Langchain Common Errors

LangChain Common Errors

Overview

Quick reference for diagnosing and resolving the most common LangChain errors.

Prerequisites

• LangChain installed and configured
• Access to application logs
• Understanding of your LangChain implementation

Error Reference

Authentication Errors

openai.AuthenticationError: Incorrect API key provided

# Cause: Invalid or missing API key
# Solution:
import os
os.environ["OPENAI_API_KEY"] = "sk-..."  # Set correct key

# Verify key is loaded
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()  # Will raise error if key invalid

anthropic.AuthenticationError: Invalid x-api-key

# Cause: Anthropic API key not set or invalid
# Solution:
os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."

# Or pass directly
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(api_key="sk-ant-...")

Import Errors

ModuleNotFoundError: No module named 'langchain_openai'

set -euo pipefail
# Cause: Provider package not installed
# Solution:
pip install langchain-openai

# For other providers:
pip install langchain-anthropic
pip install langchain-google-genai
pip install langchain-community

ImportError: cannot import name 'ChatOpenAI' from 'langchain'

# Cause: Using old import path (pre-0.2.0)
# Old (deprecated):
from langchain.chat_models import ChatOpenAI

# New (correct):
from langchain_openai import ChatOpenAI

Rate Limiting

openai.RateLimitError: Rate limit reached

# Cause: Too many API requests
# Solution: Implement retry with backoff
from langchain_openai import ChatOpenAI
from tenacity import retry, wait_exponential, stop_after_attempt

@retry(wait=wait_exponential(min=1, max=60), stop=stop_after_attempt(5))
def call_with_retry(llm, prompt):
    return llm.invoke(prompt)

# Or use LangChain's built-in retry
llm = ChatOpenAI(max_retries=3)

Output Parsing Errors

OutputParserException: Failed to parse output

# Cause: LLM output doesn't match expected format
# Solution 1: Use with_retry
from langchain.output_parsers import RetryOutputParser

parser = RetryOutputParser.from_llm(parser=your_parser, llm=llm)

# Solution 2: Use structured output (more reliable)
from pydantic import BaseModel

class Output(BaseModel):
    answer: str

llm_with_structure = llm.with_structured_output(Output)

ValidationError: field required

# Cause: Pydantic model validation failed
# Solution: Make fields optional or provide defaults
from pydantic import BaseModel, Field
from typing import Optional

class Output(BaseModel):
    answer: str
    confidence: Optional[float] = Field(default=None)

Chain Errors

ValueError: Missing required input keys

# Cause: Input dict missing required variables
# Debug:
prompt = ChatPromptTemplate.from_template("Hello {name}, you are {age}")
print(prompt.input_variables)  # ['name', 'age']

# Solution: Provide all required keys
chain.invoke({"name": "Alice", "age": 30})

TypeError: Expected mapping type as input

# Cause: Passing wrong input type
# Wrong:
chain.invoke("hello")

# Correct:
chain.invoke({"input": "hello"})

Agent Errors

AgentExecutor: max iterations reached

# Cause: Agent stuck in loop
# Solution: Increase iterations or improve prompts
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    max_iterations=20,  # Increase from default 15
    early_stopping_method="force"  # Force stop after max
)

ToolException: Tool execution failed

# Cause: Tool raised an exception
# Solution: Add error handling in tool
@tool
def my_tool(input: str) -> str:
    """Tool description."""
    try:
        # Tool logic
        return result
    except Exception as e:
        return f"Tool error: {str(e)}"

Memory Errors

KeyError: 'chat_history'

# Cause: Memory key mismatch
# Solution: Ensure consistent key names
prompt = ChatPromptTemplate.from_messages([
    MessagesPlaceholder(variable_name="chat_history"),  # Match this
    ("human", "{input}")
])

# When invoking:
chain.invoke({
    "input": "hello",
    "chat_history": []  # Must match placeholder name
})

Debugging Tips

Enable Verbose Mode

import langchain
langchain.debug = True  # Shows all chain steps

# Or per-component
agent_executor = AgentExecutor(verbose=True)

Trace with LangSmith

# Set environment variables
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-langsmith-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"

# All chains automatically traced

Check Version Compatibility

set -euo pipefail
pip show langchain langchain-core langchain-openai

# Ensure versions are compatible:
# langchain >= 0.3.0
# langchain-core >= 0.3.0
# langchain-openai >= 0.2.0

Resources

• LangChain Troubleshooting
• LangSmith Debugging
• GitHub Issues

Next Steps

For complex debugging, use langchain-debug-bundle to collect evidence.

Instructions

1. Assess the current state of the debugging configuration
2. Identify the specific requirements and constraints
3. Apply the recommended patterns from this skill
4. Validate the changes against expected behavior
5. Document the configuration for team reference

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Error Handling

Examples

Basic usage: Apply langchain common errors to a standard project setup with default configuration options.

Advanced scenario: Customize langchain common errors for production environments with multiple constraints and team-specific requirements.