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parlant

emcie-co/parlant

Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions.

GraphCanon updated 1d · GitHub synced 1d · 30 views this month

18k stars1.6k forksLast push 1mo Python Apache-2.0

Decision brief

Parlant is a specialized interaction control harness designed to ensure that AI agents, particularly those in customer service roles, behave predictably and align with predefined rules.

Good fit when

  • When you need precise control over the actions of AI agents based on specific conditions or observations.
  • For scenarios where responses must adapt to user behavior, such as distinguishing between technical experts and beginners among customers.

Avoid when

  • When the requirements for agent interactions do not demand granular rules based on observable user behavior or conditions, making this level of control unnecessary.
  • In cases where the flexibility and spontaneous responses from general-purpose LLMs are preferred without being restricted by controlled guidelines.
Pricing:
freemium - The open-source version offers community-supported functionalities which are free to use but might lack advanced support services.

Observed Jul 11, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Steady (38d since push)
As of 1d
Provenance
Not a fork · Organization account
As of 1d
Security (OSV)
2 low (2 low)
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install parlant
PyPI

How it fits your stack(12)

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Parlant is a tool designed to create and manage AI agents for customer service purposes. It allows developers to define conditions under which specific agent actions are triggered or excluded, ensuring that responses are appropriate and in line with predefined guidelines.

Capability facts

CLI
CLI entrypoint

Source: pyproject.toml:[project.scripts] · Aug 20, 2026

Languages
python

Source: github.language+pyproject.toml · Aug 20, 2026

Categories

Graph entities

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 20, 2026)

```python import parlant.sdk as p
Source link

Tags

README

Getting started

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)

Follow the 5-minute quickstart for a full walkthrough.


License

Apache 2.0 — free for commercial use.


Try it nowJoin DiscordRead the docs

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