Search Results for "databases"
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Showing 31 open source projects for "databases"
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1
Eidos
An extensible framework for Personal Data Management
...The system transforms SQLite into a flexible personal database that can store structured and unstructured information such as notes, documents, datasets, and knowledge resources. Its interface is inspired by tools like Notion, allowing users to create documents, databases, and custom views to organize personal information. Unlike cloud-based knowledge tools, Eidos runs entirely on the user’s machine, ensuring privacy and high performance through local storage. The platform integrates large language models to enable AI-assisted features such as summarizing documents, translating content, and interacting with stored data conversationally. ... -
2
Wiseflow
Enhance any agent's browser use skill
...This automated workflow helps reduce the noise associated with large information ecosystems and highlights the most important insights for users. Wiseflow can automatically categorize extracted content, assign tags, and upload processed results into databases or knowledge systems for further use. -
3
WeChatMsg
Project aimed at extracting, exporting, and analyzing chat records
WeChatMsg repository hosts an open-source project aimed at extracting, exporting, and analyzing chat records from the WeChat messaging platform. It provides tools that read local WeChat database files and allow users to convert chat data into readable formats such as HTML, Word, and CSV, making it possible to inspect conversations outside the mobile app environment. Beyond simple export, the project includes mechanisms for analyzing chat histories and generating annual reports or visual... -
4
Fast MCP
A Ruby Implementation of the Model Context Protocol
...By abstracting much of the underlying infrastructure, fast-mcp enables rapid prototyping of AI-enabled applications that can interact with external systems such as databases, APIs, or file systems. The project emphasizes performance and simplicity, making it suitable for both small prototypes and production deployments. -
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5
Anyquery
Query anything (GitHub, Notion, +40 more) with SQL and let LLMs
Anyquery is an open-source SQL query engine designed to allow users to query data from almost any source using a unified SQL interface. The system enables developers and analysts to run SQL queries on files, APIs, applications, and databases without needing separate connectors or query languages for each platform. Built on top of SQLite, the engine uses a plugin architecture that allows it to extend support to dozens of external services and data sources. Users can query structured files such as CSV, JSON, and Parquet as well as remote data sources like SaaS APIs, cloud storage services, and local applications. ... -
6
yt-fts
Search all of YouTube from the command line
...The tool returns search results with timestamps and direct links to the exact moment in the video where the phrase occurs. In addition to traditional keyword search, the system supports experimental semantic search capabilities using embeddings from AI services and vector databases. This allows users to search videos by meaning rather than only exact keywords. -
7
LangChain for Java
LangChain4j is an open-source Java library
LangChain for Java is an open-source Java framework designed to simplify the development of applications powered by large language models. The library provides a unified API that allows developers to connect Java applications to multiple AI providers and embedding databases without having to implement separate integrations for each service. Its architecture includes abstractions for prompts, chat interactions, document processing, embeddings, and vector storage, enabling developers to build complex AI workflows with minimal boilerplate code. LangChain4j also implements common design patterns used in generative AI systems, such as retrieval-augmented generation pipelines, tool calling, and intelligent agent frameworks. ... -
8
Dynamiq
An orchestration framework for agentic AI and LLM applications
...The framework focuses on simplifying the creation of complex AI workflows that involve multiple agents, retrieval systems, and reasoning steps. Instead of building each component manually, developers can use Dynamiq’s structured APIs and modular architecture to connect language models, vector databases, and external tools into cohesive pipelines. The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
9
STORM
An LLM-powered knowledge curation system that researches topics
STORM is an open-source virtual assistant framework developed by Stanford's OVAL lab. It is designed for creating natural language interfaces and assistants that can interact with APIs, databases, and services in a modular way. -
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10
Langflow
Low-code app builder for RAG and multi-agent AI applications
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database. -
11
Aix-DB
Based on the LangChain/LangGraph framework
...The platform supports multiple types of data sources and provides an end-to-end pipeline that includes intent recognition, SQL generation, database execution, and visual presentation of results. Its architecture includes multiple layers such as a web interface, API gateway, AI service layer, and data storage layer that support relational databases, vector stores, graph databases, and file systems. -
12
Langtrace
Open Telemetry based end-to-end observability tool for LLM apps
Langtrace is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations, and metrics for popular LLMs, LLM frameworks, vectors, and more.. Integrate using Typescript, and Python. Langtrace is an open-source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps. -
13
SmythOS
Cloud-native runtime for agentic AI
...It provides a foundational infrastructure layer that functions similarly to an operating system for agentic AI systems, managing resources such as language models, storage, vector databases, and caching through a unified interface. Developers can use the runtime to create, deploy, and orchestrate intelligent agents across local machines, cloud environments, or hybrid infrastructures without rewriting their application logic. The platform includes a software development kit and command-line interface that allow developers to define agent workflows, manage execution environments, and automate deployment processes. ... -
14
ReCall
Learning to Reason with Search for LLMs via Reinforcement Learning
