Laravel MongoDB 5.0: Major Release With Exciting New Features!

Rishabh Bisht
September 13, 2024 | Updated: July 21, 2025

The PHP team is happy to announce that version 5.0 of the Laravel MongoDB integration is now available!

This is a major release that introduces breaking changes to align the MongoDB integration with the Laravel framework’s conventions and behaviors, making it easy for developers to use MongoDB when they are already familiar with Eloquent.

Highlights

ID aliasing: We have introduced automatic aliasing of id to _id in MongoDB. This eliminates the need for specifying protected $primaryKey = '_id' in model classes, simplifying your code. Replace $model->_id with $model->id in your code.
Date handling enhancements: All DateTimeInterface objects, including Carbon, are now automatically converted to MongoDB’s UTCDateTime for insert and update operations. Similarly, when retrieving data, MongoDB’s UTCDateTime is automatically converted back into Carbon date with the default timezone. This change simplifies working with dates in MongoDB, reducing the need for custom Eloquent casts.
Results as objects: Query results from MongoDB will now be returned as stdClass objects instead of arrays. The object properties can now be accessed using $item->name rather than $item['name'].
Simplified model customization: $collection has been replaced by $table for customizing the collection name in Eloquent models, aligning with Laravel convention.
Provider removals: Several MongoDB-specific service providers have been removed, including Auth\PasswordResetServiceProvider, MongoDBQueueServiceProvider, and Queue\Failed\MongoFailedJobProvider. These providers are no longer necessary, as MongoDB\Connection is now fully compatible with Laravel’s classes.
Connection and logging updates: MongoDB\Laravel\Connection has been replaced with MongoDB\Connection, and the query logging now uses MongoDB Monitoring Events.

Installation

This library may be installed or upgraded with:

composer require mongodb/laravel-mongodb:^5.0

Refer to the upgrade guide to handle the breaking changes.

Resources

Documentation and other resources to get you started with Laravel and the MongoDB database are shared below:

Give it a try today and let us know what you think! Feedback on this release is welcome in the GitHub discussion, as we continue to improve and enhance the integration.

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AI Agents, Hybrid Search, and Indexing with LangChain and MongoDB

