Open models · Local or cloud

Get more from your open models

Bring open models into your own network. Ogger combines LLMs with neuro-symbolic reasoning, retrieval and lasting memory to extend what your chosen model can do. Try it hosted, or run locally with control over your infrastructure and data.

Your choice of model·Memory across sessions·OpenAI compatible

Your network. Your data boundary.

Deploy Ogger with local inference in your own network. You choose the infrastructure, models and access controls. Using a cloud model sends inference requests to that provider; a fully local configuration keeps inference inside your environment.

Open models, greater capability.

Ogger combines neural language models with symbolic tools, evidence retrieval and memory. Our ambition is frontier-level results on selected tasks with open models. That is a goal to validate against your workloads, not a claim of general frontier-model parity.

example.py
from openai import OpenAI

client = OpenAI(
    base_url="https://ogger.ai/api/v1",
    api_key="og_live_...",
)

response = client.chat.completions.create(
    model="ogger",   # the engine: grounded, planning, remembers you
    messages=[
        {"role": "user", "content": "Refactor this codebase for production"}
    ],
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

Works with your tools

◆Claude Code
◈Cline
◎Cursor
◐Windsurf
◑Roo Code
○OpenAI SDK

Any tool that supports an OpenAI-compatible base URL works out of the box.

A capable engine around your LLM

Enhance the model you choose with context, memory and tools. Results, latency and cost depend on the model and task.

Tools for stronger answers

Give your model retrieval, planning and symbolic calculations for tasks that benefit from evidence and exact computation.

Open models, clearly labeled

Choose among supported open-source and open-weight models. See the selected model in chat and review its license; availability depends on your deployment.

Memory that persists

Keep project requirements and preferences across conversations so your model can use the context you have already shared.

Local or cloud inference

Use supported local models or cloud-hosted open models. Start with the hosted experience and configure inference for your own deployment.

Visible usage and cost

Inspect reported token usage and estimated cost per query. Compare the added reasoning work with the result it produces.

Use your existing tools

Connect through OpenAI-compatible and Anthropic-compatible APIs, with API keys managed from your dashboard.

Start here. Run in your network.

Try Ogger in our hosted environment, then explore deployment in your own network with local inference. Your data boundary depends on the hosting and model provider you configure.