akgentic-catalog

Configuration management for the Akgentic multi-agent framework. Store, query, clone, validate, and resolve versioned configuration entries (teams, agents, tools, prompts, models, and any allowlisted Pydantic model) through a single unified Catalog service backed by a pluggable EntryRepository.

Table of Contents

• Overview
• Installation
• Quick Start
• Architecture
• The Entry Model
• Storage Backends
• Sharing scalars between entries
• References Between Entries
• Querying the Catalog
• CLI
• REST API
• Development
• License

Overview

Version 2 of akgentic-catalog replaces the v1 four-catalog split (templates, tools, agents, teams each with its own service, repository, model, and query) with a single Entry model, a single Catalog service, and a single EntryRepository protocol. An entry is identified by the compound key (kind, namespace, id) and carries an opaque, schema-validated payload sized for any allowlisted Pydantic model type.

Key properties:

• Unified Entry model — one Pydantic shape for every kind of configuration. Built-in kinds include team, agent, tool, prompt, and model, and arbitrary new kinds are allowed as long as the payload's model_type resolves through the akgentic.* allowlist.
• Namespaces as tenancy / environment boundaries. Each namespace is a self-contained bundle: one team root entry plus any number of sub-entries referencing it.
• Two-phase ref model — sub-entries embed sentinel {"__ref__": "<id>", "__type__": "<model_type>"} dicts where the team references them; the resolver walks these refs (with cycle detection) to produce a fully-populated runtime object.
• Pluggable storage — YAML-file-per-entry and MongoDB single-collection backends ship in the box behind the EntryRepository protocol.
• Namespace bundles — export/import a whole namespace (team + all sub-entries) as a single YAML document for round-tripping between environments.
• CLI and REST API — manage entries and bundles outside of Python.

Architecture details live in _bmad-output/akgentic-catalog/architecture/10-package-structure.md (package layout), 05-validation.md (validation rules), and 06-service-and-env.md (the service pipeline).

Installation

Workspace Installation (Recommended)

This package is designed for use within the Akgentic monorepo workspace:

git clone git@github.com:b12consulting/akgentic-quick-start.git
cd akgentic-quick-start
git submodule update --init --recursive

uv venv
source .venv/bin/activate
uv sync --all-packages --all-extras

All dependencies (akgentic-core, akgentic-llm, akgentic-tool, akgentic-team) resolve automatically via workspace configuration.

Optional Extras

Extra	Packages pulled in	Enables
api	fastapi, uvicorn	create_app() FastAPI factory
cli	typer, rich	ak-catalog console script
mongo	pymongo	MongoEntryRepository
postgres	nagra, psycopg[binary]	PostgresEntryRepository, init_db

uv sync --extra api
uv sync --extra cli
uv sync --extra mongo
uv sync --extra postgres
uv sync --all-extras

Quick Start

Create a fresh YAML-backed catalog, seed a team namespace, and resolve it:

import tempfile
from pathlib import Path

from akgentic.catalog import (
    Catalog,
    Entry,
    UNSET_NAMESPACE,
    YamlEntryRepository,
)

with tempfile.TemporaryDirectory() as tmp:
    repo = YamlEntryRepository(Path(tmp))
    catalog = Catalog(repo)

    # Create the team root with a to-be-minted namespace.
    team = Entry(
        id="research-team",
        kind="team",
        namespace=UNSET_NAMESPACE,
        user_id="u1",
        model_type="akgentic.team.models.TeamCard",
        payload={
            "name": "Research Team",
            "entry_point": {
                "__ref__": "lead-agent",
                "__type__": "akgentic.core.AgentCard",
            },
            "members": [],
        },
    )
    team = catalog.create(team)          # namespace replaced by a fresh UUID
    namespace = team.namespace

    # Create a sub-entry in the same namespace.
    agent = catalog.create(Entry(
        id="lead-agent",
        kind="agent",
        namespace=namespace,
        user_id="u1",
        model_type="akgentic.core.AgentCard",
        payload={"role": "Lead", "description": "Coordinates the team"},
    ))

    # Read / resolve.
    stored_team = catalog.get(namespace=namespace, id="research-team")
    team_card = catalog.load_team(namespace)  # TeamCard with refs populated

See the architecture shards for a namespace-bundle walkthrough and YAML authoring guidance.

