step 01
Define tools & goal
deepseek-v4-flash Give the agent its tools and its objective. Native function calling and reasoning drive the loop — the same JSON tools you already use with OpenAI.
// use cases · autonomous agents
Agents that
don't hit a wall.
Run autonomous agents and process automation on open models, tool calling, reasoning, and no token caps, so a long loop never dies on a 429.
// how it works
Plan, act, observe — without a token wall.
Tool calling, reasoning and unmetered loops from a single OpenAI-compatible endpoint — only inside the EU.
step 02
Run the loop
qwen3.6 Plan, act, observe, repeat. Agents burn tokens in bursts — and with no caps, the loop never stalls on a 429 halfway through a task.
step 03
Scale out
deepseek-v4-flash Run many agents in parallel. Limits are RPM and concurrency per key, sized to your workload on dedicated GPUs — fan out without a wall.
// drop-in
Change one line. Run the loop.
The same agent loop you already write — just pointed at Helmcode. Tools and streaming included, on private EU models with no token wall.
read_the_docsfrom openai import OpenAI client = OpenAI(api_key="sk-...", base_url="https://api.helmcode.com/v1") # plan, act, observe, repeat — no token wall to hit while not done: step = client.chat.completions.create( model="deepseek-v4-flash", messages=history, tools=tools, # native tool calling ) history += run_tools(step) # act, then observe
// why helmcode
Built for the way agents actually run.
Agent loops burst into huge token spikes — exactly the pattern metered APIs punish with 429s and surprise bills.
No caps, no 429 walls.
Agents spike into huge token bursts. No caps on total consumption and no rate-limit walls mid-loop — only RPM and concurrency per key.
Tool calling & reasoning.
Native function calling and reasoning — the substrate an agent needs to plan and act, not just chat. Flagship open models, built for the loop.
Zero logs, by architecture.
Agent traces, and the code and data they touch, are never stored and never train a model — not ours, not anyone's.
Processed in the EU.
Agents run on EU infrastructure — not on US hyperscalers subject to the Cloud Act. GDPR and AI Act native, even when they touch sensitive systems.
Open models, no lock-in.
DeepSeek V4-Flash, Qwen 3.6, Gemma 4. No vendor can deprecate the model your agents depend on, or change pricing on you overnight.
Works with your framework.
LangGraph, CrewAI, the OpenAI Agents SDK and your own loop. Change the base URL and key — tools and streaming included.
In production across
- Dev tools & coding agents
- Telco
- AI-native products
// agents faq
Agents, answered.
What platform and engineering teams ask before running agents in production.
Why does "no caps" matter for agents specifically?
Agent loops plan, call tools and re-prompt — bursting into huge token spikes. Metered APIs throttle or 429 mid-task; Helmcode has no token caps, only RPM and concurrency per key, so the loop runs to completion.
Do you support tool / function calling?
Yes — native function calling with the same JSON schema you already use with OpenAI, plus reasoning and streaming. The substrate agents need to act, not just answer.
Does it work with LangGraph, CrewAI or the OpenAI Agents SDK?
Yes. Point any OpenAI-compatible framework at our base URL with your API key — LangGraph, CrewAI, the Agents SDK and custom loops work unchanged.
Can I run many agents in parallel?
Yes. Concurrency is per API key and can be sized to your workload, with dedicated GPUs for high-throughput fleets of agents.
Do you store agent traces or the data they touch?
No. Zero logs — prompts, traces and the code and data agents read are never persisted and never train a model.
Can agents run on-premise?
Yes. Run on a dedicated GPU or fully on-premise inside your own datacenter — the same API and code, with everything kept on your network.
// get started
START BURNING TOKENS
Skip the AI infra work. Deploy your first private inference endpoint today.
Flat rate. EU data. OpenAI API compatible.