Parlant
The conversational control layer for customer-facing AI agents
Overview
The conversational control layer for customer-facing AI agents - Parlant is a context-engineering framework optimized for controlling customer interactions. The conversational control layer for customer-facing AI agents The project has 18K+ GitHub stars and is licensed under Apache-2.0.
Key Features
Source: GitHub README
- Guidelines —
- Relationships —
- Journeys —
- Canned Responses —
- Tools —
- Glossary —
- Explainability —
Getting Started
Source: GitHub README
pip install parlant
import parlant.sdk as p
async with p.Server():
agent = await server.create_agent(
name="Customer Support",
description="Handles customer inquiries for an airline",
)
# Evaluate and call tools only under the right conditions
expert_customer = await agent.create_observation(
condition="customer uses financial terminology like DTI or amortization",
tools=[research_deep_answer],
)
# When the expert observation holds, always respond
# with depth. Set the guideline to automatically match
# whenever the observation it depends on holds...
expert_answers = await agent.create_guideline(
matcher=p.MATCH_ALWAYS,
action="respond with technical depth",
dependencies=[expert_customer],
)
beginner_answers = await agent.create_guideline(
condition="customer seems new to the topic",
action="simplify and use concrete examples",
)
# When both match, beginners wins. Neither expert-level
# tool-data nor instructions can enter the agent's context.
await beginner_answers.exclude(expert_customer)
Normalized Features
Source: tool-features-normalized.json
charts, pip.
Deploy
Features
Analytics & Reporting
- Charts & Graphs
Category
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