Services
Open-source AI, adapted to how you already work.
OpenEng Labs customizes open-source tools to an enterprise’s business needs, designs and supports AI automation, and builds custom AI solutions on its own governed platform.
Every engagement is scoped to the work in front of you. There is no published price list.
- 4 live today
- 1 in development
- 1 planned for Q4 2026
Published config
publishedops-automation@1.4.0
- Build
- agents 3 · loops 1
- Connect
- cells 4 · MCP servers 1
- Govern
- gateway · token limits
Engagement
- 01Discoverdone
- 02Designdone
- 03Buildin progress
- 04Operatenext
written output per step
Engagements
Six ways to work with us.
Four are live today, one is in development, and one will open with the Enterprise and on-prem plans in Q4 2026. Each card says which.
- Live
AI Automation Support
We design, build, and support recurring automations on the OpenEng AI engine. Today global loops do the repeating while connectors, cells, and MCP servers reach your systems; schedulers and headless runners will arrive with the paid plans in Q4 2026.
What you get
- Global loops scoped to your work items and stopping rules
- Connectors, cells, and MCP servers wired to the systems you already run
- Support after go-live, with every side effect passing the governed gateway
- Live
Custom AI Solutions
We build agents for your business as authored configs, drawing on catalogs of 3,300+ building blocks and writing the agents and templates your domain needs. Each release is published from the Console as one immutable id@version.
What you get
- Agents and templates authored in the Console for your domain, with skills adopted from the catalogs
- A per-domain diff and release notes with every version you publish
- Secrets held as references in the config, never as values
- Live
Open-Source and Open-Weight Customization and Support
We adapt the open-source tools you already run and the open-weight models you want on your own hardware. We start from Ollama, the Hugging Face Hub, ONNX exports, and MCP servers.
What you get
- Open-weight models matched to your hardware, with no API key
- Models on llama.cpp, ONNX Runtime, Ollama, or an OpenAI-compatible endpoint; tools through MCP servers
- Customizations and support for the open-source tools in your stack
- Planned for Q4 2026
Private and On-Prem Agent Platforms
The local-first architecture is live today: models, threads, memory, and secrets stay on the machine, and the engine binds no inbound port. Enterprise and on-prem plans will launch in Q4 2026.
What you get
- The OpenEng AI engine on your own Linux x86_64 machines today
- Kernel-enforced isolation around each shell command and the per-user daemon
- Enterprise and on-prem plans when they launch in Q4 2026
- 0.1 in development
Agent Engineering with AEL
AEL is a native language whose compiler is first-party Rust: source to binary AEL IR to a native executable, with no LLVM and no generated C. Version 0.1 is in development and qualifies linux-x86_64 alone. Its agent features are planned.
What you get
- Agent designs reviewed against AEL’s published specification and its compact-source limits
- A written plan for carrying those designs into AEL once typed components and the agent runtime land
- Progress you can check against the dated public status record at ael.openeng.ai
- Live
Research Collaboration
We partner on applied agentic-systems research: architecture surveys, code-verified gap analyses, sandboxing, and human-in-the-loop design. Findings are written against real code and dated.
What you get
- Architecture reviews against the 2025–2026 agentic-systems survey
- Code-verified gap analyses with prioritized findings
- Sandbox and human-in-the-loop design work you can act on
Process
How an engagement runs.
Four steps, each closed by a written output you keep. Durations depend on the systems involved, so we scope them with you.
Discover
We map the systems in play and the automation candidates: what repeats, what it touches, and who signs off.
OutputAutomation map
Design
We choose the models, connectors, and governance policy, and decide what runs on which of your machines.
OutputDesign note and policy
Build
We author the configs, agents, and customizations, and verify every run on your machines before it goes live.
OutputConfig and verified runs
Operate
We support the loops in production and iterate on them as the work changes.
OutputRunbook and change log
What you keep
Artifacts you keep, on machines you own.
An engagement ends with configs, agents, connectors, loops, and runbooks in your hands, verified on your own machines. Nothing lives only in ours.
Published configs
Each release passes the Console’s validation gate and leaves as one immutable id@version with its release notes. A later release always takes a strictly higher version.
id@version
Agents, skills, and templates
Drawn from live catalogs of 1,092 agents, 885 skills, and 754 templates published as content-as-code, with agents and templates written for your domain where the catalogs stop.
catalog.json
Connectors, cells, and MCP servers
Cells and ecosystem connectors deployed into your own cloud account and wired into your config, plus the MCP servers your tools need. Thirteen ecosystem connectors are validated live today.
your cloud account
Global loops
A global loop is a request, a source (a plain repeat or a list of work items), and a stopping rule. Ready-to-run loops range from failing tests until green to a nightly PR review sweep.
request · source · stop
Foundations
Built on open foundations.
