
yams
Content addressable storage with excellent search
Stars: 347

YAMS (Yet Another Memory System) is a persistent memory system designed for Large Language Models (LLMs) and applications. It provides content-addressed storage with features such as deduplication, compression, full-text search, and vector search. The system is built with SHA-256 content-addressed store, block-level deduplication, full-text search using SQLite FTS5, semantic search with embeddings, WAL-backed durability, high-throughput I/O, and thread-safe operations. YAMS supports Linux x86_64/ARM64 and macOS x86_64/ARM64 platforms. It is recommended to build using Conan for managing dependencies and ensuring proper installation. Users can interact with YAMS through a command-line interface for tasks like initialization, adding content, searching, and retrieving data. Additionally, YAMS provides LLM-friendly patterns for caching web content, storing code diffs, and integrating with other systems through an API in C++. Troubleshooting tips include creating a default Conan profile and handling PDF support issues during the build process. The project is licensed under Apache-2.0.
README:
Persistent memory for LLMs and apps. Content‑addressed storage with dedupe, compression, full‑text and vector search.
- SHA‑256 content‑addressed storage
- Block‑level dedupe (Rabin)
- Full‑text search (SQLite FTS5) + semantic search (embeddings)
- WAL‑backed durability, high‑throughput I/O, thread‑safe
- Portable CLI and MCP server
- Extensible with Plugin Support
- SourceHut: https://sr.ht/~trvon/yams/
- GitHub mirror: https://github.com/trvon/yams
- Docs: https://yamsmemory.ai
- Discord: https://discord.gg/n7maE5MuhY
Supported platforms: Linux x86_64/ARM64, macOS x86_64/ARM64
# 1. Resolve dependencies
conan install . -of build/release -s build_type=Release -b missing
# 2. Configure
meson setup build/release \
--prefix /usr/local \
--native-file build/release/build-release/conan/conan_meson_native.ini \
--buildtype=release
# 3. Build
meson compile -C build/release
# 4. (Optional) Install
sudo meson install -C build/release
Dependencies quick ref:
- Linux: libssl-dev sqlite3 libsqlite3-dev protobuf-compiler libncurses-dev ninja-build cmake
- macOS (Homebrew): openssl@3 protobuf sqlite3 ncurses ninja cmake
- Export
OPENSSL_ROOT_DIR=$(brew --prefix openssl@3)
if CMake cannot locate OpenSSL
- Export
Common build options (Meson): -Dbuild-tests=true|false
, -Denable-tui=true|false
, -Denable-onnx=enabled|disabled|auto
, -Dplugin-onnx=true|false
, -Dyams-version=...
.
Fast iteration: set FAST_MODE=1
when running meson setup --reconfigure
to disable ONNX & tests in CI (SourceHut) or locally.
Media metadata: install mediainfo
+ dev package (e.g. libmediainfo-dev
) or FFmpeg (ffprobe
) to enable richer video parsing.
Further build documentation:
- GCC specifics / quick reference:
docs/BUILD-GCC.md
- Developer build system & internal ONNX Runtime path:
docs/developer/build_system.md
ONNX embeddings (experimental):
- The ONNX plugin is experimental and may not work as intended.
- Conan: default profiles enable
yams/*:enable_onnx=True
. With custom profiles, pass-o yams/*:enable_onnx=True
toconan install
. - Plain CMake: configure with
-DYAMS_ENABLE_ONNX=ON
and ensureonnxruntime
is discoverable (e.g., viaCMAKE_PREFIX_PATH
). - Verify configure logs include:
ONNX Runtime found - enabling local embedding generation
(and not disabled). - Internal newer ORT (GenAI headers) path: run Conan with
-o yams/*:use_conan_onnx=False
and configure with-DYAMS_BUILD_INTERNAL_ONNXRUNTIME=ON
(see developer build doc for details).
Note: Plain CMake without Conan may miss dependencies; prefer Conan builds.
export YAMS_STORAGE="$HOME/.local/share/yams"
yams init --non-interactive
# add
echo hello | yams add - --tags demo
# search
yams search hello --limit 5
# get
yams list --format minimal --limit 1
# set storage per-run
yams --data-dir /tmp/yams add -
# list (minimal for pipes)
yams list --format minimal
# fuzzy search
yams search database --fuzzy --similarity 0.8
# delete preview
yams delete --pattern "*.log" --dry-run
YAMS loads optional plugins via a stable C‑ABI host with a simple trust policy.
- Trust file:
~/.config/yams/plugins_trust.txt
(one absolute path per line; default deny) - Discovery order:
-
YAMS_PLUGIN_DIR
(exclusive override) $HOME/.local/lib/yams/plugins
-
/usr/local/lib/yams/plugins
,/usr/lib/yams/plugins
${CMAKE_INSTALL_PREFIX}/lib/yams/plugins
-
- Disable plugin subsystem: start daemon with
--no-plugins
.
ONNX plugin build/install/runtime:
- Prerequisite: onnxruntime (headers + shared libraries) must be available at build and runtime.
- Build:
yams_onnx_plugin
is built whenonnxruntime
is found and ONNX is enabled. - Install: plugin installs under
${CMAKE_INSTALL_LIBDIR}/yams/plugins
(e.g.,/usr/local/lib/yams/plugins
). - Packaging: set
-DYAMS_PACKAGE_PLUGINS=ON
(default) and runcpack
to include the plugin in binary packages. - Discovery: daemon logs a line on startup with plugin scan directories (useful for troubleshooting).
Usage (CLI):
# scan, trust, load
yams plugin scan
yams plugin trust add /usr/local/lib/yams/plugins
yams plugin load /usr/local/lib/yams/plugins/libyams_onnx_plugin.so
First‑time setup with yams init
:
- The init dialog asks whether to enable plugins; if yes, it creates and trusts
~/.local/lib/yams/plugins
. - Non‑interactive: pass
--enable-plugins
.
Dev overrides:
- Set
YAMS_PLUGIN_DIR
to your build output (e.g.,.../build/.../plugins/onnx
) to have the daemon scan it.
Behavior:
- If a trusted plugin advertises
model_provider_v1
, the daemon prefers it for embeddings. Otherwise it falls back to the legacy registry or mock/null providers (env:YAMS_USE_MOCK_PROVIDER
,YAMS_DISABLE_ONNX
).
yams serve # stdio transport
MCP config (example):
{
"mcpServers": { "yams": { "command": "/usr/local/bin/yams", "args": ["serve"] } }
}
Conan: create default profile
conan profile detect --force
PDF support issues: build with -DYAMS_ENABLE_PDF=OFF
.
Plugins not listed by yams plugin list
:
- Ensure the ONNX plugin exists in a scanned directory (install prefix or
~/.local/lib/yams/plugins
). - Ensure the directory is trusted (
yams plugin trust add <dir>
or viayams init
). - Ensure onnxruntime shared libs are resolvable by the loader (e.g.,
ldd libyams_onnx_plugin.so
). - Check the daemon startup log for:
Plugin scan directories: dir1;dir2;...
to confirm discovery paths.
Monitor with yams stats --verbose
and yams doctor
.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for yams
Similar Open Source Tools

