# 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: 2026-06-19 ## 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 ### Getting 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. ### Integrations - [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 - [OpenAI](https://waxell.ai/docs/observe/integrations/openai): Instrument OpenAI API calls with automatic or manual tracing - [Anthropic](https://waxell.ai/docs/observe/integrations/anthropic): Instrument Anthropic Claude API calls with Waxell Observe - [LiteLLM](https://waxell.ai/docs/observe/integrations/litellm): Multi-provider observability with LiteLLM's unified API - [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 - [Decorator Pattern](https://waxell.ai/docs/observe/integrations/decorator): Add observability and governance to any Python function with the @observe decorator. - [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. - [Claude Code & Cowork](https://waxell.ai/docs/observe/integrations/claude-code): Add observability, governance, and security guardrails to Claude Code and Claude Cowork sessions ### Tutorials ### Features - [LLM Call Tracking](https://waxell.ai/docs/observe/features/llm-tracking): Track every LLM API call with model, token counts, cost, and prompt/response previews. - [Cost Management](https://waxell.ai/docs/observe/features/cost-management): Track, estimate, and control LLM costs with client-side estimation, server-side calculation, and tenant-level overrides. - [Policy & Governance](https://waxell.ai/docs/observe/features/governance): Enforce execution policies with pre-run checks, budget controls, and mid-execution validation. - [Sessions](https://waxell.ai/docs/observe/features/sessions): Group related agent runs into sessions for multi-turn conversation tracking and aggregate analysis. - [User Tracking](https://waxell.ai/docs/observe/features/user-tracking): Track per-user costs, usage patterns, and agent interactions with opaque user identifiers. - [Scoring](https://waxell.ai/docs/observe/features/scoring): Attach quality scores to agent runs using numeric, categorical, or boolean values from SDK or UI. - [Evaluators (LLM-as-Judge)](https://waxell.ai/docs/observe/features/evaluators): Automate quality assessment of agent runs using configurable LLM-based evaluators and human annotation queues. - [Prompt Management](https://waxell.ai/docs/observe/features/prompt-management): Version, label, and retrieve prompts with content hashing for production traceability and a playground for testing. - [Datasets & Experiments](https://waxell.ai/docs/observe/features/datasets-experiments): Build test datasets from production data, run systematic experiments across configurations, and compare results side by side. ### API Reference - [REST API Reference](https://waxell.ai/docs/observe/api/endpoints): Complete REST API reference for the Waxell Observe endpoints, including runs, policy checks, events, model costs, LLM calls, sessions, users, scoring, and prompts. - [Python SDK Reference](https://waxell.ai/docs/observe/api/python-sdk): Complete API reference for all public classes, functions, and types in the waxell-observe Python package. ## Framework > Python SDK and Runtime for building governed AI agents ### Getting Started - [Introduction](https://waxell.ai/docs/intro): The control plane for agentic systems. Add observability to existing agents or build governed agents from scratch. - [Installation](https://waxell.ai/docs/installation): Install and configure Waxell for your Python project. ### SDK - [SDK Overview](https://waxell.ai/docs/sdk/overview): Understanding the Waxell SDK - intent-only definitions for AI agents. - [@agent Decorator](https://waxell.ai/docs/sdk/agent-spec): Define agent containers with the @agent decorator. - [@workflow Decorator](https://waxell.ai/docs/sdk/workflow-spec): Define multi-step execution flows with the @workflow decorator. - [@decision Decorator](https://waxell.ai/docs/sdk/decision-spec): Add LLM-powered decision points with the @decision decorator. - [@tool Decorator](https://waxell.ai/docs/sdk/tool-spec): Integrate external systems with the @tool decorator. - [@capability Decorator](https://waxell.ai/docs/sdk/capability-spec): Bundle reusable behaviors with the @capability decorator. ### Runtime - [Runtime Overview](https://waxell.ai/docs/runtime/overview): Understanding the Waxell Runtime - the execution engine for AI agents. - [ExecutionContext](https://waxell.ai/docs/runtime/execution-context): Managing execution state with ExecutionContext. - [WorkflowEnvelope](https://waxell.ai/docs/runtime/workflow-envelope): Durable execution boundaries with WorkflowEnvelope. - [Backends](https://waxell.ai/docs/runtime/backends): Configure runtime backends for different environments. ### Tutorials - [Build Your First Agent](https://waxell.ai/docs/tutorials/first-agent): Step-by-step tutorial to build your first Waxell agent. - [Multi-Step Workflows](https://waxell.ai/docs/tutorials/workflows): Build complex multi-step workflows with branching and composition. - [Adding Governance](https://waxell.ai/docs/tutorials/governance): Add governance policies to control agent behavior. - [Deploying to Production](https://waxell.ai/docs/tutorials/production): Deploy Waxell agents to production environments. ## Migrate > Compare frameworks and migrate from LangChain, CrewAI, or Langfuse ### Framework Comparison - [LangChain vs Waxell](https://waxell.ai/docs/migrate/langchain-vs-waxell): Side-by-side comparison of LangChain alone, LangChain with Waxell Observe, and native Waxell for building AI agents. - [CrewAI vs Waxell](https://waxell.ai/docs/migrate/crewai-vs-waxell): Side-by-side comparison of CrewAI alone, CrewAI with Waxell Observe, and native Waxell for building AI agents. - [Feature Comparison Matrix](https://waxell.ai/docs/migrate/feature-matrix): Comprehensive feature comparison across LangChain, CrewAI, custom Python agents, Waxell Observe, and Waxell Native. ### Migration Path - [Progressive Migration](https://waxell.ai/docs/migrate/overview): A phased approach to adopting Waxell. Each phase delivers standalone value -- you can stop at any phase. - [Phase 1: Add Observability](https://waxell.ai/docs/migrate/phase-1-observe): Add observability, cost tracking, and policy enforcement to your existing AI agents with waxell-observe. - [Phase 2: Add Signals](https://waxell.ai/docs/migrate/phase-2-signals): Move from ad-hoc agent triggering to webhook-driven execution with Waxell signals. - [Phase 3: Agent Builder](https://waxell.ai/docs/migrate/phase-3-agent-builder): AI-assisted migration tool that converts existing agents to native Waxell SDK definitions. - [Phase 4: Go Fully Native](https://waxell.ai/docs/migrate/phase-4-native): Manual migration guide for converting existing agents to native Waxell SDK definitions with full governance and durable workflows. ## Developer MCP > MCP server for AI agents to interact with Waxell programmatically ### MCP Server - [For Coding Agents](https://waxell.ai/docs/agents/overview) - [Setup](https://waxell.ai/docs/agents/setup) - [Capabilities](https://waxell.ai/docs/agents/capabilities) ## Guides > Architecture deep-dives, enterprise features, and best practices ### Guides - [Architecture](https://waxell.ai/docs/guides/architecture): Deep dive into Waxell's data flow and security architecture. - [Enterprise Guide](https://waxell.ai/docs/guides/enterprise): Configure enterprise security features including data residency, sub-tenants, and compliance. - [Best Practices](https://waxell.ai/docs/guides/best-practices): Best practices for building production-ready Waxell agents. ### Reference - [CLI Reference](https://waxell.ai/docs/reference/cli): The complete guide to the wax CLI — authenticate, push agents, explore runs and traces, govern, and configure Waxell from your terminal. - [Enterprise API](https://waxell.ai/docs/reference/enterprise-api): API reference for enterprise features including data residency, sub-tenants, and compliance.