Files
zeekay acaeda400a Eradicate OpenClaw — resolve merge conflicts and finish the rebrand
Three coordinated things:

1. Resolve the committed upstream/main merge conflicts. 94 files were
   sitting with raw <<<<<<< HEAD ... >>>>>>> upstream/main blocks. Every
   conflict had HanzoBot/Bot on HEAD and OpenClaw on upstream. Resolved
   by keeping HEAD everywhere — 540 conflict blocks.

2. Rewrite remaining OpenClaw text refs.
     OPENCLAW   → HANZO_BOT
     OpenClaw   → HanzoBot          (PascalCase type identifiers)
     openclaw   → bot               (codebase convention — botToken,
                                    botUsername etc.; avoids invalid
                                    hyphenated JS identifiers)
     openclaw[_]/openclaw[A-Z] → bot[_]/bot[A-Z]   (compound)
     ai.openclaw.x → ai.hanzo.bot.x  (JVM package path)

3. Delete or rename openclaw-named file paths (116 of them). Dead
   duplicates with a Bot-named canonical from the partial migration:
   deleted. Otherwise renamed.
     ai/openclaw/**           → deleted (ai/hanzo/bot/** canonical)
     Sources/OpenClaw*/       → deleted (Sources/Bot* canonical)
     Tests/OpenClawIPCTests/  → deleted (Tests/BotIPCTests canonical)
     OpenClawKit/             → deleted (BotKit canonical)
     openclaw-tools.*.ts      → deleted (bot-tools.*.ts canonical)
     openclaw-root.ts etc.    → deleted (bot-root.ts canonical)
     types.openclaw.ts        → deleted (types.bot.ts canonical)
     extensions/*/openclaw.plugin.json → renamed hanzo-bot.plugin.json
     docs/start/openclaw.md   → renamed hanzo-bot.md (+ zh-CN)
     docs/assets/openclaw-*.png, whatsapp-openclaw*.jpg → deleted
     scripts/.../openclaw-*   → deleted (bot/hanzo-bot parallels)
     openclaw.mjs             → deleted (hanzo-bot.mjs is package.bin)

Then patched 65 broken JS/TS import strings where hanzo-bot had ended
up inside an import path ('./types.hanzo-bot.js' → './types.bot.js'
etc., since the on-disk filename uses the brand-neutral bot- prefix).

Pre-existing lint debt cleaned up to get oxlint --type-aware to 0/0:
removed the dead i18n test that referenced a path no longer existing;
defined the missing DIDConfig and WalletConfig types in types.base.ts
(they were imported but never declared); dropped unused imports;
collapsed redundant type assertion and a tautological meta.bot lookup
that the openclaw→bot rename made redundant.

The a2ui.bundle.js generated artifact is kept at its pre-rebrand
contents — it gets regenerated by 'pnpm canvas:a2ui:bundle' from
sources I cannot rebuild in this commit; the source side is clean.

Verified: 0 occurrences of openclaw (case-insensitive) in tree source
content, 0 file or directory paths with openclaw in the name; oxlint
--type-aware src test reports 0/0.
2026-05-11 17:56:29 -07:00

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Run HanzoBot with vLLM (OpenAI-compatible local server)
You want to run HanzoBot against a local vLLM server
You want OpenAI-compatible /v1 endpoints with your own models
vLLM

vLLM

vLLM can serve open-source (and some custom) models via an OpenAI-compatible HTTP API. HanzoBot can connect to vLLM using the openai-completions API.

HanzoBot can also auto-discover available models from vLLM when you opt in with VLLM_API_KEY (any value works if your server doesn’t enforce auth) and you do not define an explicit models.providers.vllm entry.

Quick start

  1. Start vLLM with an OpenAI-compatible server.

Your base URL should expose /v1 endpoints (e.g. /v1/models, /v1/chat/completions). vLLM commonly runs on:

  • http://127.0.0.1:8000/v1
  1. Opt in (any value works if no auth is configured):
export VLLM_API_KEY="vllm-local"
  1. Select a model (replace with one of your vLLM model IDs):
{
  agents: {
    defaults: {
      model: { primary: "vllm/your-model-id" },
    },
  },
}

Model discovery (implicit provider)

When VLLM_API_KEY is set (or an auth profile exists) and you do not define models.providers.vllm, HanzoBot will query:

  • GET http://127.0.0.1:8000/v1/models

…and convert the returned IDs into model entries.

If you set models.providers.vllm explicitly, auto-discovery is skipped and you must define models manually.

Explicit configuration (manual models)

Use explicit config when:

  • vLLM runs on a different host/port.
  • You want to pin contextWindow/maxTokens values.
  • Your server requires a real API key (or you want to control headers).
{
  models: {
    providers: {
      vllm: {
        baseUrl: "http://127.0.0.1:8000/v1",
        apiKey: "${VLLM_API_KEY}",
        api: "openai-completions",
        models: [
          {
            id: "your-model-id",
            name: "Local vLLM Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 128000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Troubleshooting

  • Check the server is reachable:
curl http://127.0.0.1:8000/v1/models
  • If requests fail with auth errors, set a real VLLM_API_KEY that matches your server configuration, or configure the provider explicitly under models.providers.vllm.