The 10 Coolest Open-Source Software Tools Of 2025

Here’s a look at 10 open-source software tools—including software for developing AI agentic applications, managing streams of observability data, organizing data within massive data lakehouses, and building 3D animations—that’s caught our attention this year.

Keeping An Open Mind

The popularity of open-source software continues to grow because of the multiple advantages they provide including lower upfront software and hardware costs, lower total-cost-of-ownership, lack of vendor lock-in, simpler license management and support from active communities.

In the following slides we take a look at some of the most popular open-source software products that caught our attention in 2025. Some of these have been around for some time and are already widely used while others are relatively new.

Not surprisingly, the wave of AI and generative AI application development is a major driver for new open-source software products and adoption. Some of the products on this list are in the software development space or help answer the need to manage the huge volumes of data that feed AI systems.

These products are available under open-source licenses such as the MIT License, Apache 2.0 License, GNU GPL, and others. Many of them are managed by community organizations that oversee the products’ ongoing development from contributors. Others are developed by startups that offer commercial editions and services of their products in addition to open-source versions of their software.

Apache Iceberg

Apache Iceberg is an open-source, high-performance table format designed for large-scale, analytical data workloads, particularly for data lake and data lakehouse system architectures, according to the iceberg.apache.org website.

What’s the problem being addressed? As businesses and organizations look to scale up data analytics and data lake systems and develop ways to provision AI applications, AI agents and large language models with data, they find themselves hindered by siloed data, scattered across hybrid cloud environments and locked up in databases with incompatible data formats.

Apache Iceberg has quickly become a critical standard for businesses and organizations developing a data lake or data mesh strategy, according to data cloud company Snowflake, an Iceberg supporter. It provides the foundation for a unified, flexible data architecture that promises interoperability, performance, and ease of use.

Iceberg offers features such as schema evolution, time travel and hidden partitioning, enabling reliable and efficient data management across different query engines (including Spark, Trino, Flink and others.)

Apache Iceberg has become a de facto industry standard and is supported by just about every leading vendor in the big data space including Amazon Web Services, Cloudera, Databricks, Google Cloud, Oracle, Qlik, Snowflake and others.

Apache Iceberg is licensed under the Apache License 2.0. Apache Iceberg v3 Table Specification was ratified by the Iceberg community this past spring.

Apache Wayang

Analyzing data that’s distributed across many sources is a major challenge for businesses today. Apache Wayang is a cross-platform data processing framework that the Apache Software Foundation (ASF) just elevated to a top-level project.

Wayang integrates and orchestrates multiple data processing systems to provide flexibility and performance for complex data applications, according to the ASF. It unifies diverse data engines (such as Spark, Flink databases and Python) into a single system, allowing developers to write logic once and run it anywhere.

With its platform-agnostic APIs, Wayang (previously known as Rheem) decouples applications from specific systems and simplifies complex analytics across distributed data sources as if they were one. Its query optimizer automatically selects the best execution engine or combination of engines for a specific task to maximize performance.

Wayang is available under the Apache 2.0 license.

Blender

Blender is a free and open-source 3D animation software suite that can be used to create animation video, video games and even interactive applications.

Blender provides tools for a range of tasks including 3D modeling, animation visual effects and more, according to the blender.org website that describes the software as “a powerful tool used by individuals and studios alike.”

Key features and capabilities include 3D modeling, animation and rigging, rendering, simulation, video editing, motion tracking and compositing. (Rendering is the compute-intensive process of converting a 3D model into animation.)

Blender 5.0, released on November 18, offers a number of significant new capabilities and enhancements including enhanced geometry nodes, color management with ACES/HDR support, major user interface improvements, better modeling and UV tools, performance boosts for large meshes, and compositor presets for easier workflows, among others.

Blender has been around for 30 years – version 1.0 launched in January 1995. But the software is having its moment in the spotlight: Blender was used as the rendering tool in the creation of the highly acclaimed animated feature “Flow” that won both the Academy Award: Best Animated Feature Film and Golden Globe: Best Motion Picture – Animated award earlier this year.

Blender is owned by its contributors and is licensed under the GNU General Public License.

DuckDB

DuckDB is a column-oriented relational database that’s designed to handle complex OLAP (online analytical processing) queries and process large datasets.

DuckDB was developed for embedded use and runs within an application’s process to make it easy to set up and to avoid network overhead while running. It uses its own feature-rich SQL dialect for queries and offers advanced features such as vectorized data processing.

DuckDB supports multiple file formats (CSV, Parquet and JSON) and data lake formats, according to the DuckDB.org website. It runs on all popular operating systems and hardware architectures and connects to network and cloud storage.

First released in 2019, DuckDB was originally developed Mark Raasveldt and Hannes Muhleisen at the Centrum Wiskunde & Informatica in the Netherlands. Version 1.0.0 debuted in June 2024, and its continued development is overseen by DuckDB Labs.

The software is available through GitHub under the MIT License. The most recent version, DuckDB 1.4.3 LTS, was released Dec. 9, 2025.

Eidolon AI

There’s nothing hotter in the IT industry right now than AI agents. So it’s no surprise that Eidolon AI, an open-source platform designed to simplify the development and deployment of AI agents within enterprise environments, is getting noticed.

Eidolon AI provides a modular, pluggable agent SDK (software development kit) for building agentic applications and a built-in HTTP server for agent deployment, “enabling developers to efficiently create agent-based applications,” according to the eidolonai.com website.

