dspy-integration-with-langchain
Sub-skill of dspy: Integration with LangChain (+1).
Best use case
dspy-integration-with-langchain is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of dspy: Integration with LangChain (+1).
Teams using dspy-integration-with-langchain 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/integration-with-langchain/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dspy-integration-with-langchain Compares
| Feature / Agent | dspy-integration-with-langchain | 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 dspy: Integration with LangChain (+1).
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
# Integration with LangChain (+1)
## Integration with LangChain
```python
import dspy
from langchain_core.runnables import RunnableLambda
# Create DSPy module
class DSPyQA(dspy.Module):
def __init__(self):
super().__init__()
self.qa = dspy.ChainOfThought("context, question -> answer")
*See sub-skills for full details.*
## FastAPI Deployment
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import dspy
app = FastAPI()
# Load optimized module
class QAModule(dspy.Module):
def __init__(self):
*See sub-skills for full details.*Related Skills
library-evaluation-integration
Create evaluation scripts and integration tests for Python scientific libraries in the digitalmodel package. Follows the established pattern from fluids, ht, meshio, sectionproperties, and pygmt evaluations.
clean-worktree-integration-from-dirty-main
Land validated issue work from isolated worktrees when the main checkout is dirty by creating a fresh integration worktree, cherry-picking only implementation commits, re-running combined validation, and preparing push/closeout artifacts.
hermes-ecosystem-integration
Wire Hermes into workspace-hub ecosystem — multi-repo skills, config sync, session export to learning pipeline, memory cross-pollination, skill patch tracking, and cross-machine health checks.
api-integration
Integrate offshore engineering software APIs with mock testing for OrcaFlex, AQWA, and WAMIT
llm-wiki-roadmap-integration
Integrate repo-ecosystem work into an existing llm-wiki / knowledge-roadmap issue without creating duplicate GitHub issues.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
mkdocs-integration-with-python-package
Sub-skill of mkdocs: Integration with Python Package (+2).
improve-integration
Sub-skill of improve: Integration.
clean-code-pre-commit-integration
Sub-skill of clean-code: Pre-commit Integration.
agent-teams-work-queue-integration
Sub-skill of agent-teams: Work Queue Integration.
vscode-extensions-git-workflow-integration
Sub-skill of vscode-extensions: Git Workflow Integration (+1).
raycast-alfred-project-switcher-integration
Sub-skill of raycast-alfred: Project Switcher Integration.