LangChain in Orbit

LangChain is a powerful framework for building applications with large language models (LLMs). Orbit integrates with LangChain to enable advanced agent orchestration, prompt engineering, and workflow automation.

Key Features

• Chain LLMs and tools: Build complex workflows by connecting LLMs, APIs, and custom functions.
• Prompt templates: Use dynamic prompt templates for flexible agent behavior.
• Memory and context: Maintain conversation and task state across agent interactions.
• Integration with Orbit Agents: Deploy LangChain-powered agents in the Orbit app.

Example: Simple LangChain Agent

from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool

llm = OpenAI()
tools = [Tool(name="Search", func=lambda q: "Result for " + q)]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
response = agent.run("What is the capital of France?")
print(response)

Best Practices

• Use environment variables to manage API keys and secrets.
• Leverage Orbit's logging and monitoring for agent observability.
• Combine LangChain with Bedrock or other LLM providers for hybrid workflows.

See the Orbit documentation for more advanced LangChain examples and deployment guides.