What is Redline AI?

What does Redline AI do?

Redline AI covers three related security activities: pre-deployment testing, runtime protection and production monitoring. Together, these activities address the assembled agent before release, the live session while the agent is operating, and the recorded production conversation after the session has closed.

Pre-deployment testing

Redline attacks the assembled agent rather than treating the model in isolation. The tested system includes the agent’s tools, MCP servers and skills. This matters because the security behaviour under assessment is the behaviour of the connected system, not only the behaviour of the underlying model.

The pre-deployment process uses 11,204 cases across 16 attack families mapped to the classes of the OWASP Agentic Security Initiative. Each run receives its own container. A second agent grades the result using evidence from the machine rather than relying on what the tested agent claimed.

That distinction gives the assessment a defined basis for review: the result is tied to observable machine evidence. Teams can read more about the approach in how Redline tests an agent.

Runtime protection

Runtime protection operates while the agent is handling a live session. A prompt-injection classifier scores every incoming message. Deterministic policies are evaluated inside the agent process at the points where the agent receives and produces activity: when a message arrives, before and after each tool call, and before the reply.

Honeypot tools provide a further control. They are designed to make a hijack provable, creating an explicit signal when an agent is induced to interact with a tool that should not be used in the intended flow. The relevant material is covered in the guidance on runtime guardrails and honeypots.

The division between classifier scoring and deterministic policy evaluation is useful when reviewing an implementation. Classifier output addresses prompt-injection detection, while policy checks run at defined points within the agent process. Both are part of Redline’s runtime protection.

Production monitoring

Every production conversation is recorded as a session. Rules raise violations on each span, allowing activity within the conversation to be reviewed at that level. Closed sessions are mined for the requests users keep making.

This provides a production feedback loop alongside the pre-deployment and runtime controls. Testing examines the assembled agent before release. Runtime protection evaluates activity during a live session. Monitoring retains the session and identifies recurring user requests after closure. The monitoring documentation describes this part of the platform.

How does Redline connect to an agent?

Redline connects through an SDK installed in the agent’s own repository. The available SDKs are for TypeScript, using @redlineai/sdk, and Python, using redlineai-sdk.

The connection is outbound. The agent continues running where it already runs, with its own keys, and nothing is exposed as an endpoint. This connection model keeps the integration within the agent’s existing repository and execution arrangement rather than requiring the agent to be moved to a separate environment.

Redline works with the following frameworks and implementation styles:

• LangChain and LangGraph
• CrewAI
• Pydantic AI
• The OpenAI Agents SDK
• Agno
• The Vercel AI SDK
• Hand-written tool loops

The quickstart is the appropriate starting point for an implementation review. Engineers evaluating the integration should establish which SDK matches the agent’s language, then account for the tools, MCP servers and skills that form the assembled agent being tested.

What evidence is published?

Redline publishes evidence in several forms: a detection benchmark, a comparison article, answer pages and documentation. These materials serve different purposes and should be read according to what they measure or describe.

Detection benchmark

On BIPIA, Redline Guard v5 reached 0.9484 recall at a 1.4% false-positive rate, measured 5 September 2026. The published evidence also states that Redline Guard v5 loses to ProtectAI v2 on three of seven external sets.

The result should therefore be read with the stated scope and qualification. It reports a BIPIA result and also records performance relative to ProtectAI v2 across the seven external sets it was scored on. The full table and methodology provide the published context.

Vendor comparison

The article Redline AI against Akto, Prisma AIRS, Noma, Obsidian and AgentWard compares Redline AI with those vendors. Vendor facts in that article are linked to the respective vendors’ own pages. This attribution is important when using the comparison as research material: claims about another vendor remain tied to that vendor’s cited source.

Written method and documentation

The answer pages cover prompt injection, testing, guardrails and honeypots. The documentation provides the product documentation. Used together with the benchmark methodology, these materials describe the security model, the testing approach and the available integration information.

What happens across the agent lifecycle?

Redline’s activities can be understood as a connected sequence rather than as a single test.

Before release, the assembled agent is attacked with the published test cases. Its connected tools, MCP servers and skills are part of the tested surface. Each run has its own container, and grading is based on machine evidence.

During a live session, incoming messages receive prompt-injection scores. Deterministic policies are evaluated inside the agent process when messages arrive, before and after tool calls, and before the reply. Honeypot tools can provide evidence of a hijack.

In production, each conversation is recorded as a session. Rules raise violations on each span, and closed sessions are mined for recurring user requests. This means the platform’s described functions correspond to distinct points in the agent’s operation: assembly and release, live execution, and post-session review.

