langchain-upgrade-migrationClaude Skill
Plan and execute LangChain SDK upgrades and migrations.
1.4k Stars
173 Forks
2025/10/10
| name | langchain-upgrade-migration |
| description | Plan and execute LangChain SDK upgrades and migrations. Use when upgrading LangChain versions, migrating from legacy patterns, or updating to new APIs after breaking changes. Trigger with phrases like "upgrade langchain", "langchain migration", "langchain breaking changes", "update langchain version", "langchain 0.3". |
| allowed-tools | Read, Write, Edit, Bash(pip:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
LangChain Upgrade Migration
Overview
Guide for upgrading LangChain versions safely with migration strategies for breaking changes.
Prerequisites
- Existing LangChain application
- Version control with current code committed
- Test suite covering core functionality
- Staging environment for validation
Instructions
Step 1: Check Current Versions
pip show langchain langchain-core langchain-openai langchain-community # Output current requirements pip freeze | grep -i langchain > langchain_current.txt
Step 2: Review Breaking Changes
# Key breaking changes by version: # 0.1.x -> 0.2.x (Major restructuring) # - langchain-core extracted as separate package # - Imports changed from langchain.* to langchain_core.* # - ChatModels moved to provider packages # 0.2.x -> 0.3.x (LCEL standardization) # - Legacy chains deprecated # - AgentExecutor changes # - Memory API updates # Check migration guides: # https://python.langchain.com/docs/versions/migrating_chains/
Step 3: Update Import Paths
# OLD (pre-0.2): from langchain.chat_models import ChatOpenAI from langchain.prompts import ChatPromptTemplate from langchain.chains import LLMChain # NEW (0.3+): from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser # Migration script import re def migrate_imports(content: str) -> str: """Migrate old imports to new pattern.""" migrations = [ (r"from langchain\.chat_models import ChatOpenAI", "from langchain_openai import ChatOpenAI"), (r"from langchain\.llms import OpenAI", "from langchain_openai import OpenAI"), (r"from langchain\.prompts import", "from langchain_core.prompts import"), (r"from langchain\.schema import", "from langchain_core.messages import"), (r"from langchain\.callbacks import", "from langchain_core.callbacks import"), ] for old, new in migrations: content = re.sub(old, new, content) return content
Step 4: Migrate Legacy Chains to LCEL
# OLD: LLMChain (deprecated) from langchain.chains import LLMChain chain = LLMChain(llm=llm, prompt=prompt) result = chain.run(input="hello") # NEW: LCEL (LangChain Expression Language) from langchain_core.output_parsers import StrOutputParser chain = prompt | llm | StrOutputParser() result = chain.invoke({"input": "hello"})
Step 5: Migrate Agents
# OLD: initialize_agent (deprecated) from langchain.agents import initialize_agent, AgentType agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) # NEW: create_tool_calling_agent from langchain.agents import create_tool_calling_agent, AgentExecutor from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), ("human", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ]) agent = create_tool_calling_agent(llm, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools)
Step 6: Migrate Memory
# OLD: ConversationBufferMemory from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() chain = LLMChain(llm=llm, prompt=prompt, memory=memory) # NEW: RunnableWithMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory from langchain_community.chat_message_histories import ChatMessageHistory store = {} def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] chain_with_history = RunnableWithMessageHistory( chain, get_session_history, input_messages_key="input", history_messages_key="history" )
Step 7: Upgrade Packages
# Create backup of current environment pip freeze > requirements_backup.txt # Upgrade to latest stable pip install --upgrade langchain langchain-core langchain-openai langchain-community # Or specific version pip install langchain==0.3.0 langchain-core==0.3.0 # Verify versions pip show langchain langchain-core
Step 8: Run Tests
# Run test suite pytest tests/ -v # Check for deprecation warnings pytest tests/ -W error::DeprecationWarning # Run type checking mypy src/
Migration Checklist
- Current version documented
- Breaking changes reviewed
- Imports updated
- LLMChain -> LCEL migrated
- Agent initialization updated
- Memory patterns updated
- Tests passing
- Staging validation complete
Error Handling
| Error | Cause | Solution |
|---|---|---|
| ImportError | Old import path | Update to new package imports |
| AttributeError | Removed method | Check migration guide for replacement |
| DeprecationWarning | Using old API | Migrate to new pattern |
| TypeErrror | Changed signature | Update function arguments |
Resources
Next Steps
After upgrade, use langchain-common-errors to troubleshoot any issues.
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