Adrian
Adrian is open-source runtime security that reads an AI agent's chain of thought and blocks malicious plans before they execute.
Adrian targets the layer that input/output filters structurally cannot reach: the plan forming behind the prompt. If one bad tool call in your workflow costs real money or real trust, reasoning-level blocking plus a human pause is worth the self-hosting overhead, and the product page's claim of two-line integration is unusually concrete for this category. Compare it against managed prompt-and-completion filters rather than against observability dashboards, because Adrian's differentiator is stopping a run, not logging one. If nobody on your team can operate a backend, the free core will sit unread in a repo.
Verified 1d ago · liveness 71/100 · cite: rightaichoice.com/tools/adrian
- Teams running production autonomous AI agents with real credentials and real consequences
- Multi-step agent swarms that call external tools and systems
- Security teams embedding shift-left checks into agent CI/CD pipelines
- High-trust or regulated environments that need human approval before actions fire
- Teams without DevOps capacity to run and maintain a self-hosted backend
- Traditional static applications with no agentic or decision-making behaviour
- Teams prototyping casual chatbots where no production action is at risk
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Skip Adrian if your team has no one to run and maintain a self-hosted backend, or if your agents are written outside Python and do not expose a reasoning trace for the reasoning engine to review.
Self-hosting the open-source core means you fund the servers, the Python SDK integration work, and ongoing maintenance out of your own DevOps budget rather than a subscription line.
The open-source core is free to self-host, which puts Adrian below managed agent-security subscriptions on licence cost and above them on operating cost — you pay in DevOps hours and infrastructure instead of seats. That trade favours mid-size platform and security teams that already run their own tooling and need agents stopped in regulated or edge environments. Smaller teams without infrastructure staff will find the fully managed filtering vendors cheaper in real terms, even at a higher
In short
Adrian — Adrian is open-source runtime security that reads an AI agent's chain of thought and blocks malicious plans before they execute. Best for Teams running production autonomous AI agents with real credentials and real consequences, Multi-step agent swarms that call external tools and systems, Security teams embedding shift-left checks into agent CI/CD pipelines. Free to use.
What's new in Adrian
Checked 8 days agoAcross the latest 3 updates: 3 news mentions.
The bias in an LLM's subconscious
Secure Agentics examines hidden biases in LLM outputs and how they can affect agent behaviour and security posture, tying the company's research back to why reasoning-level monitoring matters.
Secure Agentics earns Honorable Mention in Black Hat 2026 Startup Spotlight
Secure Agentics was recognised with an Honorable Mention in the Black Hat 2026 Startup Spotlight for its approach to agentic security and pre-execution blocking.
Open Source Vs AI
A Secure Agentics post comparing open-source approaches with proprietary AI security tooling, relevant to how Adrian positions itself against managed filtering vendors.
What people actually say about Adrian — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
75 mentions across 6 sources (Hacker News, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 6, 2026.
Average across the 6 sources that answered — each source counts once, not each post.
- +Monitors actual chain-of-thought, not just inputs/outputs of agents.
- +Open-source codebase allows full audit and customization.
- +Blocks prompt injection and policy drift before execution.
- +Supports human-in-the-loop approval for risky actions.
- +Action sandboxing prevents malicious tool use.
- −Fail-open on errors makes it unreliable for security.
- −Sync tool execution bypasses BLOCK/HITL entirely.
- −Dashboard filter mismatch hinders investigation.
- −Very small community limits third-party validation.
- −No evidence of enterprise support or SLAs.
- • Enterprise tier pricing is unlisted; may require sales negotiation.
- • Self-hosting infrastructure costs for open-source version.
