🧾 Agentspecs

YAML-based specifications for AI agents, MCP servers, skills and more...

Overview

This repository is the source of truth for declarative specs consumed by Agent Runtimes code generation.

The YAML files in agentspecs/agentspecs are compiled into Python and TypeScript catalogs used by runtime and UI layers.

Current Repository Structure

agentspecs/
├── agents/           # Agentspecs
├── teams/            # Team orchestration specs
├── frames/           # Frame specs: owned, scoped context a Cog works under
├── cogs/             # Cog specs: an agent, equipped with Frames
├── ops/              # Op specs: Cogs, orchestrated, with a validation strategy
├── guards/           # Guard specs: a check, extending a guardrail
├── gates/            # Gate specs: what happens on what the Guards found
├── tracks/           # Track specs: the evidence kept, and for how long
├── apps/             # Application specs (Appspec): a chat, a widget, a decision, a worker
├── fragments/        # Capability fragments an agent includes
├── mcp-servers/      # MCP server specs
├── skills/           # Skill specs
├── tools/            # Runtime tool specs
├── frontend-tools/   # Frontend tool specs
├── envvars/          # Environment variable specs
├── models/           # Model specs
├── model-providers/  # Model provider specs
├── memory/           # Memory backend specs
├── guardrails/       # Guardrail policy specs
├── evals/            # Evaluator specs
├── benchmarks/       # Benchmark suite specs
├── strategies/       # Strategy specs (control-loop reasoning)
├── triggers/         # Trigger specs
├── events/           # Event specs
├── outputs/          # Output format specs
├── notifications/    # Notification channel specs
└── ui-plugins/       # UI plugin specs

Current YAML file counts:

• Agents: 168
• Teams: 11
• Frames: 5
• Cogs: 3
• Ops: 1
• Guards: 12
• Gates: 8
• Tracks: 2
• Applications: 4
• Fragments: 1
• MCP servers: 14
• Skills: 7
• Tools: 18
• Frontend tools: 6
• Env vars: 10
• Models: 39
• Model providers: 7
• Memory backends: 4
• Guardrails: 6
• Evals: 9
• Benchmarks: 8
• Strategies: 4
• Triggers: 3
• Events: 6
• Outputs: 9
• Notifications: 5
• UI plugins: 3

Versioning

All specs are versioned.

Required Version Field

Each spec includes:

• id: logical identifier
• version: semantic version string (currently 0.0.1 for all shipped specs)

Example:

id: data-acquisition
version: 0.0.1
name: Data Acquisition Agent

Versioned References

Cross-spec references should use id:version format.

Examples:

mcp_servers:
  - tavily:0.0.1

skills:
  - github:0.0.1

envvars:
  - TAVILY_API_KEY:0.0.1

agent_spec_id: comprehensive-sales-analytics:0.0.1

Runtime Catalog Aliases

Generated catalogs are keyed by unversioned id only (e.g. data-acquisition).

The get_* / get*Spec accessor functions accept both bare ids and versioned refs (data-acquisition:0.0.1), stripping the version suffix automatically.

Iterating catalog values (.values() / Object.values()) returns each spec exactly once — no deduplication is needed.

Default Version During Codegen

Code generation enforces a default spec version of 0.0.1 if omitted (scripts/codegen/versioning.py).

In practice, specs in this repository should always declare version explicitly.

Spec Types

Agents (agentspecs/agents)

Defines agent behavior and runtime defaults.

Common fields:

• id, version, name, description, enabled
• model, sandbox_variant, memory
• mcp_servers, skills, backend_tools, frontend_tools
• environment_name
• icon, emoji, color
• suggestions, welcome_message, welcome_notebook, welcome_document
• system_prompt, system_prompt_codemode_addons
• Optional workflow fields such as goal, trigger, guardrails, evals, output, notifications, advanced

Teams (agentspecs/teams)

Defines multi-agent orchestration over an underlying agent spec.

