# OpenHands > Connect OpenHands to Tokens as an OpenAI-compatible endpoint, in the web UI or the CLI, with the right model string for ids that already contain a slash. OpenHands is an open-source AI software engineer that edits code, runs commands and browses inside a sandbox. It calls models through LiteLLM, so Tokens is added as an OpenAI-compatible endpoint: a model string starting with `openai/`, a Base URL of `https://tokens.bd/v1`, and your Tokens key. Requests are OpenAI Chat Completions. :::note[Checked against the documentation] Based on the OpenHands documentation on docs.openhands.dev (the OpenAI, local LLM, LLM overview and CLI pages), checked October 2026. The pages do not state a CLI version; the install page we read pins agent server image `1.26.0-python`. We have not run OpenHands against Tokens end to end. ::: ## What you need - A Tokens key. Create one only for OpenHands, with a monthly spend cap ([API keys](/docs/api-keys)). - The model id from [/models](/models). The examples use `deepseek/deepseek-v4.1-flash`. - OpenHands installed. The CLI and the local web UI both need Python 3.12 and [uv](https://docs.astral.sh/uv/), or you can use the binary or Docker. The web UI also needs Docker running. On Windows, OpenHands says to run everything inside WSL (Ubuntu). - A model that supports tool calling. OpenHands says it needs a powerful model, and that open-weight models vary in how reliably they call tools. ## Which model string to type LiteLLM chooses the provider from the text before the first slash. For a custom OpenAI-compatible endpoint that text must be `openai/`, and OpenHands says the prefix is required in Custom Model. Tokens ids already contain a slash (`provider/model`), so the value you type has two: ```text openai/deepseek/deepseek-v4.1-flash ``` The pattern is `openai/`. OpenHands documents the same shape for proxies that have their own routing prefix (`openai//`), and its local LLM page uses `openai/qwen/qwen3.6-35b-a3b` for an LM Studio model whose id contains a slash. Neither OpenHands nor LiteLLM documents which part is sent to the endpoint. These examples rely on LiteLLM using the first `openai/` only to pick the provider and sending the rest, here `deepseek/deepseek-v4.1-flash`, which is the id Tokens expects. That is our reading of the examples, not a documented guarantee. If requests come back as `model_not_found`, see the troubleshooting section below. ## Set it up in the web UI Start the UI: ```bash uv tool install openhands --python 3.12 openhands serve ``` Open `http://localhost:3000`. Then: 1. Select the Settings button (gear icon), then the **LLM** tab. 2. Turn on the **Advanced** toggle. 3. Set **Custom Model** to `openai/deepseek/deepseek-v4.1-flash`. 4. Set **Base URL** to `https://tokens.bd/v1`. 5. Set **API Key** to your Tokens key. 6. Save the settings. OpenHands keeps its state in `~/.openhands` on your machine. Treat that folder as secret and never commit it, because it holds your settings and may hold the key. ## Set it up in the CLI On first start the CLI asks for an LLM provider and API key and saves them under `~/.openhands/`. The pages we read do not list a Base URL or Custom Model prompt in that first-run dialog, so use environment variables for Tokens: ```bash export LLM_API_KEY="tok_live_your_key" export LLM_MODEL="openai/deepseek/deepseek-v4.1-flash" export LLM_BASE_URL="https://tokens.bd/v1" openhands --override-with-envs ``` :::warning[Environment variables are ignored without the flag] OpenHands ignores `LLM_*` variables unless you start the CLI with `--override-with-envs`. The override is not saved: a plain `openhands` the next day goes back to whatever is stored in `~/.openhands/`. Put the four lines in a small shell script or alias if you use Tokens every time. ::: To change the stored model later, press `Ctrl+P` in the CLI and choose Settings, or edit the `model` field in `~/.openhands/agent_settings.json`. The CLI docs name both `settings.json` and `agent_settings.json` for the stored LLM settings, so look in the folder to see which your version uses. We could not confirm from the documentation whether the CLI's Settings screen has a Base URL field, which is why the steps above use the environment variables. On Windows, run these commands in the WSL shell, not in PowerShell. ## Check that it works First rule out OpenHands, with the id as Tokens expects it (no `openai/` prefix): ```bash curl https://tokens.bd/v1/chat/completions \ -H "Authorization: Bearer $LLM_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "deepseek/deepseek-v4.1-flash", "max_tokens": 16, "messages": [{"role": "user", "content": "Reply with OK"}]}' ``` Then, in OpenHands, ask for something small ("List the files in the workspace and tell me what the project does"). The agent should reply and run a command, and the request should show up in usage analytics on your dashboard. A reply with no actions at all usually means the model is not calling tools. ## Choosing a model OpenHands works in long loops of tool calls, so pick a model with strong tool calling and a long context. [Choosing a model](/docs/choosing-a-model) compares them, and [/models](/models) shows each model's context and output limits. OpenHands' own advice is to use the strongest model you can afford for long or high-stakes tasks. The pages we read do not mention a field for the context window or maximum output. If the model has a native tool calling switch under model customization, OpenHands says it can be toggled there. If you see malformed JSON errors or poor output, OpenHands suggests a stronger model or a larger context window before anything else. ## Limits and what to know - **Spend.** OpenHands retries failed calls (`LLM_NUM_RETRIES`, default 4) and loops until a task is done. A single task can send hundreds of requests, each carrying a growing conversation. Use a spend-capped key ([API keys](/docs/api-keys)) and watch the dashboard. OpenHands itself warns to set spending limits. - **LiteLLM costs.** Any cost OpenHands shows is an estimate from LiteLLM's own price table, which may not list Tokens ids. Your real spend is in the dashboard. - **Other LLM settings.** A few options (`LLM_API_VERSION`, `LLM_DROP_PARAMS`, `LLM_DISABLE_VISION`, `LLM_CACHING_PROMPT`) are environment variables or `config.toml` entries only, not UI fields. - **Version changes.** OpenHands changed its settings format at version 1.0.0. If you upgrade from an older install, redo the setup. ## Troubleshooting **404 `model_not_found`.** The model string reached Tokens with the wrong shape. The Custom Model value must be `openai/` followed by the exact Tokens id (`openai/deepseek/deepseek-v4.1-flash`). Without the `openai/` prefix, LiteLLM can treat the first part of the id as a provider name. If the prefix is there and it still fails, run the `curl` above with the id from [/models](/models) to confirm the id itself is right. **LiteLLM says the provider is not provided or not recognized.** The Custom Model value has no `openai/` prefix. Add it. **401 `missing_api_key` or `invalid_api_key`.** The API Key field or `LLM_API_KEY` is empty or holds a different provider's key. A Tokens key starts with `tok_live_`. **404 on every request.** The Base URL must be `https://tokens.bd/v1` and nothing more. LiteLLM adds the path itself, so do not append `/chat/completions`. **The CLI ignores my settings.** `LLM_*` variables need `--override-with-envs`. Without it the stored settings win. **403 `monthly_spend_cap_exceeded`, 402 `insufficient_credits`, or 429 `rate_limited`.** The key's cap, your balance or your rate limit stopped the agent. Raise the cap or top up in [billing](/dashboard/billing), or wait for `Retry-After`. The OpenHands retry loop makes 429 more likely during busy sessions. **From the web UI container, the endpoint is unreachable.** Tokens is a public address, so this is usually a Docker network or proxy problem on your side. Run the `curl` from the same machine first. For all codes, see [Errors](/docs/errors); for other problems, [Troubleshooting](/docs/troubleshooting). --- Page: https://tokens.bd/docs/openhands