Latest free open-source software collection:

Tiledesk

Tiledesk is a free, open-source alternative to Intercom . Key features Open-source no-code platform for building AI agents and automating customer support. Combines live chat, helpdesk, and chatbot functionality in a single solution. Drag-and-drop visual builder for creating conversational flows without code. Offers self-hosted and cloud deployment options. Platforms Linux, macOS, Windows Links Official website: https://www.tiledesk.com/ Source code: https://github.com/Tiledesk/tiledesk Pricing Freemium. Free self-hosted version under GPL-3.0 with unlimited users and conversations, plus a free cloud tier with basic features.

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Paperclip

Key features Org chart for AI agent teams Budget management for AI agent operations Governance controls for AI agent teams Links Project website Pricing Free

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Mem0

Key features Provides a persistent memory layer designed for AI agents and applications. Enables storing, retrieving, and managing user memories across multiple sessions. Supports semantic memory capabilities to help agents recall relevant context over time. Offers self-hostable open-source infrastructure for developer use. Platforms: Windows, macOS, Linux Links Official website: mem0.ai Source code: github.com/mem0ai/mem0 Pricing: Completely free and open-source under Apache-2.0; self-host and use without any licensing costs.

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LobeChat

Key features Open-source, modern AI chat interface for managing, building, and running AI agent teams. Works across multiple LLM providers, letting you switch or combine models in one place. Supports self-hosting for full control over your data and deployment. Extensible via plugins to add custom tools and capabilities. Rich feature set covering chat organization, agent management, and team workflows. Platforms: Windows, macOS, Linux Links Official website: https://lobehub.com/ Source code: https://github.com/lobehub/lobe-chat Pricing Free and fully open-source under Apache-2.0. You can self-host it or use the public demo.

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LiveKit

Key features Full-stack platform for building real-time voice, video, and AI agent applications. Built on WebRTC technology for low-latency browser and native communication. SFU-based architecture for scalable real-time media routing. Client SDKs for Windows, macOS, Linux, Android, and iOS. Open-source core server and SDKs under the Apache-2.0 license. Self-hostable alternative to proprietary real-time communication services like Twilio. Platforms: Windows, macOS, Linux, Android, iOS Links Official website: https://livekit.io Source code: https://github.com/livekit/livekit Pricing Free. The core LiveKit server and SDKs are open source under Apache-2.0, allowing self-hosting and unrestricted use.

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LiteLLM

Key features Unified Python library and gateway for calling 100+ LLM APIs, including OpenAI, Anthropic, and Azure. Built-in authentication for gateway access control. Load balancing across multiple LLM providers and models. Spend tracking to monitor API usage and costs. Lightweight and open-source, designed as a single interface for LLM API calls. Platforms: Windows, macOS, Linux Links Official website: https://www.litellm.ai Source code: https://github.com/BerriAI/litellm Pricing Free. The core is open-source under the MIT License, offering a complete LLM gateway with unified API, authentication, load balancing, and spend tracking at no cost.

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LibreChat

Key features Unifies multiple AI models — including OpenAI, Anthropic, and others — into a single conversation platform. Supports plugins to extend functionality. Enables sharing conversations and configurations. Self-hosted deployment for full control over your data and setup. Platforms: Windows, macOS, Linux Links Official website Source code repository Pricing Free. Fully free and open-source under the MIT license; self-host your own instance with no usage limits.

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Letta

Key features Open-source platform for building, deploying, and managing AI agents. Persistent memory lets agents retain context across conversations. Python framework for creating and customizing agents. Self-hostable server for full control over deployment. Licensed under Apache-2.0. Platforms: Windows, macOS, Linux. Links Official website: https://www.letta.com Source code: https://github.com/letta-ai/letta Pricing Freemium. A free, open-source self-hosted version is available under Apache-2.0, and Letta Cloud also offers a free tier.

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Langflow

Key features Visual, drag-and-drop builder for creating AI agents and RAG pipelines. Designed for LLM workflow orchestration without heavy coding. Supports building agent-based and retrieval-augmented generation applications. Platforms: Windows, macOS, Linux. Links Official website: https://www.langflow.org Source code: https://github.com/langflow-ai/langflow Pricing: Free. Open source under Apache 2.0; self-host for free.

