Imported from dbczumar/agent-plane (agent_plane/onboarding/agent/skills/agent-plane-knowledge/SKILL.md). Install upstream with npx skills add dbczumar/agent-plane --skill agent-plane-knowledge. Copyright stays with the author.

Agent Plane Knowledge Base

What is agent plane?

Agent plane is a server that hosts, manages, and executes agents via an OpenResponses-compatible API. Users create agent directories (also called agent images) that contain configuration, instructions, skills, and tools. The server loads these directories and serves them via HTTP.

Agent Directory Layout

my-agent/
├── config.yaml          # REQUIRED — agent spec
├── AGENTS.md            # Recommended — instructions/personality
├── skills/              # Optional — load-on-demand skills
│   └── <skill-name>/
│       └── SKILL.md
├── tools/               # Optional — packaged tools
│   ├── python/          # Local Python tools (auto-discovered *.py)
│   ├── typescript/      # Local TypeScript tools (auto-discovered *.ts)
│   └── mcp/             # MCP server declarations (*.yaml)
└── agents/              # Optional — sub-agent directories (recursive)
    └── <agent-name>/
        ├── config.yaml
        └── ...

config.yaml Reference

The only required file. All fields except spec_version are optional.

spec_version: 1               # REQUIRED, must be 1

name: my-agent                # Display name
description: Does X and Y.    # One-line summary

# Instructions — path to a file or inline text.
# Default: looks for AGENTS.md in the agent directory.
instructions: AGENTS.md

llm:
  # Model in LiteLLM format: provider/model-name
  # Examples: openai/gpt-5.4, anthropic/claude-sonnet-4-20250514,
  #   gemini/gemini-2.5-pro, groq/llama-4-scout-17b-16e-instruct
  model: openai/gpt-5.4

  # Provider credentials. Use ${ENV_VAR} for secrets.
  connection:
    api_key: ${OPENAI_API_KEY}
    # base_url: https://custom-endpoint.example.com/v1  # for compatible APIs

  # Optional LLM parameters (passed through to the provider)
  reasoning_effort: medium     # low | medium | high
  max_completion_tokens: 4096  # caps total output including reasoning

executor:
  # Executor type determines how the agent runs.
  type: llm          # default — agent plane manages the LLM loop
  # type: claude_sdk   — Claude SDK agent (native)
  # type: agents_sdk   — OpenAI Agents SDK agent (native)
  # type: remote        — external agent behind HTTP endpoint

  timeout: 3600        # Task deadline in seconds (default: 3600)
  max_iterations: 1000 # Max LLM calls per task (default: 1000)
  # endpoint: http://localhost:5001  # required for type: remote

interaction:
  conversational: true   # Maintain turn history (default: true)
  modalities:
    input: [text, image, file]   # default: [text]
    output: [text]               # default: [text]

tools:
  # Sub-agents this agent can spawn (must match agents/ subdirectories)
  agents:
    - researcher
    - summarizer

  # Built-in tools — string name or dict with config
  builtins:
    - web_search                 # auto-detects backend based on model provider
    - terminal_run               # persistent bash shell scoped to the conversation
    - upload_file
    - search_conversations

  timeout: 60          # Default tool timeout in seconds

params:                # Arbitrary key-value (readable by skills/tools)
  max_results: 10

Executor Types

For most new agents, use the default llm executor — it's the simplest and most capable option. Only use claude_sdk or agents_sdk if the user has existing agent code built on those SDKs.

AGENTS.md Format

Free-form markdown. This becomes the agent's system prompt. Best practices:

• Start with a clear identity statement ("You are a ...")
• List capabilities and constraints
• Reference skills by name ("You have a skill called deep-research")
• Reference sub-agents if any ("You can spawn the fact_checker agent")
• Keep it focused — the model reads this on every turn

Skills Format

Each skill lives in skills/<skill-name>/SKILL.md:

---
name: deep-research
description: Investigate a topic in depth using web search and source synthesis.
---

When researching a topic:

1. Search broadly first using web search...
2. Cross-reference multiple sources...

Rules:

• YAML frontmatter with name and description (both required)
• name must match the directory name, be lowercase, use [a-z0-9-]+
• Body is markdown instructions loaded on demand by the agent
• Referenced in AGENTS.md or config.yaml

Tools

Built-in tools

Call list_builtin_tools to get the current set of available built-in tools and their descriptions. Do not rely on a hardcoded list — new tools may be added at any time.

