ollama-proxy-plus 0.1.0

Speak the Ollama API protocol — route requests to OpenAI, Gemini, Anthropic, AWS Bedrock, Azure, Groq, xAI, Mistral, DeepSeek, Together, Perplexity, Kimi, and any OpenAI-compatible server.

ollama-proxy

A lightweight server that speaks the Ollama API protocol and routes every request to a cloud or self-hosted LLM backend.

Distributed on PyPI as ollama-proxy-plus (the ollama-proxy name was taken). Install with pip install ollama-proxy-plus. Everything else — import path, CLI command, endpoints — stays as ollama-proxy.

Any tool that already works with Ollama — Open WebUI, Continue, Cursor, GitHub Copilot, LangChain, LlamaIndex, the ollama Python SDK — connects with zero code changes.

Your App  ---->  POST /api/chat            (Ollama protocol)
         ---->  POST /v1/chat/completions  (OpenAI protocol)
                        |
                 ollama-proxy
                        |  routes by model-name prefix
       +----------------+----------------+------- ... -------+
  openai/*        gemini/*        anthropic/*           custom/*
  OpenAI API    Gemini API      Anthropic API     your vLLM / Ray server

The model name prefix is the only routing key. The proxy speaks two wire formats simultaneously — the Ollama protocol on /api/* and the OpenAI protocol on /v1/* — so it works with both ecosystems.

---

Install

# Core (OpenAI / Gemini / xAI / Groq / Mistral / DeepSeek / Together / Perplexity / Kimi / vLLM / etc.)
pip install ollama-proxy-plus

# With Anthropic SDK
pip install "ollama-proxy-plus[anthropic]"

# With AWS Bedrock support
pip install "ollama-proxy-plus[bedrock]"

# Everything
pip install "ollama-proxy-plus[all]"

Or run without installing using uv:

uvx ollama-proxy-plus

Quick start

# 1. Set API keys for the providers you'll use
export OPENAI_API_KEY=sk-...
export GEMINI_API_KEY=AIza...

# 2. Start the proxy on port 11434 (Ollama's default)
ollama-proxy

# 3. Use any Ollama-compatible client pointed at http://localhost:11434
curl http://localhost:11434/api/chat \
  -d '{"model": "openai/gpt-4o-mini", "messages": [{"role":"user","content":"Hi"}]}'

Optional: copy .env.example to .env and put keys there instead of exporting them.

---

CLI

ollama-proxy [serve]    # run the proxy (default subcommand)
ollama-proxy doctor     # check config + provider connectivity
ollama-proxy list-models
ollama-proxy test openai/gpt-4o-mini --prompt "Say hello"

serve flags:

• --host 0.0.0.0 (default)
• --port 11434 (default)
• --debug — verbose request/response logging
• --reload — auto-reload on code changes (dev)
• --config path.yaml — load YAML config
• --workers N — number of worker processes

Or python -m ollama_proxy ... works too.

---

Adding models

Three ways, in increasing permanence:

1. Direct call (no registration)

Any <prefix>/<model-id> works as long as the prefix is configured. The model just won't appear in /api/tags discovery.

curl http://localhost:11434/api/chat -d '{
  "model": "openai/gpt-5-preview",
  "messages": [{"role": "user", "content": "Hi"}]
}'

2. EXTRA_MODELS env var

export EXTRA_MODELS="openai/gpt-5,gemini/gemini-3-pro,kimi/kimi-k2.7"
ollama-proxy

3. YAML config file

# ollama-proxy.yaml
models:
  - openai/gpt-5
  - gemini/gemini-3-pro

ollama-proxy --config ollama-proxy.yaml

---

Adding providers

OpenAI-compatible (most providers, including self-hosted)

Option A — environment variables (auto-discovered):

export PROVIDERS_VLLM_API_KEY=EMPTY
export PROVIDERS_VLLM_BASE_URL=http://localhost:8000/v1
ollama-proxy

Use vllm/<model-id> in your client. No code changes needed.

