Top 4 open source LLM prompt management platforms.
Published: 2023-12-14 16:13:13

### PromptDesk
 
The easiest and fastest way to build prompt-based applications.

![PromptDesk screenshot](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0hwgh67pn9j4a8fan7jm.png)

#### Top 4 features:

1. Collaborative GUI Prompt Builder: Featuring a user-friendly and sophisticated interface, this builder streamlines the creation of complex prompts, enabling users to craft intricate prompt structures with ease.

2. 100% LLM Support: PromptDesk offers seamless integration with all large language models without restriction, limit or wait.

3. Fine-Tuning and Data Management: Users have access to detailed logs and histories, facilitating the fine-tuning of datasets and prompts for optimized performance and tailored application responses.

4. Python SDK: Accelerate prompt-to-code which allows for effortless integration of prompts created in the GUI with Python source code.

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### LiteLLM

Call 100+ LLM models using the OpenAI format.

#### Top 4 features:

1. Unified API Format: It allows calling various LLM APIs using the OpenAI format, simplifying integration with multiple providers like Azure, Cohere, Anthropic, etc.

2. Consistent Output and Exception Mapping: Guarantees consistent output format and maps common exceptions across different providers to OpenAI exception types.

3. Load Balancing and Proxy Management: Supports load balancing across multiple deployments and manages calling 100+ LLMs in OpenAI format.

4. Logging and Observability: Provides predefined callbacks for integration with various logging and monitoring tools.

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### LLMClient

A caching and debugging proxy server for LLM users.

#### Top 4 features:

1. Multi-LLM Support: It supports various language models, including OpenAI's GPT models, Anthropic's Claude, Azure's AI models, Google's AI Text models, and more.

2. Function (API) Calling with Reasoning (CoT): Enables language models to reason through tasks and interact with external data via API calls. This includes built-in functions like a code interpreter.

3. Detailed Debug Logs and Troubleshooting Support: Provides tools for debugging, including comprehensive logs and a Web UI for tracing and metrics.

4. Long Term Memory and Vector DB Support (Built-in RAG): Supports long-term memory for maintaining context in conversations and retrieval-augmented generation (RAG) with vector database support for enhanced query responses.

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### GPTCache

A semantic cache for LLMs that fully integrates with LangChain and llama_index.

#### Top 4 features:

1. Semantic Caching: Utilizes semantic analysis to cache similar queries, enhancing efficiency and reducing redundant API calls to language models.

2. Modular Design: Offers flexibility in integrating various components like LLM adapters, multimodal adapters, and embedding generators for customized caching solutions.

3. Support for Multiple LLMs and Multimodal Models: Compatible with a range of large language models and multimodal models, facilitating broad application scenarios.

4. Diverse Storage and Vector Store Options: Supports a variety of cache storage systems and vector stores, allowing for scalable and adaptable cache management.

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