Architecture

flowchart LR
    PY[Python API] --> CAT
    CLI[ak-catalog CLI] --> CAT
    API[FastAPI /catalog] --> CAT
    CAT[Catalog service] --> RES[resolver.py]
    CAT --> REPO[EntryRepository]
    REPO --> YAML[(YamlEntryRepository)]
    REPO --> MONGO[(MongoEntryRepository)]
    REPO --> POSTGRES[(PostgresEntryRepository)]

The runtime layout under src/akgentic/catalog/ mirrors shard 10:

src/akgentic/catalog/
    __init__.py          Public API (Catalog, Entry, EntryKind, EntryQuery, ...)
    catalog.py           Unified Catalog service (CRUD + clone + resolve + load_team)
    resolver.py          Two-phase ref resolver + allowlisted model loader
    env.py               ${VAR} substitution for YAML payloads
    serialization.py     Namespace bundle load/dump
    validation.py        Namespace-level validation report
    models/              Entry, EntryKind, EntryQuery, CloneRequest, errors
    repositories/        EntryRepository protocol + YAML + Mongo impls
    api/                 FastAPI app + /catalog router
    cli/                 Typer ak-catalog app

Layered invariants (enforced by Catalog)

• Namespace bootstrap — non-team entries require a pre-existing team entry in the same namespace.
• Namespace minting — creating a team with namespace=UNSET_NAMESPACE mints a fresh UUID before any other pipeline step runs.
• Ownership propagation — every sub-entry inherits the team's user_id.
• Delete guards — deleting an entry referenced by another entry in the same namespace raises CatalogValidationError listing inbound referrers.
• Clone atomicity — clone collects every intended write in memory and emits them in a single pass; partial failures leave the destination untouched.

The Entry Model

Every catalog row is an Entry:

from akgentic.catalog import Entry, EntryKind

Entry(
    id="lead-agent",              # stable within (kind, namespace)
    kind=EntryKind.AGENT,          # "team" | "agent" | "tool" | "prompt" | "model" | ...
    namespace="tenant-42",         # tenancy / environment boundary
    user_id="u1",                  # ownership; propagated from the team
    model_type="akgentic.core.AgentCard",  # allowlisted Pydantic class
    payload={"role": "Lead", "description": "..."},
)

model_type is a dotted path to a Pydantic BaseModel subclass under the akgentic.* allowlist; the resolver calls akgentic.catalog.resolver.load_model_type to materialize it. Payloads validate against that class at create/update time.

Storage Backends

YAML (default)

YamlEntryRepository(root) lays out one file per entry, namespaced directory per namespace, partitioned by kind:

<root>/
  <namespace>/
    team/research-team.yaml
    agent/lead-agent.yaml
    tool/web-search.yaml

from akgentic.catalog import Catalog, YamlEntryRepository
catalog = Catalog(YamlEntryRepository("./catalog"))

MongoDB

MongoEntryRepository stores every entry in a single collection indexed by the compound (kind, namespace, id) key. Install the mongo extra and provide a connection:

from akgentic.catalog import Catalog, MongoCatalogConfig, MongoEntryRepository

cfg = MongoCatalogConfig(
    connection_string="mongodb://localhost:27017",
    database="akgentic",
)
catalog = Catalog(MongoEntryRepository(cfg))

PostgreSQL

PostgresEntryRepository stores every entry in a single catalog_entries table keyed by the compound (namespace, id) primary key. Install the postgres extra and provide a DSN via one of three supply channels (flag-wins precedence on the CLI, explicit kwarg on the API factory):

Supply channel	Consumer
PostgresCatalogConfig(connection_string=)	create_app() factory
--postgres-conn-string CLI flag	ak-catalog
DB_CONN_STRING_PERSISTENCE env var	CLI + init-container

from akgentic.catalog import Catalog
from akgentic.catalog.api.app import create_app
from akgentic.catalog.repositories.postgres import (
    PostgresCatalogConfig,
    PostgresEntryRepository,
)

# Programmatic / API path.
cfg = PostgresCatalogConfig(
    connection_string="postgresql://postgres:pw@localhost:5432/catalog",
)
app = create_app(backend="postgres", postgres_config=cfg)

# Direct repository path.
catalog = Catalog(PostgresEntryRepository(cfg.connection_string))

Deployment prerequisite. The PostgresEntryRepository constructor does NOT create the schema — it only validates the DSN. Before starting the catalog service against a fresh database, run the runnable init-container module once per environment (Kubernetes initContainer / Nomad prestart pattern):

DB_CONN_STRING_PERSISTENCE=postgresql://postgres:pw@localhost:5432/catalog \
  python -m akgentic.catalog.scripts.init_db

Exit code 0 on success, 2 when the env var is missing, 1 on any other failure (unreachable host, malformed DSN, driver error).

All three backends expose the same EntryRepository protocol; parity tests under tests/repositories/test_entry_repository_contract.py keep them interchangeable.

Sharing scalars between entries

When several entries need to reuse the same bare scalar — a prompt body, a default role label, a model id — the catalog ships a sanctioned wrapper called NativeValue. A NativeValue entry carries a single value field; the resolver unwraps the value at the ref-splice site so a typed str / int / bool field on the consuming entry receives the bare scalar instead of the wrapper.