Option B — YAML config:

providers:
  myvllm:
    api_key: EMPTY
    base_url: http://localhost:8000/v1
  ray:
    api_key: ${RAY_API_KEY}
    base_url: http://ray-head:8000/v1
    retries: 2
    fallback: openai/gpt-4o-mini

Non-compatible provider (Cohere, Vertex AI, etc.)

For wire formats incompatible with OpenAI:

1. Subclass BaseProvider in ollama_proxy/providers/myprovider_provider.py
2. Implement chat(), chat_stream(), optionally chat_full() and chat_stream_raw() for tool support, and embed()
3. Add a routing branch in ollama_proxy/server.py::get_provider()

---

Built-in providers

OpenAI-compatible (one adapter, many providers)

Prefix	Provider	Base URL	Env Variable
openai/	OpenAI	https://api.openai.com/v1	OPENAI_API_KEY
gemini/	Google Gemini	https://generativelanguage.googleapis.com/v1beta/openai/	GEMINI_API_KEY
xai/	xAI / Grok	https://api.x.ai/v1	XAI_API_KEY
groq/	Groq	https://api.groq.com/openai/v1	GROQ_API_KEY
mistral/	Mistral AI	https://api.mistral.ai/v1	MISTRAL_API_KEY
deepseek/	DeepSeek	https://api.deepseek.com	DEEPSEEK_API_KEY
together/	Together AI	https://api.together.xyz/v1	TOGETHER_API_KEY
perplexity/	Perplexity	https://api.perplexity.ai	PERPLEXITY_API_KEY
kimi/	Kimi / Moonshot	https://api.moonshot.ai/v1	KIMI_API_KEY

Endpoints can change. Verify against each provider's docs before relying on them in production.

Self-hosted servers

Any OpenAI-compatible REST server works the same way (typical defaults):

Server	Base URL
vLLM, Ray Serve (vLLM backend)	http://localhost:8000/v1
Hugging Face TGI, LocalAI, Llamafile	http://localhost:8080/v1
LM Studio	http://localhost:1234/v1
Ollama (real instance)	http://localhost:11434/v1
Jan	http://localhost:1337/v1

Native SDK providers

Prefix	Provider	Auth	Install
anthropic/	Anthropic / Claude	ANTHROPIC_API_KEY	pip install ollama-proxy-plus[anthropic]
azure/	Azure OpenAI	AZURE_OPENAI_* vars	(core)
bedrock/	AWS Bedrock	AWS credential chain	pip install ollama-proxy-plus[bedrock]

---

Azure OpenAI

AZURE_OPENAI_API_KEY=<key>
AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/
AZURE_OPENAI_API_VERSION=2024-08-01-preview

The part after azure/ is your deployment name. For multi-resource setups, populate AZURE_DEPLOYMENTS in config.py.

AWS Bedrock

Uses the standard AWS credential chain — no API key. Just have AWS creds configured via env vars, profile, or IAM role:

AWS_REGION=us-east-1
# plus access keys, profile, or IAM role

Use the model ID or cross-region inference profile after bedrock/:

bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0
bedrock/us.amazon.nova-pro-v1:0
bedrock/us.meta.llama3-1-70b-instruct-v1:0

Enable model access in the Bedrock console first.

---

YAML configuration

The proxy reads --config <file> or $PROXY_CONFIG_FILE. See ollama-proxy.example.yaml. Highlights:

providers:
  openai:
    api_key: ${OPENAI_API_KEY}
    retries: 2                            # transient-error retries
    fallback: groq/llama-3.3-70b-versatile  # used after retries exhausted

  myvllm:
    api_key: EMPTY
    base_url: http://localhost:8000/v1

models:
  - openai/gpt-4o
  - myvllm/meta-llama/Llama-3.1-8B-Instruct

${ENV_VAR} references are expanded. YAML config layers on top of env-var defaults.

