Most teams don’t struggle with AI because the models aren’t capable.
They struggle because there’s no reliable way to operate AI in real systems.
Bring your own agents. Works with LangChain, CrewAI, LlamaIndex, and any Python framework.
Free. 2-line setup.
Waxell is a governance and observability layer that sits above your existing agents, enforcing policies, tracking cost, and recording every decision in real time.
Already running agents?
Keep what you have.
Add two lines of Python. From that point on, Waxell Observe captures every LLM call, tool invocation, cost, and agent decision — automatically. Governance policies apply by default. This is agentic governance without re-platforming.
No wrapper classes. No changes to your agent logic. No re-platforming.
Everything that runs after this line is observed and governed.
Auto-instruments 200+ libraries. No code changes required.
Waxell is not an agent framework and it is not an application.
Waxell is a governance and orchestration layer that sits above agents, models, and integrations. It defines the conditions under which work is allowed to occur and records what happens when it does.
This separation allows agent behavior to evolve while control remains stable.
Some teams have clear ideas for how agents could augment their work, but no operating model to support them.
Others already run agents in production, but struggle with governance, visibility, or cost as usage grows.
Waxell supports both starting points. Start with Observe — add governance to the agents you already have without rebuilding anything.
Move to the full Waxell SDK when you need a native production runtime with durable workflows and full infrastructure support.
Every step delivers standalone value. No commitment required until you're ready.
Autonomous systems are not adopted all at once. They begin as experiments, then become workflows, then become infrastructure.
Waxell is implemented incrementally, so governance can be introduced early without blocking execution.
The goal is systems that can be expanded deliberately while remaining operable by the teams that run them.
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Add decorators and context managers when you want more structure.
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Deploy to the Waxell runtime when you're ready for policy dashboards, scheduling, and full production infrastructure.
Autonomy without governance introduces fragility.
Governance without autonomy introduces friction.
Waxell exists to balance the autonomy and governance, so that agentic systems can be expanded deliberately while remaining predictable and controllable.
The goal is systems that continue to function when attention moves elsewhere.
FAQ
Does Waxell work with my existing agents?
Yes. Waxell Observe adds governance to any Python agent — LangChain, CrewAI, LlamaIndex, or custom. No changes to your agent logic.
How is Waxell different from LangSmith or Langfuse?
Other tools observe and record. Waxell also enforces — blocking actions that violate policy before they execute, not logging them after the fact.
What does "governance" actually mean in practice?
Cost budgets that enforce themselves, content policies that block PII before it leaves your stack, rate limits that stop runaway loops, and an audit trail of every decision. Eleven policy categories, configured in the dashboard, enforced during execution.
How long does integration take?
Two lines of Python. pip install, initialize before your imports, done. Every agent that runs after that line is observed and governed.