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Hexabot

Key features Self-hosted platform for building AI agents, chatbots, and automation workflows — a free, open-source alternative to ManyChat. Deploy your chatbots across multiple messaging channels from a single interface. Design automation workflows to handle complex, multi-step user interactions. Maintain full control over your data and infrastructure thanks to self-hosting. Completely free to use with no feature limitations under the GPL-3.0 license. Platforms: Linux Links Official website: https://hexabot.ai Source code: https://github.com/hexabot-ai/hexabot Pricing: Free. Completely free and open-source under the GPL-3.0 license; self-host without limitations.

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Dify

Key features Build AI agents and RAG (retrieval-augmented generation) pipelines through a visual interface. Deploy the resulting AI agents and RAG pipelines from the same platform. Free Community Edition available under a custom license. The custom license restricts offering multitenant LLM app development platforms as a service. Source code hosted publicly on GitHub. Links Official website: https://dify.ai Source code repository: https://github.com/langgenius/dify Pricing Freemium. The Dify Community Edition is free to use under a custom license, but the license restricts offering multitenant LLM app development platforms as a service.

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Botpress

Key features Open-source platform for building AI agents and chatbots powered by large language models. Drag-and-drop flow design for creating conversational workflows without heavy coding. Full source code available for customization and self-hosting. Runs on Windows, macOS, and Linux. Platforms: Windows, macOS, Linux. Links: Official website · Source code Pricing: Freemium. The self-hosted open-source version is available under the MIT license and includes all core features.

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Beam

Beam is a free, open-source software / service you can self-host or use without paying. Key features Commercial serverless GPU computing platform designed for running AI and compute workloads without managing infrastructure. Offers sub-second cold starts, enabling fast scaling of GPU-backed applications. Operates as a cloud service rather than a self-hosted or open-source solution. Available under a freemium tier, allowing limited use at no cost. Links Official website: https://beam.cloud Pricing Freemium. Note that there is no open-source offering — this is a paid cloud service.

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Agno

Agno is a free, open-source alternative to Microsoft Copilot Studio . Key features Lightweight, model-agnostic Python library for building intelligent AI agents. Built-in support for memory, knowledge, tools, and reasoning. Multimodal framework capable of handling diverse input and output types. Free and open-source core under the MPL-2.0 license, with an optional paid cloud service. Platforms: Windows, macOS, Linux Links Official website: https://agno.com Source code: https://github.com/agno-ai/agno Pricing Freemium. The core library is free and open-source under the MPL-2.0 license; optional Agno Cloud paid services are available.

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Arize Phoenix

Arize Phoenix is a free, open-source alternative to LangSmith . Key features Open-source LLM tracing and evaluation platform for AI applications. Monitor, debug, and optimize LLM workflows with built-in observability. Self-hosted deployment for full control over tracing and evaluation data. Supports experimentation with LLM tracing and evaluation using the Phoenix library. Designed for machine-learning and AI observability use cases. Platforms Windows, macOS, Linux Links Official website Source code repository Pricing Free. The free plan includes self-hosted LLM tracing, evaluation, and experimentation with the open-source Phoenix library.

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Agenta

Agenta is a free, open-source alternative to LangSmith . Key features Build, evaluate, and improve AI agents and LLM applications in a single collaborative platform. Manage prompts with versioning and organization tools for LLM workflows. Run evaluations to test and compare AI agent and LLM outputs. Gain observability into LLM application behavior and performance. Support LLMOps practices for teams developing with large language models. Platforms Windows, macOS, Linux Links Official website: https://agenta.ai Source code: https://github.com/Agenta-AI/agenta Pricing Free. Free and open-source self-hosted platform under Apache-2.0 license.

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ACI.dev

ACI.dev is a free, open-source software / service you can self-host or use without paying. Build reliable AI agents with unified tool integration

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Parlant

Parlant is a free, open-source software / service you can self-host or use without paying. Structured control layer for customer-facing AI agents