Tool recommendation guide:

• "I want a research agent" → web_search + web_fetch
• "I want a coding agent" → terminal_run + upload_file
• "I want a data analysis agent" → terminal_run + upload_file + download_file
• "I want a conversational assistant" → no tools needed (or web_search for current info)
• "I want an agent that can access external APIs" → consider MCP servers (see below)

MCP servers (external tool integrations)

MCP (Model Context Protocol) lets agents connect to external services — databases, APIs, Slack, GitHub, etc. Each MCP server is declared as a YAML file in tools/mcp/:

my-agent/
  tools/
    mcp/
      github.yaml
      slack.yaml

MCP server config format (tools/mcp/github.yaml):

transport: http
url: https://mcp-server.example.com/sse
headers:
  Authorization: Bearer ${GITHUB_TOKEN}

• transport: must be http
• url: the MCP server's SSE endpoint URL
• headers: optional auth headers (use ${ENV_VAR} for secrets)

When to recommend MCP:

• User wants to connect to an external service (database, API, SaaS tool)
• User mentions Slack, GitHub, Jira, Postgres, etc.
• The integration isn't covered by built-in tools

Finding MCP servers: Use web_search (if available) or web_fetch to search for available MCP servers. Good starting points:

• https://modelcontextprotocol.io — official MCP directory
• https://github.com/modelcontextprotocol — official GitHub org
• Search for " MCP server" (e.g. "Slack MCP server", "Postgres MCP server")

If the user mentions a specific service they want to connect to, use web_search or web_fetch to find if an MCP server exists for it and how to configure it.

What to tell the user: MCP servers are external processes that expose tools via HTTP. The user needs to run the MCP server separately (or use a hosted one) and provide the URL in the config.

Local tools (custom Python/TypeScript)

Python files in tools/python/ are auto-discovered. Each @tool-decorated module-level function in those files becomes a separate tool — one file may export many tools. The decorator derives the JSON schema from the function's type hints and Google-style docstring.

# tools/python/my_tools.py
from agent_plane.tools import tool


@tool
def my_tool(text: str, count: int = 1) -> str:
    """
    Repeat the text count times.

    Args:
        text: The text to repeat.
        count: Number of repetitions (default 1).
    """
    return text * count

Authoring rules:

• Decorate a module-level function — not a class method, lambda, or nested function (the decorator rejects those at decoration time with a clear error).
• Type hints on parameters drive the LLM-facing JSON schema. Use concrete types — Any and object produce permissive schemas with no validation.
• The function name becomes the LLM-facing tool name. Names must not collide with built-in tools or with other custom tools in the same agent (collisions fail loud at agent load).
• Both def and async def are supported. Sync def bodies are wrapped in asyncio.to_thread automatically so they don't block the event loop.
• Pydantic BaseModel arguments are first-class — they get expanded into the schema correctly with full validation.

When to recommend local tools: When the user needs custom logic that isn't covered by builtins or MCP servers.

Example: Minimal Agent

spec_version: 1
name: my-assistant
description: A helpful assistant.
llm:
  model: anthropic/claude-sonnet-4-20250514
  connection:
    api_key: ${ANTHROPIC_API_KEY}
instructions: |
  You are a helpful assistant. Answer questions clearly and concisely.

This is the simplest valid agent — just a name, model, and instructions. No skills, no tools, no sub-agents.

Example: Research Agent with Tools and Skills

spec_version: 1
name: researcher
description: A research agent that searches the web and synthesizes findings.
llm:
  model: openai/gpt-5.4
  connection:
    api_key: ${OPENAI_API_KEY}
tools:
  builtins:
    - web_search
    - upload_file
interaction:
  modalities:
    input: [text, file]
    output: [text]
instructions: AGENTS.md

Sub-Agents (multi-agent systems)

An agent can spawn child agents to delegate tasks. Sub-agents are full agents with their own config.yaml, living in the agents/ directory:

my-agent/
  config.yaml
  AGENTS.md
  agents/
    researcher/
      config.yaml        # sub-agent spec
    fact-checker/
      config.yaml        # another sub-agent

Declaring sub-agents

The parent's config.yaml lists sub-agent names under tools.agents:

tools:
  agents:
    - researcher
    - fact-checker
  builtins:
    - web_search

Each name must match a directory under agents/.

Sub-agent config

Each sub-agent has its own complete config.yaml:

# agents/researcher/config.yaml
spec_version: 1
name: researcher
description: Sub-agent that searches the web for information.
llm:
  model: openai/gpt-5.4
  connection:
    api_key: ${OPENAI_API_KEY}
tools:
  builtins:
    - web_search
    - web_fetch
instructions: |
  You are a researcher. When given a topic, search the web
  and return a summary with sources.

How spawning works

The parent agent gets spawn_sub_agent (singular), check_task, and cancel_task tools automatically when sub-agents are declared. The parent's AGENTS.md should reference them:

You have two sub-agents you can delegate to:
- **researcher** — searches the web for information
- **fact-checker** — verifies claims with evidence

Call `spawn_sub_agent(type="<name>", input="<task>")` to
dispatch one. Emit multiple `spawn_sub_agent` tool calls in the
same response to run sub-agents in parallel. Each result auto-
delivers as a system message when ready — `check_task` polls,
`cancel_task` aborts.

When to recommend sub-agents

• User wants specialized roles (researcher + summarizer + reviewer)
• User wants parallel execution (search multiple sources at once)
• User wants separation of concerns (each sub-agent has focused instructions)

For simple agents, sub-agents are overkill. Only suggest them when the user describes a workflow with distinct steps or roles.

Running an Agent

Once the agent directory is created:

# Start the server with the agent pre-registered
ap server --agent ./my-agent/

# Or deploy to a running server
ap deploy ./my-agent/ --server http://localhost:8000