Eidolon AI is designed to treat AI agents as services—a concept that facilitates the creation of complex AI systems with multiple interacting agents. The platform’s modular architecture allows for easy component swapping, according to eidolanai.com, making it possible to customize agents with different large language models (LLMs), RAG (retrieval augmented generation) implementations, and other tools without extensive rewrites.

The system also offers pre-built agents. In addition to the built-in HTTP server, agents can be directly deployed to Kubernetes.

Eidolon AI is available under the Apache 2.0 open-source license.

LangChain

The value of generative AI applications can be hampered by the fact that the large language models that power them are limited to the data they were trained on. The real value of AI is realized when businesses and organizations can get their own data into the language models and generate unique content.

LangChain is an open-source framework that makes it possible to connect large language models with external data sources, providing tools and abstractions that businesses and organizations use to leverage their own proprietary data for AI applications and AI agents without the need to retrain the LLMs.

Because it offers a pre-built agent architecture, prompt templates, Python libraries, integrations to hundreds of LLMs and integrations with hundreds of other development tools, databases, APIs and more, LangChain simplifies and speeds up the development of AI applications and agents.

While LangChain was initially released in 2022, LangChain 1.0, the first major stable released became generally available on Oct. 22, 2025. LangChain is managed by the LangChain Community and is freely available under the open-source MIT License.

MCP Toolbox for Databases

With development of AI applications, agents and other software happening so rapidly, a growing problem is how best to integrate these new AI systems with other IT systems and data sources.

One increasingly popular answer is Model Text Protocol (MCP), an open standard developed by Anthropic for connecting AI applications, including agents and chatbots, with external tools and data sources.

MCP Toolbox for Databases is an open-source MCP server that allows developers to easily and safely connect AI systems and their large language models to structured data repositories usingh a standardized protocol, eliminating the need to develop custom integrations.

With MCP Toolbox for Databases, AI applications and agents can be linked to a range of SQL databases, including PostgreSQL and MySQL, to access data for operational and analytical tasks.

Previously known as Gen AI Toolbox for Databases, MCP Toolbox for Databases was developed by Google, which now makes it available under the Apache 2.0 open-source license. It is available through GitHub where it has been riding high on the website’s trending list.

Mistral Devstral

Devstral is an open-source language model that is purpose-built for developing AI agentic applications.

Unveiled on May 21, Devstral is the result of a collaborative effort between Mistral AI, one of the industry’s leading AI model developers, and All Hands AI, a startup that offers tools that automate routine developer tasks for building AI agents.

Devstral is based on Mistral-Small-3.1. One of the biggest advantages of Devstral is its lightweight design – it operates with just 24 billion parameters. The model is capable of operating on a single Nvidia RTX 4090 GPU and can run on a laptop computer.

Another key capability: Devstral can process substantial amounts of code and instructions at a time, according to a DigitalOcean review, allowing it to handle complex problems with large codebases.

Devstral is designed to act as a full software engineering agent, optimized for integration into agentic frameworks like OpenHands, SWE-Agent and OpenDevin. The model also has a 128k context window. All this means that Devstral is capable of such tasks as navigating large codebases, resolving complicated issues and generating code, according to Mistral.

Devstral is available under the Apache 2.0 license.

OpenTelemetry

IT estates today sprawl across on-premises data centers and multiple cloud platforms. That makes the chore of collecting information from the applications that operate across those environments—a critical step for monitoring and managing application performance—increasingly complex.

OpenTelemetry is an open-source framework that standardizes the collection, processing and export of observability data (including log, metric, and trace data) from applications. Its proponents say it offers a vendor-neutral way for IT managers to gain insights into their IT systems by providing a collector and consistent APIs and SDKs that can send telemetry data to a compatible backend observability system for analysis.

Critical OpenTelemetry components include the OpenTelemetry Collector, a vendor-agnostic proxy for receiving, processing and exporting telemetry data, and the OpenTelemetry Protocol standard for transmitting telemetry data.

Many of the leading developers of observability platforms, including Datadog, Dynatrace, LogicMonitor, New Relic and Splunk (a Cisco company), support OpenTelemetry and some contribute to its development.

The OpenTelemetry project is managed by the Cloud Native Computing Foundation, part of the nonprofit Linux Foundation, and it is listed on the cncf.io/projects website as an incubating project. Core OpenTelemetry components are available under the Apache 2.0 license.

Vortex

Vortex is an extensible, next-generation columnar data storage file format and toolkit that its developers say is designed to handle the high-throughput data processing demands of today’s AI workloads.

The Vortex technology was developed by former Palantir and Citadel engineers who founded Spiral, a New York-based startup that is developing data infrastructure—including the SpiralDB next-generation database—for managing and processing multimodal data. (Spiral officially launched in September with $22 million in seed and Series A funding, the latter led by the General Catalyst venture capital firm.)

In August, Spiral donated the Vortex technology to the LF AI & Data Foundation, part of the Linux Foundation, which announced that Vortex is a new incubation-stage project. The project includes contributions and support from other industry giants including Microsoft, Palantir and Snowflake.

Vortex backers say that legacy data storage file formats, such as Apache Parquet, were designed for structured data analysis tasks and can’t meet the processing demands of today’s AI workloads.

Vortex, according to the LF AI & Data Foundation, “bridges the gap between cloud storage and heterogeneous compute” with its ability to handle data across memory, disk and network file formats while maintaining compression throughout. Vortex is optimized to support multimodal data, wide schemas, GPU-based training workloads, and high performance reads from cloud object stores such as Amazon S3 and Google Cloud Storage.

Vortex is available under the Apache 2.0 license, according to the GitHub Vortex page.