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CodeWhale

CodeWhale is a free, open-source alternative to Claude Code . Codewhale Codewhale is an open source coding agent for your terminal, built in Rust and improved in public with the people who use it. 简体中文 · 日本語 · Tiếng Việt · Bahasa Indonesia · 한국어 · Español · Português · Русский · Українська · Français · Deutsch · 繁體中文 · हिन्दी · Türkçe · Italiano · Polski · العربية · Català Install npm install -g codewhale codewhale The first run helps you connect a provider or stay offline. Codewhale also supports Cargo, Docker, Nix, Scoop, prebuilt archives, Android/Termux, and a CNB mirror. See the installation guide . Use Talk to Codewhale the same way you would talk to a teammate: Fix the failing tests and explain what changed. Or run a task without opening the TUI: codewhale exec " fix the failing tests and explain what changed " Codewhale can read your repository, edit files, run commands, inspect results, and keep working toward a goal. You decide how much access it has. Why Codewhale Use the model you want. Connect hosted providers or local models through Ollama, vLLM, or SGLang. Switch provider and model with /model . Stay in control. Plan is read-only. Ask, Auto-Review, and Full Access make approval behavior visible. /undo reverts the last turn and /restore returns the workspace to an earlier snapshot. Keep long work organized. Save sessions, set a durable /goal , review workflows before they run, and coordinate agents without turning their internal instructions into your transcript. Extend the agent you already have. Connect MCP servers and skills, configure hooks, and keep agent roles as readable files in your project or personal settings. Run /help in the TUI for commands and keyboard shortcuts. Safety Codewhale runs on your machine with the access you grant it. Approval modes and repository rules limit what the agent may do; optional OS sandboxing adds a stronger execution boundary where supported. Unknown model prices stay unknown instead of being reported as free. Read authorization order for the exact policy stack and configuration for local settings. Documentation Providers and local models Agent teams MCP , hooks , and configuration Local web client All documentation Join the community Codewhale gets better when people use it, report what feels wrong, and help fix it. If a provider is missing, a workflow is awkward, or the terminal UI gets in your way, open an issue . If you know how to improve it, open a pull request . First contributions are welcome, and contributors keep credit for the work that lands. Join the Discord , or add Hunter on WeChat ( hunterbown ) and ask to join the Whale Brothers group. Project history Codewhale began as deepseek-tui and still preserves that configuration and session compatibility. It is now provider-neutral and independently maintained; it is not affiliated with any model provider. Thanks to every contributor and to the open source communities that helped the project grow. See the contributor record . License MIT

