{"record":{"id":"1fbbc75b662385dc","repo":"mem0ai/mem0","slug":"failed-to-generate-response-e","errorCode":null,"errorMessage":"Failed to generate response: {e}","messagePattern":"Failed to generate response: (.+?)","errorType":"exception","errorClass":"RuntimeError","httpStatus":null,"severity":"error","filePath":"mem0/llms/aws_bedrock.py","lineNumber":468,"sourceCode":"            tools: List of tools for function calling\n            tool_choice: Tool choice method\n            stream: Whether to stream the response\n            **kwargs: Additional parameters\n\n        Returns:\n            Generated response\n        \"\"\"\n        try:\n            if tools and self.supports_tools:\n                # Use converse method for tool-enabled models\n                return self._generate_with_tools(messages, tools, stream)\n            else:\n                # Use standard invoke_model method\n                return self._generate_standard(messages, stream)\n\n        except Exception as e:\n            logger.error(f\"Failed to generate response: {e}\")\n            raise RuntimeError(f\"Failed to generate response: {e}\")\n\n    @staticmethod\n    def _convert_tools_to_converse_format(tools: List[Dict]) -> List[Dict]:\n        \"\"\"Convert OpenAI-style tools to Converse API format.\"\"\"\n        if not tools:\n            return []\n\n        converse_tools = []\n        for tool in tools:\n            if tool.get(\"type\") == \"function\" and \"function\" in tool:\n                func = tool[\"function\"]\n                converse_tool = {\n                    \"toolSpec\": {\n                        \"name\": func[\"name\"],\n                        \"description\": func.get(\"description\", \"\"),\n                        \"inputSchema\": {\n                            \"json\": func.get(\"parameters\", {})\n                        }","sourceCodeStart":450,"sourceCodeEnd":486,"githubUrl":"https://github.com/mem0ai/mem0/blob/001c235229be8795e3834520467bd0d661ed8f34/mem0/llms/aws_bedrock.py#L450-L486","documentation":"RuntimeError raised by AWSBedrockLLM.generate_response as the outer wrapper around ANY exception during response generation — tool-enabled paths (_generate_with_tools) and standard paths (_generate_standard) both funnel here. The original exception is logged (logger.error) and appended to the message; the real cause is almost always the inner AWS error (throttle, model access, malformed messages, context length).","triggerScenarios":"Calling Memory.add/search which invokes the LLM; Bedrock ThrottlingException under load; ModelStreamErrorException or AccessDeniedException for a model the account lacks access to; context-length exceeded for the converse API; unsupported tool schema in _convert_tools_to_converse_format raising inside the tool path","commonSituations":"Production bursts hitting Bedrock TPS limits; account without model access granted for the specific model ID; malformed chat history (invalid role alternation) rejected by converse API; partial JSON responses from the model breaking downstream parsing in the tool loop.","solutions":["Read the '{e}' portion of the message and the mem0 error log to identify the underlying AWS exception, then fix that specifically","For throttling: add exponential-backoff retry around Memory.add/search calls or reduce concurrency","For model access: enable the model in the Bedrock console for the region","For context-length errors: reduce prompt/history size or switch to a model with a larger context window","Update mem0ai — Bedrock integration fixes (tool conversion, streaming) land regularly"],"exampleFix":"// before\nresult = memory.add(\"user likes tea\", user_id=\"a\")  # RuntimeError: Failed to generate response: ThrottlingException\n\n# after\nimport time\nfor attempt in range(5):\n    try:\n        result = memory.add(\"user likes tea\", user_id=\"a\")\n        break\n    except RuntimeError as e:\n        if \"Throttling\" not in str(e) or attempt == 4:\n            raise\n        time.sleep(2 ** attempt)","handlingStrategy":"retry","validationCode":"# preflight before heavy use: verify model invocation works\nresp = boto3.client(\"bedrock-runtime\", region_name=region).invoke_model(\n    modelId=model_id, body=b'{\"prompt\":\"hi\",\"max_tokens\":1}')\nassert resp[\"body\"].read(), \"model invocation failed\"","typeGuard":null,"tryCatchPattern":"import time\n\ndef generate_with_backoff(fn, *args, retries=5, **kw):\n    for i in range(retries):\n        try:\n            return fn(*args, **kw)\n        except RuntimeError as e:\n            msg = str(e)\n            transient = any(t in msg for t in (\"Throttling\", \"ServiceUnavailable\", \"timeout\", \"Timeout\"))\n            if not transient or i == retries - 1:\n                raise\n            time.sleep(2 ** i)","preventionTips":["Wrap Memory.add/search in retry-with-backoff for throttling","Keep prompts under the model's context window","Log the inner exception text — the wrapper hides the cause","Upgrade mem0ai for Bedrock tool/streaming fixes"],"tags":["python","aws","bedrock","runtime","llm","mem0"],"backgroundTag":null,"analyzedSha":"001c235229be8795e3834520467bd0d661ed8f34","analyzedAt":"2026-08-15T01:55:42.685Z","schemaVersion":2},"datasetVersion":"2026-08-15T22:17:37.221Z"}