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Basic Information

Promptulate is an AI Agent application development framework created by Cogit Lab that provides a concise, Pythonic SDK for building LLM-powered agent applications. It centralizes common agent concepts such as LLMs, Agents, Tools, RAG, and Planners into a small, composable API where most tasks can be performed using a single pne.chat call. The project targets developers who want to assemble autonomous agents that can plan, reason, call external tools, and produce structured outputs. It integrates with litellm for broad model provider compatibility and supports many model backends. The README includes examples for chat usage, agent planning with tool calls, structured output via Pydantic schemas, and atomized components like Planner to enable modular customization and lifecycle hooks for extensibility.

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Features
Promptulate emphasizes a Pythonic code style and exposes a compact interface (notably pne.chat) to cover typical LLM application needs. It supports a wide variety of base models through litellm and accepts provider/model_name syntax for third-party model calls. The framework supplies diverse agent types (WebAgent, ToolAgent, CodeAgent) and a Plan-and-Execute pattern to enable planning, tool usage, and reflection. It can wrap Python functions as tools, interoperate with LangChain tools, provides lifecycle hooks and rich hook points, includes prompt caching to reduce repetition, offers a built-in OpenAI-like wrapper, terminal/client integration for debugging, and Streamlit/Gradio components and examples for rapid prototyping.
Use Cases
Promptulate lowers the barrier to building autonomous LLM applications by unifying common patterns into concise APIs and examples. Developers can prototype chatbots, planner-driven agents, or tool-enabled workflows with minimal code while leveraging many model providers. Structured output support via Pydantic helps produce reliable data responses. Integration with existing tooling like LangChain and the ability to convert Python functions into agent tools simplifies adding capabilities. Lifecycle hooks and atomized components enable customization and debugging, while prompt caching, terminal integration, and example apps (Streamlit, Gradio) accelerate development and debugging. The framework is useful for teams wanting a modular, interoperable foundation for agent-based applications.

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