yams
YAMS (Yet Another Memory System) is a persistent memory system designed for Large Language Models (LLMs) and applications. It provides content-addressed storage with features such as deduplication, compression, full-text search, and vector search. The system is built with SHA-256 content-addressed store, block-level deduplication, full-text search using SQLite FTS5, semantic search with embeddings, WAL-backed durability, high-throughput I/O, and thread-safe operations. YAMS supports Linux x86_64/ARM64 and macOS x86_64/ARM64 platforms. It is recommended to build using Conan for managing dependencies and ensuring proper installation. Users can interact with YAMS through a command-line interface for tasks like initialization, adding content, searching, and retrieving data. Additionally, YAMS provides LLM-friendly patterns for caching web content, storing code diffs, and integrating with other systems through an API in C++. Troubleshooting tips include creating a default Conan profile and handling PDF support issues during the build process. The project is licensed under Apache-2.0.

Flowise
Flowise is a tool that allows users to build customized LLM flows with a drag-and-drop UI. It is open-source and self-hostable, and it supports various deployments, including AWS, Azure, Digital Ocean, GCP, Railway, Render, HuggingFace Spaces, Elestio, Sealos, and RepoCloud. Flowise has three different modules in a single mono repository: server, ui, and components. The server module is a Node backend that serves API logics, the ui module is a React frontend, and the components module contains third-party node integrations. Flowise supports different environment variables to configure your instance, and you can specify these variables in the .env file inside the packages/server folder.

