The risk engine for your AI agents
RiskKernel puts deterministic cost, loop, and time budgets around your agents, with a real kill switch, crash-resumable runs, and human-approval gates. Self-hosted, your keys, no telemetry. Point it at the agents you already run with one environment variable.
One Go binary. Works with OpenAI, Anthropic, and your existing stack.
Agents break in production the same handful of ways
Frameworks orchestrate agents. They do not put hard limits around them. So the same failures keep shipping to production, and they cost real money the moment an agent runs unattended.
Deterministic guardrails, in compiled code
Budgets, kill switches, and approvals belong in statically-typed code that runs the same way every time, not in a prompt. RiskKernel is that layer.
Hard cost ceilings
Set a dollar and token budget per run. The kill switch fires the moment the ceiling is hit, mid-loop, before the spend lands.
Loop and time budgets
Cap iterations and wall-clock per run. Runaway loops die deterministically instead of grinding until someone notices.
Crash-resumable runs
Send kill -9 mid-run and resume from the last checkpoint. No re-spending the work already paid for.
Human-approval gates
Block any side-effecting tool call and route it for approval over CLI, web, or webhook. Framework-agnostic, the LLM cannot bypass it.
OpenTelemetry export
Emit GenAI spans for cost, loops, and checkpoints to Datadog, Grafana, Honeycomb, or whatever backend you already run.
Your keys, no telemetry
One self-hosted binary on your infra. BYO provider keys, never stored in plaintext. Nothing phones home. It is verifiable in the source.
Three ways to adopt it, one deterministic core
Start with zero code changes through the proxy, then reach deeper when you want loop-level and tool-level control. The enforcement logic is the same Go core underneath.
One environment variable
An OpenAI-compatible endpoint. Set OPENAI_BASE_URL and every call is metered, capped, logged, and forwarded with your key. No rewrite.
Python SDK and adapters
Wrap a run for loop counts, time budgets, checkpoints, and approval gates. Adapters for LangChain, the Claude Agent SDK, and the OpenAI Agents SDK.
OpenTelemetry in and out
Ingest GenAI spans from apps you have already instrumented, and export to the observability backend you already pay for.
The LLM proposes. Deterministic code disposes.
Reasoning stays with the model. Every budget, gate, and kill switch is plain compiled logic that runs the same way every time.
Free to self-host, forever
The runtime is Apache 2.0 and stays feature-complete. No license gates, no phone-home, no lock-in. It is a single Go binary you run on your own infrastructure, with your own provider keys.
Get on the waitlist
The runtime is open source today. The hosted dashboard, runs, spend, and approvals in one place, is in private beta. Join to get early access and launch updates. No spam.
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Get in touch
Building agents in production, evaluating RiskKernel for your team, or want to be a design partner? Reach out.