The control layer for AI agents
- Your account lives in our own infrastructure - passwords are securely hashed, or skip them entirely and sign in with GitHub or an emailed link
- Prismor is designed with zero-retention architecture: we extract vulnerability insights without ever storing your proprietary code.
- Login via GitHub to scan your private repos
The control layer
for AI agents
- Your account lives in our own infrastructure - passwords are securely hashed, or skip them entirely and sign in with GitHub or an emailed link
- Prismor is designed with zero-retention architecture: we extract vulnerability insights without ever storing your proprietary code.
- Login via GitHub to scan your private repos
What developers are saying
AI agents can run commands before you get a chance to review them. This repo adds a checkpoint
Your agent is about to call a tool and you have no pause button in the session
Your guardrail can warn you and still wave the command through. Prismor's docs make the distinction explicit: observe only logs; enforce can block, but warn/log actions still allow the call
a seatbelt for claude code
Looking for a promising tool: Prismor. Tell me something is wrong in the agentic coding landscape without telling me something's wrong
open-source tooling that answers a real bill question always finds users
if you use Codex or Claude Code, you probably need this
bookmarked, thanks for sharing
Coding agents can run shell commands, install packages, call MCP tools and touch your secrets. Maybe we should watch them
Love the MCP gateway angle. One policy layer across everything is the right abstraction
bro this is actually clean, i've had claude code almost run rm -rf on my server once. policy checks would've saved me
the observe mode is the part i'd want tested: how often does it flag legitimate shell commands, and who maintains those policies when the repo changes?
Honestly, AI trading bots need this more than humans
gm, open source control plane that gates every tool call sounds like the missing guardrail. how heavy is the latency?
enforce mode saves wallets tbh
the enforce vs observe switch is smart! teams can see the blast radius before
This is a real gap. The agent part gets all the attention, but visibility and control are what make it usable in production
nice take on agent iam, the guardrail scoping feels solid. how do you handle secret rotation across delegated sub agents?
Local first security for agents is a strong direction. Secret redaction is table stakes, but policy enforcement around tool calls and package installs is where this gets really interesting
this is genuinely needed. most agent stacks rn are a black box once you deploy them, and the "we'll add observability later" crowd finds out the hard way when something exfiltrates a token
Open source is the way to go! Superb demo and great product
super cool product!
Tried it for 3 of my repositories. Super awesome man!
This is awesomeee
I think this is a very solid approach
too good
Hats off for providing us with this!!
security is key especially as models become increasingly capable!
As enterprises are adapting agents into their workforce, security becomes very crucial part of the workflow. Looking forward for great work
so cool!
AI agents can run commands before you get a chance to review them. This repo adds a checkpoint
Your agent is about to call a tool and you have no pause button in the session
Your guardrail can warn you and still wave the command through. Prismor's docs make the distinction explicit: observe only logs; enforce can block, but warn/log actions still allow the call
a seatbelt for claude code
Looking for a promising tool: Prismor. Tell me something is wrong in the agentic coding landscape without telling me something's wrong
open-source tooling that answers a real bill question always finds users
if you use Codex or Claude Code, you probably need this
bookmarked, thanks for sharing
Coding agents can run shell commands, install packages, call MCP tools and touch your secrets. Maybe we should watch them
Love the MCP gateway angle. One policy layer across everything is the right abstraction
bro this is actually clean, i've had claude code almost run rm -rf on my server once. policy checks would've saved me
the observe mode is the part i'd want tested: how often does it flag legitimate shell commands, and who maintains those policies when the repo changes?
Honestly, AI trading bots need this more than humans
gm, open source control plane that gates every tool call sounds like the missing guardrail. how heavy is the latency?
enforce mode saves wallets tbh
the enforce vs observe switch is smart! teams can see the blast radius before
This is a real gap. The agent part gets all the attention, but visibility and control are what make it usable in production
nice take on agent iam, the guardrail scoping feels solid. how do you handle secret rotation across delegated sub agents?
Local first security for agents is a strong direction. Secret redaction is table stakes, but policy enforcement around tool calls and package installs is where this gets really interesting
this is genuinely needed. most agent stacks rn are a black box once you deploy them, and the "we'll add observability later" crowd finds out the hard way when something exfiltrates a token
Open source is the way to go! Superb demo and great product
super cool product!
Tried it for 3 of my repositories. Super awesome man!
This is awesomeee
I think this is a very solid approach
too good
Hats off for providing us with this!!
security is key especially as models become increasingly capable!
As enterprises are adapting agents into their workforce, security becomes very crucial part of the workflow. Looking forward for great work
so cool!