facefusion-docker
FaceFusion Docker is an industry leading face manipulation platform that provides a seamless way to manipulate faces in images and videos. The repository offers Docker containers for CPU, CUDA, TensorRT, and ROCm environments, allowing users to easily set up and run the platform. Users can access different containers through specific ports to browse and interact with the face manipulation features. The platform is designed to be user-friendly and efficient for various face manipulation tasks.

docutranslate
Docutranslate is a versatile tool for translating documents efficiently. It supports multiple file formats and languages, making it ideal for businesses and individuals needing quick and accurate translations. The tool uses advanced algorithms to ensure high-quality translations while maintaining the original document's formatting. With its user-friendly interface, Docutranslate simplifies the translation process and saves time for users. Whether you need to translate legal documents, technical manuals, or personal letters, Docutranslate is the go-to solution for all your document translation needs.

orbit
ORBIT (Open Retrieval-Based Inference Toolkit) is a middleware platform that provides a unified API for AI inference. It acts as a central gateway, allowing you to connect various local and remote AI models with your private data sources like SQL databases, vector stores, and local files. ORBIT uses a flexible adapter architecture to connect your data to AI models, creating specialized 'agents' for specific tasks. It supports scenarios like Knowledge Base Q&A and Chat with Your SQL Database, enabling users to interact with AI models seamlessly. The tool offers a RESTful API for programmatic access and includes features like authentication, API key management, system prompts, health monitoring, and file management. ORBIT is designed to streamline AI inference tasks and facilitate interactions between users and AI models.

rust-genai
genai is a multi-AI providers library for Rust that aims to provide a common and ergonomic single API to various generative AI providers such as OpenAI, Anthropic, Cohere, Ollama, and Gemini. It focuses on standardizing chat completion APIs across major AI services, prioritizing ergonomics and commonality. The library initially focuses on text chat APIs and plans to expand to support images, function calling, and more in the future versions. Version 0.1.x will have breaking changes in patches, while version 0.2.x will follow semver more strictly. genai does not provide a full representation of a given AI provider but aims to simplify the differences at a lower layer for ease of use.

crssnt
crssnt is a tool that converts RSS/Atom feeds into LLM-friendly Markdown or JSON, simplifying integration of feed content into AI workflows. It supports LLM-optimized conversion, multiple output formats, feed aggregation, and Google Sheet support. Users can access various endpoints for feed conversion and Google Sheet processing, with query parameters for customization. The tool processes user-provided URLs transiently without storing feed data, and can be self-hosted as Firebase Cloud Functions. Contributions are welcome under the MIT License.

LEANN
LEANN is an innovative vector database that democratizes personal AI, transforming your laptop into a powerful RAG system that can index and search through millions of documents using 97% less storage than traditional solutions without accuracy loss. It achieves this through graph-based selective recomputation and high-degree preserving pruning, computing embeddings on-demand instead of storing them all. LEANN allows semantic search of file system, emails, browser history, chat history, codebase, or external knowledge bases on your laptop with zero cloud costs and complete privacy. It is a drop-in semantic search MCP service fully compatible with Claude Code, enabling intelligent retrieval without changing your workflow.

candle-vllm
Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

obsei
Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

wzry_ai
This is an open-source project for playing the game King of Glory with an artificial intelligence model. The first phase of the project has been completed, and future upgrades will be built upon this foundation. The second phase of the project has started, and progress is expected to proceed according to plan. For any questions, feel free to join the QQ exchange group: 687853827. The project aims to learn artificial intelligence and strictly prohibits cheating. Detailed installation instructions are available in the doc/README.md file. Environment installation video: (bilibili) Welcome to follow, like, tip, comment, and provide your suggestions.

yomitoku
YomiToku is a Japanese-focused AI document image analysis engine that provides full-text OCR and layout analysis capabilities for images. It recognizes, extracts, and converts text information and figures in images. It includes 4 AI models trained on Japanese datasets for tasks such as detecting text positions, recognizing text strings, analyzing layouts, and recognizing table structures. The models are specialized for Japanese document images, supporting recognition of over 7000 Japanese characters and analyzing layout structures specific to Japanese documents. It offers features like layout analysis, table structure analysis, and reading order estimation to extract information from document images without disrupting their semantic structure. YomiToku supports various output formats such as HTML, markdown, JSON, and CSV, and can also extract figures, tables, and images from documents. It operates efficiently in GPU environments, enabling fast and effective analysis of document transcriptions without requiring high-end GPUs.

