Open Source Database Projects
Databases organize and manage information so applications and people can store, retrieve, update, and analyze it reliably. They address challenges such as handling growing data volumes, supporting concurrent access, preserving consistency, and making information searchable. Different designs suit different needs, from structured records and transactions to rapid lookups, distributed data, and large-scale analytics.
Open source database tools include database engines, embedded storage libraries, query interfaces, migration utilities, monitoring systems, and administration tools. When choosing one, consider its maturity, license, maintenance activity, data model, performance characteristics, deployment requirements, backup options, and integrations with your existing stack. These tools are useful to application developers, data teams, system administrators, and organizations seeking control over how their data is stored and managed.
84 repositories · updated October 14, 2026
ai-observer: Monitor AI Coding Assistant Usage Locally
AI Observer is a self-hosted OpenTelemetry backend and dashboard for tracking usage across local AI coding assistants. It brings token and cost data, traces, logs, and metrics together in DuckDB, with both OTLP ingestion and file-based import or watching for supported tools.
ActiveHealth 38/100
agentfs: Store and Audit AI Agent State in SQLite
AgentFS stores an AI agent’s files, key-value state, and tool-call history in a SQLite database. Use its SDK or CLI when you need portable, queryable agent state and filesystem-level isolation.
QuietHealth 36/100
magic-context: Manage Coding Agent Context and Memory
Magic Context manages coding-agent context and project memory across sessions, reducing reliance on disruptive compaction and manual note-taking. It is designed for developers using OpenCode, Pi, or Oh My Pi who want persistent, searchable project knowledge.
ActiveHealth 71/100
OpenViking: Organize Agent Memory, Knowledge, and Skills
OpenViking is a context database for AI agents that organizes knowledge, memories, and skills in a browsable virtual filesystem. It supports scoped retrieval and agent integrations, with options to self-host or use a hosted service.
ActiveHealth 83/100
graphrag: Build Graph-Powered Retrieval and Chat
TigerGraph GraphRAG combines a TigerGraph knowledge graph and vector search with LLMs to answer questions over documents and structured data. It suits teams already using TigerGraph who want a self-hosted chat and ingestion application, including an optional agentic retrieval engine.
ActiveHealth 35/100
code-session-memory: Search AI Coding Sessions Across Tools
code-session-memory automatically indexes coding-agent conversations into a shared vector database for semantic search. It suits developers who want to retrieve decisions and solutions across OpenCode, Claude Code, Cursor, VS Code, Codex, and Gemini CLI sessions.
MaintainedHealth 32/100
computer: Give Agents a Durable Filesystem and Execution Runtime
Cloudflare Computer gives agents a durable workspace backed by Durable Object SQLite and a choice of container or Worker execution. It suits prototypes that need persistent files and flexible execution, but the project is preview-only and not for production use.
NewHealth 57/100
cortex-mem: Give AI Agents Persistent Long-Term Memory
Cortex Memory is a Rust framework for storing, extracting, and searching long-term memories for AI agents and applications. It combines durable file-based storage with semantic search, and offers CLI, REST API, and MCP access.
ActiveHealth 31/100
mrq: Run Distributed Python Task Queues
MRQ is a Python task queue that uses Redis, MongoDB, and gevent to schedule and run distributed jobs. It suits teams needing queue management, worker visibility, and parallel execution for mixed I/O- and CPU-bound tasks.
InactiveHealth 29/100
mixer: Generate Test Data for Python Models
Mixer creates test instances with generated values for Django, SQLAlchemy, and other Python object models. It suits developers replacing hand-written fixtures with configurable test data that can be saved to a database or kept in memory.
InactiveHealth 26/100
fake2db: Generate Test Databases with Fake Data
fake2db generates databases populated with realistic-looking data for development and testing. It supports several SQL and NoSQL backends, with options for row counts, locales, repeatable seeds, and custom fields.
InactiveHealth 25/100
mimesis: Generate Realistic Fake Data in Python
Mimesis generates realistic synthetic data across languages and locales for Python development and testing. Use it to create test records, API fixtures, sample datasets, or structured and relational data.
ActiveHealth 49/100