# data/catalog/agent-team/prompt/id_team_template.yaml
id: id_team_template
kind: prompt
namespace: agent-team
user_id: anonymous
model_type: akgentic.catalog.NativeValue
description: System-prompt template body for team members
payload:
  value: "You are {role}. Collaborate with your team."

# data/catalog/agent-team/prompt/id_team_role.yaml
id: id_team_role
kind: prompt
namespace: agent-team
user_id: anonymous
model_type: akgentic.catalog.NativeValue
description: Default role label for team members
payload:
  value: "a helpful team member"

# data/catalog/agent-team/prompt/id_team_prompt.yaml
id: id_team_prompt
kind: prompt
namespace: agent-team
user_id: anonymous
model_type: akgentic.llm.prompts.PromptTemplate
description: Default system prompt for team members
payload:
  template: { __ref__: "id_team_template" }   # resolves to str
  params:
    role: { __ref__: "id_team_role" }         # resolves to str

Two things are worth pinning explicitly:

• The resolver unwraps .value at ref-splice time. From every other layer's perspective — repositories, CLI, HTTP, bundle export — a NativeValue entry is a normal entry with a {"value": <scalar>} payload. The unwrap fires only when a __ref__ marker targets a NativeValue; direct retrieval via Catalog.get returns the Entry like any other entry.
• NativeValue.value: dict[str, Any] is for JSON literals at boundaries, NOT for structured catalog content. Storing a typed structure under value as an untyped dict effectively bypasses the "payload is a BaseModel" invariant — the consuming side has nothing to validate against. If you need typed structured content, write a real BaseModel. The catalog does not mechanically block this anti-pattern; the discipline is on the catalog author.

See _bmad-output/akgentic-catalog/decisions/adr-15-native-value-refs.md for the full rationale and design.

References Between Entries

Sub-entries are embedded in the team payload (and in each other) as sentinel ref dicts, not by plain ID strings. A ref is a two-key dict:

{"__ref__": "<entry-id>", "__type__": "<model_type>"}

The constants REF_KEY and TYPE_KEY are re-exported from akgentic.catalog for construction/inspection. The resolver walks these refs in two phases — populate_refs (ensures every ref resolves to a known entry) and resolve (materializes the runtime Pydantic object) — with cycle detection. See architecture/05-validation.md and architecture/06-service-and-env.md for the full rules.

Querying the Catalog

EntryQuery is the single query model for all kinds. Any subset of filters may be provided; unspecified filters are ignored.

from akgentic.catalog import EntryQuery

# Every entry in a namespace.
catalog.list_by_namespace("tenant-42")

# Cross-namespace filter.
catalog.list(EntryQuery(kind="agent", user_id="u1"))

# Description substring search.
catalog.list(EntryQuery(kind="tool", description_contains="search"))

CLI

The optional ak-catalog console script (enabled by --extra cli) mounts a Typer app with one subcommand group per kind plus top-level verbs for namespace-scoped and schema operations.

# Kind-scoped CRUD.
ak-catalog --root ./catalog team list --namespace tenant-42
ak-catalog --root ./catalog agent get --namespace tenant-42 lead-agent
ak-catalog --root ./catalog agent create ./lead-agent.yaml

# Namespace bundle round-trip.
ak-catalog --root ./catalog export --namespace tenant-42 > tenant-42.yaml
ak-catalog --root ./catalog import ./tenant-42.yaml

# Validation & schema.
ak-catalog --root ./catalog validate --namespace tenant-42
ak-catalog --root ./catalog validate ./tenant-42.yaml   # dry-run from bundle
ak-catalog schema akgentic.core.AgentCard
ak-catalog model-types                                  # list allowlisted types

Full reference: docs/cli-usage-guide.md.

REST API

The optional FastAPI app (enabled by --extra api) mounts the /catalog router. Start it in-process:

uvicorn "akgentic.catalog:create_app" --factory

Backend is selected via environment variables at app-factory time (YAML by default; set AKGENTIC_CATALOG_BACKEND=mongo plus connection fields for MongoDB). Error responses map catalog exceptions to HTTP:

Status	Cause
404	EntryNotFoundError
409	CatalogValidationError
422	Pydantic ValidationError on payload

See src/akgentic/catalog/api/router.py for the full endpoint surface (CRUD per kind, namespace bundle export/import, schema, resolve, validate).

Development

Prerequisites

• Python 3.12+
• uv package manager

Setup

uv sync --all-extras

Commands

# Run tests
uv run pytest tests/

# Run tests with coverage
uv run pytest tests/ --cov=akgentic.catalog --cov-fail-under=80

# Lint
uv run ruff check src/ tests/

# Format
uv run ruff format src/ tests/

# Type check
uv run mypy src/

License

See the repository root for license information.