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redis

redis is a free, open-source alternative to Redis Enterprise . This document serves as both a quick start guide to Redis and a detailed resource for building it from source. New to Redis? Start with What is Redis and Getting Started Ready to build from source? Jump to Build Redis from Source Want to contribute? See the Code contributions section and CONTRIBUTING.md Looking for detailed documentation? Navigate to redis.io/docs Table of contents What is Redis? Key use cases Why choose Redis? What is Redis Open Source? Getting started Redis starter projects Using Redis with client libraries Using Redis with redis-cli Using Redis with Redis Insight Redis data types, processing engines, and capabilities Cloud hosted Redis Community Build Redis from source Install dependencies and build Building Redis - flags and general notes Fixing build problems with dependencies or cached build options Fixing problems building 32 bit binaries Allocator Monotonic clock Verbose build Running Redis with TLS Code contributions Redis Trademarks What is Redis? For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine. Key use cases Redis excels in various applications, including: Caching: Supports multiple eviction policies, key expiration, and hash-field expiration. Distributed Session Store: Offers flexible session data modeling (string, JSON, hash). Data Structure Server: Provides low-level data structures (strings, lists, sets, hashes, sorted sets, JSON, etc.) with high-level semantics (counters, queues, leaderboards, rate limiters) and supports transactions & scripting. NoSQL Data Store: Key-value, document, and time series data storage. Search and Query Engine: Indexing for hash/JSON documents, supporting vector search, full-text search, geospatial queries, ranking, and aggregations via Redis Search. Event Store & Message Broker: Implements queues (lists), priority queues (sorted sets), event deduplication (sets), streams, and pub/sub with probabilistic stream processing capabilities. Vector Store for GenAI: Integrates with AI applications (e.g. LangGraph, mem0) for short-term memory, long-term memory, LLM response caching (semantic caching), and retrieval augmented generation (RAG). Real-Time Analytics: Powers personalization, recommendations, fraud detection, and risk assessment. Why choose Redis? Redis is a popular choice for developers worldwide due to its combination of speed, flexibility, and rich feature set. Here's why people choose Redis for: Performance: Because Redis keeps data primarily in memory and uses efficient data structures, it achieves extremely low latency (often sub-millisecond) for both read and write operations. This makes it ideal for applications demanding real-time responsiveness. Flexibility: Redis isn't just a key-value store, it provides native support for a wide range of data structures and capabilities listed in What is Redis? Extensibility: Redis is not limited to the built-in data structures, it has a modules API that makes it possible to extend Redis functionality and rapidly implement new Redis commands Simplicity: Redis has a simple, text-based protocol and well-documented command set Ubiquity: Redis is battle tested in production workloads at a massive scale. There is a good chance you indirectly interact with Redis several times daily Versatility : Redis is the de facto standard for use cases such as: Caching: quickly access frequently used data without needing to query your primary database Session management: read and write user session data without hurting user experience or slowing down every API call Querying, sorting, and analytics: perform deduplication, full text search, and secondary indexing on in-memory data as fast as possible Messaging and interservice communication: job queues, message brokering, pub/sub, and streams for communicating between services Vector operations: Long-term and short-term LLM memory, RAG content retrieval, semantic caching, semantic routing, and vector similarity search In summary, Redis provides a powerful, fast, and flexible toolkit for solving a wide variety of data management challenges. If you want to know more, here is a list of starting points: Introduction to Redis data types The full list of Redis commands Redis for AI Redis documentation What is Redis Open Source? Redis Community Edition (Redis CE) was renamed Redis Open Source with the v8.0 release. Redis Ltd. also offers Redis Software , a self-managed software with additional compliance, reliability, and resiliency for enterprise scaling, and Redis Cloud , a fully managed service integrated with Google Cloud, Azure, and AWS for production-ready apps. Read more about the differences between Redis Open Source and Redis here . Getting started If you want to get up and running with Redis quickly without needing to build from source, use one of the following methods: Redis Cloud Official Redis Docker images (Alpine/Debian) docker run -d -p 6379:6379 redis:latest Redis binary distributions Snap Homebrew RPM Debian Redis quick start guides If you prefer to build Redis from source - see instructions below. Redis starter projects To get started as quickly as possible in your language of choice, use one of the following starter projects: Python (redis-py) C#/.NET (NRedisStack/StackExchange.Redis) Go (go-redis) JavaScript (node-redis) Java/Spring (Jedis) Using Redis with client libraries To connect your application to Redis, you will need a client library. Redis has documented client libraries in most popular languages, with community-supported client libraries in additional languages. Python (redis-py) Python (RedisVL) C#/.NET (NRedisStack/StackExchange.Redis) JavaScript (node-redis) Java (Jedis) Java (Lettuce) Go (go-redis) PHP (Predis) C (hiredis) Full list of client libraries Using Redis with redis-cli redis-cli is Redis' command line interface. It is available as part of all the binary distributions and when you build Redis from source. You can start a redis-server instance, and then, in another terminal try the following: cd src ./redis-cli redis> ping PONG redis> set foo bar OK redis> get foo "bar" redis> incr mycounter (integer) 1 redis> incr mycounter (integer) 2 redis> Using Redis with Redis Insight For a more visual and user-friendly experience, use Redis Insight - a tool that lets you explore data, design, develop, and optimize your applications while also serving as a platform for Redis education and onboarding. Redis Insight integrates Redis Copilot , a natural language AI assistant that improves the experience when working with data and commands. Redis Insight documentation Redis Insight GitHub repository Redis data types, processing engines, and capabilities Redis provides a variety of data types, processing engines, and capabilities to support a wide range of use cases: String: Sequences of bytes, including text, serialized objects, and binary arrays used for caching, counters, and bitwise operations. JSON: Nested JSON documents that are indexed and searchable using JSONPath expressions and with Redis Search Array: Sparse, index-addressable collection of string values Hash: Field-value maps used to represent basic objects and store groupings of key-value pairs with support for hash field expiration (TTL) Redis Search: Use Redis as a document database, a vector database, a secondary index, and a search engine. Define indexes for hash and JSON documents and then use a rich query language for vector search, full-text search, geospatial queries, and aggregations. List: Linked lists of string values used as stacks, queues, and for queue management. Set: Unordered collection of unique strings used for tracking unique items, relations, and common set operations (intersections, unions, differences). Sorted set: Collection of unique strings ordered by an associated score used for leaderboards and rate limiters. Vector set (beta): Collection of vector embeddings used for semantic similarity search, semantic caching, semantic routing, and Retrieval Augmented Generation (RAG). Geospatial indexes: Coordinates used for finding nearby points within a given radius or bounding box. Bitmap: A set of bit-oriented operations defined on the string type used for efficient set representations and object permissions. Bitfield: Binary-encoded strings that let you set, increment, and get integer values of arbitrary bit length used for limited-range counters, numeric values, and multi-level object permissions such as role-based access control (RBAC) Hyperloglog: A probabilistic data structure for approximating the cardinality of a set used for analytics such as counting unique visits, form fills, etc. * Bloom filter: A probabilistic data structure to check if a given value is present in a set. Used for fraud detection, ad placement, and unique column (i.e. username/email/slug) checks. * Cuckoo filter: A probabilistic data structure for checking if a given value is present in a set while also allowing limited counting and deletions used in targeted advertising and coupon code validation. * t-digest: A probabilistic data structure used for estimating the percentile of a large dataset without having to store and order all the data points. Used for hardware/software monitoring, online gaming, network traffic monitoring, and predictive maintenance. * Top-k: A probabilistic data structure for finding the most frequent values in a data stream used for trend discovery. * Count-min sketch: A probabilistic data structure for estimating how many times a given value appears in a data stream used for sales volume calculations. Time series: Data points indexed in time order used for monitoring sensor data, asset tracking, and predictive analytics Pub/sub : A lightweight messaging capability. Publishers send messages to a channel, and subscribers receive messages from that channel. Stream : An append-only log with random access capabilities and complex consumption strategies such as consumer groups. Used for event sourcing, sensor monitoring, and notifications. Transaction: Allows the execution of a group of commands in a single step. A request sent by another client will never be served in the middle of the execution of a transaction. This guarantees that the commands are executed as a single isolated operation. Programmability: Upload and execute Lua scripts on the server. Scripts can employ programmatic control structures and use most of the commands while executing to access the database. Because scripts are executed on the server, reading and writing data from scripts is very efficient. Cloud hosted Redis Fully-managed Redis with real-time performance at scale. Redis Cloud Community Redis Community Resources Build Redis from source This section refers to building Redis from source. If you want to get up and running with Redis quickly without needing to build from source see the Getting started section . These instructions apply to Redis 8.10 and above. For versions lower than 8.10, see the 8.8 build instructions . Configuration files : the build steps below tell you to run ./src/redis-server redis.conf . Release tarballs bake the bundled modules' loadmodule lines and per-module settings directly into redis.conf during packaging, so an extracted release tarball is ready to run as-is. When building from a git checkout instead, that module config lives in the auto-generated redis-full.conf produced by make modules-update (and regenerated by make sync-redis-conf ) — run ./src/redis-server redis-full.conf there. Edit Redis-core settings in redis.conf . See