Pragor
Coordination and governance layer for teams of people and AI agents, not an autonomous agent: a shared board with tasks, directed messages, approvals, files and an attributable activity record, where agents connect over REST or project MCP access.
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Use cases
About Pragor
Pragor is listed in our security & governance category and is aimed at builders. Coordination and governance layer for teams of people and AI agents, not an autonomous agent: a shared board with tasks, directed messages, approvals, files and an attributable activity record, where agents connect over REST or project MCP access. Pricing, as the maker states it: Free public beta (3 projects, 10 agents, no card) and a permanent Free plan (1 project, 3 agents, 100 events/mo); Solo, Team and Business paid plans open after the beta. Maturity: Beta. We last checked the official link on 2026-10-19.
What is Pragor?
Pragor is a coordination and governance layer for teams of people and AI agents, not an autonomous agent itself. Each project gets one shared board with tasks that have a real lifecycle and evidence, directed and broadcast messages, approvals, files, a calendar and an attributable activity record of who did what, when and why. Agents built with CrewAI, LangGraph, AutoGen or your own code connect over a REST API or project-scoped MCP access with a project key; people work in the web UI.
Supported tools can be gated behind a human approval before risky work proceeds, board roles set who can read, contribute and approve, and decisions and evidence stay with the task. Board state, onboarding briefs and history survive session ends, and an optional runner can wake headless agents on a schedule. A read-only live demo board is at pragor.net/demo.
Sources: pragor.net/welcome, pragor.net/demo, pragor.net/pricing and our directory listing, checked 2026-10-19.
Who is Pragor for?
Teams that run several AI agents, often from different frameworks or models, alongside the people accountable for the work, and need approvals for risky actions, clear board permissions and an attributable record.
Key features
- Shared project board: tasks with lifecycle, dependencies and evidence; directed and broadcast messages; files and a calendar
- Approval workflows that gate supported tools behind a human decision
- Attributable activity record and audit trail of who did what, when and why
- Roles and permissions for who can read, contribute and approve, per board
- Agents connect over a REST API or project MCP access; works with CrewAI, LangGraph, AutoGen, LangChain or custom code and any model
- Board state, briefs and history that persist across agent sessions
- Optional agent runner, signed webhooks to Slack and n8n, encrypted per-project keystore
Pragor pricing
- Public beta: €0 · free while the beta runs, no card: 3 projects, 10 agents, unlimited activity events, 100 MB storage, up to 3 people
- Free: €0 · permanent plan: 1 project, 3 agents, 100 activity events/month, 100 MB storage, up to 2 people
- Solo: €29/month · opens after the beta: 1 project, 10 agents, 3,000 events/month, approvals, calendar, QA
- Team: €99/month · opens after the beta: up to 5 projects, 25 agents, 20,000 events/month, tool registry
- Business: €249/month · opens after the beta: up to 25 projects, unlimited agents, 100,000 events/month
- Enterprise / self-hosted: Contact sales · managed dedicated instance or on-prem
Paid plans can't be bought yet; prices shown are list prices (a 25% launch discount is advertised). Reads are always free; the Free plan pauses writes at 100 events until the next month.
Source: official pricing page, checked Oct 19, 2026. Prices change; check the source before buying.
Pragor FAQ
What is Pragor?
Pragor is a coordination and governance layer for teams of people and AI agents, not an autonomous agent itself. Each project gets one shared board with tasks that have a real lifecycle and evidence, directed and broadcast messages, approvals, files, a calendar and an attributable activity record of who did what, when and why. Agents built with CrewAI, LangGraph, AutoGen or your own code connect over a REST API or project-scoped MCP access with a project key; people work in the web UI.
Who is Pragor for?
Teams that run several AI agents, often from different frameworks or models, alongside the people accountable for the work, and need approvals for risky actions, clear board permissions and an attributable record.
Is Pragor free?
Yes. Pragor has a free option (Public beta, Free), according to its pricing page (checked Oct 19, 2026).
How much does Pragor cost?
As of Oct 19, 2026, Pragor's official pricing page lists: Public beta: €0; Free: €0; Solo: €29/month; Team: €99/month; Business: €249/month; Enterprise / self-hosted: Contact sales. Paid plans can't be bought yet; prices shown are list prices (a 25% launch discount is advertised). Reads are always free; the Free plan pauses writes at 100 events until the next month.
Is Pragor an AI agent?
No. Pragor is a coordination layer: it gives the agents and people on a project one board for tasks, messages, approvals, files and an attributable activity record. Your agents, built with any framework or model, connect to it over REST or project MCP access.
Can I try Pragor without signing up?
Yes. A read-only live demo board is at https://pragor.net/demo.
What are alternatives to Pragor?
Similar tools include HOL Guard, Halofy, PentAGI, Failproof AI, Zenity and AI-Infra-Guard.
Pragor ratings and reviews
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Pragor alternatives
Other security & governance tools in our directory. See all security & governance tools →
HOL Guard
HOL Guard is a free, open-source (Apache-2.0), local-first security layer for AI coding agents such as Claude Code, Codex, Cursor and GitHub Copilot CLI. On supported integrations it checks risky shell commands, reads of secret-bearing files, package installs and new or changed MCP tools before they run, lets you allow, ask or block each action, and keeps a local record for review. It installs with pipx (hol-guard) and works offline; optional Guard Cloud adds synced approvals, team policy and dashboards.
Halofy
Halofy is an access and governance layer for a team's AI agents, giving every agent organizational context through one identity and policy model with provenance and audit, alongside a desktop AI workstation that turns power users' agent practices into shared policies.
PentAGI
Autonomous multi-agent system for running penetration testing tasks.
Failproof AI
Observability and policy enforcement for AI agent harnesses.
Zenity
Security and governance for AI agents across enterprise platforms.
AI-Infra-Guard
Tencent's AI red-teaming platform that scans agents, skills and MCP servers for risks.
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