> ## Documentation Index > Fetch the complete documentation index at: https://docs.nanny.run/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > What Nanny is, what it is not, and when to use it. You deploy a multi-agent system on Friday. Monday morning your CFO sends a Slack: "Why did we spend \$4,000 over the weekend?" One agent got stuck in a loop. Nobody stopped it. No audit trail. Nothing. This is happening right now at hundreds of companies. **Nanny is the enforcement primitive that prevents it.** Nanny is an open-source enforcement primitive for autonomous agents and multi-agent systems. You tell it how far each agent is allowed to go — in steps, tokens, wall-clock time, and which tools it can touch — and the moment any limit is crossed, Nanny kills the process immediately and emits a structured log saying exactly what happened and why. No grace period. No soft warnings. No recovery logic. No negotiation. That boundary is deterministic, auditable, and structurally impossible for any agent to bypass. *** ## What nanny guarantees When you run an agent under nanny, these three things are true: * It **will not** take more steps than you allow. * It **will not** spend more than your token budget. * It **will not** run longer than your timeout. If any limit is breached, Nanny kills the process immediately — the agent cannot catch, delay, or prevent the stop. An `ExecutionStopped` event is emitted with the exact reason, and Nanny exits with a non-zero status code. *** ## What nanny is not Nanny sets limits and enforces them. It adds no intelligence — it doesn't interpret your agent's behavior, suggest better limits, or make any decisions. If you set a limit, it holds. *** ## Who it is for Nanny is for developers and teams running agents in production — or preparing to. It is a good fit if you: * Are building **multi-agent systems** where different agents have different roles, tool access, and budget ceilings — and you need enforcement that fires per-role, not just globally * Are running **autonomous agents** that call external tools, browse the web, or write to APIs * Need hard guarantees that an agent **cannot exceed a token budget or run indefinitely** * Want a **structured audit trail** of every tool call and stop reason for every execution * Are building with **CrewAI, LangChain, or any Python or Rust agent framework** * Want enforcement that is **not tied to any agent framework** — use CrewAI, LangGraph, or any Python or Rust framework without lock-in *** ## The multi-agent scenario A fintech team builds a system where a manager agent spawns 12 specialists: one checks regulations, one pulls market data, one drafts reports. They deploy on Friday. One agent gets stuck looping on a market data API call over the weekend. The team has no per-role kill switch and no audit trail of which agent made which call. With Nanny, each specialist has its own named limit set in `nanny.toml`: ```toml theme={null} [limits.analysis] steps = 60 tokens = 200 # tight — this agent makes expensive calls timeout = 60000 [limits.reporter] steps = 20 tokens = 50 # loose — this agent just writes a file timeout = 30000 ``` The analysis agent activates `[limits.analysis]` when it runs. The reporter activates `[limits.reporter]`. Each has its own tool allowlist — the analysis agent cannot call `write_report`, the reporter cannot call `compute_stats`. The moment any agent exceeds its ceiling or reaches for the wrong tool, Nanny stops it. The event log shows exactly which agent, which tool, which limit, and when. **Scope today:** This works for any multi-agent framework that runs agents within a single process — CrewAI, LangGraph, AutoGen, plain Python. See [`examples/python/metrics_crew`](https://github.com/nanny-run/nanny/tree/main/examples/python/metrics_crew) for the complete working example. Cross-process and cross-machine enforcement is supported via the governance server. *** ## The nanny ecosystem Nanny is designed to meet you where you are and grow with you. **Nanny CLI** — The enforcement entry point. Governs any agent process in any language as its parent process supervisor. Install it once as a system tool and use `nanny run` from any project that has a `nanny.toml` with a `[start]` command configured. ```bash theme={null} nanny run # reads [start].cmd from nanny.toml nanny run --limits=researcher # activates a named limit set ``` **Rust SDK** — For Rust agents, go deeper. Annotate individual functions with `#[nanny::tool]`, `#[nanny::rule]`, and `#[nanny::agent]` to get per-function token accounting, allowlist enforcement, and custom rules. See the [Rust SDK guide](/v0.4/guides/rust-sdk). **Python SDK** — The same model as the Rust SDK, as Python decorators. `@tool`, `@rule`, `@agent`. Each agent in your fleet gets its own budget ceiling, tool allowlist, and custom rules. Works with LangChain, CrewAI, or any Python agent framework. See [Python SDK](/v0.4/guides/python-sdk). **Nanny Cloud** *(coming soon)* — Durable audit logs, team dashboards, org-level budget aggregation, and managed enforcement across all your agents. The OSS runtime stays unchanged — Cloud is the observability and coordination layer above it. *** ## Open source The Nanny runtime is fully open source under the **Apache 2.0 licence**. Source code, issues, and contributions live at [github.com/nanny-run/nanny](https://github.com/nanny-run/nanny). Cloud is the managed layer above the OSS primitive — not a replacement for it. *** ## Next steps Install nanny and run your first governed agent in under five minutes. Install, upgrade, and uninstall on macOS, Linux, and Windows. Understand the enforcement model and passthrough mode. Learn how timeout, steps, and token limits work. Full schema for the configuration file. Per-function governance with `#[nanny::tool]`, `#[nanny::rule]`, `#[nanny::agent]`. Per-function governance with `@tool`, `@rule`, `@agent` decorators. Works with LangChain, CrewAI, and any Python agent framework. Cross-process and cross-machine enforcement with shared budget and mTLS.