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import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

[OLD PROXY 👉 NEW proxy here] Local OpenAI Proxy Server

A fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs.

:::info

Docs outdated. New docs 👉 here

:::

Usage 

pip install litellm[proxy]

$ litellm --model ollama/codellama 

#INFO: Ollama running on http://0.0.0.0:8000

Test

In a new shell, run: 

$ litellm --test

Replace openai base 

import openai 

openai.api_base = "http://0.0.0.0:8000"

print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))

Other supported models:

Assuming you're running vllm locally 
$ litellm --model vllm/facebook/opt-125m

$ litellm --model openai/<model_name> --api_base <your-api-base>

$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
$ litellm --model claude-instant-1

$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1

$ export TOGETHERAI_API_KEY=my-api-key
$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k

$ export REPLICATE_API_KEY=my-api-key
$ litellm \
  --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3

$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf

$ export PALM_API_KEY=my-palm-key
$ litellm --model palm/chat-bison

$ export AZURE_API_KEY=my-api-key
$ export AZURE_API_BASE=my-api-base

$ litellm --model azure/my-deployment-name

$ export AI21_API_KEY=my-api-key
$ litellm --model j2-light

$ export COHERE_API_KEY=my-api-key
$ litellm --model command-nightly

Tutorial: Use with Multiple LLMs + LibreChat/Chatbot-UI/Auto-Gen/ChatDev/Langroid,etc.

Replace openai base: 

import openai 

openai.api_key = "any-string-here"
openai.api_base = "http://0.0.0.0:8080" # your proxy url

# call openai
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey"}])

print(response)

# call cohere
response = openai.ChatCompletion.create(model="command-nightly", messages=[{"role": "user", "content": "Hey"}])

print(response)

1. Clone the repo 

git clone https://github.com/danny-avila/LibreChat.git

2. Modify docker-compose.yml 

OPENAI_REVERSE_PROXY=http://host.docker.internal:8000/v1/chat/completions

3. Save fake OpenAI key in .env 

OPENAI_API_KEY=sk-1234

4. Run LibreChat: 

docker compose up

1. Clone the repo 

git clone https://github.com/dotneet/smart-chatbot-ui.git

2. Install Dependencies 

npm i

3. Create your env 

cp .env.local.example .env.local

4. Set the API Key and Base 

OPENAI_API_KEY="my-fake-key"
OPENAI_API_HOST="http://0.0.0.0:8000

5. Run with docker compose 

docker compose up -d

pip install pyautogen

from autogen import AssistantAgent, UserProxyAgent, oai
config_list=[
    {
        "model": "my-fake-model",
        "api_base": "http://0.0.0.0:8000",  #litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL", # just a placeholder
    }
]

response = oai.Completion.create(config_list=config_list, prompt="Hi")
print(response) # works fine

llm_config={
    "config_list": config_list,
}

assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list)

Credits @victordibia for this tutorial. 

from autogen import AssistantAgent, GroupChatManager, UserProxyAgent
from autogen.agentchat import GroupChat
config_list = [
    {
        "model": "ollama/mistralorca",
        "api_base": "http://0.0.0.0:8000",  # litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL",  # just a placeholder
    }
]
llm_config = {"config_list": config_list, "seed": 42}

code_config_list = [
    {
        "model": "ollama/phind-code",
        "api_base": "http://0.0.0.0:8000",  # litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL",  # just a placeholder
    }
]

code_config = {"config_list": code_config_list, "seed": 42}

admin = UserProxyAgent(
    name="Admin",
    system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.",
    llm_config=llm_config,
    code_execution_config=False,
)


engineer = AssistantAgent(
    name="Engineer",
    llm_config=code_config,
    system_message="""Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor.
Don't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
""",
)
planner = AssistantAgent(
    name="Planner",
    system_message="""Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.
The plan may involve an engineer who can write code and a scientist who doesn't write code.
Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.
""",
    llm_config=llm_config,
)
executor = UserProxyAgent(
    name="Executor",
    system_message="Executor. Execute the code written by the engineer and report the result.",
    human_input_mode="NEVER",
    llm_config=llm_config,
    code_execution_config={"last_n_messages": 3, "work_dir": "paper"},
)
critic = AssistantAgent(
    name="Critic",
    system_message="Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.",
    llm_config=llm_config,
)
groupchat = GroupChat(
    agents=[admin, engineer, planner, executor, critic],
    messages=[],
    max_round=50,
)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)


admin.initiate_chat(
    manager,
    message="""
""",
)

Credits @Nathan for this tutorial.

