agentscope-ai/agentscope · error · RuntimeError

"AgentScope embedding model returned no embeddings."

Error message

"AgentScope embedding model returned no embeddings."

What it means

embed() calls the AgentScope embedding model and expects a non-empty embeddings list in the response. If the model returns an EmbeddingResponse with empty embeddings, there is no vector to return and a RuntimeError is raised.

Source

Thrown at src/agentscope/middleware/_longterm_memory/_mem0/_agentscope_adapter.py:271

                f"EmbeddingModelBase, got "
                f"{type(self.config.model).__name__}.",
            )
        self._agentscope_model: EmbeddingModelBase = self.config.model
        self._bridge = _AsyncBridge()

    # ----- EmbeddingBase interface -----
    # pylint: disable=unused-argument
    def embed(
        self,
        text: str | list[str],
        memory_action: str | None = None,  # mem0 contract — unused
    ) -> list[float]:
        """mem0 ``EmbeddingBase`` entry — runs the AgentScope embedding
        model synchronously and returns the first vector."""
        text_list = [text] if isinstance(text, str) else list(text)
        response = self._bridge.run(self._agentscope_model(text_list))
        if not response.embeddings:
            raise RuntimeError(
                "AgentScope embedding model returned no embeddings.",
            )
        # AgentScope EmbeddingResponse.embeddings is List[List[float]];
        # mem0 expects a single vector for a single-text call.
        return response.embeddings[0]


# ----------------------------------------------------------------------
# Build a mem0 MemoryConfig wired to AgentScope models
# ----------------------------------------------------------------------

# The provider name we register under in mem0's factory + config layer.
_AGENTSCOPE_PROVIDER = "agentscope"


def build_mem0_config(
    *,
    chat_model: ChatModelBase | None = None,

View on GitHub (pinned to e90f1c7592)

Solutions

1. Test the embedding model directly to verify it returns vectors
2. Fix mocks to include at least one vector
3. Check provider API key/quota and input text validity

Example fix

// before
mock_model.return_value = EmbeddingResponse(embeddings=[])
// after
mock_model.return_value = EmbeddingResponse(embeddings=[[0.1, 0.2, 0.3]])

resp = await embedding_model(['ping'])
if not resp.embeddings:
    raise RuntimeError('embedding model returned no vectors — check provider/key')

try:
    vec = emb.embed('text')
except RuntimeError as e:
    if 'no embeddings' in str(e):
        vec = retry_with_backoff(lambda: emb.embed('text'))
    else:
        raise

When it happens

Trigger: A misbehaving or mocked embedding model returning EmbeddingResponse(embeddings=[]) or embedding=False; provider returning an empty body.

Common situations: Mocked models in tests that forget to populate embeddings; provider errors that still parse into a response object.

Related errors

• "AgentScopeEmbedding requires `model` in the config to be an
• f"AgentScopeEmbedding `model` must be an EmbeddingModelBase,
• Path {path_file} exists but is not a file.
• "AgentScopeLLM requires `model` in the config to be an Agent
• f"AgentScopeLLM `model` must be a ChatModelBase, got {type(s

AI-assisted analysis of agentscope-ai/agentscope@e90f1c7592 (2026-08-28). Data as JSON: /api/errors/9abe6861e769994d. Report an issue: GitHub.