Four model runtimes work out of the box; vLLM, SGLang, ExLlamaV2 and V3, MLX, TensorRT-LLM, and Transformers run once their toolchain is installed. Most of the systems we connect to are already in your stack.
What an engagement starts from.
Counts read from the live catalogs on September 25, 2026: starting material, not a promise of scope.
3,300+
Building blocks across six catalogs: agents, skills, templates, models, cells, and plugins
Source: six catalog.json files, 3,323 entries
6
Ready-to-run global loops for tests, backlogs, tickets, inboxes, alerts, and pull requests
Source: loops.openeng.app
Four runtimes out of the box.
Embedded GGUF
In-process llama.cpp with weights fetched from the Hugging Face Hub, checksum-verified.
Ollama library
Any tag in your local Ollama library; public tags are pulled for you at provisioning.
ONNX exports
Standard Optimum and onnx-community exports on ONNX Runtime; TensorFlow models run via ONNX export.
Network endpoint
Any OpenAI-compatible endpoint, as a secondary, per-role option.
The OpenEng platform itself is not open source; it runs open-weight models and builds on open-source tooling.
Where it runs
Your machines today. Fleet and on-prem plans next.
The OpenEng AI engine is live on Linux x86_64, and the cloud pieces are source you deploy yourself. Plans for per-fleet licensing and a whole-platform on-prem license will launch in Q4 2026.
- Live
Your Linux x86_64 machines
The OpenEng AI engine runs on your own Linux x86_64 machines today, installed with one checksum-verified command. It binds no inbound port.
- Live
Your own cloud account
Cells and ecosystem connectors ship as source-only Python that you deploy into your AWS, GCP, or Azure account, behind your own API gateway. No binary is shipped to your cloud.
- Coming
macOS and Windows engines
Only the Linux x86_64 engine is published today. macOS and Windows builds are coming, and general availability of the platform is planned for Q4 2026.
- Planned for Q4 2026
Enterprise and on-prem plans
Priced per fleet rather than per seat: one account will run your configs, schedulers, and headless runners at fleet scale, or the whole platform on-prem. Pricing will be announced at launch.
Status as of September 25, 2026. The Enterprise and on-prem plans cannot be purchased yet; we scope work on your machines now and carry it over when the plans launch.
Guarantees
Governed by construction.
The automations and agents we build for you run inside the OpenEng AI engine, so the guarantees come from its architecture, not from a promise in a contract.
One gateway, fail-closed
Every side effect passes one gateway with a fail-closed policy floor: a configuration can add policy above it but never remove it. A build-time gate enforces the no-bypass rule.
Kernel sandbox by default
Shell commands run in a kernel sandbox by default wherever the OS provides one: a read-only root, writes only inside the workspace, and the network off unless you allow it.
Secrets by reference only
The Console stores ${ENV} references and never a value. Each reference resolves on your machine at run time, so no secret leaves it.
No telemetry
The OpenEng AI engine sends no telemetry and binds no inbound port. Models, threads, memory, and secrets stay on the machine.
These guarantees describe the OpenEng AI engine, which is live on Linux x86_64 as of September 25, 2026. They will apply the same way under the Enterprise and on-prem plans that will launch in Q4 2026.
Questions
Before you write to us.
What services does OpenEng Labs provide?
Six: AI automation support, custom AI solutions, open-source and open-weight customization and support, private and on-prem agent platforms (the plans will launch in Q4 2026), agent engineering with AEL (version 0.1 in development; agent features planned), and research collaboration. Every engagement is scoped; there is no published price list.
Do you work with open-source tools?
Yes. We adapt the open-source tools an enterprise already runs, such as Ollama, the Hugging Face Hub, ONNX Runtime, and MCP servers, and the OpenEng AI engine runs open-weight models on your own machines. The OpenEng platform itself is not open source.
Can the platform run entirely on our own machines?
The OpenEng AI engine runs on your own Linux x86_64 machines today: models, threads, memory, and secrets stay there, it sends no telemetry, and it binds no inbound port. Running the entire platform inside your own infrastructure will come with the on-prem plan in Q4 2026.
How do we start?
Write to hello@openeng.ai or use the Contact Us page. We begin with a discovery conversation about your systems and the automation candidates, then scope a first engagement in writing.
Start a conversation about AI automation.
Tell us which systems are in play and what repeats. We will come back with a scoped first step.