Nüm Agents SDK Skill

Name: num-agents
Rating: 62
Author: Creativityliberty

Scaffold, design, and build AI agent systems using the Nüm Agents SDK. The SDK uses a universe-based architecture: declare which functional modules your agent needs, and the framework generates the scaffolding automatically.

Install: pip install num-agents Repo: https://github.com/Creativityliberty/numagents

Core Concepts

code

Universe   ← functional module (PocketFlowCore, KnowledgeLayer, etc.)
Node       ← single execution step with retry + hooks
Flow       ← graph of nodes with conditional transitions
SharedStore← dict passed through all nodes (global state)
Agent Spec ← YAML declaration → auto-generates agent code

See references/universe-catalog.md for all available universes. See references/flow-patterns.md for Node, Flow, ConditionalNode patterns. See references/agent-spec-schema.md for the YAML spec schema.

Workflow

1. Understand the Agent

Ask the user:

•What does the agent need to do? (e.g. summarize docs, call APIs, orchestrate sub-agents)
•Which universes does it need? (see universe-catalog.md)
•Does it need memory, event bus, scheduler, metrics, tracing?
•What LLM backend? (gpt-4o, claude-sonnet-4-6, etc.)

2. Write the Agent Spec (YAML)

Generate agent.yaml following the schema in references/agent-spec-schema.md:

yaml

agent:
  name: "MyAgent"
  description: "What this agent does"
  univers:
    - PocketFlowCore       # node/flow execution engine
    - StructureAgentIA     # structured output + reasoning
    - KnowledgeLayer       # memory and retrieval
  protocol: N2A
  llm: claude-sonnet-4-6
  memory: true
  eventbus: false
  scheduler: false
  metrics: true
  tracing: true

3. Generate the Scaffold

bash

num-agents generate --spec agent.yaml --catalog univers_catalog.yaml

If no catalog exists yet:

bash

num-agents generate --spec agent.yaml  # uses default catalog

4. Implement Nodes

For each step in the agent's workflow, implement a Node:

python

from num_agents import Node, SharedStore

class MyNode(Node):
    def __init__(self):
        super().__init__(
            name="MyStep",
            retry_count=3,
            enable_logging=True
        )

    def exec(self, shared: SharedStore):
        data = shared.get("input_key")
        result = process(data)
        shared.set("output_key", result)
        return result

For conditional branching: see references/flow-patterns.md.

5. Wire the Flow

python

from num_agents import Flow

flow = Flow(name="MyPipeline", enable_logging=True)
flow.add_node(node_a)
flow.add_node(node_b)
flow.set_start(node_a)
node_a.connect(node_b)  # linear connection

6. Add Hooks

python

import time

node.add_before_hook(lambda shared: shared.set("start_time", time.time()))
node.add_after_hook(lambda shared, result:
    shared.set("duration", time.time() - shared.get("start_time")))
node.add_error_hook(lambda shared, error:
    logger.error(f"Node failed: {error}"))

7. Run the Agent

python

results = flow.execute(initial_data={
    "config": {...},
    "input": "user request here"
})
print(results)

8. Dev Setup (new project)

bash

git clone https://github.com/Creativityliberty/numagents.git
cd numagents
make install-dev   # installs deps + pre-commit hooks
make check         # lint + type-check + tests
make test-cov      # coverage report

Wrap Up

•✅ Agent spec: agent.yaml created
•✅ Nodes implemented: [list]
•✅ Flow wired: [node_a → node_b → ...]
•✅ Hooks added: [before/after/error]
•Ask: "Do you want me to add metrics/tracing, a ConditionalNode, or run make check?"