listen
Listen is a Solana Swiss-Knife toolkit for algorithmic trading, offering real-time transaction monitoring, multi-DEX swap execution, fast transactions with Jito MEV bundles, price tracking, token management utilities, and performance monitoring. It includes tools for grabbing data from unofficial APIs and works with the $arc rig framework for AI Agents to interact with the Solana blockchain. The repository provides miscellaneous tools for analysis and data retrieval, with the core functionality in the `src` directory.

aim
Aim is a command-line tool for downloading and uploading files with resume support. It supports various protocols including HTTP, FTP, SFTP, SSH, and S3. Aim features an interactive mode for easy navigation and selection of files, as well as the ability to share folders over HTTP for easy access from other devices. Additionally, it offers customizable progress indicators and output formats, and can be integrated with other commands through piping. Aim can be installed via pre-built binaries or by compiling from source, and is also available as a Docker image for platform-independent usage.

openai-edge-tts
This project provides a local, OpenAI-compatible text-to-speech (TTS) API using `edge-tts`. It emulates the OpenAI TTS endpoint (`/v1/audio/speech`), enabling users to generate speech from text with various voice options and playback speeds, just like the OpenAI API. `edge-tts` uses Microsoft Edge's online text-to-speech service, making it completely free. The project supports multiple audio formats, adjustable playback speed, and voice selection options, providing a flexible and customizable TTS solution for users.

dexto
Dexto is a lightweight runtime for creating and running AI agents that turn natural language into real-world actions. It serves as the missing intelligence layer for building AI applications, standalone chatbots, or as the reasoning engine inside larger products. Dexto features a powerful CLI and Web UI for running AI agents, supports multiple interfaces, allows hot-swapping of LLMs from various providers, connects to remote tool servers via the Model Context Protocol, is config-driven with version-controlled YAML, offers production-ready core features, extensibility for custom services, and enables multi-agent collaboration via MCP and A2A.
For similar tasks

metaflow
Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

ck
Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

client-python
The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

ComposeAI
ComposeAI is an Android & iOS application similar to ChatGPT, built using Compose Multiplatform. It utilizes various technologies such as Compose Multiplatform, Material 3, OpenAI Kotlin, Voyager, Koin, SQLDelight, Multiplatform Settings, Coil3, Napier, BuildKonfig, Firebase Analytics & Crashlytics, and AdMob. The app architecture follows Google's latest guidelines. Users need to set up their own OpenAI API key before using the app.

aiohttp-devtools
aiohttp-devtools provides dev tools for developing applications with aiohttp and associated libraries. It includes CLI commands for running a local server with live reloading and serving static files. The tools aim to simplify the development process by automating tasks such as setting up a new application and managing dependencies. Developers can easily create and run aiohttp applications, manage static files, and utilize live reloading for efficient development.

minefield
BitBom Minefield is a tool that uses roaring bit maps to graph Software Bill of Materials (SBOMs) with a focus on speed, air-gapped operation, scalability, and customizability. It is optimized for rapid data processing, operates securely in isolated environments, supports millions of nodes effortlessly, and allows users to extend the project without relying on upstream changes. The tool enables users to manage and explore software dependencies within isolated environments by offline processing and analyzing SBOMs.

gez
Gez is a high-performance micro frontend framework based on ESM. It uses Rspack compilation and maps modules to URLs with strong caching and content-based hashing. Gez embraces modern micro frontend architecture by leveraging ESM and importmap for dependency management, providing reliable isolation with module scope, seamless integration with any modern frontend framework, intuitive development experience, and optimal performance with zero runtime overhead and reliable caching strategies.

forge
Forge is a powerful open-source tool for building modern web applications. It provides a simple and intuitive interface for developers to quickly scaffold and deploy projects. With Forge, you can easily create custom components, manage dependencies, and streamline your development workflow. Whether you are a beginner or an experienced developer, Forge offers a flexible and efficient solution for your web development needs.
For similar jobs

db2rest
DB2Rest is a modern low-code REST DATA API platform that simplifies the development of intelligent applications. It seamlessly integrates existing and new databases with language models (LMs/LLMs) and vector stores, enabling the rapid delivery of context-aware, reasoning applications without vendor lock-in.

mage-ai
Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

airbyte
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

airflow
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

airbyte-platform
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's low-code Connector Development Kit (CDK). Airbyte is used by data engineers and analysts at companies of all sizes to move data for a variety of purposes, including data warehousing, data analysis, and machine learning.

chronon
Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.