memory sync failed use | Friends of the Crustacean 🦞🤝 | Page 1

vernal flax May 17, 2026, 10:42 PM

#

arn
memory
memory sync failed (session-start): Error: openai embeddings failed: 401 {
"error": {
"message": "Incorrect API key provided: $NVIDIA_***_KEY. You can find your API key at https://platform.openai.com/account/api-keys.",
"type": "invalid_request_error",
"param": null,
"code": "invalid_api_key"
}
}

sullen moatBOT May 17, 2026, 10:42 PM

#

To help others find answers, you can mark your question as solved via Right click solution message -> Apps -> ✅ Mark Solution

placid mossBOT May 17, 2026, 10:43 PM

#

vernal flax arn memory memory sync failed (session-start): Error: openai embeddings failed: ...

First thing: please share openclaw --version, since memory/auth behavior has changed a bit between builds.

That error means the memory embedding path is using the OpenAI embeddings adapter, but the key it resolved is an NVIDIA key/value. OpenAI is rejecting it before memory sync can start.

Most likely one of these is true:

OPENAI_API_KEY is set to an NVIDIA key, or literally to something like $NVIDIA_..._KEY.
models.providers.openai.apiKey points at the NVIDIA env var.
You meant to use an OpenAI-compatible NVIDIA endpoint, but only changed the key, not the embeddings base URL/model.

Quick checks:

openclaw memory status --deep
openclaw models status
openclaw status --all

placid mossBOT May 17, 2026, 10:43 PM

#

vernal flax arn memory memory sync failed (session-start): Error: openai embeddings failed: ...

Fix depends on intent:

If you want OpenAI embeddings: set a real OPENAI_API_KEY from OpenAI.
If you want a different embedding provider: configure agents.defaults.memorySearch.provider to that provider and use its matching key.
If you want NVIDIA/OpenAI-compatible embeddings: configure memory search with the compatible remote.baseUrl, remote.apiKey, and embedding model; otherwise OpenClaw will send that NVIDIA key to OpenAI’s API and get this exact 401.

Docs:
https://docs.openclaw.ai/reference/memory-config
https://docs.openclaw.ai/help/faq

Paste the version plus the relevant redacted agents.defaults.memorySearch and models.providers.openai config if you want me to sanity-check the shape.

vernal flax May 17, 2026, 10:49 PM

#

OpenClaw 2026.5.12 (f066dd2)-- OpenClaw 2026.5.12 (f066dd2) — I speak fluent bash, mild sarcasm, and aggressive tab-completion energy.

│
◇
Memory Search (standard)
Provider: openai (requested: auto)
Model: text-embedding-3-small
Sources: memory
Indexed: 0/81 files · 0 chunks
Dirty: yes
Store: ~/.openclaw/memory/standard.sqlite
Workspace: ~/.openclaw/workspace
Dreaming: off
Embeddings: unavailable
Embeddings error: openai embeddings failed: 401 {
"error": {
"message": "Incorrect API key provided: nvapi-fn**********************************************************YnYT. You can find your API key at https://platform.openai.com/account/api-keys.",
"type": "invalid_request_error",
"param": null,
"code": "invalid_api_key"
}
}

placid mossBOT May 17, 2026, 10:49 PM

#

vernal flax OpenClaw 2026.5.12 (f066dd2)-- OpenClaw 2026.5.12 (f066dd2) — I speak fluent bas...

Yep, this confirms it: auto-detect picked openai, and the key OpenClaw resolved for OpenAI starts with nvapi-. That is an NVIDIA key being sent to OpenAI’s embeddings endpoint, so the 401 is expected.

Check where that key is coming from, but don’t paste the full value here:

printenv OPENAI_API_KEY | sed 's/./*/g' | head
openclaw config get models.providers.openai.apiKey
openclaw config get agents.defaults.memorySearch

Likely fixes:

# If you want OpenAI embeddings:
export OPENAI_API_KEY="sk-..."   # real OpenAI key, not nvapi
openclaw gateway restart

placid mossBOT May 17, 2026, 10:49 PM

#

vernal flax OpenClaw 2026.5.12 (f066dd2)-- OpenClaw 2026.5.12 (f066dd2) — I speak fluent bas...

openclaw memory status --deep

Or set memory to a provider you actually have credentials for, for example Gemini/Voyage/Mistral/etc. The key detail is: agents.defaults.memorySearch.provider: "openai" expects an OpenAI-compatible embeddings setup, and by default uses OPENAI_API_KEY.

If this NVIDIA key was put into OPENAI_API_KEY for chat/model routing, that’s the root problem. Put NVIDIA auth under the NVIDIA/custom provider config instead, and keep OPENAI_API_KEY reserved for actual OpenAI.

