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Edgee’s OpenAI-compatible API works seamlessly with LangChain, allowing you to leverage LangChain’s powerful features like chains, agents, memory, and RAG while reducing token costs by up to 50% through intelligent compression and 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)

​
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()

​
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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Compression & Tags via Headers

Control Agentic Token Compression and add tags for observability using the default_headers parameter: 
from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    base_url="https://api.edgee.ai/v1",
    api_key=os.getenv("API_KEY"),
    model="gpt-5.2",
    default_headers={
        "x-edgee-enable-compression": "true",
        "x-edgee-compression-rate": "0.8",  # Target 80% compression
        "x-edgee-tags": "production,langchain,rag-pipeline",
    }
)
Available Headers:
HeaderTypeDescription
x-edgee-enable-compression"true" or "false"Enable token compression (overrides console settings)
x-edgee-compression-ratestringTarget compression rate (0.0-1.0, default 0.75)
x-edgee-tagsstringComma-separated tags for analytics and filtering
You can also enable compression per API key in the Edgee console. Headers override console settings.

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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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Tags

You can add tags to your requests for analytics and filtering using the default_headers parameter: 
from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    base_url="https://api.edgee.ai/v1",
    api_key=os.getenv("API_KEY"),
    model="mistral-small",
    default_headers={
        "x-edgee-tags": "production,user-123,langchain",
    },
)

# All requests from this client will include these tags
response = llm.invoke("What is LangChain?")
Tags are comma-separated strings in the header. They help you categorize and filter requests in Edgee’s analytics dashboard.

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Authentication

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