applied-cli 0.5.40

CLI and shared client library for Applied Labs AI support agents

Applied Labs CLI

CLI and client library for Applied Labs AI support agents.

Installation

pip install applied-cli

CLI Usage

# Authenticate
applied login

# List agents
applied agents
applied agents --format json

# Query conversations
applied conversations --resolution escalated --limit 10
applied conversation <id> --messages

# Query tickets
applied tickets --status open

# Knowledge base
applied knowledge --type qa --search "refund"

# Taxonomy
applied taxonomy --type topics
applied taxonomy-counts --start 2026-03-01 --end 2026-03-18 --format csv

Library Usage

from applied_cli import AppliedClient, tools

# Create client
client = AppliedClient(token="al_xxx")

# Use tools
agents = await tools.agent_list(client, output_format="csv")
conversations = await tools.conversation_query(
    client,
    filters={"resolution": "escalated"},
    fields=["id", "title", "contact.email"],
    limit=20,
)

Tools

Tool	Description
agent_list	List all AI agents
conversation_get	Get single conversation with messages
conversation_query	Search/filter conversations
ticket_query	Search/filter tickets
knowledge_list	List knowledge base items
taxonomy_list	List topics, intents, and flags
taxonomy_counts	Aggregate conversation counts by topic and intent

Examples

# Find escalated conversations
await tools.conversation_query(
    client,
    filters={"resolution": "escalated"},
    fields=["id", "title", "contact.email"],
    output_format="csv"
)

# Get conversation with transcript
await tools.conversation_get(
    client,
    conversation_id="abc-123",
    include_messages=True,
    message_limit=50
)

# Search knowledge base
await tools.knowledge_list(
    client,
    kb_type="qa",
    search="refund policy",
    output_format="json"
)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check --fix .
ruff format .

License

MIT