Omnigent Knowledge Base

What is Omnigent?

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.

yaml
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

executor:
  # REQUIRED area. type must be one of: claude_sdk | agents_sdk | omnigent.
  # There is NO `llm` executor type.
  type: claude_sdk     # Anthropic Claude SDK, in-process (simplest)
  # type: agents_sdk   — OpenAI Agents SDK, in-process
  # type: omnigent     — subprocess harness; requires config.harness below

  # Only for type: omnigent — pick the harness that runs the loop.
  # One of: claude-native | claude-sdk | codex-native | codex |
  #         openai-agents | open-responses | pi
  # config:
  #   harness: claude-native
  #   permission_mode: bypassPermissions   # claude-native headless
  #   yolo: true                           # codex-native headless

  # Model is OPTIONAL — omit to use the configured provider's default.
  # Pin one directly on the executor when needed:
  # model: anthropic/claude-sonnet-4-20250514   # LiteLLM provider/model
  # model: databricks-claude-opus-4-7           # or a serving-endpoint name
  # connection:                                 # provider credentials
  #   api_key: ${ANTHROPIC_API_KEY}
  # auth:                                        # or Databricks profile auth
  #   type: databricks
  #   profile: oss

  timeout: 3600        # Task deadline in seconds (default: 3600)
  max_iterations: 1000 # Max LLM calls per task (default: 1000)

# os_env — grant filesystem/shell access (harness agents). Exposes
# sys_os_read / sys_os_write / sys_os_edit / sys_os_shell.
os_env:
  type: caller_process
  cwd: .
  sandbox:
    type: none         # or linux_bwrap / darwin_seatbelt to sandbox

# guardrails — runtime policy gates (optional).
guardrails:
  ask_timeout: 86400   # seconds to wait on an approval prompt
  policies:
    blast_radius:
      type: function
      function:
        path: omnigent.inner.nessie.policies.blast_radius

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

| Type | When to use | How it works | |------|------------|--------------| | claude_sdk | New simple agents; existing Claude SDK code | In-process Anthropic Claude SDK; it manages its own loop | | agents_sdk | New simple agents; existing OpenAI Agents SDK code | In-process OpenAI Agents SDK runner | | omnigent | Coding/CLI harnesses, shell + file tools, sub-agents | Spawns a subprocess harness selected by config.harness |

There is no llm executor type — the only valid values are claude_sdk, agents_sdk, and omnigent. For most new simple agents, use claude_sdk (or agents_sdk) — in-process, no extra config. Use omnigent when the agent needs a specific harness, shell/file access, or sub-agents; it requires a config.harness:

| config.harness | What it is | |------------------|------------| | claude-native (alias claude) | Claude Code — full coding tools, native permissions | | claude-sdk | Claude Agent SDK loop | | codex-native / codex | Codex CLI / harness | | openai-agents | OpenAI Agents harness (any gateway model) | | open-responses | OpenResponses-compatible harness | | pi | Headless multi-model worker (bridged sys_os_* tools) |

AGENTS.md Format

Free-form markdown. This becomes the agent-authored portion of the system prompt; Omnigent may append framework-owned lifecycle or metadata instructions at runtime. 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:

markdown
---
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):

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 "<service-name> 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.

python
# tools/python/my_tools.py
from omnigent.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

yaml
spec_version: 1
name: my-assistant
description: A helpful assistant.
executor:
  type: claude_sdk
instructions: |
  You are a helpful assistant. Answer questions clearly and concisely.

This is the simplest valid agent — a name, an executor, and instructions. No model is pinned, so it uses the configured provider's default. No skills, no tools, no sub-agents.

Example: Research Agent with Tools and Skills

yaml
spec_version: 1
name: researcher
description: A research agent that searches the web and synthesizes findings.
executor:
  type: agents_sdk
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:

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

Each name must match a directory under agents/. The parent must use executor.type: omnigent — that's what provides the spawn tools. Each sub-agent is a full agent and may use any executor (claude_sdk, agents_sdk, or omnigent).

Sub-agent config

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

yaml
# agents/researcher/config.yaml
spec_version: 1
name: researcher
description: Sub-agent that searches the web for information.
executor:
  type: claude_sdk
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 sys_session_send (singular), check_task, and sys_cancel_task tools automatically when sub-agents are declared. The parent's AGENTS.md should reference them:

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

Call `sys_session_send(type="<name>", input="<task>")` to
dispatch one. Emit multiple `sys_session_send` 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,
`sys_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:

bash
# 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:6767