Setup ChatDev (Docs) 

git clone https://github.com/OpenBMB/ChatDev.git
cd ChatDev
conda create -n ChatDev_conda_env python=3.9 -y
conda activate ChatDev_conda_env
pip install -r requirements.txt

Run ChatDev w/ Proxy 

export OPENAI_API_KEY="sk-1234"

export OPENAI_API_BASE="http://0.0.0.0:8000"

python3 run.py --task "a script that says hello world" --name "hello world"

pip install langroid

from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIGPT

# configure the LLM
my_llm_config = OpenAIGPTConfig(
    # where proxy server is listening 
    api_base="http://0.0.0.0:8000", 
)

# create llm, one-off interaction
llm = OpenAIGPT(my_llm_config)
response = mdl.chat("What is the capital of China?", max_tokens=50)

# Create an Agent with this LLM, wrap it in a Task, and 
# run it as an interactive chat app:
from langroid.agent.base import ChatAgent, ChatAgentConfig
from langroid.agent.task import Task

agent_config = ChatAgentConfig(llm=my_llm_config, name="my-llm-agent")
agent = ChatAgent(agent_config)

task = Task(agent, name="my-llm-task")
task.run()

Credits @pchalasani and Langroid for this tutorial.

Local Proxy

Here's how to use the local proxy to test codellama/mistral/etc. models for different github repos 

pip install litellm

$ ollama pull codellama # OUR Local CodeLlama  

$ litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048

Tutorial: Use with Multiple LLMs + Aider/AutoGen/Langroid/etc. 

$ litellm

#INFO: litellm proxy running on http://0.0.0.0:8000

Send a request to your proxy 

import openai 

openai.api_key = "any-string-here"
openai.api_base = "http://0.0.0.0:8080" # your proxy url

# call gpt-3.5-turbo
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey"}])

print(response)

# call ollama/llama2
response = openai.ChatCompletion.create(model="ollama/llama2", messages=[{"role": "user", "content": "Hey"}])

print(response)

Continue-Dev brings ChatGPT to VSCode. See how to install it here.

In the config.py set this as your default model.

  default=OpenAI(
      api_key="IGNORED",
      model="fake-model-name",
      context_length=2048, # customize if needed for your model
      api_base="http://localhost:8000" # your proxy server url
  ),

Credits @vividfog for this tutorial. 

$ pip install aider 

$ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key

pip install pyautogen

from autogen import AssistantAgent, UserProxyAgent, oai
config_list=[
    {
        "model": "my-fake-model",
        "api_base": "http://localhost:8000",  #litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL", # just a placeholder
    }
]

response = oai.Completion.create(config_list=config_list, prompt="Hi")
print(response) # works fine

llm_config={
    "config_list": config_list,
}

assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list)

Credits @victordibia for this tutorial. 

from autogen import AssistantAgent, GroupChatManager, UserProxyAgent
from autogen.agentchat import GroupChat
config_list = [
    {
        "model": "ollama/mistralorca",
        "api_base": "http://localhost:8000",  # litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL",  # just a placeholder
    }
]
llm_config = {"config_list": config_list, "seed": 42}

code_config_list = [
    {
        "model": "ollama/phind-code",
        "api_base": "http://localhost:8000",  # litellm compatible endpoint
        "api_type": "open_ai",
        "api_key": "NULL",  # just a placeholder
    }
]

code_config = {"config_list": code_config_list, "seed": 42}

admin = UserProxyAgent(
    name="Admin",
    system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.",
    llm_config=llm_config,
    code_execution_config=False,
)


engineer = AssistantAgent(
    name="Engineer",
    llm_config=code_config,
    system_message="""Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor.
Don't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
""",
)
planner = AssistantAgent(
    name="Planner",
    system_message="""Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.
The plan may involve an engineer who can write code and a scientist who doesn't write code.
Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.
""",
    llm_config=llm_config,
)
executor = UserProxyAgent(
    name="Executor",
    system_message="Executor. Execute the code written by the engineer and report the result.",
    human_input_mode="NEVER",
    llm_config=llm_config,
    code_execution_config={"last_n_messages": 3, "work_dir": "paper"},
)
critic = AssistantAgent(
    name="Critic",
    system_message="Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.",
    llm_config=llm_config,
)
groupchat = GroupChat(
    agents=[admin, engineer, planner, executor, critic],
    messages=[],
    max_round=50,
)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)


admin.initiate_chat(
    manager,
    message="""
""",
)

Credits @Nathan for this tutorial.