Since we announced integration with LangChain last year, MongoDB has been building out tooling to help developers create advanced AI applications with LangChain . With recent releases, MongoDB has made it easier to develop agentic AI applications (with a LangGraph integration), perform hybrid search by combining Atlas Search and Atlas Vector Search , and ingest large-scale documents more effectively. For more on each development—plus new support for the LangChain Indexing API—please read on! Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. The rise of AI agents Agentic applications have emerged as a compelling next step in the development of AI. Imagine an application able to act on its own, working towards complicated goals and drawing on context to create a strategy. These applications leverage large language models (LLMs) to dynamically determine their execution path, breaking free from the constraints of traditional, deterministic logic. Consider an application tasked with answering a question like "In our most profitable market, what is the current weather?" While a traditional retrieval-augmented generation (RAG) app may falter, unable to obtain information about “current weather,” an agentic application shines. The application can intelligently deduce the need for an external API call to obtain current weather information, seamlessly integrating this with data retrieved from a vector search to identify the most profitable market. These systems take action and gather additional information with limited human intervention, supplementing what they already know. Building such a system is easier than ever thanks to MongoDB’s continued work with LangGraph. Unleashing the power of AI agents with LangGraph and MongoDB Because it now offers LangGraph—a framework for performing multi-agent orchestration—LangChain is more effective than ever at simplifying the creation of applications using LLMs, including AI agents. These agents require memory to maintain context across multiple interactions, allowing users to engage with them repeatedly while the agent retains information from previous exchanges. While basic agentic applications can utilize in-memory structures, for more complicated use cases these structures are not sufficient. MongoDB allows developers to build stateful, multi-actor applications with LLMs, storing and retrieving the “checkpoints” needed by LangGraph.js. The new MongoDBSaver class makes integration simpler than ever before, as LangGraph.js is able to utilize historical user interactions to enhance agentic AI. By segmenting this history into checkpoints, the library allows for persistent session memory, easier error recovery, and even the ability to “time travel”—allowing users to jump back in the graph to a previous state to explore alternative execution. The MongoDBSaver class implements all of this functionality right into LangGraph.js, with sensible defaults and MongoDB-specific optimization. To learn more, please visit the source code , the documentation , and our new tutorial (which includes both a written and video version). Improve retrieval accuracy with Hybrid Search Retriever Hybrid search is particularly well-suited for queries that have both semantic and keyword-based components. Let’s look at an example, a query such as "find recent scientific papers about climate change impacts on coral reefs that specifically mention ocean acidification". This query would use a hybrid search approach, combining semantic search to identify papers discussing climate change effects on coral ecosystems, keyword matching to ensure "ocean acidification" is mentioned, and potential date-based filtering or boosting to prioritize recent publications. This combination allows for more comprehensive and relevant results than either semantic or keyword search alone could provide. With our recent release of Retrievers in LangChain-MongoDB, building such advanced retrieval patterns is more accessible than ever. Retrievers are how LangChain integrates external data sources into LLM applications. MongoDB has added two new custom, purpose-built Retrievers to the langchain-mongodb Python package, giving developers a unified way to perform hybrid search and full-text search with sensible defaults and extensive code annotation. These new classes make it easier than ever to use the full capabilities of MongoDB Vector Search with LangChain. The new MongoDBAtlasFullTextSearchRetriever class performs full-text searches using the Best Match 25 (BM25) analyzer. The MongoDBAtlasHybridSearchRetriever class builds on this work, combining the above implementation with vector search, fusing the results with Reciprocal Rank Fusion (RRF) algorithm. The combination of these two techniques is a potent tool for improving the retrieval step of a Retrieval-Augmented Generation (RAG) application, enhancing the quality of the results. To find out more, please dive into the MongoDBAtlasHybridSearchRetriever and MongoDBAtlasFullTextSearchRetriever classes. Seamless synchronization using LangChain Indexing API In addition to these releases, we’re also excited to announce that MongoDB now supports the LangChain Indexing API, allowing for seamless loading and synchronization of documents from any source into MongoDB, leveraging LangChain's intelligent indexing features. This new support will help users avoid duplicate content, minimize unnecessary rewrites, and optimize embedding computations. The LangChain Indexing API's record management system ensures efficient tracking of document writes, computing hashes for each document, and storing essential information like write time and source ID. This feature is particularly valuable for large-scale document processing and retrieval applications, offering flexible cleanup modes to manage documents effectively in MongoDB vector search. To read more about how to use the Indexing API, please visit the LangChain Indexing API Documentation . We’re excited about these LangChain integrations and we hope you are too. Here are some resources to further your learning: Check out our written and video tutorial to walk you through building your own JavaScript AI agent with LangGraph.js and MongoDB. Experiment with Hybrid search retrievers to see the power of Hybrid search for yourself. Read the previous announcement with LangChain about Semantic Caching.

September 12, 2024

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That’s a Wrap: MongoDB’s 2025 in Review & 2026 Predictions