modules/MODULES.md for the full config flow. Install dependencies and build Building Redis with all data structures (JSON, time series, Bloom / cuckoo / count-min / top-k, t-digest, and the Query Engine) needs a build toolchain plus a few version-sensitive dependencies — GCC/Clang, LLVM 21 , CMake 3.25–3.31.6 , Rust 1.94 , OpenSSL, Python 3, and assorted -dev libraries. Instead of a per-OS package list, the repo installs them for you with make bootstrap , which detects your OS and installs each bundled module's prerequisites. CMake version range matters. The modules require 3.25 ≤ CMake ≤ 3.31.6 — CMake 4.x is not supported and the build will fail with it. On distros that ship CMake 4.x by default (e.g. Ubuntu 26.04), pin a supported version, e.g. pip3 install 'cmake==3.31.6' . Note make bootstrap only installs CMake when it's missing or too old; it won't downgrade a pre-installed 4.x, so remove/pin that yourself. 1. Get the source Either works — the release tarball already bundles the module sources; a git checkout needs one extra step to fetch them: # A) Release tarball (recommended for building/running a release). # Replace <version>, e.g. 8.10.0 — extracts into redis-<version>/: wget -O redis- < version > .tar.gz https://github.com/redis/redis/releases/download/ < version > /redis-full.tar.gz tar xvf redis- < version > .tar.gz && cd redis- < version > # B) git checkout — clone the bundled modules once: git clone https://github.com/redis/redis.git && cd redis make modules-update 2. Install the build dependencies Pick whichever option fits your environment: Build inside the Docker build environment — recommended. The repo ships docker/Dockerfile.noble (Ubuntu 24.04) with every prerequisite baked in, so you build inside the container and never touch your host toolchain: docker build -f docker/Dockerfile.noble -t redis-build:noble . # Multi-arch (requires `docker buildx` configured): docker buildx build --platform linux/amd64,linux/arm64 \ -f docker/Dockerfile.noble -t redis-build:noble . # Build with the working tree mounted: docker run --rm -it -v " $PWD " :/workspace -w /workspace redis-build:noble \ bash -lc ' make -j"$(nproc)" && make run ' Install everything on a fresh machine or container. On a clean environment (for example a throwaway ubuntu:24.04 container), let bootstrap install every prerequisite for Redis core and all cloned modules: make bootstrap ⚠️ make bootstrap installs system packages and may override existing versions of shared tools (compiler, CMake, LLVM, …). Prefer option 1, or run it in a disposable container, if that matters on your machine. See only what's missing. To inspect which prerequisites are absent before installing anything, print one deduped list across Redis core and all modules: make bootstrap list Then install just the reported packages yourself. (Version-gated deps are shown as name (>= X) ; optional test/coverage deps are listed separately and don't fail the check.) Get the exact install commands to copy-paste. To run exactly what make bootstrap would, but only for the missing dependencies, use dry-run — it prints the precise install command for each missing dependency and installs nothing: make bootstrap dry-run The commands are printed per module , so a dependency shared by several modules appears once for each. Work through them iteratively: Copy-paste the commands for a module to install its dependencies. Re-run make bootstrap dry-run — the deps you just installed no longer show, so you now see only what's still missing for the remaining modules. Repeat until make bootstrap dry-run prints no install commands. Manual, per-OS install (no Docker, and you'd rather not let make bootstrap touch your host): follow the per-OS dependency instructions in the 8.8 README, which still lists them explicitly — https://github.com/redis/redis/tree/8.8#readme . 3. Build and run export BUILD_TLS=yes # optional — TLS support (needs OpenSSL dev libs) make -j " $( nproc ) " # Release tarball (module config is baked into redis.conf): ./src/redis-server redis.conf # From a git checkout, use the auto-generated module config instead: ./src/redis-server redis-full.conf make (same as make build / make all ) builds whatever is cloned under modules/*/src alongside Redis core. To build just the core data structures — even with modules cloned — use make build redis . Building Redis - flags and general notes Redis can be compiled and used on Linux, OSX, OpenBSD, NetBSD, FreeBSD. We support big endian and little endian architectures, and both 32 bit and 64-bit systems. It may compile on Solaris derived systems (for instance SmartOS) but our support for this platform is best effort and Redis is not guaranteed to work as well as on Linux, OSX, and *BSD. To build Redis with all the data structures (including JSON, time series, Bloom filter, cuckoo filter, count-min sketch, top-k, and t-digest) and with Redis Query Engine, make sure first that all the prerequisites are installed (see Install dependencies and build above), then clone the bundled modules once and build: make modules-update make make (same as make build / make all ) always builds whatever's cloned under modules/*/src alongside Redis core — there's no separate flag to opt in. If nothing is cloned yet, you get a core-only build. To build Redis with just the core data structures — even if modules are already cloned — use: make build redis To build with TLS support, you need OpenSSL development libraries (e.g. libssl-dev on Debian/Ubuntu) and the following flag in the make command: make BUILD_TLS=yes To build with systemd support, you need systemd development libraries (such as libsystemd-dev on Debian/Ubuntu or systemd-devel on CentOS), and the following flag: make USE_SYSTEMD=yes To append a suffix to Redis program names, add the