Ollama Proxy

Ollama proxy — one endpoint that routes to multiple Ollama instances. Drop-in Ollama proxy replacement for localhost:11434. Same Ollama API, same model names, but the Ollama proxy routes requests to the best device. Auto-discovers Ollama nodes, scores on 7 signals, retries on failure. Works with Open WebUI, LangChain, Aider. Ollama代理 | proxy Ollama

Ollama Proxy — One Endpoint for All Your Ollama Instances

You have Ollama running on multiple machines. Instead of hardcoding IPs and manually picking which Ollama instance to hit, point everything at the Ollama proxy. The Ollama proxy routes to the best available device automatically.

text

Before:  App → http://macmini:11434  (one Ollama instance, hope it's not busy)
After:   App → http://ollama-proxy:11435   (Ollama proxy picks the best machine)

Set up the Ollama proxy

bash

pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/

On one machine (the Ollama proxy):

bash

herd    # starts the Ollama proxy on port 11435

On every machine running Ollama:

bash

herd-node    # discovers the Ollama proxy automatically on your network

Now point your apps at http://ollama-proxy:11435 instead of http://localhost:11434. Same Ollama API, same model names, same streaming — the Ollama proxy handles smarter routing.

Drop-in Ollama proxy replacement

Every Ollama API endpoint works through the Ollama proxy:

bash

# Chat via Ollama proxy (same as direct Ollama)
curl http://ollama-proxy:11435/api/chat -d '{
  "model": "llama3.3:70b",
  "messages": [{"role": "user", "content": "Hello via Ollama proxy"}]
}'

# Generate via Ollama proxy (same as direct Ollama)
curl http://ollama-proxy:11435/api/generate -d '{
  "model": "qwen3:32b",
  "prompt": "Explain quantum computing via Ollama proxy"
}'

# List models via Ollama proxy (aggregated from all Ollama nodes)
curl http://ollama-proxy:11435/api/tags

# List loaded models via Ollama proxy (across all Ollama nodes)
curl http://ollama-proxy:11435/api/ps

# Pull a model via Ollama proxy (auto-selects best node)
curl -N http://ollama-proxy:11435/api/pull -d '{"name": "codestral"}'

OpenAI-compatible Ollama proxy API

The Ollama proxy also exposes an OpenAI-compatible endpoint — same models, no code changes:

python

from openai import OpenAI

# Point at the Ollama proxy instead of direct Ollama
ollama_proxy_client = OpenAI(base_url="http://ollama-proxy:11435/v1", api_key="not-needed")
ollama_proxy_response = ollama_proxy_client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Hello via Ollama proxy"}],
    stream=True,
)

What the Ollama proxy does that direct Ollama doesn't

Feature	Direct Ollama	Ollama Proxy (Herd)
Multiple machines	Manual IP switching	Ollama proxy routes automatically
Load balancing	None	Ollama proxy scores on 7 signals
Failover	None	Ollama proxy auto-retries on next node
Model discovery	Per-machine Ollama	Ollama proxy aggregates fleet-wide
Queue management	None	Ollama proxy manages per-node:model queues
Dashboard	None	Ollama proxy provides real-time web UI
Health checks	None	Ollama proxy runs 15 automated checks
Request tracing	None	Ollama proxy logs to SQLite trace store
Image generation	None	Ollama proxy routes mflux + DiffusionKit
Speech-to-text	None	Ollama proxy routes Qwen3-ASR

Ollama proxy works with your existing tools

Just change the Ollama URL to the Ollama proxy — no other configuration needed:

Tool	Before (direct Ollama)	After (Ollama proxy)
Open WebUI	http://localhost:11434	http://ollama-proxy:11435
Aider	--openai-api-base http://localhost:11434/v1	--openai-api-base http://ollama-proxy:11435/v1
Continue.dev	Ollama at localhost	Ollama proxy at ollama-proxy:11435
LangChain	Ollama(base_url="http://localhost:11434")	Ollama(base_url="http://ollama-proxy:11435")
LiteLLM	ollama/llama3.3:70b	ollama/llama3.3:70b (point at Ollama proxy)
CrewAI	OPENAI_API_BASE=http://localhost:11434/v1	OPENAI_API_BASE=http://ollama-proxy:11435/v1

How the Ollama proxy routes requests

When a request arrives at the Ollama proxy, it scores all Ollama nodes that have the requested model:

1. Thermal state — is the model already loaded in the Ollama instance (hot)?
2. Memory fit — does the Ollama node have enough free RAM?
3. Queue depth — is the Ollama node busy with other requests?
4. Latency history — how fast has this Ollama node been recently?
5. Role affinity — the Ollama proxy sends big models to big machines
6. Availability trend — is this Ollama node reliably available?
7. Context fit — does the loaded context window match the request?

The highest-scoring Ollama node wins. If it fails, the Ollama proxy retries on the next best node automatically.

Monitor your Ollama proxy fleet

Ollama proxy dashboard at http://ollama-proxy:11435/dashboard — see every Ollama node, every model, every queue in real time.

bash

# Ollama proxy fleet overview
curl -s http://ollama-proxy:11435/fleet/status | python3 -m json.tool

# Ollama proxy health checks
curl -s http://ollama-proxy:11435/dashboard/api/health | python3 -m json.tool

Full documentation

• Agent Setup Guide — setting up the Ollama proxy
• API Reference — all Ollama proxy endpoints
• Configuration — Ollama proxy settings

Contribute

Ollama Herd (the Ollama proxy) is open source (MIT). We welcome contributions:

• Star on GitHub — help others find the Ollama proxy
• Open an issue — bug reports, feature requests
• PRs welcome — CLAUDE.md gives AI agents full Ollama proxy context. 444 tests, async Python.

Guardrails

• No automatic model downloads — the Ollama proxy requires explicit user confirmation for model pulls.
• Model deletion requires explicit user confirmation via the Ollama proxy.
• All Ollama proxy requests stay local — no data leaves your network.
• Never delete or modify files in ~/.fleet-manager/.