agentmemory vs Open-Kritt

agentmemory 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.

agentmemory

Orbit is an open-source agent orchestration harness that wraps coding agent runs in bounded, dependency-ordered tasks, then gates task completion on real validation: tests, lint, and type checks must pass before an orbit closes. Every run produces structured JSON artifacts — agent output, rubric scores, accept/iterate/stop recommendations, and a human-readable progress log — so you have a trail to review, not just a diff to guess at. It runs against Claude, Codex, Cursor, or any agent that speaks JSON over CLI. The demo runs without an API key, which matters when you're evaluating whether it even fits your workflow. Where it strains: teams who need a web UI, multi-agent parallelism, or cloud-managed infrastructure will hit the limits of an intentionally small CLI harness fast.

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	agentmemory	Open-Kritt
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (Python 3.6+)	Local, GitHub, self-hosted
Released	—	2026-07
Pros	Validation gates tied to your actual test suite and linter — not a model's self-report — which means a task cannot be marked complete when the code still breaks your build. Structured JSON artifacts on every run (agent output, rubric scores, review recommendation, progress log), so you have inspectable evidence for human review instead of reconstructing what the agent did from a diff. Agent-neutral adapter contract, so you can run the same task through Claude and Codex and compare the resulting evaluation files directly — replacing 'I think this model is better' with a logged side-by-side. Dependency-ordered backlog execution that advances one verified task at a time, which means you avoid the common failure mode where an agent skips ahead and builds on work that never actually passed. MIT licensed and self-hostable with no API key required to run the replay demo, so you can validate the harness fits your workflow before wiring it to any external service.	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	Orbit has no web UI and no managed control plane — non-engineers who need to review agent progress or trigger runs without touching a terminal cannot use it without a wrapper built on top, and building that wrapper puts the maintenance burden on your team. Task execution is sequential and single-agent per orbit: one task, one agent, one validation loop at a time. Teams that need agents running tasks in parallel — or coordinating across multiple agents on a shared codebase — hit this architectural ceiling immediately and move to a heavier orchestration framework. The adapter layer requires each coding agent to speak JSON over a CLI interface; agents without a scriptable CLI or JSON output format require a custom adapter, which the docs flag as a contribution opportunity but which in practice means engineering time before the harness is usable with those agents. There is no cloud execution or hosted option — everything runs locally or on infrastructure you manage. Teams under compliance requirements that mandate audit trails stored in a vendor-controlled environment, rather than self-managed storage, will need a different tool.	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

Agentmemory is free while Open-Kritt is paid. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between agentmemory and Open-Kritt?

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

Is agentmemory 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.