Compare Libraries
See which libraries have better AI support across different models
Format: owner/repo — max 5 repositories
Knowledge cutoff: 2025-08-31
diesel
diesel-rs
A safe, extensible ORM and Query Builder for Rust
sea-orm
SeaQL
🐚 A powerful relational ORM for Rust
sqlx
launchbadge
🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
mongo-rust-driver
mongodb
The official MongoDB Rust Driver
serde
serde-rs
Serialization framework for Rust
Summary for GPT-5.2-Codex
| Library | Overall | Coverage | Adoption | Docs | AI Ready | Momentum | Maint. |
|---|---|---|---|---|---|---|---|
| B · 73 | 83 | 53 | 90 | 70 | 60 | 85 | |
| C · 69 | 83 | 66 | 100 | 40 | 80 | 75 | |
| C · 64 | 83 | 56 | 100 | 30 | 35 | 70 | |
| C · 64 | 83 | 57 | 85 | 30 | 60 | 85 | |
| C · 62 | 83 | 67 | 65 | 30 | 50 | 70 |
Score by LLM
See how each library scores across different AI models
| Library | GPT-5.2-Codex | Claude 4.5 Opus | Claude 4.5 Sonnet | Gemini 3 Pro |
|---|---|---|---|---|
| diesel | 73 | 72 | 72 | 71 |
| sea-orm | 69 | 68 | 68 | 67 |
| sqlx | 64 | 63 | 63 | 62 |
| mongo-rust-driver | 64 | 64 | 63 | 63 |
| serde | 62 | 61 | 61 | 60 |
AI Evaluation
Rust Data LibrariesGenerated 1/29/2026
The Rust data ecosystem in 2026 continues to be defined by the tension between strict compile-time safety and async-first developer ergonomics. While Diesel remains the definitive choice for type-safe relational mapping with its robust v2.2+ ecosystem, higher-level async ORMs like Sea-ORM have matured significantly, offering a more productive path for modern web architectures. Serde persists as the foundational serialization framework, maintaining its ubiquity despite its age, while specialized drivers like the official MongoDB Rust driver provide enterprise-grade reliability for non-relational workloads.
Recommendations by Scenario
New Projects
Sea-ORM offers the most comprehensive 'batteries-included' experience for new Rust projects, combining async-native performance with an intuitive API that significantly reduces the boilerplate typically associated with Rust database code. Its integration with SQLx provides a high-performance foundation while allowing developers to escape to raw SQL when necessary.
AI Coding
Diesel's strictly typed DSL and schema-driven architecture create a highly predictable environment for AI coding assistants. Because Diesel's queries are expressed through Rust's type system rather than raw strings, LLMs can leverage the compiler's own constraints to generate code that is far less prone to runtime errors or SQL injection vulnerabilities.
Migrations
With its mature CLI and proven track record in complex schema evolutions, Diesel remains the industry leader for long-term database maintenance. Its migration system is designed for stability and reliability, providing better guardrails for production environments that require zero-downtime updates and strict schema versioning.
Library Rankings
Enterprise applications where data integrity is the highest priority and teams are comfortable with a steeper learning curve to achieve maximum safety.
Strengths
- +Unrivaled compile-time type safety ensures that every query is validated against your database schema at build time.
- +A mature ecosystem with extensive community support, plugins, and deep integration with the broader Rust web stack.
- +Excellent support for complex relational patterns and custom SQL types, maintaining type safety throughout.
Weaknesses
- -High cognitive overhead for developers unfamiliar with its trait-heavy DSL; error messages can be intimidating for beginners.
- -The core library is synchronous, necessitating the use of specialized crates like 'diesel-async' for modern async-first projects.
Rapid application development and startups building modern web APIs where async performance and developer velocity are critical.
Strengths
- +First-class async support built on the battle-tested SQLx driver, enabling high-concurrency microservices with minimal effort.
- +Dynamic query builder allows for extremely flexible API design while still benefiting from Rust's type checks.
- +Industry-leading documentation that includes detailed guides, interactive examples, and clear migration paths from other libraries.
Weaknesses
- -Introduces a layer of abstraction that may carry a slight performance penalty compared to raw SQL execution.
- -Relatively high adoption of dynamic features can occasionally lead to less 'discoverable' code compared to Diesel's explicit schema mapping.
High-scale applications requiring horizontal distribution, real-time analytics, or flexible document-based data structures.
Strengths
- +Official maintenance by the MongoDB team guarantees early access to new database features and high-velocity security patches.
- +Seamless integration with Serde allows for idiomatic Rust data modeling with NoSQL flexibility.
- +Optimized for high-performance async workflows with native support for major runtimes like Tokio.
Weaknesses
- -Strictly limited to the MongoDB ecosystem, lacking the cross-database versatility of relational ORMs.
- -Dynamic schema model shifts more responsibility to the application layer to maintain data consistency over time.
Performance-sensitive services where developers want full control over SQL syntax while retaining basic type safety.
Strengths
- +Compile-time checked SQL allows you to use your existing database knowledge without the burden of learning a proprietary DSL.
- +Pure Rust implementation with zero dependencies on external C libraries, simplifying deployment and cross-compilation.
- +Supports PostgreSQL, MySQL, and SQLite through a unified async API that is both powerful and lightweight.
Weaknesses
- -Requires a persistent database connection or a 'preparation' step for offline compilation to verify SQL queries.
- -Momentum has slowed as the library reaches maturity, with fewer innovative features compared to the rapid growth of Sea-ORM.
Data serialization and deserialization across any format; a must-have dependency for almost every Rust project.
Strengths
- +The universal standard for serialization in Rust; if you're using Rust data, you're almost certainly using Serde.
- +Incredible training data coverage ensures that AI tools generate correct attributes and logic nearly 100% of the time.
- +Zero-copy deserialization capabilities enable world-class performance for high-throughput data processing.
Weaknesses
- -The project has reached a maintenance plateau; while extremely stable, it lacks the documentation depth of newer libraries.
- -Reliance on complex procedural macros can significantly increase compile times as your data model grows in size.