following flag: make PROG_SUFFIX= " -alt " You can build a 32 bit Redis binary using: make 32bit After building Redis, it is a good idea to test it using: make test If TLS is built, running the tests with TLS enabled (you will need tcl-tls installed): ./utils/gen-test-certs.sh ./runtest --tls Redis supports compression of replication stream via zstd as of 8.10. To build with compression support you have to install zstd development libraries (e.g libzstd-dev on Debian/Ubuntu) and use the following flag when invoking the make command: make BUILD_COMPRESSION=yes Fixing build problems with dependencies or cached build options Redis has some dependencies which are included in the deps directory. make does not automatically rebuild dependencies even if something in the source code of dependencies changes. When you update the source code with git pull or when code inside the dependencies tree is modified in any other way, make sure to use the following command in order to really clean everything and rebuild from scratch: make distclean This will clean: jemalloc, lua, hiredis, linenoise and other dependencies. Also, if you force certain build options like 32bit target, no C compiler optimizations (for debugging purposes), and other similar build time options, those options are cached indefinitely until you issue a make distclean command. Fixing problems building 32 bit binaries If after building Redis with a 32 bit target you need to rebuild it with a 64 bit target, or the other way around, you need to perform a make distclean in the root directory of the Redis distribution. In case of build errors when trying to build a 32 bit binary of Redis, try the following steps: Install the package libc6-dev-i386 (also try g++-multilib). Try using the following command line instead of make 32bit : make CFLAGS="-m32 -march=native" LDFLAGS="-m32" Allocator Selecting a non-default memory allocator when building Redis is done by setting the MALLOC environment variable. Redis is compiled and linked against libc malloc by default, except for jemalloc being the default on Linux systems. This default was picked because jemalloc has proven to have fewer fragmentation problems than libc malloc. To force compiling against libc malloc, use: make MALLOC=libc To compile against jemalloc on Mac OS X systems, use: make MALLOC=jemalloc Monotonic clock By default, Redis will build using the POSIX clock_gettime function as the monotonic clock source. On most modern systems, the internal processor clock can be used to improve performance. Cautions can be found here: http://oliveryang.net/2015/09/pitfalls-of-TSC-usage/ On ARM aarch64 systems, the hardware clock is enabled by default because the ARM Generic Timer is architecturally guaranteed to be available and monotonic on all ARMv8-A processors (see the “The Generic Timer in AArch64 state” section of the Arm Architecture Reference Manual for Armv8-A ). To build with support for the processor's internal instruction clock on other architectures, use: make CFLAGS= " -DUSE_PROCESSOR_CLOCK " Verbose build Redis will build with a user-friendly colorized output by default. If you want to see a more verbose output, use the following: make V=1 Running Redis with TLS Please consult the TLS.md file for more information on how to use Redis with TLS. Running Redis with the Query Engine and optional proprietary Intel SVS-VAMANA optimisations License Disclaimer If you are using Redis Open Source under AGPLv3 or SSPLv1, you cannot use it together with the Intel Optimizations (Leanvec and LVQ binaries). The reason is that the Intel SVS license is not compatible with those licenses. The Leanvec and LVQ techniques are closed source and are only available for use with Redis Open Source when distributed under the RSALv2 license. For more details, please refer to the information provided by Intel here . By default, Redis with the Redis Query Engine supports SVS-VAMANA index with global 8-bit quantisation. To compile Redis with the Intel SVS-VAMANA optimisations, LeanVec and LVQ, use the following: make BUILD_INTEL_SVS_OPT=yes Alternatively, you can export the variable before running the build step for your platform: export BUILD_INTEL_SVS_OPT=yes make Code contributions By contributing code to the Redis project in any form, including sending a pull request via GitHub, a code fragment or patch via private email or public discussion groups, you agree to release your code under the terms of the Redis Software Grant and Contributor License Agreement. Please see the CONTRIBUTING.md file in this source distribution for more information. For security bugs and vulnerabilities, please see SECURITY.md and the description of the ability of users to backport security patches under Redis Open Source 7.4+ under BSDv3. Open Source Redis releases are subject to the following licenses: Version 7.2.x and prior releases are subject to BSDv3. These contributions to the original Redis core project are owned by their contributors and licensed under the 3BSDv3 license as referenced in the REDISCONTRIBUTIONS.txt file. Any copy of that license in this repository applies only to those contributions; Versions 7.4.x to 7.8.x are subject to your choice of RSALv2 or SSPLv1; and Version 8.0.x and subsequent releases are subject to the tri-license RSALv2/SSPLv1/AGPLv3 at your option as referenced in the LICENSE.txt file. Redis Trademarks The purpose of a trademark is to identify the goods and services of a person or company without causing confusion. As the registered owner of its name and logo, Redis accepts certain limited uses of its trademarks, but it has requirements that must be followed as described in its Trademark Guidelines available at: https://redis.io/legal/trademark-policy/ .

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