Common fields:

• id, version, name, description, enabled
• agent_spec_id (versioned)
• orchestration_protocol, execution_mode, supervisor
• agents (team members), reaction_rules, health_monitoring
• notifications, output

Frames (agentspecs/frames)

Defines the context work happens in — the rules, the vocabulary, the goals, the style and the norms of an organization, a department, a team, a project, a role or a relationship — and the Guards an output has to pass. The concept is the Intelligence Hub whitepaper's.

Common fields:

• id, version, name, description, enabled
• scope (organization, department, team, project, role, relationship) and owner — both required
• extends (the parent Frame, versioned)
• rules, terminology, goals, style, norms, process, architecture, prompts
• skills, backend_tools, mcp_servers (versioned references)
• guards (id, category, description, required)

from agentspecs.frames import compose_frames, render_frames

context = compose_frames(["sales-pipeline", "board-reporting"])
print(render_frames(context))

Cogs (agentspecs/cogs)

Defines an AI worker you can hold to account: a Cog extends an agent spec and is equipped with Frames.

Common fields:

• id, version, name, description, enabled
• extends (the agent spec, versioned) — required
• frames (the Frames it works under, in order, versioned) — required
• kind (context, model, combined)
• any agent field, overriding or appending to the agent's

id: cog-crawler
version: 0.0.1
name: Crawler Cog
extends: worker-crawler:0.0.1
frames:
  - web-research:0.0.1

from agentspecs.cogs import get_resolved_cog

cog = get_resolved_cog("cog-crawler")   # the agent, the Cog's changes and its Frames, flat
cog["frame_context"]["guards"]          # what its output answers to

Ops, Guards, Gates and Tracks

The execution and accountability model of the whitepaper: Frames guide the work. Cogs perform the work. Ops orchestrate the work. Guards verify the work. Gates decide whether the work proceeds. Tracks make the work accountable.

• Ops (agentspecs/ops): owner, cogs, frames, supervisor, and a validation strategy — guards by stage (preflight, in_flight, post_run, continuous), gates, track. An Op without one is refused.
• Guards (agentspecs/guards): a Guard extends a guardrail — the policy it verifies — and adds category (the seven of the whitepaper), stages, method, check and the signals it reports.
• Gates (agentspecs/gates): guards, when (a condition on their signals, or always), then and otherwise (proceed, pause, retry, human review or approval, expert review, stop), reviewers.
• Tracks (agentspecs/tracks): retain_for, include, readers, redact; never exchangeable.

ops/op-sales-pipeline-board-report.yaml is the comprehensive example: one Cog with its Frames, twelve Guards of all seven categories at all four stages, eight Gates and a seven-year Track.

from agentspecs.ops import get_resolved_op

op = get_resolved_op("op-sales-pipeline-board-report")
op["guards"]["post_run"]     # each Guard, with the guardrail it extends
op["gates"], op["track"]

Applications (agentspecs/apps)

An application is what a person uses: an agent with an interface, rules, tests and a place to run. One spec — the Appspec, schema: loop.app/v1 — for a chat, a widget, a decision and a worker. It stands alone: agent, connections, rules, interface, tests, record and deployment in plain fields; Guards, Gates and a Track optional, under checks.

A rule is written in a person's words and enforced on what a tool does: applies_to is an action class — read, write, send, buy, delete, publish — or named tools, and behaviour is do_it, if_asked, ask_first or leave_to_me. Every tool of tools/ carries its class (action:), and every MCP server says the class of each tool it serves (actions:); a tool nobody classed is unknown, and unknown is the most restricted.

from agentspecs.apps import behaviour_for, get_app

triage = get_app("inbox-triage")
behaviour_for(triage, "google-workspace.send_gmail_message")   # ask_first

MCP Servers (agentspecs/mcp-servers)

Defines MCP integrations and process startup configuration.

Common fields:

• id, version, name, description
• command, args, transport
• env, envvars (usually versioned)
• tags, icon, emoji

Skills (agentspecs/skills)

Defines reusable skill modules.