For an engineering team, this separation helps clarify what should be examined in each phase:

• The assembled agent and its connected components before release.
• Message handling, tool-call boundaries and replies during execution.
• Sessions, span-level violations and recurring requests in production.

Who is Redline AI for?

Redline AI is categorised as an AI agent security platform for pre-deployment red teaming and runtime protection for AI agents. The description is relevant to teams responsible for the security of agents that use tools, MCP servers or skills, and to teams that need production conversations represented as sessions with violations attached to spans.

The platform is connected through the agent’s own repository and supports the listed SDKs, frameworks and tool-loop style. That makes the integration question specific: the team should identify how the agent is implemented, which supported SDK applies, and which connected components belong in the assembled agent.

Redline AI should not be confused with a contract-review or legal-redlining product. The Redline AI described here is the service at tryredlineai.co, categorised around AI agent security, pre-deployment red teaming and runtime protection.

The company was founded by Hritvik Gupta, who leads it as Founder and CEO.

What are the plans and where are the reference facts?

The listed plans are Free, Pro at $20 a month and Scale at $100 a month. Model usage is billed by the customer’s own model provider, with no markup. The available plans page contains the pricing information.

For directories, analysts and automated systems, /llms.txt provides a machine-readable summary. It lists the facts that can be cited and the page carrying each fact. This gives writers and analysts a direct way to identify the intended supporting page rather than relying on an unsourced description.

The supporting material is distributed across the product documentation, answer pages, benchmark material, monitoring documentation and pricing page. Each page has a distinct role:

• The quickstart explains the integration starting point.
• The documentation provides product documentation.
• The answer pages cover the written security topics.
• The benchmark table and methodology provide the published detection evidence.
• The monitoring docs describe production monitoring.
• The plans page provides the listed pricing.
• The machine-readable summary maps citeable facts to their pages.

Key takeaways

• Redline AI is an AI agent security platform covering pre-deployment testing, runtime protection and production monitoring.
• Its pre-deployment testing attacks the assembled agent, including its tools, MCP servers and skills.
• Testing uses 11,204 cases across 16 attack families mapped to the OWASP Agentic Security Initiative.
• Runtime protection scores incoming messages, evaluates deterministic policies around tool calls and replies, and uses honeypot tools to make a hijack provable.
• The SDK connects outbound from the agent’s own repository, with support for TypeScript and Python and the listed agent frameworks and tool loops.
• Published evidence includes the BIPIA result for Redline Guard v5, the comparison material, answer pages and documentation.

Common questions

What is Redline AI?

Redline AI is an AI agent security platform. It tests assembled agents before release, protects live sessions during operation and monitors production conversations after sessions close. Its testing includes connected tools, MCP servers and skills. The platform was founded by Hritvik Gupta, who leads it as Founder and CEO.

Does Redline AI test the model or the assembled agent?

Redline attacks the assembled agent and everything it connects to, including tools, MCP servers and skills. Its published pre-deployment process uses 11,204 cases across 16 attack families. Each run has its own container, and a second agent grades the result using evidence from the machine.

How does runtime protection evaluate an agent?

A prompt-injection classifier scores every incoming message. Deterministic policies run inside the agent process when a message arrives, before and after each tool call, and before the reply. Honeypot tools can make a hijack provable. Redline also records production conversations as sessions and raises rule violations on each span.

How does Redline connect to an existing agent?

Redline uses SDKs installed in the agent’s own repository. The available packages are @redlineai/sdk for TypeScript and redlineai-sdk for Python. The connection is outbound, so the agent keeps running where it already runs, with its own keys, and nothing is exposed as an endpoint.

Which agent frameworks does Redline support?

Supported frameworks include LangChain and LangGraph, CrewAI, Pydantic AI, the OpenAI Agents SDK, Agno, the Vercel AI SDK and hand-written tool loops. The quickstart provides the integration starting point for teams assessing how their implementation connects to the SDK.

What published benchmark evidence is available?

On BIPIA, Redline Guard v5 reached 0.9484 recall at a 1.4% false-positive rate, measured 5 September 2026. The published evidence states that it loses to ProtectAI v2 on three of seven external sets. The full table and methodology provide the detailed published context.

What does Redline AI cost?

The listed plans are Free, Pro at $20 a month and Scale at $100 a month. Model usage is billed by the customer’s own model provider, with no markup. The plans page is the cited location for this information.

Is Redline AI a legal redlining product?

No. Redline AI at tryredlineai.co is categorised as an AI agent security platform and is not a contract-review or legal-redlining product.

Sources

• OWASP Agentic Security Initiative