Docs for the exact config knobs: https://docs.openclaw.ai/reference/memory-config

vernal flax May 17, 2026, 10:49 PM

#

By source:
memory · 0/81 files · 0 chunks
Vector store: ready
Semantic vectors: ready
Vector path: /opt/homebrew/lib/node_modules/openclaw/node_modules/sqlite-vec-darwin-arm64/vec0.dylib
FTS: ready
Embedding cache: enabled (0 entries)
Batch: disabled (failures 0/2)
Recall store: 1 entries · 0 promoted · 1 concept-tagged · 0 spaced · scripts=1 latin
Recall path: ~/.openclaw/workspace/memory/.dreams/short-term-recall.json
Recall updated: 2026-05-03T18:37:17.593Z
Dreaming artifacts: diary absent · 0 corpus files · ingestion state absent
Dream corpus: ~/.openclaw/workspace/memory/.dreams/session-corpus
Dream ingestion: ~/.openclaw/workspace/memory/.dreams/session-ingestion.json--anujsharma@Anuj-Mac memory-core % openclaw models status

🦞 OpenClaw 2026.5.12 (f066dd2) — Hot reload for config, cold sweat for deploys.

placid mossBOT May 17, 2026, 10:49 PM

#

vernal flax By source: memory · 0/81 files · 0 chunks Vector store: ready Semantic vectors...

Good, that output means the embedding provider is now reachable: Semantic vectors: ready.

The remaining issue is just that the index is empty: 0/81 files · 0 chunks. Force a rebuild now:

openclaw memory index --force --verbose
openclaw memory status --deep
openclaw memory search "test"

Or one-shot:

openclaw memory status --deep --index --verbose

After indexing, you should see nonzero chunks. If it still says 0 chunks, paste the verbose indexing output; that’ll show whether the files are being skipped, unreadable, or chunking to nothing.

placid mossBOT May 17, 2026, 10:49 PM

#

vernal flax By source: memory · 0/81 files · 0 chunks Vector store: ready Semantic vectors...

Docs: https://docs.openclaw.ai/cli/memory

vernal flax May 17, 2026, 10:49 PM

#

Config : ~/.openclaw/openclaw.json
Agent dir : ~/.openclaw/agents/standard/agent
Default : custom-integrate-api-nvidia-com/qwen/qwen3-coder-480b-a35b-instruct
Fallbacks (4) : custom-integrate-api-nvidia-com/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning, custom-integrate-api-nvidia-com/qwen/qwen3-next-80b-a3b-thinking, custom-integrate-api-nvidia-com/meta/llama-3.1-70b-instruct, custom-integrate-api-nvidia-com/openai/gpt-oss-120b
Image model : -
Image fallbacks (0): -
Aliases (10) : vision -> custom-integrate-api-nvidia-com/meta/llama-3.2-11b-vision-instruct, brain -> custom-integrate-api-nvidia-com/openai/gpt-oss-120b, coder -> custom-integrate-api-nvidia-com/qwen/qwen3-coder-480b-a35b-instruct, agent -> custom-integrate-api-nvidia-com/mistralai/mistral-nemotron, medical -> custom-integrate-api-nvidia-com/meta/llama-3.1-70b-instruct, thinking -> custom-integrate-api-nvidia-com/qwen/qwen3-next-80b-a3b-thinking, omni -> custom-integrate-api-nvidia-com/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning, N-dsflash -> custom-integrate-api-nvidia-com/deepseek-ai/deepseek-v4-flash, eyes -> google/gemini-2.5-pro, gflash -> google/gemini-2.5-flash
Configured models (10): custom-integrate-api-nvidia-com/meta/llama-3.2-11b-vision-instruct, custom-integrate-api-nvidia-com/openai/gpt-oss-120b, custom-integrate-api-nvidia-com/qwen/qwen3-coder-480b-a35b-instruct, custom-integrate-api-nvidia-com/mistralai/mistral-nemotron, custom-integrate-api-nvidia-com/meta/llama-3.1-70b-instruct, custom-integrate-api-nvidia-com/qwen/qwen3-next-80b-a3b-thinking, custom-integrate-api-nvidia-com/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning, custom-integrate-api-nvidia-com/deepseek-ai/deepseek-v4-flash, google/gemini-2.5-pro, google/gemini-2.5-flash

#

Auth overview
Auth store : ~/.openclaw/agents/standard/agent/auth-profiles.json
Shell env : off
Providers w/ OAuth/tokens (0): -

