agenta-langchain-integration
Sub-skill of agenta: Langchain Integration.
Best use case
agenta-langchain-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of agenta: Langchain Integration.
Teams using agenta-langchain-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langchain-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agenta-langchain-integration Compares
| Feature / Agent | agenta-langchain-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of agenta: Langchain Integration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Langchain Integration
## Langchain Integration
```python
"""
Use Agenta for prompt management in Langchain applications.
"""
import agenta as ag
from agenta import Agenta
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from typing import Dict, Any
class AgentaPromptLoader:
"""
Load prompts from Agenta into Langchain.
"""
def __init__(self, app_name: str):
self.app_name = app_name
self.client = Agenta()
self._cache: Dict[str, PromptTemplate] = {}
def get_prompt(
self,
variant_name: str = None,
use_cache: bool = True
) -> PromptTemplate:
"""
Get a Langchain PromptTemplate from Agenta.
Args:
variant_name: Variant to load (None for default)
use_cache: Whether to use cached prompts
Returns:
Langchain PromptTemplate
"""
cache_key = variant_name or "default"
if use_cache and cache_key in self._cache:
return self._cache[cache_key]
# Get variant from Agenta
if variant_name:
variant = self.client.get_variant_by_name(
app_name=self.app_name,
variant_name=variant_name
)
else:
variant = self.client.get_default_variant(app_name=self.app_name)
# Create Langchain prompt
template = variant.config.get("template", "{input}")
prompt = PromptTemplate.from_template(template)
# Cache
self._cache[cache_key] = prompt
return prompt
def create_chain(
self,
variant_name: str = None,
model: str = "gpt-4",
temperature: float = 0.3
):
"""
Create a Langchain chain from Agenta prompt.
Args:
variant_name: Variant to use
model: Model name
temperature: Temperature setting
Returns:
Langchain chain
"""
prompt = self.get_prompt(variant_name)
llm = ChatOpenAI(model=model, temperature=temperature)
return prompt | llm | StrOutputParser()
# Usage
ag.init()
loader = AgentaPromptLoader("qa-app")
# Get prompt template
prompt = loader.get_prompt("concise-v1")
print(f"Template: {prompt.template}")
# Create and use chain
chain = loader.create_chain(variant_name="detailed-v2")
result = chain.invoke({"input": "What is machine learning?"})
print(f"Result: {result}")
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