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Custodyn.app VS mlsql

Compare Custodyn.app VS mlsql and see what are their differences

Custodyn.app

Runtime security and trust infrastructure for AI agents: Policy enforcement, human approvals, and tamper-evident audit logs for every AI agent action. Stop runaway agents before they cause real damage. The security layer your AI agents are missing.

Rating
0 reviews
Pricing
Open source Freemium Free trial $49 / Monthly
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Alternatives Is it good?
mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
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Alternatives Is it good?
happySEO
Create ai humanized SEO content tailored for ecommerce and small businesses with HappySEO tools. From ai writer to keyword research and analyze tools. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
0 vs 2
AI Agents popularity
100% vs 0%
alternatives listed
12 vs 1

Base details

Website, pricing, platforms and company facts side by side.

Custodyn.app
mlsql
Website custodyn.app github.com
Pricing
Open source Freemium Free trial $49 / Monthly Official pricing
—
Company Startup from India · 2026 —
Listed in
AI Agents LLM Security Return Policy Generator AI Gateway Audit Trail Auditing And Compliance AI +4
Education LMS Online Learning Web App +1

About Custodyn.app and mlsql

In their own words, as submitted to SaaSHub.

Custodyn.app
mlsql

Custodyn is a runtime security and trust infrastructure platform for AI agents. As AI agents become autonomous — executing code, sending emails, making API calls, and taking real-world actions without human oversight — the security risks are real and largely ungoverned. Custodyn sits inside agent...

Read more about Custodyn.app

No description of mlsql yet.

Features and specs

What each product offers, as listed by its team.

Custodyn.app 5 features
mlsql 0 features
  • Policy Enforcement Engine
    Define exactly what your AI agents can and cannot do. Block dangerous actions automatically before they execute.
  • Human Approval Queues
    Risky agent actions pause and wait for human sign-off before proceeding. Full control stays with your team.
  • Multi-Framework SDK
    Drop-in Python and JS SDKs with integrations for LangChain, CrewAI, AutoGen, MCP, Claude Code, and OpenAI Agents SDK.
  • Open-Core Model
    MIT licensed SDK for developer adoption. Paid SaaS platform for teams that need policy management, dashboards, and compliance.
  • Tamper-Evident Audit Logs
    Every agent action — allowed, blocked, or approved — is logged in a cryptographically verifiable audit trail. SOC2 ready.

No features have been listed yet.

Analysis

An editorial look at what each product does well and who it suits.

Custodyn.app
mlsql

No analysis of Custodyn.app yet.

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Custodyn.app
mlsql
100% 100%
AI Agents
0% 0%
0% 0%
Education
100% 100%
100% 100%
Auditing And Compliance
0% 0%
0% 0%
Online Learning
100% 100%

Questions & Answers

As answered by people managing Custodyn.app and mlsql.

Why should a person choose your product over its competitors?

Custodyn.app's answer

Most AI security tools sit outside the agent — monitoring network traffic or endpoint activity. Custodyn sits inside the agent code itself. It's developer-first, open-core, and self-serve — pip install or npm install and you're running in minutes. No demo call, no sales process. The MIT licensed SDK means developers adopt it freely, the paid platform gives teams policy management, dashboards, and audit compliance.

What's the story behind your product?

Custodyn.app's answer

Built by a solo founder with 15+ years in information security and penetration testing. In early 2026, AI agents started taking real-world autonomous actions with no security layer governing them. The July 2026 incidents — OpenAI agent breaching Hugging Face infrastructure, Anthropic's agentic misalignment report — confirmed the problem was real and urgent. Custodyn was built to be the security layer AI agents were missing.

What makes your product unique?

Custodyn.app's answer

Custodyn is the only AI agent security platform that works at the SDK layer — embedded directly inside agent code. Unlike endpoint-based solutions, Custodyn intercepts actions inside custom LangChain, CrewAI, and AutoGen pipelines that no endpoint sensor can reach. Human-in-the-loop approval queues mean dangerous actions wait for human sign-off before executing — not just blocked or allowed, but genuinely governed.

How would you describe the primary audience of your product?

Custodyn.app's answer

Developers and security teams building production AI agent systems using LangChain, CrewAI, AutoGen, OpenAI Agents SDK, MCP, or Claude Code — who need runtime governance, human oversight, and audit trails before deploying agents in high-stakes environments.

Which are the primary technologies used for building your product?

Custodyn.app's answer

Python Flask (backend) SQLite (database) Python SDK (PyPI) JavaScript SDK (npm) Railway (deployment) Razorpay (billing)

Who are some of the biggest customers of your product?

Custodyn.app's answer

No enterprise customers yet - pre-revenue, currently seeking first customers and open to acquisition

User comments

Share your experience with using Custodyn.app and mlsql. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Custodyn.app 0 mentions
mlsql 2 mentions

Tracking Custodyn.app since Aug 2026.

  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

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