custom-integrate-api-nvidia-com effective=profiles:~/.openclaw/agents/standard/agent/auth-profiles.json | profiles=2 (oauth=0, token=0, api_key=2) | custom-integrate-api-nvidia-com:key1=ref(env:NVIDIA_API_KEY), custom-integrate-api-nvidia-com:key2=ref(env:NVIDIA_API_KEY_2)
deepseek effective=profiles:~/.openclaw/agents/main/agent/auth-profiles.json | profiles=1 (oauth=0, token=0, api_key=1) | deepseek:default=ref(env:DEEPSEEK_API_KEY) | env=sk-7b0bf...e9159ec7 | source=env: DEEPSEEK_API_KEY
google effective=profiles:~/.openclaw/agents/main/agent/auth-profiles.json | profiles=1 (oauth=0, token=0, api_key=1) | google:default=AIzaSyAd...dwIxzU0o | env=AIzaSyAd...dwIxzU0o | source=env: GOOGLE_API_KEY | models.json=AIzaSyAd...dwIxzU0o | source=models.json: ~/.openclaw/agents/standard/agent/models.json
nvidia effective=env:nvapi-fn...1RozYnYT | env=nvapi-fn...1RozYnYT | source=env: NVIDIA_API_KEY
openai effective=env:nvapi-fn...1RozYnYT | env=nvapi-fn...1RozYnYT | source=env: OPENAI_A

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Config : ~/.openclaw/openclaw.json Agent dir : ~/.openclaw/agents/sta...

That models output is useful, but it’s separate from the memory embedding provider.

Your default chat model can be NVIDIA/custom while memory search still uses OpenAI embeddings. In your earlier output, memory said:

Provider: openai (requested: auto)
Model: text-embedding-3-small

So memory is not using custom-integrate-api-nvidia-com/... for embeddings. It auto-selected OpenAI because OpenAI credentials/config were present, then failed because the resolved “OpenAI” key was actually nvapi-....

If you want to keep NVIDIA for chat and use Gemini for memory, set memory explicitly to Gemini since you already have Google models configured:

{
  agents: {

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Config : ~/.openclaw/openclaw.json Agent dir : ~/.openclaw/agents/sta...

    defaults: {
      memorySearch: {
        provider: "gemini",
        model: "gemini-embedding-001"
      }
    }
  }
}

Then make sure GEMINI_API_KEY or models.providers.google.apiKey is set, restart the gateway, and run:

openclaw memory status --deep
openclaw memory index --force --verbose

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Config : ~/.openclaw/openclaw.json Agent dir : ~/.openclaw/agents/sta...

If you specifically want NVIDIA embeddings, you’ll need an OpenAI-compatible embeddings endpoint/model and configure agents.defaults.memorySearch.remote.baseUrl, remote.apiKey, and model. The configured chat model list alone won’t do that.

Docs: https://docs.openclaw.ai/reference/memory-config

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Auth overview Auth store : ~/.openclaw/agents/standard/agent/auth-profiles.js...

There’s the culprit:

openai effective=env:nvapi-... | source=env: OPENAI_A...

So OpenClaw sees an OpenAI env var, but its value is your NVIDIA key. Since memory auto-detection checks OpenAI before Google/Gemini, it picks OpenAI and fails.

Fix it one of these ways:

# Option A: remove the bad OpenAI env var if you are not using OpenAI
unset OPENAI_API_KEY

Then remove it wherever it is persisted too: shell profile, launchd env, service env, .env, etc. After that:

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Auth overview Auth store : ~/.openclaw/agents/standard/agent/auth-profiles.js...

openclaw gateway restart
openclaw memory status --deep

Since you already have Google auth, memory should auto-detect Gemini after OpenAI disappears. I’d still make it explicit to avoid future confusion:

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "gemini",
        model: "gemini-embedding-001"
      }
    }

placid mossBOT May 17, 2026, 10:50 PM

#

vernal flax Auth overview Auth store : ~/.openclaw/agents/standard/agent/auth-profiles.js...

}
}

Then rebuild:

openclaw memory index --force --verbose

If you do want OpenAI embeddings instead, replace OPENAI_API_KEY with a real OpenAI sk-... key. Don’t put nvapi-... in OPENAI_API_KEY.

vernal flax May 17, 2026, 10:57 PM

#

no i want to use the custom-integrate-api-nvidia-com as provider and I want does memeory need any specific kind of model does it need vision or does it need to be "custom-integrate-api-nvidia-com/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning",
"custom-integrate-api-nvidia-com/qwen/qwen3-next-80b-a3b-thinking",
"custom-integrate-api-nvidia-com/meta/llama-3.1-70b-instruct",
"custom-integrate-api-nvidia-com/openai/gpt-oss-120b"
]"custom-integrate-api-nvidia-com/qwen/qwen3-coder-480b-a35b-instruct what kind of model is better suited for memory seaarch . also in my openclaw .json memorySearch: was not defined i guess it default to openAI so i coult not fix it to i tried to see if set my key to openai and if it work

placid mossBOT May 17, 2026, 10:58 PM

#

vernal flax no i want to use the custom-integrate-api-nvidia-com as provider and I want does...

Memory search does not want a chat/coder/reasoning/vision model. It needs an embedding model: a model whose API returns vectors from text via /v1/embeddings.

So these are the wrong kind for memory embeddings:

qwen3-coder-480b...
qwen3-next-80b...thinking
llama-3.1-70b-instruct
gpt-oss-120b
nemotron...reasoning/omni

Those are generation models. They can answer questions, but they are not the vectorizer used by memory search.