Common fields:

• id, version, name, description, module
• envvars, optional_env_vars, dependencies
• tags, icon, emoji

Backend Tools (agentspecs/backend-tools)

Defines the tools that run on the runtime, in Python: their metadata and implementation binding. An agent names them under backend_tools, as it names the page's under frontend_tools; the old tools field is refused.

Common fields:

• id, version, name, description, enabled
• approval
• runtime.language, runtime.package, runtime.method
• tags, icon, emoji

Frontend Tools (agentspecs/frontend-tools)

Names tools that run on the page rather than on the runtime (frontend_tools of an agent), a set at a time: the notebook's, the Lexical document's, the decks'.

Common fields:

• id, version, name, description, enabled
• toolset (the names of the tools of the set)
• tags, icon, emoji

Marks: icon and emoji

An MCP server, a skill, a backend tool and a frontend tool set each have an icon and an emoji (agentspecs.marks, its JSON Schema MARKS_SCHEMA, checked by tests/test_marks.py). The icon says the package it is in: <package>:<name>, the package one of @datalayer/icons-react and @primer/octicons-react, the name in kebab case as that package names it — @datalayer/icons-react:odoo, @primer/octicons-react:mark-github. A page draws the icon, the emoji where there is no icon, and nothing where there is neither.

Env Vars (agentspecs/envvars)

Defines environment variable metadata.

Common fields:

• id, version, name, description
• registrationUrl, tags, icon, emoji

Models (agentspecs/models)

Defines model options available to specs.

Common fields:

• id, version, name, description, provider
• default
• required_env_vars

Other Catalogs

• memory: memory backend options
• guardrails: security and policy profiles
• evals: evaluator definitions
• benchmarks: benchmark suites (with evaluator dependencies)
• triggers: reusable trigger templates
• outputs: output format templates/capabilities
• notifications: notification channel templates

Extension and Composition

A spec is built out of other specs rather than copied from them: extends (one parent, at most three deep, cycles refused) and includes (fragments). An agent extends an agent, a Frame extends a Frame, a Cog extends an agent, and a Guard extends a guardrail. Lists append and are deduplicated, a child's scalar wins, and !remove / !replace cover the rest. The rules are in the documentation and applied by agentspecs.compose.

Adding or Updating Specs

1. Add or edit YAML in the relevant folder under agentspecs/agentspecs.
2. Always set id and version.
3. Use versioned cross-references (name:version) in fields that reference other specs.
4. Keep IDs stable; bump version when introducing breaking changes.
5. Regenerate catalogs in Agent Runtimes (make specs) and validate consumers.

Parameters (Launch-Time Inputs)

Agentspecs support a parameters field using JSON Schema. This lets one spec be reused across multiple launches while keeping runtime inputs validated and explicit.

What parameters provide

• Validation: enforce type, enum, required, and defaults.
• Templating: inject values into text fields using {{parameter_name}}.
• Reusability: same agent spec, different runtime contexts.

Typical template targets

• system_prompt
• welcome_message
• pre_hooks.sandbox
• other template-aware text fields

Example

id: demo-parameters
version: 0.0.1

parameters:
  type: object
  properties:
    project:
      type: string
      default: Orbit
    role:
      type: string
      enum:
        - product analyst
        - engineering lead
        - support specialist
      default: product analyst
  required:
    - project

welcome_message: >
  This runtime was launched for project {{project}}.

system_prompt: >
  You are an assistant dedicated to {{project}}.

pre_hooks:
  sandbox:
    - |
      project_name = """{{project}}"""

Validation notes

• Missing required parameters fail validation.
• Invalid enum values fail validation.
• Optional parameters use defaults when available.

Best Practices

• Use kebab-case IDs for most specs (analyze-support-tickets).
• Use UPPER_SNAKE_CASE for env var IDs (TAVILY_API_KEY).
• Keep descriptions concise and action-oriented.
• Prefer explicit versioned references, even when alias lookup works.
• Maintain backward compatibility by preserving old versions when possible.

License

Copyright (c) 2025-2026 Datalayer, Inc.

Distributed under the terms of the Modified BSD License.