# Agentfield Personal > Build and install a personal AI agent on this machine's AgentField: real source in ~/agentfield-agents, packaged with agentfield-package.yaml, installed with `af install`, started with `af run`, registered on the local control plane, and visible in AgentField Desktop with a keys form and an auto-start toggle. Use when the user wants an agent that lives on their machine as a persistent capability — a pricing agent, a support agent, a research agent — rather than a deployable project. A standalone repository with Docker Compose is the `agentfield` skill; calling agents that already exist is the `agentfield-use` skill. - Skill: `agent-field/agentfield-personal` (Agent Skill) - Install (CLI): `npx skillmds@latest add agent-field/agentfield-personal` - Raw SKILL.md: https://api.skillmd.com/api/skills/agent-field/agentfield-personal/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: Agent-Field (https://skillmd.com/u/agent-field) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/agent-field/agentfield-personal --- # Building a personal AgentField agent A personal agent is a capability installed on this machine. Once it's running, the local control plane routes calls to it, other agents and coding assistants can discover and delegate to it, and the AgentField Desktop app shows it with its keys and lifecycle controls. The deliverable is not a repository — it is a working, registered, callable agent. This skill is the workflow for getting that done. It does not use Docker, Docker Compose, a new Git repository, or a project `CLAUDE.md` unless the user independently asks for one of those. ## Before building Check once whether an installed agent already covers the request: `af list` for what's installed, and the control plane's discovery (`GET /api/v1/discovery/capabilities`) for what each running agent's reasoners actually do (the `agentfield-use` skill documents this surface). If a healthy installed agent already does the job, say so and offer to use it instead of building a duplicate — unless the user explicitly asked to build a new or replacement agent, in which case build it. A stopped-but-capable installation is not a reason to duplicate either; offer to start it with `af run `. For the agent's design, fetch the live SDK docs first — `https://agentfield.ai/llms.txt` (and `llms-full.txt` for depth) — that is the SDK ground truth. Decompose the job into reasoners the same way the `agentfield` skill teaches: by cognitive jobs, not by a single catch-all prompt. Personal agents are usually small — a handful of reasoners on one node is normal — but the design bar is the same. ## Workflow 1. **Build stable real source.** Choose one filesystem-safe kebab-case package/name/node ID, ``, and author the agent at `~/agentfield-agents/`. This directory is the durable source of truth the user will edit later. Do not author in a temporary directory, a disposable checkout, or the generated `~/.agentfield` installation copy. Run language-native syntax checks and tests on the source before installing. 2. **Package the source.** Write the manifest at `~/agentfield-agents//agentfield-package.yaml`. Put `config_version: v1` at the top — the manifest schema version, distinct from the agent release `version`. Declare `name`, release `version`, `description`, `author`, `language`, a runnable `entrypoint.start` that matches the source and language, `entrypoint.healthcheck: /health`, `agent_node.node_id` equal to ``, its matching `agent_node.default_port`, and only install dependencies the source needs. ```yaml config_version: v1 name: pricing-agent version: 0.1.0 description: Answers pricing questions from the product catalog author: language: python entrypoint: start: python main.py healthcheck: /health agent_node: node_id: pricing-agent default_port: 9301 dependencies: python: [requests] user_environment: - name: OPENROUTER_API_KEY description: LLM provider key used for all reasoning calls type: secret scope: global ``` 3. **Declare secrets safely.** For every external key the source actually uses, declare a `user_environment` entry with `name`, an actionable `description`, `type: secret`, and an explicit scope. Use `scope: global` only for deliberately reusable credentials such as a model-provider key; use `scope: node` for credentials or configuration specific to this agent. Do not declare invented keys. 4. **Install and configure.** Run `af install ~/agentfield-agents/`. Configure each declared global key with `af secrets set KEY` and each node key with `af secrets set --node KEY`, letting the CLI prompt/stdin take the value. Never invent, echo, commit, put into `agentfield-package.yaml`, or include secret values in a handoff. 5. **Start and verify registration.** Run `af run `, then poll `GET ${AGENTFIELD_SERVER:-http://localhost:8080}/api/v1/nodes` until the node ID is registered in an active/healthy state. An install entry, `af list` entry, or successful process spawn alone is not success. 6. **Invoke live.** Invoke the public entry reasoner through the control plane with a representative request. For nontrivial work use async execution and poll (the `agentfield-use` skill documents the execute/poll surface); require a terminal successful result before calling the build done. 7. **Handle failures honestly.** Diagnose and safely retry correctable failures from installation, secret setup, startup, registration, or invocation (`af logs ` is the first stop). If a required secret value is known only to the user, stop with a blocking handoff that names the needed key and scope but never its value. Do not claim completion until healthy registration and a live reasoner result both succeed. 8. **Hand off.** Tell the user the agent is installed, running, and now appears in the AgentField Desktop app, where its declared keys are presented as a form and its lifecycle has an auto-start toggle. Include: the stable source path, the manifest path, the installed name, the public entry reasoner's invocation target, the registration and live-call verification results, and the commands to restart (`af stop && af run `), stop (`af stop `), inspect logs (`af logs `), and update after source edits (`af install ~/agentfield-agents/` followed by `af run `).