Important config nuance: memorySearch.provider does not use arbitrary chat provider IDs like custom-integrate-api-nvidia-com. The documented embedding providers are bedrock, gemini, github-copilot, local, mistral, ollama, openai, voyage. For a custom OpenAI-compatible NVIDIA endpoint, you still use the openai embedding adapter, but override the remote endpoint/key/model:

placid mossBOT May 17, 2026, 10:58 PM

#

vernal flax no i want to use the custom-integrate-api-nvidia-com as provider and I want does...

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
        model: "PUT_NVIDIA_EMBEDDING_MODEL_ID_HERE",
        remote: {
          baseUrl: "https://integrate.api.nvidia.com/v1",
          apiKey: { source: "env", provider: "default", id: "NVIDIA_API_KEY" }
        }
      }
    }
  }
}

placid mossBOT May 17, 2026, 10:58 PM

#

vernal flax no i want to use the custom-integrate-api-nvidia-com as provider and I want does...

The model must be an NVIDIA embedding model from their catalog, not one of the chat models in your fallback list. No vision is needed for normal memory unless you are deliberately indexing images/audio with multimodal memory.

Also, yes: if memorySearch is missing, OpenClaw auto-detects. It picked OpenAI because OPENAI_API_KEY existed, and that env var had your NVIDIA key in it. Don’t use OPENAI_API_KEY for NVIDIA; point memory’s remote.apiKey at NVIDIA_API_KEY instead.

After editing:

openclaw gateway restart
openclaw memory status --deep
openclaw memory index --force --verbose

Docs: https://docs.openclaw.ai/reference/memory-config

vernal flax May 17, 2026, 11:05 PM

#

are thes model classified as Text-to-Embedding model ?

placid mossBOT May 17, 2026, 11:05 PM

#

vernal flax are thes model classified as Text-to-Embedding model ?

No — the ones you listed are not text-to-embedding models. They’re chat/generation models:

qwen/qwen3-coder-480b-a35b-instruct      -> code/chat generation
qwen/qwen3-next-80b-a3b-thinking         -> reasoning/chat generation
meta/llama-3.1-70b-instruct              -> instruct/chat generation
openai/gpt-oss-120b                      -> chat generation
nvidia/nemotron-3-nano-omni...reasoning  -> multimodal/reasoning generation

For memory search you need a model that NVIDIA’s catalog/API explicitly exposes as embedding, text embedding, or retrieval embedding, and it must work on the /v1/embeddings endpoint.

Quick test shape:

curl https://integrate.api.nvidia.com/v1/embeddings \

placid mossBOT May 17, 2026, 11:05 PM

#

vernal flax are thes model classified as Text-to-Embedding model ?

  -H "Authorization: Bearer $NVIDIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"EMBEDDING_MODEL_ID_HERE","input":"hello world"}'

If it returns a numeric embedding vector, it’s the right kind. If it errors like “model not supported for embeddings,” it’s a chat model and won’t work for memory search.

vernal flax May 17, 2026, 11:07 PM

#

I am search on nvdia catlog and it list by use case i can see on optioin called text-to-embeding fro eaxample llama-nemotron-embed-1b-v2
Downloadable
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

placid mossBOT May 17, 2026, 11:08 PM

#

vernal flax I am search on nvdia catlog and it list by use case i can see on optioin called ...

Yes — text-to-embedding is the category you want.

llama-nemotron-embed-1b-v2 sounds like the right type of model for memory search: it turns text into vectors for retrieval/search. That is much more appropriate than coder/reasoning/chat models.

One catch: “Downloadable” may mean it is not hosted on NVIDIA’s /v1/embeddings API. OpenClaw can use it in one of two ways:

Hosted NVIDIA embeddings endpoint: if NVIDIA exposes that model through https://integrate.api.nvidia.com/v1/embeddings, configure it as the memory embedding model.
Local/self-hosted: if it is only downloadable, you need to serve it with an embeddings-compatible server, then point OpenClaw at that server’s OpenAI-compatible /v1/embeddings endpoint.

