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Waxell
Waxell is a platform for building governed AI agents with built-in observability, policy enforcement, and production-grade infrastructure. This file provides an index of all documentation for LLM consumption.
- Authority
- 50 reach 5 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://waxell.ai/llms.txt
- llms-full.txt
- https://waxell.ai/llms-full.txt
- Sections
- 16
- Links
- 230
- Size
- 51603 bytes
- Popularity rank
- #987005
- Categories
- AI and machine learning
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-19T10:45:35.074Z
- Last checked
- 2026-10-21T23:22:03.085Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-21T23:21:57.328Z. Cached copy · Source on waxell.ai
# Waxell
> Waxell is a platform for building governed AI agents with built-in observability, policy enforcement, and production-grade infrastructure. This file provides an index of all documentation for LLM consumption.
> Last generated: 2026-10-21
## Links
- [Website](https://waxell.ai)
- [Documentation](https://waxell.ai/docs/)
- [Dashboard](https://waxell.dev)
- [Status](https://status.waxell.dev)
- [Community Forum](https://community.waxell.dev)
- [Security](https://waxell.ai/docs/security)
- [Full Documentation for LLMs](https://waxell.ai/docs/llms-full.txt)
## Waxell Observe
> LLM observability, cost tracking, and governance for AI applications
### Get Started
- [Waxell Observe](https://waxell.ai/docs/observe/overview): Lightweight observability and governance for any Python AI agent framework. Track LLM calls, manage costs, and enforce policies without rewriting your agents.
- [Quickstart: Observe Your Agents](https://waxell.ai/docs/observe/quickstart): Add full observability to your AI agents -- auto-instrumentation in 2 lines, decorators for structure, WaxellContext for full control.
- [Installation & Configuration](https://waxell.ai/docs/observe/installation): Install waxell-observe and configure API credentials via environment variables, CLI config file, or programmatic setup.
### Instrumentation
- [Auto-Instrumentation](https://waxell.ai/docs/observe/integrations/auto-instrumentation): Zero-code observability for 200+ AI/ML libraries including LLM providers, vector databases, agent frameworks, and more
- [Decorator Pattern](https://waxell.ai/docs/observe/integrations/decorator): Add observability and governance to any Python function with the @observe decorator.
- [Behavior Tracking](https://waxell.ai/docs/observe/features/behavior-tracking): Track tool calls, retrievals, decisions, reasoning, retries, and steps with decorators, convenience functions, or manual context methods.
- [Advanced: Context Manager](https://waxell.ai/docs/observe/integrations/context-manager): Use WaxellContext for fine-grained control over observability and governance in complex agent workflows.
- [Streaming](https://waxell.ai/docs/observe/integrations/streaming): Capture streaming LLM responses with proper token counting
- [Multi-Agent](https://waxell.ai/docs/observe/integrations/multi-agent): Trace correlated multi-agent systems with shared sessions
### Tutorials
- [Tell the Story of a Run](https://waxell.ai/docs/observe/tutorials/story-of-a-run): Turn a trace from a list of spans into a narrative anyone can read — task, steps, decisions, result, and what degraded — across all three integration modes.
- [Auto-Instrumentation: Activity, Not Story](https://waxell.ai/docs/observe/tutorials/narrate-auto-instrumentation): What the two-line init() gives you, exactly where it stops, and the one-line change that turns activity into a narrated run.
- [Decorator: Narrate With Three Lines](https://waxell.ai/docs/observe/tutorials/narrate-decorator): Start
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