...The project builds on earlier work focused on teaching models how to search for information during reasoning tasks and extends that idea to a broader system where models can call a variety of external tools such as APIs, databases, or computation engines. Instead of relying purely on static knowledge stored inside the model, ReCall allows the language model to dynamically decide when it should retrieve information or invoke external capabilities during the reasoning process. The framework uses reinforcement learning to train models to perform these tool calls effectively while solving multi-step reasoning tasks. -
15
CAG
Cache-Augmented Generation: A Simple, Efficient Alternative to RAG
CAG, or Cache-Augmented Generation, is an experimental framework that explores an alternative architecture for integrating external knowledge into large language model responses. Traditional retrieval-augmented generation systems rely on real-time retrieval of documents from databases or vector stores during inference. CAG proposes a different approach by preloading relevant knowledge into the model’s context window and precomputing the model’s key-value cache before queries are processed. This strategy allows the model to generate responses using the cached context directly, eliminating the need for repeated retrieval operations during runtime. ... -
16
Vanna 2.0
Chat with your SQL database
Vanna is an open-source Python framework that enables natural language interaction with databases by converting user questions into executable SQL queries using large language models. The framework uses a retrieval-augmented generation architecture that learns from database schemas, documentation, and past query examples to generate accurate queries tailored to a specific dataset. Vanna can be integrated into many environments, including notebooks, web applications, messaging platforms, and data dashboards, making it flexible for analytics and data exploration workflows. ... -
17
Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
Neuron AI is a PHP agentic framework for building production-ready AI applications that connect models, memory, vector databases, and tools into working agents. It is designed for developers who want to create systems such as RAG pipelines, multi-agent workflows, and business process automations without having to hand-build every integration from scratch. The framework provides an Agent class that can be extended to inherit core capabilities like memory, tools, function calling, and retrieval-augmented generation. ... -
18
LangChain Rust
LangChain for Rust, the easiest way to write LLM-based programs
LangChain Rust is an open-source Rust implementation inspired by the LangChain ecosystem for building applications powered by large language models. The library aims to provide Rust developers with a structured framework for orchestrating prompts, chains, agents, and external tools within LLM-driven workflows. By adapting LangChain concepts to the Rust programming language, the project emphasizes performance, safety, and efficient memory management. Developers can use the framework to build... -
19
Slack MCP Server
The most powerful MCP Slack Server with no permission requirements
...MCP is a standardized protocol that allows large language models and AI agents to securely interact with external tools and data sources such as messaging platforms, databases, or file systems. The slack-mcp-server acts as an intermediary layer that exposes Slack data and messaging functionality to AI clients while enforcing access rules and communication standards. Through this architecture, AI assistants can read message histories, interact with channels, and retrieve contextual information from Slack conversations in order to perform tasks such as automated analysis, collaboration assistance, or contextual code review. ... -
20
Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
...The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. Developers define data transformations and AI operations using computed columns on tables, allowing pipelines to evolve incrementally as new data or models are added. The framework supports multimodal content including images, video, text, and audio, enabling applications such as retrieval-augmented generation systems, semantic search, and multimedia analytics. -
21
AgentGuide
AI Agent Development Guide, LangGraph in Action, Advanced RAG
AgentGuide is an open-source learning resource designed to provide a structured pathway for understanding and building AI agents. The project aggregates tutorials, research papers, frameworks, and practical resources related to agent development with large language models. Instead of presenting scattered resources, the repository organizes them into a systematic learning roadmap that guides learners from foundational concepts to advanced AI agent systems. The guide covers topics such as... -
22
LLMSurvey
A Survey of Large Language Models
...By structuring the literature in a navigable format, LLMSurvey allows researchers and practitioners to quickly explore important publications in the field without manually searching through multiple databases. -
23
RAG from Scratch
Demystify RAG by building it from scratch
...Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently. -
24
dataline
AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake
dataline is an open-source AI data analysis and visualization platform that allows users to interact with datasets using natural language. The system enables both technical and non-technical users to explore data by asking questions conversationally, which the platform translates into database queries and analytical operations. It supports connections to multiple structured data sources such as PostgreSQL, MySQL, Snowflake, SQLite, Excel files, CSV datasets, and other database systems. Once... -
25
trench
Open-Source Analytics Infrastructure
...The system is built on top of high-performance data technologies including Apache Kafka and ClickHouse, which allows it to ingest and process very large volumes of events while maintaining fast query performance. It was originally developed to solve scaling challenges in product analytics systems where traditional relational databases become inefficient as event tables grow. The platform enables developers to collect events such as page views, user actions, and behavioral metrics while storing them in a column-oriented analytics database optimized for time-series workloads. By combining streaming ingestion with fast analytical queries, the system supports use cases such as product analytics dashboards, observability pipelines, and machine learning data preparation.