Viability Score
How well maintained and how widely used is Adrian? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Chain-of-thought monitoring via a Python SDK wrapped around your agent
- Real-time capture of agent action telemetry alongside its reasoning
- Reasoning engine that evaluates agent plans against your security policies
- Prompt injection detection before the agent executes an action
- Detection of policy violations and out-of-remit reasoning
- Overt-harm detection such as cryptomining and data exfiltration
- Contextual-harm detection, e.g. an e-commerce agent resetting a user password
- Block mode that pauses each agent action for review before it proceeds
- Alert mode that pushes security alerts to Slack, Discord, or WhatsApp
- Human-in-the-loop approval workflows for high-risk actions
- Action sandboxing so an agent can attempt an action without committing
- Behavioural interrogation to surface manipulation and reasoning drift
- Agent-aware monitoring built for systems that reason and act
- Self-hosted open-source core with GitHub access
- Shift-left security for agent development pipelines
About Adrian
Adrian is an open-source runtime security monitoring and control toolkit for AI agents, built by Secure Agentics. Rather than inspecting traffic at the perimeter, it deploys inside the agent: a Python SDK wraps your agent framework or orchestration layer, captures action telemetry alongside the agent's reasoning, and streams both to a local or hosted backend. The premise is that prompt injection is architectural and unlikely to be solved by filtering, so the useful defence is stopping an agent before it acts. A reasoning engine reviews the chain of thought against your security policies while the agent is still planning, separating overt harm such as cryptomining or data exfiltration from contextual harm such as an e-commerce agent resetting a customer's password. It flags prompt injection, policy violations and out-of-remit reasoning before any tool call fires. The response is configurable: alert mode pushes notifications to Slack, Discord or WhatsApp, while block mode pauses each agent action for review, with human-in-the-loop approval and action sandboxing available. Behavioural interrogation looks for manipulation and drift over long-running tasks rather than single-shot attacks. Secure Agentics took an Honorable Mention in the Black Hat 2026 Startup Spotlight for the approach. The product page describes integration as two lines of code, with an Apache-style core on GitHub that you self-host. It suits teams running autonomous agents, multi-step agent swarms, production systems with real-world impact, edge AI and Critical National Infrastructure deployments.
Behind the Verdict
Most agent security tooling in 2026 still watches. It captures trajectories, renders dashboards, and tells you after the fact which tool call drained an account. Adrian makes a narrower and more interesting bet: that the only place to intervene is between the reasoning trace and the action, and that this must happen inside the agent rather than at a boundary. The architecture follows from that. A Python SDK wraps your framework or orchestration layer, captures action telemetry alongside the reasoning in real time, and ships both to a local or hosted backend. A reasoning engine then evaluates the plan against your policies while the agent is still deciding. What is genuinely differentiated is the block path. Alert mode routes to Slack, Discord or WhatsApp, which is table stakes. Block mode pauses each agent action and holds it for review, with human-in-the-loop approval and action sandboxing so an agent can attempt something without committing. Behavioural interrogation extends detection across long-running tasks, picking up manipulation and drift rather than just single-shot attacks. That combination — policy evaluation on the chain of thought, then a hard pause — is what the product page means by 'watching is not defending.' Where it gets honest: this is a young project, and the trade is DevOps effort instead of a subscription. You own the integration and the infrastructure. Effectiveness depends on your agent framework exposing usable reasoning traces, so heavily abstracted agents give the reasoning engine less to work with. The homepage's own step 04 promises leverage of existing security triage and operational platforms, but the scrape surfaces only Slack, Discord, WhatsApp alerting and a GitHub presence, so any SIEM routing is your own plumbing. Agents written outside Python, or those that do not expose a reasoning trace, are out of scope today. The positioning against managed filters is a real fork in the road, not a marketing distinction. Managed filters arbitrate prompts and completions; Adrian arbitrates intent before execution and can halt the run. Teams that cannot staff a self-hosted backend should weigh that cost honestly before adopting, because the free core is not a hosted service. For teams already running agents against real credentials in regulated, edge or Critical National Infrastructure settings, the ability to pause and approve is the feature that matters, and it is the reason the Black Hat 2026 Startup Spotlight honourable mention landed.
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Real-world workflow fit
Concrete scenarios for the personas Adrian actually fits — and what changes day-one when you adopt it.
Wrap the agent framework with the Adrian Python SDK so action telemetry and reasoning stream to a self-hosted backend, then run the agent against staging traffic in alert mode with notifications going to Slack.
Outcome: The team sees which planned actions trip policy violations — such as an agent reaching for credential resets — before enabling block mode on production credentials.
Configure block mode so each agent action pauses when the reasoning engine flags prompt injection or out-of-remit reasoning, with human-in-the-loop approval routed to the on-call operator.
Outcome: High-risk tool calls cannot fire without a named human approving them, giving the team an auditable approval step on autonomous actions.
Turn on behavioural interrogation during a multi-step agent run to look for manipulation attempts and reasoning drift rather than single-shot attacks.
Outcome: Drift surfaces as a behavioural signal in the chain of thought, so the team can sandbox the offending action and adjust the agent's plan instead of discovering the outcome from logs.
Use Cases
- Pause and review an agent action before it executes when the reasoning trace shows a policy violation.
- Detect prompt injection in the thought process rather than filtering it at the input boundary.
- Enforce custom security policies on agent tool use and decision-making.
- Route agent security alerts into operator channels like Slack, Discord, or WhatsApp.
- Add shift-left agent security checks into your development pipeline.
- Give a human approver control over high-risk autonomous actions in regulated environments.
