reminix-langchain 0.0.3

Reminix adapter for LangChain - serve agents as REST APIs

reminix-langchain

Reminix Runtime adapter for LangChain. Serve any LangChain runnable as a REST API.

Ready to go live? Deploy to Reminix for zero-config hosting, or self-host on your own infrastructure.

Installation

pip install reminix-langchain

This will also install reminix-runtime as a dependency.

Quick Start

from langchain_openai import ChatOpenAI
from reminix_langchain import wrap_and_serve

llm = ChatOpenAI(model="gpt-4o")
wrap_and_serve(llm, name="my-chatbot", port=8080)

For more flexibility (e.g., serving multiple agents), use wrap and serve separately:

from langchain_openai import ChatOpenAI
from reminix_langchain import wrap
from reminix_runtime import serve

llm = ChatOpenAI(model="gpt-4o")
agent = wrap(llm, name="my-chatbot")
serve([agent], port=8080)

Your agent is now available at:

• POST /agents/my-chatbot/invoke - Stateless invocation
• POST /agents/my-chatbot/chat - Conversational chat

API Reference

wrap_and_serve(runnable, name, port, host)

Wrap a LangChain runnable and serve it immediately. Combines wrap and serve for single-agent setups.

Parameter	Type	Default	Description
runnable	Runnable	required	Any LangChain runnable (LLM, chain, agent, etc.)
name	str	"langchain-agent"	Name for the agent (used in URL path)
port	int	8080	Port to serve on
host	str	"0.0.0.0"	Host to bind to

wrap(runnable, name)

Wrap a LangChain runnable for use with Reminix Runtime. Use this with serve from reminix_runtime for multi-agent setups.

Parameter	Type	Default	Description
runnable	Runnable	required	Any LangChain runnable (LLM, chain, agent, etc.)
name	str	"langchain-agent"	Name for the agent (used in URL path)

Returns: LangChainAdapter - A Reminix adapter instance

Example with a Chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from reminix_langchain import wrap
from reminix_runtime import serve

# Create a chain
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}")
])
llm = ChatOpenAI(model="gpt-4o")
chain = prompt | llm

# Wrap and serve
agent = wrap(chain, name="my-chain")
serve([agent], port=8080)

Endpoint Input/Output Formats

POST /agents/{name}/invoke

Stateless invocation for task-oriented operations.

Request:

{
  "input": {
    "prompt": "Summarize this text: ..."
  }
}

Or with messages:

{
  "input": {
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }
}

Response:

{
  "output": "Hello! How can I help you today?"
}

POST /agents/{name}/chat

Conversational chat with message history.

Request:

{
  "messages": [
    {"role": "user", "content": "What is the capital of France?"}
  ]
}

Response:

{
  "output": "The capital of France is Paris.",
  "messages": [
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."}
  ]
}

Runtime Documentation

For information about the server, endpoints, request/response formats, and more, see the reminix-runtime package.

Deployment

Ready to go live?

• Deploy to Reminix - Zero-config cloud hosting
• Self-host - Run on your own infrastructure

Links

• GitHub Repository
• LangChain Documentation

License

Apache-2.0