ollama-memory-embeddingsConfigure OpenClaw memory search to use Ollama as the embeddings server (OpenAI-compatible /v1/embeddings) instead of the built-in node-llama-cpp local GGUF loading. Includes interactive model selection and optional import of an existing local embedding GGUF into Ollama.
Install via ClawdBot CLI:
clawdbot install vidarbrekke/ollama-memory-embeddingsThis skill configures OpenClaw memory search to use Ollama as the **embeddings
server** via its OpenAI-compatible /v1/embeddings endpoint.
Embeddings only. This skill does not affect chat/completions routing —
it only changes how memory-search embedding vectors are generated.
~/.openclaw/skills/ollama-memory-embeddingsembeddinggemma (default — closest to OpenClaw built-in)nomic-embed-text (strong quality, efficient)all-minilm (smallest/fastest)mxbai-embed-large (highest quality, larger) ollama create (currently detects embeddinggemma, nomic-embed, all-minilm,
and mxbai-embed GGUFs in known cache directories)
:latest tag automatically)agents.defaults.memorySearch in OpenClaw config (surgical — onlytouches keys this skill owns):
provider = "openai"model = :latest remote.baseUrl = "http://127.0.0.1:11434/v1/"remote.apiKey = "ollama" (required by client, ignored by Ollama) restart methods: openclaw gateway restart, systemd, launchd)
openclaw memory index --force --verbose) 1. Checks model exists in ollama list
2. Calls the embeddings endpoint and validates the response
enforce.sh)watchdog.sh)bash ~/.openclaw/skills/ollama-memory-embeddings/install.shFrom this repository:
bash skills/ollama-memory-embeddings/install.shbash ~/.openclaw/skills/ollama-memory-embeddings/install.sh \
--non-interactive \
--model embeddinggemma \
--reindex-memory autoBulletproof setup (install watchdog):
bash ~/.openclaw/skills/ollama-memory-embeddings/install.sh \
--non-interactive \
--model embeddinggemma \
--reindex-memory auto \
--install-watchdog \
--watchdog-interval 60Note: In non-interactive mode, --import-local-gguf auto is treated as
no(safe default). Use--import-local-gguf yesto explicitly opt in.
Options:
--model : one of embeddinggemma, nomic-embed-text, all-minilm, mxbai-embed-large--import-local-gguf : default no (safer default; opt in with yes)--import-model-name : default embeddinggemma-local--restart-gateway : default no (restart only when explicitly requested)--skip-restart: deprecated alias for --restart-gateway no--openclaw-config : config file path override--install-watchdog: install launchd drift auto-heal watchdog (macOS)--watchdog-interval : watchdog interval (default 60)--reindex-memory : memory rebuild mode (default auto)--dry-run: print planned changes and commands; make no modifications~/.openclaw/skills/ollama-memory-embeddings/verify.shUse --verbose to dump raw API response on failure:
~/.openclaw/skills/ollama-memory-embeddings/verify.sh --verboseManually enforce desired state (safe to run repeatedly):
~/.openclaw/skills/ollama-memory-embeddings/enforce.sh \
--model embeddinggemma \
--openclaw-config ~/.openclaw/openclaw.jsonCheck for drift only:
~/.openclaw/skills/ollama-memory-embeddings/enforce.sh \
--check-only \
--model embeddinggemmaRun watchdog once (check + heal):
~/.openclaw/skills/ollama-memory-embeddings/watchdog.sh \
--once \
--model embeddinggemmaInstall watchdog via launchd (macOS):
~/.openclaw/skills/ollama-memory-embeddings/watchdog.sh \
--install-launchd \
--model embeddinggemma \
--interval-sec 60The installer searches for embedding GGUFs matching these patterns in known
cache directories (~/.node-llama-cpp/models, ~/.cache/node-llama-cpp/models,
~/.cache/openclaw/models):
embeddinggemma.ggufnomic-embed.ggufall-minilm.ggufmxbai-embed.ggufOther embedding GGUFs are not auto-detected. You can always import manually:
ollama create my-model -f /path/to/Modelfile:latest tag for consistent Ollama interaction.retrieval mismatch across incompatible vector spaces.
--reindex-memory auto, installer reindexes only when the effective embedding fingerprint changed (provider, model, baseUrl, apiKey presence).
"ollama" value.agents.defaults.memorySearch is absent,the enforcer reads known legacy paths and mirrors writes to preserve compatibility.
Generated Mar 1, 2026
A research team using OpenClaw for AI development wants to switch from built-in GGUF embeddings to Ollama's embedding models for better performance and model variety. They need to maintain existing memory search functionality while upgrading to more advanced embedding models like mxbai-embed-large for higher quality research document retrieval.
A company migrating their internal knowledge base to OpenClaw needs to configure memory search with specific embedding models that match their existing infrastructure. They want to use Ollama's OpenAI-compatible endpoint for easier integration with their existing monitoring and deployment systems while optionally importing their pre-trained embedding models.
Individual developers using OpenClaw for coding assistance want to switch to faster embedding models like all-minilm for quicker memory search responses. They need a simple way to configure their local setup without disrupting their existing chat/completions functionality while ensuring config drift doesn't break their workflow.
An educational platform using OpenClaw for student assistance needs to configure memory search with embedding models optimized for educational content. They want to use Ollama's embedding server for better scalability and the ability to switch between different embedding models based on subject matter requirements.
A customer support team automating responses with OpenClaw needs to improve their knowledge base retrieval accuracy. They want to upgrade from default embeddings to higher quality models like nomic-embed-text while maintaining the ability to reindex existing memory vectors and monitor configuration health automatically.
Consultants help organizations configure and optimize their OpenClaw memory search with Ollama embeddings. Services include model selection guidance, performance tuning, and ongoing configuration management using the skill's enforcement and watchdog features for enterprise reliability.
Providers offer fully managed OpenClaw deployments with pre-configured Ollama memory embeddings. This includes automated installation, model updates, drift monitoring, and 24/7 support using the skill's verification and auto-healing capabilities for hands-off operation.
Platforms offer specialized embedding models optimized for different industries that can be easily integrated into OpenClaw via Ollama. The skill's model selection and import capabilities create a distribution channel for model developers to reach OpenClaw users.
💬 Integration Tip
Always run the verification script after installation and consider enabling the watchdog for production environments to automatically detect and fix configuration drift.
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