Description
ManagePrompt serves as a crucial tool for developers integrating Large Language Models (LLMs) into their applications. This open-source, local LLM call debugger empowers developers to meticulously track and analyze every interaction with LLM APIs. By capturing comprehensive details of each request and response, ManagePrompt offers unparalleled visibility into the LLM call lifecycle.
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The debugger provides a local web UI that displays vital information in real-time. Developers can examine full request and response bodies, visualize the flow of data, and monitor token usage, including input, output, and cache read/write tokens for every individual call. This granular tracking is essential for understanding model behavior and optimizing performance.
Beyond technical details, ManagePrompt offers practical insights into development costs. It provides real-time cost estimations, calculated using pricing data from models.dev, allowing developers to manage their LLM expenditures effectively. The tool automatically handles both streaming and non-streaming LLM calls, ensuring no interaction is missed. Its WebSocket-powered live updates keep the UI synchronized with ongoing calls, providing an immediate feedback loop.
For project management and data persistence, ManagePrompt utilizes a project-scoped SQLite database, requiring zero configuration. This local storage ensures that all debugging data is kept within the developer's environment. Integration is straightforward, often requiring just two lines of code to add the necessary middleware to AI SDKs. ManagePrompt is designed to be versatile, working seamlessly with a wide range of LLM APIs, including those from OpenAI, Anthropic, Google, and Mistral, among others.
The target audience for ManagePrompt includes AI engineers, machine learning engineers, software developers, and data scientists who are building applications that leverage LLMs. It is particularly beneficial for those working on complex LLM integrations, fine-tuning models, or optimizing LLM-powered features where detailed debugging and performance analysis are critical. The tool simplifies the often-complex process of troubleshooting LLM interactions, accelerating development cycles and improving the quality of AI-driven applications.
- CategoryPrompt Engineering Tools
- Pricing ModelOpen Source
- DeploymentLocal
- PlatformsCross-platform (via installation methods)
ManagePrompt LLM Debugger's Core Features
Captures all LLM API requests and responses
Visualizes request/response flow
Tracks input, output, and cache tokens per call
Estimates LLM call costs in real-time
Supports both streaming and non-streaming LLM calls
Provides live, real-time UI updates via WebSockets
Uses a project-scoped SQLite database for local data storage
Offers zero-configuration setup for local databases
Integrates easily with AI SDKs via middleware
Compatible with multiple LLM providers (OpenAI, Anthropic, Google, Mistral, etc.)
Open-source local LLM call debugger
How to use ManagePrompt LLM Debugger?
Install: Use Homebrew, Go, or npm/pnpm to install ManagePrompt.
Integrate: Add `devToolsMiddleware()` to your AI SDK configuration.
Capture: Wrap your LLM calls with the `capture()` function.
Run UI: Start the ManagePrompt server to access the local web UI.
Debug: Analyze request/response details, token usage, and costs in the UI.
ManagePrompt LLM Debugger's Use Cases
- LLM API Debugging
- Performance Optimization
- Cost Management
- Request/Response Analysis
- Real-time Monitoring
- Multi-Provider Integration
