent` class that wraps the full loop - LLM calls, tool invocation, and iteration - into a single component. LangGraph offers an equivalent shortcut through `create_react_agent` in `langgraph.prebuilt`. Both produce a ReAct-style agent that handles tool calling and multi-step reasoning automatically.
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+
+
+ {`# pip install haystack-ai anthropic-haystack
+
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(
+ model="claude-sonnet-4-5-20250929",
+ generation_kwargs={"temperature": 0},
+ ),
+ tools=[multiply, add],
+ system_prompt="You are a helpful assistant that performs arithmetic.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("What is 3 multiplied by 7, then add 5?")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-anthropic langgraph
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage, SystemMessage
+
+@tool
+def multiply(a: int, b: int) -> int:
+ """Multiply \`a\` and \`b\`."""
+ return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+ """Add \`a\` and \`b\`."""
+ return a + b
+
+# Create an agent - the agentic loop is handled automatically
+model = ChatAnthropic(
+ model="claude-sonnet-4-5-20250929",
+ temperature=0,
+)
+agent = create_agent(
+ model,
+ tools=[multiply, add],
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that performs arithmetic."
+ ),
+)
+
+result = agent.invoke({
+ "messages": [HumanMessage(content="What is 3 multiplied by 7, then add 5?")]
+})
+print(result["messages"][-1].content)`}
+
+
+
+ {`# pip install haystack-ai sentence-transformers
+
+from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+# Embed and write documents to the document store
+document_store = InMemoryDocumentStore()
+
+doc_embedder = SentenceTransformersDocumentEmbedder(
+ model="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+docs = [
+ Document(content="Paris is the capital of France."),
+ Document(content="Berlin is the capital of Germany."),
+ Document(content="Tokyo is the capital of Japan."),
+]
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+document_store.write_documents(docs_with_embeddings)`}
+
+
+ {`# pip install langchain-community langchain-huggingface sentence-transformers
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+
+# Embed and add documents to the vector store
+embeddings = HuggingFaceEmbeddings(
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+vectorstore = InMemoryVectorStore(embedding=embeddings)
+vectorstore.add_documents([
+ Document(page_content="Paris is the capital of France."),
+ Document(page_content="Berlin is the capital of Germany."),
+ Document(page_content="Tokyo is the capital of Japan."),
+])`}
+
+
+
+ {`from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+# ChatPromptBuilder expects a List[ChatMessage] as template
+template = [ChatMessage.from_user("""
+Given the following documents, answer the question.
+{% for doc in documents %}{{ doc.content }}{% endfor %}
+Question: {{ question }}
+""")]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(
+ "text_embedder",
+ SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+)
+rag_pipeline.add_component(
+ "retriever", InMemoryEmbeddingRetriever(document_store=document_store)
+)
+rag_pipeline.add_component(
+ "prompt_builder", ChatPromptBuilder(template=template)
+)
+rag_pipeline.add_component(
+ "llm", AnthropicChatGenerator(model="claude-sonnet-4-5-20250929")
+)
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+result = rag_pipeline.run({
+ "text_embedder": {"text": "What is the capital of France?"},
+ "prompt_builder": {"question": "What is the capital of France?"},
+})
+print(result["llm"]["replies"][0].text)`}
+
+
+ {`from langchain_anthropic import ChatAnthropic
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def format_docs(docs):
+ return "\\n".join(doc.page_content for doc in docs)
+
+retriever = vectorstore.as_retriever()
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+template = """
+Given the following documents, answer the question.
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | model
+ | StrOutputParser()
+)
+
+result = rag_chain.invoke("What is the capital of France?")
+print(result)`}
+
+
+
+ {`# pip install haystack-ai mcp-haystack anthropic-haystack
+
+from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
+from haystack.components.agents import Agent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+from haystack.dataclasses import ChatMessage
+
+# Connect to an MCP server - tools are auto-discovered
+toolset = MCPToolset(
+ server_info=StdioServerInfo(
+ command="uvx",
+ args=["mcp-server-fetch"],
+ )
+)
+
+agent = Agent(
+ chat_generator=AnthropicChatGenerator(model="claude-sonnet-4-5-20250929"),
+ tools=toolset,
+ system_prompt="You are a helpful assistant that can fetch web content.",
+)
+
+result = agent.run(messages=[
+ ChatMessage.from_user("Fetch the content from https://haystack.deepset.ai")
+])
+print(result["messages"][-1].text) # or print(result["last_message"].text)`}
+
+
+ {`# pip install langchain-mcp-adapters langgraph langchain-anthropic
+
+import asyncio
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain.agents import create_agent
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import HumanMessage, SystemMessage
+
+model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+
+async def run():
+ client = MultiServerMCPClient(
+ {
+ "fetch": {
+ "command": "uvx",
+ "args": ["mcp-server-fetch"],
+ "transport": "stdio",
+ }
+ }
+ )
+ tools = await client.get_tools()
+ agent = create_agent(
+ model,
+ tools,
+ system_prompt=SystemMessage(
+ content="You are a helpful assistant that can fetch web content."
+ ),
+ )
+ result = await agent.ainvoke(
+ {
+ "messages": [
+ HumanMessage(content="Fetch the content from https://haystack.deepset.ai")
+ ]
+ }
+ )
+ print(result["messages"][-1].content)
+
+
+asyncio.run(run())`}
+
+