Files
openclaw/extensions/llama-cpp
Peter Steinberger 6aa27d6ecd refactor: retire August compat windows (embedding API, pi aliases, target parser, spawning hook, setup exports, WhatsApp inbound aliases) (#124416)
* refactor(plugin-sdk): retire embedded Pi aliases

* refactor(channels): retire explicit target compatibility

* refactor(plugins): retire subagent spawning hook

* refactor(plugin-sdk): retire shipped channel setup exports

* refactor(whatsapp): retire inbound callback aliases

Proof: focused build and WhatsApp E2E green; broad WhatsApp suite 188/189 files green. extensions/whatsapp/src/monitor-inbox.policy.test.ts flakes only in the parallel batch and passes isolated (10/10).

* refactor(plugin-sdk): retire memory embedding registrar

Migrate every bundled provider and manifest to registerEmbeddingProvider and contracts.embeddingProviders. Preserve memory-specific batching, local-service acquisition, index identity, and auto-selection through the canonical generic registry adapter, then remove the parallel registrar, registry, diagnostics, contracts, tests, and docs.

* chore(plugin-sdk): tighten retired surface budgets

Pin the post-retirement public SDK surface to 144 entrypoints, 4,312 exports, 2,564 callable exports, and 1,133 deprecated exports; agent-harness-runtime now permits exactly nine deprecated exports.
2026-08-15 22:43:47 -07:00
..

@openclaw/llama-cpp-provider

Official managed llama.cpp provider for OpenClaw GGUF chat and embeddings.

The plugin installs a pinned, integrity-verified llama-server and configures OpenClaw's existing localService supervisor. Model traffic uses the normal OpenAI-compatible chat and embedding transports.

Install

openclaw plugins install @openclaw/llama-cpp-provider

Restart the Gateway after installing or updating the plugin, then choose llama.cpp once during interactive onboarding or configuration.

Configure text inference

After explicit consent, OpenClaw installs the matching server build and downloads Gemma 4 E4B IT Q4_K_M (approximately 5.0 GB) plus EmbeddingGemma (approximately 0.3 GB). The default chat download is offered only on machines with at least 16 GiB of RAM.

Custom GGUF models remain supported through params.modelPath. Rerun llama.cpp setup after changing the model so OpenClaw can verify the file and regenerate the managed router preset.

See the llama.cpp provider guide for platform requirements, custom GGUF configuration, diagnostics, and repair.

Configure embeddings

Set memory.search.provider to local. The plugin preserves the historical local embedding provider and index identity while serving requests through the managed server's /v1/embeddings endpoint.

Package

  • Plugin id: llama-cpp
  • Package: @openclaw/llama-cpp-provider
  • Minimum OpenClaw host: 2026.6.2