It’s nearly the end of the year—again! That means it’s time for an end-of-year blog post that expresses disbelief at the passage of time. Which, as the saying goes, flies when you’re having fun. And definitely when you’re as busy as MongoDB was in 2025. It was a big year for the company—and more importantly, for the tens of thousands of customers and millions of developers who rely on MongoDB’s modern data platform for their most mission-critical workloads. At MongoDB, everything we do starts with our obsession with customers and their needs, and if there’s a theme to MongoDB’s 2025, it was (and will continue to be) enabling customer innovation and helping them succeed in the AI era. So here are a few highlights of how MongoDB acted on behalf of customers in 2025. From the acquisition of Voyage AI to customer success across industries, a lot happened in 2025. Let’s go!* *Read to the end for 2026 thoughts. 2025: The (MongoDB) year that was Voyage AI, modernization, and search In February, MongoDB announced the acquisition of Voyage AI, a pioneer in embedding and reranking models, to enhance the accuracy of AI applications. Integrating Voyage AI's advanced retrieval technology with MongoDB’s modern, AI-ready data platform addresses a critical challenge: LLM model hallucinations caused by a lack of context. By improving retrieval accuracy for specialized domains like finance and law, the integration enables businesses to deploy AI for mission-critical use cases. To learn more, see the MongoDB Voyage AI page. Then, in September, we launched MongoDB AMP, an AI-powered Application Modernization Platform. AMP is designed to accelerate the transformation of legacy applications through a combination of AI-powered tooling, a proven delivery framework, and expert guidance (tools, techniques, and talent) to help enterprises reduce technical debt and modernize 2-3 times faster. Want more? Sure you do! Check out this short video. MongoDB also announced the addition of search and vector search capabilities to MongoDB Community Edition and MongoDB Enterprise Server. This allows developers to build and test AI-native applications, including those using retrieval-augmented generation (RAG), in local or on-premises environments. Previously exclusive to MongoDB Atlas, these features enable secure, hybrid deployments where sensitive data can remain on-premises while still leveraging advanced search tools. Here’s a (slightly less short) video about search and vector search on Enterprise Server. Growing and scaling with MongoDB As noted, everything we do at MongoDB starts with our obsession with customers. 2025 was another banner year for customer success and innovation—we were inspired by what organizations of every shape and size, across industries and geographies, built with MongoDB in 2025. Here are just two of the many stories our customers shared in 2025; much more can be found in my colleague Katie Palmer’s blog series, Innovating with MongoDB. Factory By combining the Atlas modern data platform with Voyage AI’s high-performance embeddings, the AI-native startup Factory—which uses AI agents called Droids to accelerate software development lifecycles for organizations—consolidated its fragmented tech stack. This enabled superior code retrieval, simplified operations, and provided the scalability needed to process billions of tokens daily. McKesson McKesson, a global pharmaceutical distributor, replaced its monolithic legacy infrastructure with MongoDB Atlas to meet strict drug tracing mandates. By adopting our modern cloud data platform, McKesson scaled its operations 300x, managing tracking data for 1.2 billion containers annually without latency, and ensuring compliance and patient safety while reducing developer complexity. For more, check out the video of McKesson at MongoDB.local NYC from September. From niche NoSQL to enterprise powerhouse As senior MongoDB engineer and Technical Fellow Ashish Kumar put it earlier this year, “through a sustained and deliberate engineering effort,” MongoDB has gone from a (seemingly) niche NoSQL solution to a trusted enterprise standard, and now delivers “the high availability, tunable consistency, ACID transactions, and robust security that enterprises demand.” A new era of leadership The face of MongoDB has also changed—our CFO, Mike Berry, joined the company in April, and Dev Ittycheria stepped down as CEO in November, after more than 11 years leading the company (including its 2017 IPO). In a LinkedIn post about his role, new MongoDB CEO CJ Desai noted that the company is “at the forefront of a new data revolution, unlocking the next wave of productivity and intelligence.” “Having spent my career building and scaling technology platforms, I’ve always been drawn to companies defined by clarity of vision, relentless organic innovation, and a customer-first culture. MongoDB exemplifies all three,” said Desai. We couldn’t agree more. Onward! Reading the 2026 tea leaves So what might 2026 bring (for MongoDB and tech at large)? Here are a handful of our leaders’ predictions: “As much as people want to talk about Artificial General Intelligence (AGI), we’re still in the phase where most AI use cases automate redundant tasks but benefit from human-in-the-loop checks. Organizations that use AI to complete work that historically is a drain on human resources—but then uses people to carefully verify what AI builds, apply governance frameworks, and maintain accountability across the data lifecycle—will be more successful.” —Pete Johnson, Field CTO, AI, MongoDB “After years of inflated expectations and unsustainable spending, the AI industry is trapped in a bubble where companies reflexively attempt to deploy LLMs at every problem, driving up costs with minimal to no return. Businesses that break free from this spending cycle are the ones that understand the need to ground LLM responses in factual data and learn from prior mistakes. We believe the best way to do this will be with highly accurate embedding models and rerankers for reliable data retrieval.” —Frank Liu, Staff Product Manager, MongoDB "In 2026, cloud independence will evolve from strategic preference to existential imperative across enterprises of every scale. The outages and disruptions of recent years have exposed a fundamental truth: in an always-on digital economy—where commerce, mobility, governance, and even public safety depend on uninterrupted access to cloud services—single-provider reliance is no longer a calculated risk, but a systemic vulnerability. Compounding this is the inexorable rise of data sovereignty. Regulatory regimes worldwide now demand precise jurisdictional control over data residency, rendering rigid cloud commitments incompatible with compliance at global scale. The defining competitive advantage will belong to organizations that transcend fragile prevention theater and engineer true infrastructural resilience: architectures inherently portable, data frictionlessly mobile, and operations autonomously sustained across heterogeneous clouds through AI-orchestrated redundancy. In short, the winners will not merely mitigate downtime—they will design systems that render the concept obsolete." —Ben Cefalo, SVP, Head of Core Products, MongoDB Happy holidays and happy New Year, everyone!

December 22, 2025