Promptix Python is an open-source framework for building autonomous AI agents in Python. With a simple installation via pip, you can instantiate agents powered by any major LLM, register domain-specific tools, configure in-memory or persistent data stores, and orchestrate multi-step decision loops. The SDK supports real-time streaming of token outputs, callback handlers for logging or custom processing, and built-in memory modules to retain context across interactions. Developers can leverage this library to prototype chatbot assistants, automations, data pipelines, or research agents in minutes. Its modular design allows swapping models, adding custom tools, and extending memory backends, providing flexibility for a wide range of AI agent use cases.

Scoopika provides a powerful platform that allows developers to effortlessly create advanced AI agents. These agents are capable of seeing, talking, listening, learning, and taking actions. Designed to integrate seamlessly with external tools, data validation, and real-time streaming, Scoopika offers a robust solution for building intelligent, multimodal AI assistants. Developers can get started for free and leverage the platform’s capabilities to enhance their applications.

Inngest AgentKit provides a comprehensive framework for developing AI agents within a Node.js environment. It leverages Inngest’s event-driven architecture to trigger agent workflows based on external events such as HTTP requests, scheduled tasks, or webhook calls. The toolkit includes template rendering utilities for crafting dynamic responses, built-in state management to maintain context over sessions, and seamless integration with external APIs and language models. Agents can stream partial responses in real time, manage complex logic, and orchestrate multi-step processes with error handling and retries. By abstracting infrastructure and workflow concerns, AgentKit enables developers to focus on designing intelligent behaviors, reducing boilerplate code and accelerating deployment of conversational assistants, data-processing pipelines, and task automation bots.

Claude-Code-OpenAI transforms Anthropic’s Claude API into a drop-in replacement for OpenAI models in Python applications. After installing via pip and configuring your OPENAI_API_KEY and CLAUDE_API_KEY environment variables, you can use familiar methods like openai.ChatCompletion.create(), openai.Completion.create(), or openai.Embedding.create() with Claude model names (e.g., claude-2, claude-1.3). The library intercepts calls, routes them to the corresponding Claude endpoints, and normalizes responses to match OpenAI’s data structures. It supports real-time streaming, rich parameter mapping, error handling, and prompt templating. This allows teams to experiment with Claude and GPT models interchangeably without refactoring code, enabling rapid prototyping for chatbots, content generation, semantic search, and hybrid LLM workflows.