# Helixdevelopment Helixagent Langchain Sdk Patterns

> LangChain SDK Patterns

- Skill: `tomevault-io/helixdevelopment-helixagent-langchain-sdk-patterns` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/helixdevelopment-helixagent-langchain-sdk-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/helixdevelopment-helixagent-langchain-sdk-patterns/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/helixdevelopment-helixagent-langchain-sdk-patterns

---


# LangChain SDK Patterns

## Overview
Production-ready patterns for LangChain applications including LCEL chains, structured output, and error handling.

## Prerequisites
- Completed `langchain-install-auth` setup
- Familiarity with async/await patterns
- Understanding of error handling best practices

## Core Patterns

### Pattern 1: Type-Safe Chain with Pydantic
```python
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

class SentimentResult(BaseModel):
    """Structured output for sentiment analysis."""
    sentiment: str = Field(description="positive, negative, or neutral")
    confidence: float = Field(description="Confidence score 0-1")
    reasoning: str = Field(description="Brief explanation")

llm = ChatOpenAI(model="gpt-4o-mini")
structured_llm = llm.with_structured_output(SentimentResult)

prompt = ChatPromptTemplate.from_template(
    "Analyze the sentiment of: {text}"
)

chain = prompt | structured_llm

# Returns typed SentimentResult
result: SentimentResult = chain.invoke({"text": "I love LangChain!"})
print(f"Sentiment: {result.sentiment} ({result.confidence})")
```

### Pattern 2: Retry with Fallback
```python
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import RunnableWithFallbacks

primary = ChatOpenAI(model="gpt-4o")
fallback = ChatAnthropic(model="claude-3-5-sonnet-20241022")

# Automatically falls back on failure
robust_llm = primary.with_fallbacks([fallback])

response = robust_llm.invoke("Hello!")
```

### Pattern 3: Async Batch Processing
```python
import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("Summarize: {text}")
chain = prompt | llm

async def process_batch(texts: list[str]) -> list:
    """Process multiple texts concurrently."""
    inputs = [{"text": t} for t in texts]
    results = await chain.abatch(inputs, config={"max_concurrency": 5})
    return results

# Usage
results = asyncio.run(process_batch(["text1", "text2", "text3"]))
```

### Pattern 4: Streaming with Callbacks
```python
from langchain_openai import ChatOpenAI
from langchain_core.callbacks import StreamingStdOutCallbackHandler

llm = ChatOpenAI(
    model="gpt-4o-mini",
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()]
)

# Streams tokens to stdout as they arrive
for chunk in llm.stream("Tell me a story"):
    # Each chunk contains partial content
    pass
```

### Pattern 5: Caching for Cost Reduction
```python
from langchain_openai import ChatOpenAI
from langchain_core.globals import set_llm_cache
from langchain_community.cache import SQLiteCache

# Enable SQLite caching
set_llm_cache(SQLiteCache(database_path=".langchain_cache.db"))

llm = ChatOpenAI(model="gpt-4o-mini")

# First call hits API
response1 = llm.invoke("What is 2+2?")

# Second identical call uses cache (no API cost)
response2 = llm.invoke("What is 2+2?")
```

## Output
- Type-safe chains with Pydantic models
- Robust error handling with fallbacks
- Efficient async batch processing
- Cost-effective caching strategies

## Error Handling

### Standard Error Pattern
```python
from langchain_core.exceptions import OutputParserException
from openai import RateLimitError, APIError

def safe_invoke(chain, input_data, max_retries=3):
    """Invoke chain with error handling."""
    for attempt in range(max_retries):
        try:
            return chain.invoke(input_data)
        except RateLimitError:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise
        except OutputParserException as e:
            # Handle parsing failures
            return {"error": str(e), "raw": e.llm_output}
        except APIError as e:
            raise RuntimeError(f"API error: {e}")
```

## Resources
- [LCEL Documentation](https://python.langchain.com/docs/concepts/lcel/)
- [Structured Output](https://python.langchain.com/docs/concepts/structured_outputs/)
- [Fallbacks](https://python.langchain.com/docs/how_to/fallbacks/)
- [Caching](https://python.langchain.com/docs/how_to/llm_caching/)

## Next Steps
Proceed to `langchain-core-workflow-a` for chains and prompts workflow.

---
> Source: [HelixDevelopment/HelixAgent](https://github.com/HelixDevelopment/HelixAgent) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-16 -->