CLI and shared client library for Applied Labs AI support agents
Project description
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
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 |
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
Project details
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