For hosted NVIDIA, the config shape is:

{
  agents: {
    defaults: {
      memorySearch: {

placid mossBOT May 17, 2026, 11:08 PM

#

vernal flax I am search on nvdia catlog and it list by use case i can see on optioin called ...

        provider: "openai",
        model: "llama-nemotron-embed-1b-v2",
        remote: {
          baseUrl: "https://integrate.api.nvidia.com/v1",
          apiKey: { source: "env", provider: "default", id: "NVIDIA_API_KEY" }
        }
      }
    }
  }
}

Then test:

placid mossBOT May 17, 2026, 11:08 PM

#

vernal flax I am search on nvdia catlog and it list by use case i can see on optioin called ...

openclaw gateway restart
openclaw memory status --deep
openclaw memory index --force --verbose

If NVIDIA returns “model not found” or “not supported for embeddings,” use the exact model ID from the API docs/catalog, or switch to a hosted embedding model NVIDIA exposes on /v1/embeddings.

vernal flax May 17, 2026, 11:12 PM

#

I hvae tessted it on anujsharma@Anuj-Mac memory-core % curl -X POST "https://integrate.api.nvidia.com/v1/embeddings"
-H "Authorization: Bearer $NVIDIA_API_KEY"
-H "Content-Type: application/json"
-d '{
"input": ["What is the capital of France?"],
"model": "nvidia/llama-nemotron-embed-1b-v2",
"encoding_format": "float",
"input_type": "query",
"truncate": "NONE"
}'
{"object":"list","data":[{"index":0,"embedding":[-0.0196075439453125,0.040802001953125,-0.01251983642578125,0.028564453125,0.04254150390625,0.0157318115234375,-0.033050537109375,-0.0007967948913574219,-0.040863037109375,0.002529144287109375,0.0013093948364257812,-0.0118560791015625,-0.0166778564453125,-0.0038166046142578125,-0.0016

placid mossBOT May 17, 2026, 11:12 PM

#

vernal flax I hvae tessted it on anujsharma@Anuj-Mac memory-core % curl -X POST "https://int...

Nice, that proves nvidia/llama-nemotron-embed-1b-v2 is a valid hosted embedding model.

Use this exact model id in memory config:

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
        model: "nvidia/llama-nemotron-embed-1b-v2",
        remote: {
          baseUrl: "https://integrate.api.nvidia.com/v1",
          apiKey: { source: "env", provider: "default", id: "NVIDIA_API_KEY" }
        }
      }

placid mossBOT May 17, 2026, 11:12 PM

#

vernal flax I hvae tessted it on anujsharma@Anuj-Mac memory-core % curl -X POST "https://int...

    }
  }
}

One thing to check: OpenClaw’s generic OpenAI-compatible embedding adapter sends the standard body shape:

{ "model": "...", "input": ["..."] }

It does not currently add NVIDIA-specific fields like input_type, truncate, or encoding_format. So before editing config, test NVIDIA with the minimal OpenAI-compatible body:

curl -X POST "https://integrate.api.nvidia.com/v1/embeddings" \

placid mossBOT May 17, 2026, 11:12 PM

#

vernal flax I hvae tessted it on anujsharma@Anuj-Mac memory-core % curl -X POST "https://int...

  -H "Authorization: Bearer $NVIDIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["What is the capital of France?"],
    "model": "nvidia/llama-nemotron-embed-1b-v2"
  }'

If that returns embeddings too, OpenClaw should work with the config above.

Then:

openclaw gateway restart
openclaw memory status --deep

placid mossBOT May 17, 2026, 11:12 PM

#

vernal flax I hvae tessted it on anujsharma@Anuj-Mac memory-core % curl -X POST "https://int...

openclaw memory index --force --verbose

Also remove/fix the bad OPENAI_API_KEY=nvapi-... env var so auto-detection doesn’t keep confusing things later.

vernal flax May 17, 2026, 11:24 PM

#

does this look okay curl -X POST https://integrate.api.nvidia.com/v1/embeddings
-H "Content-Type: application/json"
-H "Authorization: Bearer $NVIDIA_API_KEY"
-d '{
"input": ["What is the capital of France?"],
"model": "nvidia/nv-embed-v1"
}'
{"object":"list","data":[{"index":0,"embedding":[0.001781463623046875,0.0009927749633789062,-0.0074615478515625,-0.005855560302734375,0.01482391357421875,0.00394439697265625,-0.0165863037109375,0.0001957416534423828,0.0163116455078125,0.01039886474609375,0.01270294189453125,-0.0131988525390625,-0.0008902549743652344,0.0219268798828125,-0.02146911621

placid mossBOT May 17, 2026, 11:24 PM

#

vernal flax does this look okay curl -X POST https://integrate.api.nvidia.com/v1/embeddings ...

Yes, that looks good. That response shape is exactly what OpenClaw’s OpenAI-compatible embedding adapter expects: data[0].embedding is a numeric vector, and your request worked without NVIDIA-only fields.

Use this config:

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
        model: "nvidia/nv-embed-v1",
        remote: {
          baseUrl: "https://integrate.api.nvidia.com/v1",
          apiKey: { source: "env", provider: "default", id: "NVIDIA_API_KEY" }
        }
      }

placid mossBOT May 17, 2026, 11:24 PM

#

vernal flax does this look okay curl -X POST https://integrate.api.nvidia.com/v1/embeddings ...

