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      • Import repositories to test
      • Invite team members to sign up
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      • CVSSF metric
      • Glossary
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      • What is DAST?
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      • Assign vulnerability remediation to a team member
      • See details of the reported security vulnerabilities
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      • Break the build
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      • Types of authentication used
      • Fix code automatically with gen AI
      • Get AI-generated guides for remediation
      • Contribute to enhancing the scanners
      • Fluid Attacks' scanners
      • Know and reproduce the scanner’s OWASP Benchmark results
      • Access recent downloads
      • Check your compliance with standards
      • View analytics common to orgs, groups and portfolios
      • Download a report of detected vulnerabilities
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      • View analytics for the portfolio level only
      • Use analytics charts options
      • View and download logs
      • Pentesters' tools
    • Machine
      • Import repositories fast and safely with OAuth
      • Manage environments
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      • Resolve events impeding tests
      • See retrieved repositories not yet added to any group
      • Invite contributing developers
      • Manage members
      • Manage your organization's authors
      • Understand roles
      • Create and delete groups
      • Create another organization
      • Know your Groups section
      • Manage a group's configuration
      • Register payment information
      • See the target of evaluation's status and SBOM
      • Sort groups into portfolios
      • Accept vulnerabilities
      • Manage fix prioritization policies
      • Manage security gates
      • Prevent the deployment of builds with vulnerabilities
      • View details of the security of your builds
      • Analyze your supply chain security
      • Assign treatments
      • Correlate your threat model to vulnerabilities
      • Examine the evidence of exploitability
      • Request a vulnerability be dismissed as Zero Risk
      • See vulnerabilities assigned to you
      • See where vulnerabilities are and more details
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      • Enable and disable notifications
      • Explore the user menu
      • Leave group
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      • Platform sections and header items
      • Sign-up and login authentication
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        • 2023
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      • Supported evidence formats
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      • Supported package managers
      • Supported remediation
      • Supported SCM systems
      • Supported secrets
      • Supported standards
      • Supported ticketing systems
      • CVSS score adjustment
      • Find reachable dependency vulnerabilities
      • Vulnerability signature update
      • What is SCA?
      • APK scanner configuration file
      • DAST scanner configuration file
      • SAST scanner configuration file
      • SCA scanner configuration file
      • Scan with a configuration file
      • Ask the AI Agent
      • Ask via chat
      • Post comments
      • Send Fluid Attacks an email
      • Talk to a Pentester
      • Watch certifiable tutorial videos or get a demo
    • Use the Platform
      • Manage repositories
      • See vulnerabilities
      • Exclude findings from scan reports
      • Run scans locally
      • Understand the scanner output
      • Use standalone scanners
      • Use the scanners in CI/CD
  • Integrations
      • Local tools
      • Access Talk to a Pentester and help from Jira issues
      • Automate Jira issue creation
      • Create Jira issues for vulnerabilities
      • Go to vulnerability evidence and more from Jira issues
      • Install the Fluid Attacks app for Jira Cloud
      • Link vulnerabilities to Jira issues or unlink them
      • Request reattacks from Jira issues
      • Set up the Jira integration
      • Set up the Azure DevOps integration
      • Set up the GitLab integration
      • Install the VS Code extension
      • View vulnerable lines, use fix options and more
      • VS Code extension error and solution catalog
      • Identify and address vulnerabilities from IntelliJ
      • Install the IntelliJ plugin
      • Identify and address vulnerabilities from Cursor
      • Install the Cursor extension
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      • Encryption at rest
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      • No personal gain
      • Personnel NDA
      • Secure deletion
      • Awareness
      • Certification Hub
      • Certified cloud provider
      • Certified security analysts
      • Comprehensive reporting
      • Developing for integrity
      • Extensive hiring process
      • Monitoring
      • Production data not used for dev or test
      • Secure emails
      • Software Artifacts SLSA levels
      • Static website
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      • Everything as code
      • Extensive logs
      • Data privacy policy
      • Data policies
      • Email obfuscation
      • Employee time tracking software
      • Manual for the National Database Registry (NDR)
      • OTR messaging
      • Polygraph tests
      • Project pseudonymization
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      • Transparent use of cookies
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      • Everything is decentralized
      • Redundant roles
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      • Information security responsibility
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      • Quality policy
      • Status page
      • Testing our technology
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    • 42Crunch
    • 7 Way Security
    • Aikido
    • Anvil Secure
    • Apiiro
    • AppCheck
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    • Appknox
    • Aqua
    • ArmorCode
    • Arnica
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  • Log in to the platform 

On This Page

  • Rationale
  • Alternatives
  • LangChain
  • Usage​
StackDependenciesPydantic AI

Pydantic AI

Rationale

Pydantic AI  is a Python-first agent framework for building production-grade, type-safe AI applications. It integrates with major model providers and emphasizes predictable, validated I/O, real-time observability, and straightforward Python composition. Pydantic AI offers type-safe design, real-time debugging, and performance monitoring through Pydantic Logfire. It is ideal for AI-driven projects that require flexible and efficient agent composition using standard Python best practices.

In summary, these are its strengths:

  • Model-agnostic: Supports OpenAI, Anthropic, Gemini, DeepSeek, Ollama, Groq, Cohere, and Mistral; simple interface to add others.
  • Structured responses: Pydantic validation enforces exact schemas for consistent outputs across runs.
  • Type-safe by design: Strong typing improves clarity and refactoring.
  • Logfire integration: Real-time debugging, performance monitoring, and behavior tracing for LLM apps.
  • MCP support: Agents act as an MCP client to connect to MCP servers and use their tools.
  • Pythonic control: Simple dependency injection, branching, and testing using standard Python.
  • User-friendly: Enterprise-ready for high-accuracy apps; predictable behavior; minimal boilerplate; easy model swaps.

Alternatives

LangChain

LangChain  is a general-purpose framework with extensive integrations and patterns (chains, tools, agents, graphs) for LLM applications.

Pros:

  • Highly flexible and feature-rich
  • Road ecosystem and integrations
  • It supports complex pipelines and agent/graph patterns

Cons:

  • The flip side of LangChain’s flexibility is complexity: steep learning curve; multiple overlapping abstractions.
  • Integrations are split across lightweight packages. Changing models often needs extra installs and code adjustments; this may involve more boilerplate and configuration compared to Pydantic AI.
  • MCP integration can be painful. MCP Toolbox documentation is not clear about its usage.
  • Type-safety lags behind Pydantic AI.

Usage​

We use Pydantic AI for programming our AI-MCP agent:

  • Agent runs
  • MCP integration with AI Agent
Last updated on February 13, 2026
Platform authorizationReact
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