---

Retry & fallback

Any provider can have retries and fallback. On transient failures (429, 5xx, timeouts, connection errors) the proxy retries with exponential backoff, then falls back to the configured model:

providers:
  kimi:
    retries: 2
    fallback: openai/gpt-4o-mini

Set per provider; client sees a successful response from the fallback if the primary is degraded.

---

Health checks

Endpoint	Description
/health	Lightweight: status, version, configured providers, model count
/health/providers	Pings each OpenAI-compat provider's /models endpoint

Useful for monitoring, load-balancer probes, and quickly seeing which provider is down right now.

---

Logging & observability

Every request gets a unique x-request-id (returned in response headers and included in every log line for that request).

# Verbose request/response logs
ollama-proxy --debug

# JSON-formatted logs (for log aggregators)
PROXY_LOG_JSON=1 ollama-proxy --debug

Sample debug output:

2026-06-04 12:30:15 [DEBUG] [a3f2b1c4d5e6] ollama-proxy: REQ /v1/chat/completions model=gemini/gemini-2.5-pro stream=True messages=10 tools=74 format=None
2026-06-04 12:30:18 [DEBUG] [a3f2b1c4d5e6] ollama-proxy: RES /v1/chat/completions status=200 finish=stop

Run ollama-proxy doctor to validate your configuration and check upstream connectivity in one shot.

---

Endpoints

Ollama protocol

Endpoint	Notes
GET /	"Ollama is running"
GET /api/version	Reports 0.6.5
GET /api/tags	All registered models
GET /api/ps	Always empty (no VRAM concept)
POST /api/chat	Streaming + non-streaming, tools, format
POST /api/generate	Streaming + non-streaming, format
POST /api/embed / /api/embeddings	Both formats supported
POST /api/show	Model details
POST /api/pull	Mocked (3 progress events)

OpenAI protocol (Copilot, OpenAI SDK, Continue)

Endpoint	Notes
POST /v1/chat/completions	Streaming SSE + non-streaming, tools, response_format
GET /v1/models	Lists registered models

Health

Endpoint	Notes
GET /health	Basic status
GET /health/providers	Connectivity check across all OpenAI-compat providers

---

Usage examples

ollama Python SDK

import ollama
client = ollama.Client(host="http://localhost:11434")
resp = client.chat(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hi"}])
print(resp["message"]["content"])

OpenAI SDK

from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="dummy")
resp = client.chat.completions.create(
    model="kimi/kimi-k2.6",
    messages=[{"role": "user", "content": "Hi"}],
)
print(resp.choices[0].message.content)

Open WebUI

Settings → Connections → Ollama URL: http://localhost:11434. All registered models appear in the dropdown.

GitHub Copilot

In VS Code settings, configure the Ollama provider with http://localhost:11434 and pick any registered model.

---

Development

git clone https://github.com/skamalj/ollama-proxy
cd ollama-proxy
uv sync --extra all
uv run ollama-proxy --debug

Run tests:

uv run pytest

---

Project structure

ollama_proxy/
├── __init__.py              # Package version + app re-export
├── __main__.py              # python -m ollama_proxy
├── cli.py                   # CLI entry point
├── server.py                # FastAPI app + endpoint handlers
├── config.py                # Config loading (env + YAML + auto-discovery)
├── logging_config.py        # Structured logging + correlation IDs
├── retry.py                 # Retry / fallback orchestration
├── commands/                # CLI subcommands
│   ├── doctor.py
│   ├── list_models.py
│   └── test.py
└── providers/
    ├── base.py
    ├── openai_compat_provider.py   # OpenAI, Gemini, Groq, Kimi, vLLM, ...
    ├── anthropic_provider.py       # Anthropic / Claude
    ├── azure_provider.py           # Azure OpenAI
    └── bedrock_provider.py         # AWS Bedrock (Converse API)

---

License

MIT — see LICENSE.