    }
  }
}

Then run:

openclaw gateway restart
openclaw memory status --deep
openclaw memory index --force --verbose

Two cautions:

Keep provider: "openai" here because that means “use the OpenAI-compatible embeddings adapter,” not “bill OpenAI.”

placid mossBOT May 17, 2026, 11:24 PM

#

vernal flax does this look okay curl -X POST https://integrate.api.nvidia.com/v1/embeddings ...

Don’t leave OPENAI_API_KEY set to the NVIDIA key. This config points directly at NVIDIA_API_KEY, so OPENAI_API_KEY should be unset unless it is a real OpenAI key.

If it indexes successfully, please right-click the solution message → Apps → ✅ Mark Solution so Answer Overflow can index it for the next person.

vernal flax May 17, 2026, 11:36 PM

#

Memory Search (standard)
Provider: openai (requested: openai)
Model: nvidia/nv-embed-v1
Sources: memory
Indexed: 0/81 files · 0 chunks
Dirty: yes
Store: ~/.openclaw/memory/standard.sqlite
Workspace: ~/.openclaw/workspace
Dreaming: off
Embeddings: ready
By source:
memory · 0/81 files · 0 chunks
Vector store: ready
Semantic vectors: ready
Vector path: /opt/homebrew/lib/node_modules/openclaw/node_modules/sqlite-vec-darwin-arm64/vec0.dylib
FTS: ready
Embedding cache: enabled (0 entries)
Batch: disabled (failures 0/2)
Recall store: 1 entries · 0 promoted · 1 concept-tagged · 0 spaced · scripts=1 latin
Recall path: ~/.openclaw/workspace/memory/.dreams/short-term-recall.json
Recall updated: 2026-05-03T18:37:17.593Z
Dreaming artifacts: diary absent · 0 corpus files · ingestion state absent
Dream corpus: ~/.openclaw/workspace/memory/.dreams/session-corpus
Dream ingestion: ~/.openclaw/workspace/memory/.dreams/session-ingestion.json
How does this help agent and how to use it ?
can this be configured ford this allow cross memory sharing btw agents?

placid mossBOT May 17, 2026, 11:36 PM

#

vernal flax Memory Search (standard) Provider: openai (requested: openai) Model: nvidia/nv-e...

That looks healthy on the embedding side now:

Provider: openai (requested: openai)
Model: nvidia/nv-embed-v1
Embeddings: ready
Semantic vectors: ready

The remaining thing is indexing. It still says:

Indexed: 0/81 files · 0 chunks
Dirty: yes

Run:

placid mossBOT May 17, 2026, 11:36 PM

#

vernal flax Memory Search (standard) Provider: openai (requested: openai) Model: nvidia/nv-e...

openclaw memory index --force --verbose
openclaw memory status --deep
openclaw memory search "something from my memory"

How it helps the agent: OpenClaw chunks MEMORY.md and memory/*.md, embeds those chunks, then the agent can pull only relevant memory back into context instead of stuffing all memory into every prompt. So it improves recall without blowing up context.

For cross-agent sharing, there are a few levels:

Shared Markdown memory: easiest path. Put shared notes in a common folder and add it with agents.defaults.memorySearch.extraPaths if every agent should see it, or per-agent agents.list[].memorySearch.extraPaths if only some agents should.

{
  agents: {
    defaults: {

placid mossBOT May 17, 2026, 11:36 PM

#

vernal flax Memory Search (standard) Provider: openai (requested: openai) Model: nvidia/nv-e...

      memorySearch: {
        extraPaths: ["/Users/anuj/shared-openclaw-memory"]
      }
    }
  }
}

Session transcript memory: opt-in. Add "sessions" to sources and enable experimental session memory if you want prior chats indexed too.

{
  agents: {
    defaults: {
      memorySearch: {

placid mossBOT May 17, 2026, 11:36 PM

#

vernal flax Memory Search (standard) Provider: openai (requested: openai) Model: nvidia/nv-e...

        sources: ["memory", "sessions"],
        experimental: { sessionMemory: true }
      }
    }
  }
}

Cross-agent transcript search: that’s more advanced and is documented for the QMD backend using memorySearch.qmd.extraCollections. For normal builtin memory, think “per-agent SQLite index plus shared extraPaths” as the practical route.

Docs:
https://docs.openclaw.ai/concepts/memory-search
https://docs.openclaw.ai/reference/memory-config

vernal flax May 17, 2026, 11:48 PM

#

anujsharma@Anuj-Mac memory-core % openclaw memory index --force --verbose

🦞 OpenClaw 2026.5.12 (f066dd2) — Runs on a Raspberry Pi. Dreams of a rack in Iceland.

Memory Index (standard)
Provider: openai (requested: openai)
Model: nvidia/nv-embed-v1
Sources: memory (MEMORY.md + ~/.openclaw/workspace/memory/*.md)---repat so put on loast few lines00:37:38 [memory] embeddings: batch start
00:37:38 [memory] embeddings: batch start
Memory index updated (standard).
anujsharma@Anuj-Mac memory-core % anujsharma@Anuj-Mac memory-core % openclaw memory status --deep

🦞 OpenClaw 2026.5.12 (f066dd2) — I don't just autocomplete—I auto-commit (emotionally), then ask you to review (logically).

