Integrations

The Pattern

Almost every AI tool supports either the OpenAI API or the Ollama API. Ollama Herd speaks both. The integration is always the same:

1. Find where the tool configures its LLM endpoint
2. Change the URL to your Herd router (http://router-ip:11435)
3. Done, the tool thinks it's talking to one Ollama or one OpenAI endpoint, but the fleet handles routing

Replace router-ip with your router's LAN IP address. If the tool runs on the same machine as the router, use localhost.

OpenAI SDK (Python)

Works for chat completions, embeddings, and image generation.

from openai import OpenAI

client = OpenAI(
    base_url="http://router-ip:11435/v1",
    api_key="not-needed",
)

response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Hello from the fleet!"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

OpenAI SDK (Node.js)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://router-ip:11435/v1",
  apiKey: "not-needed",
});

const stream = await client.chat.completions.create({
  model: "llama3.3:70b",
  messages: [{ role: "user", content: "Hello from the fleet!" }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

Open WebUI

Open WebUI supports multiple Ollama connections. Point it at Herd instead of a single Ollama:

1. Go to Settings > Connections
2. Set Ollama URL to http://router-ip:11435
3. Save

Open WebUI's built-in multi-instance support uses random selection. With Herd, you get 8-signal intelligent routing instead.

LangChain

from langchain_ollama import ChatOllama

llm = ChatOllama(
    base_url="http://router-ip:11435",
    model="llama3.3:70b",
)

response = llm.invoke("Hello from LangChain!")
print(response.content)

Or with the OpenAI-compatible interface:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="http://router-ip:11435/v1",
    api_key="not-needed",
    model="llama3.3:70b",
)

CrewAI

from crewai import Agent, LLM

llm = LLM(
    model="ollama/llama3.3:70b",
    base_url="http://router-ip:11435",
)

agent = Agent(
    role="Researcher",
    goal="Find information",
    llm=llm,
)

CrewAI agents fire many concurrent requests. With a fleet, each agent's requests fan out across devices instead of queuing on one.

OpenClaw (Claude Code)

Edit ~/.config/openclaw/openclaw.json5:

{
  "models": {
    "providers": {
      "ollama": {
        "baseUrl": "http://router-ip:11435",
        "apiKey": "ollama-local",
        "api": "ollama",
        "models": [
          {
            "id": "llama3.3:70b",
            "name": "Llama 3.3 70B",
            "reasoning": false,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }
          }
        ]
      }
    }
  }
}

OpenClaw makes 5–20+ LLM calls per task. The fleet prevents these from queuing on a single machine.

Aider

aider --model ollama/llama3.3:70b --ollama-api-base http://router-ip:11435

Or set the environment variable:

export OLLAMA_API_BASE=http://router-ip:11435
aider --model ollama/llama3.3:70b

Continue.dev

In your Continue configuration (.continue/config.json):

{
  "models": [
    {
      "title": "Llama 3.3 70B (Fleet)",
      "provider": "ollama",
      "model": "llama3.3:70b",
      "apiBase": "http://router-ip:11435"
    }
  ]
}

LlamaIndex

from llama_index.llms.ollama import Ollama

llm = Ollama(
    base_url="http://router-ip:11435",
    model="llama3.3:70b",
)

response = llm.complete("Hello from LlamaIndex!")

LiteLLM

LiteLLM is a cloud API gateway. Herd sits between LiteLLM and your local Ollama fleet:

import litellm

response = litellm.completion(
    model="ollama/llama3.3:70b",
    messages=[{"role": "user", "content": "Hello!"}],
    api_base="http://router-ip:11435",
)

This gives you local + cloud in one stack: LiteLLM routes between providers, Herd routes between local devices.

curl

OpenAI format:

curl http://router-ip:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.3:70b",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": false
  }'

Ollama format:

curl http://router-ip:11435/api/chat -d '{
  "model": "llama3.3:70b",
  "messages": [{"role": "user", "content": "Hello!"}],
  "stream": false
}'

Embeddings:

curl http://router-ip:11435/api/embed -d '{
  "model": "nomic-embed-text",
  "input": "Hello from the fleet!"
}'

Image generation:

curl http://router-ip:11435/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-image-turbo",
    "prompt": "a cat sitting on a laptop",
    "size": "1024x1024"
  }'

Request Tagging

Any integration can add tags for per-tag analytics. Use the X-Herd-Tags header or the metadata.tags field:

# OpenAI SDK with tags
response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Hello!"}],
    extra_headers={"X-Herd-Tags": "my-app,production"},
)

# curl with tags
curl http://router-ip:11435/api/chat \
  -H "X-Herd-Tags: my-app, production" \
  -d '{"model": "llama3.3:70b", "messages": [{"role": "user", "content": "Hello!"}]}'

The dashboard's Tags tab breaks down usage, latency, and error rates per tag.

Next Steps

• Quickstart, Get the fleet running first
• Deployment, Production setup and monitoring
• API Reference, Full endpoint documentation