AI Analysis
Final verdict: SAFE
The package 10xscale-agentflow v0.7.5.1 has a moderate code coverage and uses standard badges for versioning, license, and supported Python versions. No specific malicious activities or unusual patterns were detected.
- Package uses standard PyPI and GitHub badges.
- Moderate code coverage reported.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
lib.request with urllib.request.urlopen(url, timeout=10) as resp: # noqa: S310 # nosec B31request with urllib.request.urlopen(url, timeout=10) as resp: # noqa: S310 # nosec B31
Code Obfuscation
score 8.0
Found 4 obfuscation pattern(s)
part.thought_signature = base64.b64decode(sig_b64) else: part.thought_signature ="image/png" raw = base64.b64decode(b64_data) return [types.Part.from_bytes(data=rawreturn [] raw = base64.b64decode(b64_data) return [types.Part.from_bytes(data=raw, miif data: raw = base64.b64decode(data) return [types.Part.from_bytes(data=raw, mi
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: 10xscale.ai>
Suspicious Page Links
score 4.0
Found 2 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:8000/mcpNon-HTTPS external link: http://127.0.0.1:8000
Git Repository History
Repository 10xHub/agentflow appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with 10xscale-agentflow
Create a fully-functional mini-application called 'SmartTaskManager' that leverages the 10xscale-agentflow Python package to manage and execute complex tasks in a distributed environment. The application should allow users to define tasks as nodes in a directed acyclic graph (DAG), where each node represents a task and edges represent dependencies between tasks. Users should be able to specify task details such as task type (e.g., data processing, model training), required resources, and execution parameters. Additionally, the application should support the following features: 1. Task Scheduling: Automatically schedule tasks based on their dependencies and available resources. 2. Task Execution: Execute tasks using different providers' LLMs (such as OpenAI, Google GenAI, Anthropic) depending on the specified task requirements. 3. Task Monitoring: Monitor the status of tasks in real-time, providing alerts when tasks fail or complete. 4. Task Persistence: Ensure that the state of tasks is persisted across restarts, allowing for resumption of operations after failures or maintenance. 5. Graph Visualization: Provide a visual representation of the task graph, highlighting active, pending, and completed tasks. 6. Customizable Workflows: Allow users to create custom workflows by adding new types of tasks and modifying existing ones. 7. Scalability: Design the application to scale horizontally, supporting multiple agents and LLM providers. 8. Security: Implement security measures to protect sensitive information, such as API keys and task data. The 'SmartTaskManager' application should demonstrate the core capabilities of the 10xscale-agentflow package, including its ability to handle complex workflows, integrate with various LLM providers, and manage task states durably. Your goal is to showcase how this package simplifies the development of sophisticated multi-agent systems while maintaining high performance and reliability.