Helixdevelopment Helixagent 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'

# 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

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.

Source: HelixDevelopment/HelixAgent — distributed by TomeVault.