│
◇
Memory Search (standard)
Provider: openai (requested: openai)
Model: nvidia/nv-embed-v1
Sources: memory
Indexed: 81/81 files · 257 chunks
Dirty: no
Store: ~/.openclaw/memory/standard.sqlite
Workspace: ~/.openclaw/workspace
Dreaming: off
Embeddings: ready
By source:
memory · 81/81 files · 257 chunks
Vector store: ready
Semantic vectors: ready
Vector dims: 4096
Vector path: /opt/homebrew/lib/node_modules/openclaw/node_modules/sqlite-vec-darwin-arm64/vec0.dylib
FTS: ready
Embedding cache: enabled (257 entries)
Batch: disabled (failures 0/2)
Recall store: 1 entries · 0 promoted · 1 concept-tagged · 0 spaced · scripts=1 latin
Recall path: ~/.openclaw/workspace/memory/.dreams/short-term-recall.json
Recall updated: 2026-05-03T18:37:17.593Z
Dreaming artifacts: diary absent · 0 corpus files · ingestion state absent
Dream corpus: ~/.openclaw/workspace/memory/.dreams/session-corpus
Dream ingestion: ~/.openclaw/workspace/memory/.dreams/session-ingestion.json

placid mossBOT May 17, 2026, 11:48 PM

#

vernal flax anujsharma@Anuj-Mac memory-core % openclaw memory index --force --verbose 🦞 Op...

That’s fixed. ✅

The key lines are:

Indexed: 81/81 files · 257 chunks
Dirty: no
Embeddings: ready
Semantic vectors: ready
Vector dims: 4096
Embedding cache: enabled (257 entries)

So memory search is now using NVIDIA embeddings successfully and the index is populated.

Quick usage check:

placid mossBOT May 17, 2026, 11:48 PM

#

vernal flax anujsharma@Anuj-Mac memory-core % openclaw memory index --force --verbose 🦞 Op...

openclaw memory search "some topic you know is in MEMORY.md"

In normal agent chats, you usually don’t manually “use” it. The memory search tool can retrieve relevant chunks when the agent needs prior facts/context. If you want more material available, add Markdown notes under ~/.openclaw/workspace/memory/ or MEMORY.md, then re-run openclaw memory index --force or let the watcher pick it up.

Please mark the solution so it gets indexed for future users: right-click the solution message → Apps → ✅ Mark Solution.

vernal flax May 17, 2026, 11:51 PM

#

does this mean that memoerry will hve to be updated manulay to have all the filees stoed as emebeding by openclaw memory index --force , also what is the purpose of memory-core skill is this internall using that skill,can you Session transcript memory: as you would explain to 12 year old

placid mossBOT May 17, 2026, 11:51 PM

#

vernal flax does this mean that memoerry will hve to be updated manulay to have all the file...

No, you usually should not need to run openclaw memory index --force every time.

For normal memory files, OpenClaw watches:

~/.openclaw/workspace/MEMORY.md
~/.openclaw/workspace/memory/*.md

When those files change, it should reindex automatically after a short delay. openclaw memory index --force is the manual “rebuild everything now” button, useful after changing providers/models, fixing a broken index, or debugging.

memory-core is the built-in memory plugin/backend that provides the openclaw memory ... commands and memory search behavior. It is not a “skill” like an agent prompt skill; it is the internal plugin that indexes files, stores embeddings, searches them, and exposes memory tools.

Session transcript memory, explained for a 12-year-old:

Imagine your agent has two notebooks:

placid mossBOT May 17, 2026, 11:51 PM

#

vernal flax does this mean that memoerry will hve to be updated manulay to have all the file...

Memory files: notes you intentionally wrote down, like “I prefer pnpm” or “My server is called home-lab.”
Session transcripts: chat logs from old conversations.

By default, OpenClaw mainly reads the first notebook. Session transcript memory means OpenClaw can also make old chat logs searchable, so if you talked about something last week, the agent may find that old conversation later.

But it is opt-in because chat logs can contain private/noisy stuff and can make the memory index bigger. To enable it:

{
  agents: {
    defaults: {
      memorySearch: {
        sources: ["memory", "sessions"],
        experimental: { sessionMemory: true }
      }
    }

placid mossBOT May 17, 2026, 11:51 PM

#

vernal flax does this mean that memoerry will hve to be updated manulay to have all the file...

}
}

Then restart and index/check:

openclaw gateway restart
openclaw memory status --deep
openclaw memory index --force --verbose

My recommendation: keep regular MEMORY.md / memory/*.md for important durable facts, and only enable session transcript memory if you really want old chats searchable too.

vernal flax May 17, 2026, 11:56 PM

#

You also mentioned it keep track of changes so i made a change then my agetn will be able to recover the past change ( i had to do lot automate pushing openclaw.json to github0) so memery toll can do the same if yes what would haave ask the agent to get the information

placid mossBOT May 17, 2026, 11:56 PM

#

vernal flax You also mentioned it keep track of changes so i made a change then my agetn wil...