Setup ChatDev (Docs) 

git clone https://github.com/OpenBMB/ChatDev.git
cd ChatDev
conda create -n ChatDev_conda_env python=3.9 -y
conda activate ChatDev_conda_env
pip install -r requirements.txt

Run ChatDev w/ Proxy 

export OPENAI_API_KEY="sk-1234"

export OPENAI_API_BASE="http://0.0.0.0:8000"

python3 run.py --task "a script that says hello world" --name "hello world"

pip install langroid

from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIGPT

# configure the LLM
my_llm_config = OpenAIGPTConfig(
    #format: "local/[URL where LiteLLM proxy is listening]
    chat_model="local/localhost:8000", 
    chat_context_length=2048,  # adjust based on model
)

# create llm, one-off interaction
llm = OpenAIGPT(my_llm_config)
response = mdl.chat("What is the capital of China?", max_tokens=50)

# Create an Agent with this LLM, wrap it in a Task, and 
# run it as an interactive chat app:
from langroid.agent.base import ChatAgent, ChatAgentConfig
from langroid.agent.task import Task

agent_config = ChatAgentConfig(llm=my_llm_config, name="my-llm-agent")
agent = ChatAgent(agent_config)

task = Task(agent, name="my-llm-task")
task.run()

Credits @pchalasani and Langroid for this tutorial. GPT-Pilot helps you build apps with AI Agents. For more

In your .env set the openai endpoint to your local server. 

OPENAI_ENDPOINT=http://0.0.0.0:8000
OPENAI_API_KEY=my-fake-key
A guidance language for controlling large language models. https://github.com/guidance-ai/guidance

NOTE: Guidance sends additional params like stop_sequences which can cause some models to fail if they don't support it.

Fix: Start your proxy using the --drop_params flag

litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params

import guidance

# set api_base to your proxy
# set api_key to anything
gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:8000", api_key="anything")

experts = guidance('''
{{#system~}}
You are a helpful and terse assistant.
{{~/system}}

{{#user~}}
I want a response to the following question:
{{query}}
Name 3 world-class experts (past or present) who would be great at answering this?
Don't answer the question yet.
{{~/user}}

{{#assistant~}}
{{gen 'expert_names' temperature=0 max_tokens=300}}
{{~/assistant}}
''', llm=gpt4)

result = experts(query='How can I be more productive?')
print(result)

:::note Contribute Using this server with a project? Contribute your tutorial here!

:::

Advanced

Logs 

$ litellm --logs

This will return the most recent log (the call that went to the LLM API + the received response).

All logs are saved to a file called api_logs.json in the current directory.

Configure Proxy

If you need to:

  • save API keys
  • set litellm params (e.g. drop unmapped params, set fallback models, etc.)
  • set model-specific params (max tokens, temperature, api base, prompt template)

You can do set these just for that session (via cli), or persist these across restarts (via config file).

Save API Keys 

$ litellm --api_key OPENAI_API_KEY=sk-...

LiteLLM will save this to a locally stored config file, and persist this across sessions.

LiteLLM Proxy supports all litellm supported api keys. To add keys for a specific provider, check this list:

$ litellm --add_key HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]

$ litellm --add_key ANTHROPIC_API_KEY=my-api-key

$ litellm --add_key PERPLEXITYAI_API_KEY=my-api-key

$ litellm --add_key TOGETHERAI_API_KEY=my-api-key

$ litellm --add_key REPLICATE_API_KEY=my-api-key

$ litellm --add_key AWS_ACCESS_KEY_ID=my-key-id
$ litellm --add_key AWS_SECRET_ACCESS_KEY=my-secret-access-key

$ litellm --add_key PALM_API_KEY=my-palm-key

$ litellm --add_key AZURE_API_KEY=my-api-key
$ litellm --add_key AZURE_API_BASE=my-api-base

$ litellm --add_key AI21_API_KEY=my-api-key

$ litellm --add_key COHERE_API_KEY=my-api-key

E.g.: Set api base, max tokens and temperature.

For that session: 

litellm --model ollama/llama2 \
  --api_base http://localhost:11434 \
  --max_tokens 250 \
  --temperature 0.5

# OpenAI-compatible server running on http://0.0.0.0:8000

Performance

We load-tested 500,000 HTTP connections on the FastAPI server for 1 minute, using wrk.

There are our results: 

Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   156.38ms   25.52ms 361.91ms   84.73%
    Req/Sec    13.61      5.13    40.00     57.50%
  383625 requests in 1.00m, 391.10MB read
  Socket errors: connect 0, read 1632, write 1, timeout 0

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