- Monitor agent behaviour for manipulation and drift across long-running tasks.
- Self-host agent security telemetry where data cannot leave your infrastructure.
Limitations
- Adrian is open source and self-hosted, so you own the integration and the infrastructure — a Python SDK wraps the agent and requires DevOps capacity to run.
- Its effectiveness depends on the agent framework or orchestration layer exposing usable reasoning traces; heavily abstracted agents give the reasoning engine less to analyse.
- The project is young, so the ecosystem and community are still maturing compared to established I/O filtering vendors.
- The scrape surfaces a Slack, Discord and WhatsApp alerting path plus GitHub, so SIEM routing depends on your own plumbing.
- Agents written outside Python, or that do not expose a reasoning trace for review, are not covered today.
- Secure Agentics lists enterprise options above the open-source core, and the public product pages do not spell out what those add.
as of 2026-10-10
Verification history
We have re-verified Adrian 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Adrian tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (Community)
$0
Ideal for
Engineering and security teams running autonomous agents in production who have DevOps capacity to self-host and want pre-execution blocking without a licence fee.
What this tier adds
Starting tier: free, self-hosted core with the Python SDK, chain-of-thought monitoring, prompt injection and policy detection, alert and block modes, and action sandboxing.
Enterprise
Custom
Ideal for
Regulated or Critical National Infrastructure deployments that need commercial support and enterprise deployment terms on top of the open-source runtime.
What this tier adds
Adds commercial support and enterprise deployment options over the free core, scoped to the specific deployment.
Where the pricing makes sense
The company stage and team size where Adrian's pricing actually pencils out — and where peers do it cheaper.
The open-source core is free to self-host, which puts Adrian below managed agent-security subscriptions on licence cost and above them on operating cost — you pay in DevOps hours and infrastructure instead of seats. That trade favours mid-size platform and security teams that already run their own tooling and need agents stopped in regulated or edge environments. Smaller teams without infrastructure staff will find the fully managed filtering vendors cheaper in real terms, even at a higher
Setup time & first value
How long it actually takes to get something useful out of Adrian — broken out by persona, not the marketing-page minute.
For an engineer already familiar with the agent's framework: integration is documented as two lines of code, so first signal in alert mode is achievable inside a working session, assuming a backend to receive telemetry. Add hours to days for policy authoring against your own tool inventory. Teams that need block mode, human approval routing and SIEM plumbing should budget a multi-week rollout
Switching to or from Adrian
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From input/output filtering tools such as Lakera or Robust Intelligence: keep the boundary filter for prompt hygiene and add Adrian inside the agent to arbitrate intent before execution.
- →From agent observability dashboards: keep the dashboard for trajectory history and add the Adrian Python SDK so reasoning traces are evaluated against policy in real time, not reviewed after the run.
- →From post-execution log review: wrap the orchestration layer with the SDK and move policy checks to the planning stage so block mode can intervene before the action fires.
- →From static rule-based agent guardrails: replace signature matching with the reasoning engine's behavioural detection, then route alerts to your existing Slack, Discord or WhatsApp channel.
- ↗To a fully managed agent security SaaS: if operating a self-hosted backend stops being viable, move policy enforcement to a vendor-hosted filter and accept losing pre-execution block mode.
- ↗To an APM or LLM observability platform: if you only need trajectory logging and drift dashboards rather than intervention, instrument the agent for tracing and drop the policy engine.
- ↗To native framework guardrails: if your agent framework ships its own pre-tool-call hooks, you can implement a narrower block check in-framework, at the cost of the dedicated reasoning engine.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Adrian”, and we withheld 6: 6 could not be judged, because “Adrian” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Adrian.
Official links
Featured Head-to-Head Comparisons
Adrian vs Sublime Security
Choose Adrian if you need runtime security for AI agents—especially autonomous systems in production—and want an open-source, low-overhead toolkit. Choose Sublime Security if your primary concern is advanced email threats like BEC/VEC and you need deep integration with Microsoft 365 or Google Workspace with custom detection rules.
Adrian vs Push Security
Choose Push Security if your priority is securing employee browser usage against AI-powered phishing, session hijacking, and data leakage to AI tools. Choose Adrian if you're building autonomous AI agents and need runtime monitoring of their chain-of-thought to catch prompt injection or policy drift. They solve different problems: human security vs. agent security.
Adrian vs Audioeye
Choose Adrian if you're building autonomous AI agents and need runtime security to catch prompt injections and policy violations before they execute. Choose AudioEye if your priority is achieving ADA/WCAG compliance quickly with automated scanning and legal backup — they serve completely different needs.
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