--- name: langchain-core-workflow-b-623d4577e143 description: | Build LangChain agents with tools for autonomous task execution. Use when creating AI agents, implementing tool calling, or building autonomous workflows with decision-making. Trigger with phrases like "langchain agents", "langchain tools", "tool calling", "langchain autonomous", "create agent", "function calling". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore --- # LangChain Core Workflow B: Agents & Tools ## Overview Build autonomous agents that can use tools, make decisions, and execute multi-step tasks using LangChain's agent framework. ## Prerequisites - Completed `langchain-core-workflow-a` (chains) - Understanding of function/tool calling concepts - Familiarity with async programming ## Instructions ### Step 1: Define Tools ```python from langchain_core.tools import tool from pydantic import BaseModel, Field class SearchInput(BaseModel): query: str = Field(description="The search query") @tool(args_schema=SearchInput) def search_web(query: str) -> str: """Search the web for information.""" # Implement actual search logic return f"Search results for: {query}" @tool def calculate(expression: str) -> str: """Evaluate a mathematical expression.""" try: result = eval(expression) # Use safer alternative in production return str(result) except Exception as e: return f"Error: {e}" @tool def get_current_time() -> str: """Get the current date and time.""" from datetime import datetime return datetime.now().isoformat() tools = [search_web, calculate, get_current_time] ``` ### Step 2: Create Agent with Tools ```python from langchain_openai import ChatOpenAI from langchain.agents import create_tool_calling_agent, AgentExecutor from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder llm = ChatOpenAI(model="gpt-4o-mini") prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant with access to tools."), MessagesPlaceholder(variable_name="chat_history", optional=True), ("human", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ]) agent = create_tool_calling_agent(llm, tools, prompt) agent_executor = AgentExecutor( agent=agent, tools=tools, verbose=True, max_iterations=10, handle_parsing_errors=True ) ``` ### Step 3: Run the Agent ```python # Simple invocation result = agent_executor.invoke({ "input": "What's 25 * 4 and what time is it?" }) print(result["output"]) # With chat history from langchain_core.messages import HumanMessage, AIMessage history = [ HumanMessage(content="Hi, I'm Alice"), AIMessage(content="Hello Alice! How can I help you?") ] result = agent_executor.invoke({ "input": "What's my name?", "chat_history": history }) ``` ### Step 4: Streaming Agent Output ```python async def stream_agent(): async for event in agent_executor.astream_events( {"input": "Search for LangChain news"}, version="v2" ): if event["event"] == "on_chat_model_stream": print(event["data"]["chunk"].content, end="", flush=True) elif event["event"] == "on_tool_start": print(f"\n[Using tool: {event['name']}]") ``` ## Output - Typed tool definitions with Pydantic schemas - Configured agent executor with error handling - Working agent that can reason and use tools - Streaming output for real-time feedback ## Advanced Patterns ### Custom Tool with Async Support ```python from langchain_core.tools import StructuredTool async def async_search(query: str) -> str: """Async search implementation.""" import aiohttp async with aiohttp.ClientSession() as session: # Implement async search return f"Async results for: {query}" search_tool = StructuredTool.from_function( func=lambda q: "sync fallback", coroutine=async_search, name="search", description="Search the web" ) ``` ### Agent with Memory ```python from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory message_history = ChatMessageHistory() agent_with_memory = RunnableWithMessageHistory( agent_executor, lambda session_id: message_history, input_messages_key="input", history_messages_key="chat_history" ) result = agent_with_memory.invoke( {"input": "Remember, I prefer Python"}, config={"configurable": {"session_id": "user123"}} ) ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | Tool Not Found | Tool name mismatch | Verify tool names in prompt | | Max Iterations | Agent stuck in loop | Increase limit or improve prompts | | Parse Error | Invalid tool call format | Enable `handle_parsing_errors` | | Tool Error | Tool execution failed | Add try/except in tool functions | ## Resources - [Agents Conceptual Guide](https://python.langchain.com/docs/concepts/agents/) - [Tool Calling](https://python.langchain.com/docs/concepts/tool_calling/) - [Agent Types](https://python.langchain.com/docs/how_to/agent_executor/) ## Next Steps Proceed to `langchain-common-errors` for debugging guidance. --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-core-workflow-b/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License (with OpenAI/Anthropic Rider) Copyright (c) 2026 Jeffrey Emanuel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic): This rider is part of the "conditions" of this License. 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Any breach of this rider automatically and immediately terminates the permissions granted by this License. Upon termination, you must immediately cease all use and distribution of the Software and any Derivative Works and destroy all copies under your control. You agree that a breach of this rider would cause irreparable harm and that Jeffrey Emanuel may seek injunctive or other equitable relief to enforce this rider, in addition to any other remedies available at law. To the maximum extent permitted by applicable law, the prevailing party in any action to enforce this rider shall be entitled to recover reasonable attorneys' fees and costs. For purposes of this rider, "Affiliate" means any entity that directly or indirectly controls, is controlled by, or is under common control with the specified party. "Control" means ownership of more than 50% of the voting securities or other ownership interest, or the power to direct management or policies by contract or otherwise. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-core-workflow-b/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Growth Product Manager, ML / AI Engineer, Product Manager Specialties: API Tools: Langchain Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 136b5601ef23f54d74996332fe5745f4f2082db11ec93e503a007ffb079a236b