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Overview

Edgee’s OpenAI-compatible API works seamlessly with LangChain, allowing you to leverage LangChain’s powerful features like chains, agents, memory, and RAG while maintaining control over your LLM infrastructure.

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Installation

Using uv with inline dependencies (PEP 723): 
#!/usr/bin/env -S uv run
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "langchain",
#     "langchain-openai",
# ]
# ///
Or install manually: 
pip install langchain langchain-openai

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Basic Usage

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
import os

# Initialize the LLM with Edgee endpoint
llm = ChatOpenAI(
    base_url="https://api.edgee.ai/v1",
    api_key=os.getenv("API_KEY"),
    model="mistral-small",  # or any model available through Edgee
    timeout=30.0,
)

# Simple chat
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is LangChain?"),
]

response = llm.invoke(messages)
print(response.content)

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Command-Line Script

Complete script with argument parsing: 
#!/usr/bin/env -S uv run
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "langchain",
#     "langchain-openai",
# ]
# ///

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
import os
import argparse

def main():
    parser = argparse.ArgumentParser(description="LangChain with Edgee")
    parser.add_argument("--model", type=str, default="mistral-small", help="Model name")
    parser.add_argument("--message", type=str, required=True, help="Message to send")
    parser.add_argument("--system", type=str, default="You are a helpful assistant.", help="System prompt")

    args = parser.parse_args()

    llm = ChatOpenAI(
        base_url="https://api.edgee.ai/v1",
        api_key=os.getenv("API_KEY"),
        model=args.model,
        timeout=30.0,
    )

    messages = [
        SystemMessage(content=args.system),
        HumanMessage(content=args.message),
    ]

    response = llm.invoke(messages)
    print(response.content)

if __name__ == "__main__":
    main()

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Usage Examples

# Basic usage (uses mistral-small by default)
uv run langchain_script.py --message "Tell me a joke"

# With custom model
uv run langchain_script.py --model "gpt-4" --message "Explain quantum computing"

# With custom system prompt
uv run langchain_script.py \
  --model "mistral-small" \
  --message "Write a haiku" \
  --system "You are a creative poet"

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Advanced Features

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Chains

from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(
    base_url="https://api.edgee.ai/v1",
    api_key=os.getenv("API_KEY"),
    model="mistral-small",
)

# Create a prompt template
prompt = PromptTemplate.from_template("Write a brief summary about {topic}")

# Create the chain using LCEL (LangChain Expression Language)
chain = prompt | llm | StrOutputParser()

# Run the chain
result = chain.invoke({"topic": "artificial intelligence"})
print(result)

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Streaming Responses

llm = ChatOpenAI(
    base_url="https://api.edgee.ai/v1",
    api_key=os.getenv("API_KEY"),
    model="mistral-small",
    streaming=True,
)

for chunk in llm.stream("Tell me a long story"):
    print(chunk.content, end="", flush=True)

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Authentication

Edgee uses standard Bearer token authentication. Set your API key as an environment variable: 
export API_KEY="your-edgee-api-key"
The api_key parameter in ChatOpenAI automatically formats the header as: 
Authorization: Bearer {api_key}

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Benefits of Using LangChain with Edgee

Unified Infrastructure

Access all LLM providers through Edgee while using LangChain’s developer-friendly interface.

Cost Control

Leverage Edgee’s cost tracking and routing while building complex LangChain applications.

Reliability

Combine LangChain’s agent capabilities with Edgee’s automatic failover and load balancing.

Observability

Monitor your LangChain applications with Edgee’s built-in observability features.

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Next Steps