Small but important distinction: memory search is not version control.

It keeps the searchable index up to date with the current contents of your memory files. If you edit openclaw.json, memory search will not automatically remember the old version unless one of these is true:

you wrote the change/history into MEMORY.md or memory/*.md
session transcript memory was enabled and the conversation/log about that change got indexed
you kept the old file in git, backups, or another history system

For config history like openclaw.json, git is still the right tool. Memory is for recall, not rollback.

Also: be careful pushing openclaw.json to GitHub. It can contain tokens, API keys, channel IDs, local paths, auth refs, or other private config. Prefer committing a redacted example config, or use secret refs/env vars and double-check before pushing.

If you want the agent to remember a config change, add a short note to memory, for example:

## OpenClaw config changes

placid mossBOT May 17, 2026, 11:56 PM

#

vernal flax You also mentioned it keep track of changes so i made a change then my agetn wil...

- 2026-05-17: Memory embeddings configured to use NVIDIA hosted embeddings via provider=openai, model=nvidia/nv-embed-v1, remote.baseUrl=https://integrate.api.nvidia.com/v1, apiKey from NVIDIA_API_KEY.

Then ask the agent things like:

What memory embedding provider did I configure?
Why did we switch away from auto OpenAI memory detection?
Find my notes about the NVIDIA memory embedding setup.
What changed in my OpenClaw memory config recently?

If you want actual “what did this file look like before?” use git:

placid mossBOT May 17, 2026, 11:56 PM

#

vernal flax You also mentioned it keep track of changes so i made a change then my agetn wil...

git log -- openclaw.json
git show HEAD~1:openclaw.json
git diff HEAD~1 -- openclaw.json

Memory can help you remember the decision. Git helps you recover the exact old file.

vernal flax May 18, 2026, 12:03 AM

#

so my mmeory block look like this
"sources": ["memory", "sessions"],
"experimental": { "sessionMemory": true },
"provider": "openai",
"model": "nvidia/nv-embed-v1",
"remote": {
"baseUrl": "https://integrate.api.nvidia.com/v1",
"apiKey": { "source": "env", "provider": "default", "id": "NVIDIA_API_KEY" }
}
......any updates or suggestion or recommendatoin ?

placid mossBOT May 18, 2026, 12:03 AM

#

vernal flax so my mmeory block look like this "sources": ["memory", "sessio...

That block looks basically right for what you want: NVIDIA hosted embeddings via the OpenAI-compatible adapter, plus session transcript indexing.

I’d make three practical tweaks/checks:

Make sure it is nested under agents.defaults.memorySearch:

{
  agents: {
    defaults: {
      memorySearch: {
        sources: ["memory", "sessions"],
        experimental: { sessionMemory: true },
        provider: "openai",
        model: "nvidia/nv-embed-v1",
        remote: {

placid mossBOT May 18, 2026, 12:03 AM

#

vernal flax so my mmeory block look like this "sources": ["memory", "sessio...

          baseUrl: "https://integrate.api.nvidia.com/v1",
          apiKey: { source: "env", provider: "default", id: "NVIDIA_API_KEY" }
        }
      }
    }
  }
}

Keep OPENAI_API_KEY unset unless it is a real OpenAI key. Your memory config now uses NVIDIA_API_KEY directly, so you don’t need the NVIDIA key in any OpenAI env var.
Think carefully about sessions. It makes old conversations searchable, which is useful, but it also indexes more private/noisy data and can increase embedding cost. If you only want curated long-term memory, use:

sources: ["memory"]

placid mossBOT May 18, 2026, 12:03 AM

#

vernal flax so my mmeory block look like this "sources": ["memory", "sessio...

If you want old chat recall too, your current ["memory", "sessions"] is fine.

After editing, verify with:

openclaw gateway restart
openclaw memory status --deep
openclaw memory index --force --verbose
openclaw memory search "NVIDIA memory embedding"

Given your last status showed 81/81 files · 257 chunks, the NVIDIA embedding setup itself is working. The only real decision left is whether session transcripts should be included or kept out for privacy/noise/cost reasons.

vernal flax May 18, 2026, 12:14 AM

#

how do mark this as solution

placid mossBOT May 18, 2026, 12:14 AM

#

vernal flax how do mark this as solution

In Discord, right-click the message that solved it, then choose:

Apps → ✅ Mark Solution

If you’re on mobile, long-press the solution message, then look for Apps / Mark Solution in the action menu.

Pick the message with the final working config or the one confirming Indexed: 81/81 files · 257 chunks, whichever best captures the fix.

#memory sync failed use