Microsoft Agent Framework vs Open-Kritt

Microsoft Agent Framework and Open-Kritt are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Microsoft Agent Framework

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

Open-Kritt

The tool runs parallel AI agents across a codebase, so vulnerability discovery that would serialize into hours on a single-context scan distributes across concurrent analysis threads. It targets security researchers and bug bounty teams who need to sweep repositories at scale, not review a function at a time. Self-hosting is supported under AGPL-3.0, which means your code and findings never leave your infrastructure — a requirement for any org with compliance constraints. The open-source core is inspectable and forkable, but managed scans are a paid-only feature, so teams that want the hosted workflow face a significant spend threshold. The page describes GitHub integration as a first-class path, making it a practical fit for teams already running security workflows inside existing CI infrastructure.

Attribute	Microsoft Agent Framework	Open-Kritt
Pricing	Free	Paid
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Python and .NET with consistent APIs. Available for both .NET and Python	Local, GitHub, self-hosted
Languages	Python, C# (.NET)	—
Released	2025-10	2026-07
Pros	Unifies the enterprise-ready foundations of Semantic Kernel with the innovative orchestration of AutoGen Full framework support for both Python and C#/.NET implementations with consistent APIs and built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging Open standards & interoperability — MCP, A2A, and OpenAPI ensure agents are portable and vendor-neutral Supports integration with any API via OpenAPI, collaboration across runtimes with Agent2Agent (A2A), and dynamic tool connections using MCP Enterprise readiness — built-in observability, approvals, security, and long-running durability	Parallel agent analysis across large codebases, so security researchers are not bottlenecked by single-context limits that cause coverage gaps on repositories too large for one model pass. AGPL-3.0 open-source license with self-hosting support, which means organizations with compliance requirements can audit the tool's behavior and keep all code and findings on their own infrastructure rather than routing through a third-party service. Direct GitHub repository integration, so teams can point the tool at existing repos without building a separate code ingestion or preprocessing step. Support for Codex and Claude Code model backends, so teams can align the analysis engine with the model their organization already has access to or trusts for security-sensitive tasks. Inspectable agent orchestration code under an open license, which means a security team can verify exactly what the agents are executing — a requirement that opaque SaaS tools cannot satisfy.
Cons	Public preview released October 1, 2025, with AutoGen and Semantic Kernel entering maintenance mode Requires understanding of agentic AI concepts and orchestration patterns Dependent on external model providers for LLM capabilities	Managed scans are a paid-only feature with a spend threshold the validator context confirms is substantial; independent researchers and small bug bounty teams operating on limited budgets hit this wall immediately and are forced to self-host, which shifts the burden of infrastructure provisioning, scaling, and maintenance entirely onto the team. Self-hosting the agent infrastructure requires operational capacity that security research teams — typically focused on findings, not DevOps — often lack; teams without a dedicated infrastructure engineer end up spending sprint time on setup and uptime instead of auditing, and those teams frequently abandon self-hosted options for managed security tooling that absorbs that operational cost. No API is available per the tool's current documentation, which means teams that want to embed Kritt.ai's analysis into an existing CI/CD pipeline or trigger scans programmatically from another system face a hard integration ceiling; teams requiring API-driven automation switch to tools with exposed endpoints.

Bottom line

Microsoft Agent Framework is free while Open-Kritt is paid; Open-Kritt is open source; only Microsoft Agent Framework exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Microsoft Agent Framework and Open-Kritt?

Microsoft Agent Framework is Free, while Open-Kritt is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Microsoft Agent Framework better than Open-Kritt?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.