# Helixdevelopment Helixagent Langchain Core Workflow A

> LangChain Core Workflow A: Chains & Prompts

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

---


# LangChain Core Workflow A: Chains & Prompts

## Overview
Build production-ready chains using LangChain Expression Language (LCEL) with prompt templates, output parsers, and composition patterns.

## Prerequisites
- Completed `langchain-install-auth` setup
- Understanding of prompt engineering basics
- Familiarity with Python type hints

## Instructions

### Step 1: Create Prompt Templates
```python
from langchain_core.prompts import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder
)

# Simple template
simple_prompt = ChatPromptTemplate.from_template(
    "Translate '{text}' to {language}"
)

# Chat-style template
chat_prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template(
        "You are a {role}. Respond in {style} style."
    ),
    MessagesPlaceholder(variable_name="history", optional=True),
    HumanMessagePromptTemplate.from_template("{input}")
])
```

### Step 2: Build LCEL Chains
```python
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser

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

# Basic chain: prompt -> llm -> parser
basic_chain = simple_prompt | llm | StrOutputParser()

# Invoke the chain
result = basic_chain.invoke({
    "text": "Hello, world!",
    "language": "Spanish"
})
print(result)  # "Hola, mundo!"
```

### Step 3: Chain Composition
```python
from langchain_core.runnables import RunnablePassthrough, RunnableParallel

# Sequential chain
chain1 = prompt1 | llm | StrOutputParser()
chain2 = prompt2 | llm | StrOutputParser()

sequential = chain1 | (lambda x: {"summary": x}) | chain2

# Parallel execution
parallel = RunnableParallel(
    summary=prompt1 | llm | StrOutputParser(),
    keywords=prompt2 | llm | StrOutputParser(),
    sentiment=prompt3 | llm | StrOutputParser()
)

results = parallel.invoke({"text": "Your input text"})
# Returns: {"summary": "...", "keywords": "...", "sentiment": "..."}
```

### Step 4: Branching Logic
```python
from langchain_core.runnables import RunnableBranch

# Conditional branching
branch = RunnableBranch(
    (lambda x: x["type"] == "question", question_chain),
    (lambda x: x["type"] == "command", command_chain),
    default_chain  # Fallback
)

result = branch.invoke({"type": "question", "input": "What is AI?"})
```

## Output
- Reusable prompt templates with variable substitution
- Type-safe LCEL chains with clear data flow
- Composable chain patterns (sequential, parallel, branching)
- Consistent output parsing

## Examples

### Multi-Step Processing Chain
```python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

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

# Step 1: Extract key points
extract_prompt = ChatPromptTemplate.from_template(
    "Extract 3 key points from: {text}"
)

# Step 2: Summarize
summarize_prompt = ChatPromptTemplate.from_template(
    "Create a one-sentence summary from these points: {points}"
)

# Compose the chain
chain = (
    {"points": extract_prompt | llm | StrOutputParser()}
    | summarize_prompt
    | llm
    | StrOutputParser()
)

summary = chain.invoke({"text": "Long article text here..."})
```

### With Context Injection
```python
from langchain_core.runnables import RunnablePassthrough

def get_context(input_dict):
    """Fetch relevant context from database."""
    return f"Context for: {input_dict['query']}"

chain = (
    RunnablePassthrough.assign(context=get_context)
    | prompt
    | llm
    | StrOutputParser()
)

result = chain.invoke({"query": "user question"})
```

## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Missing Variable | Template variable not provided | Check input dict keys match template |
| Type Error | Wrong input type | Ensure inputs match expected schema |
| Parse Error | Output doesn't match parser | Use more specific prompts or fallback |

## Resources
- [LCEL Conceptual Guide](https://python.langchain.com/docs/concepts/lcel/)
- [Prompt Templates](https://python.langchain.com/docs/concepts/prompt_templates/)
- [Runnables](https://python.langchain.com/docs/concepts/runnables/)

## Next Steps
Proceed to `langchain-core-workflow-b` for agents and tools workflow.

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