A list of 13 open-source software for building and managing production-ready AI applications. The tools cover various aspects of AI development, including LLM tool integration, vector databases, RAG pipelines, model training and deployment, LLM routing, data pipelines, AI agent monitoring, LLM observability, and AI app development.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
Hugging Face introduces a unified tool use API across multiple model families, making it easier to implement tool use in language models.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
A curated list of awesome tools and libraries for large language models.
This page lists various tools that can be integrated with LangChain, categorized by their functionalities. These tools range from search engines and code interpreters to API connectors and data manipulation tools.
- Composio: Streamline agent development with tool integrations.
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
This article introduces Google's top AI applications, providing a guide on how to start using them, including Google Gemini, Google Cloud, TensorFlow, Experiments with Google, and AI Hub.
This article guides you through the process of building a simple agent in LangChain using Tools and Toolkits. It explains the basics of Agents, their components, and how to build a Mathematics Agent that can perform simple mathematical operations.
