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Introduction

Welcome to Parlant!โ€‹

Parlant Funny Agent

Parlant is a framework that transforms how AI agents make decisions in customer-facing scenarios.

With Parlant, you can not only spin up and serve an LLM agent in minutesโ€”with a full-fledged & responsive conversation management APIโ€”but, more importantly, you can continuously guide and improve its decision making and general behavior, easily and reliably.

๐Ÿฅœ Parlant in a Nutshellโ€‹

Unlike traditional approaches that rely on prompt engineering or conversational flow charts, Parlant introduces a dynamic control system that ensures agents follow your specific business rules, in the form of behavioral guidelines that you provide, by matching and activating the appropriate combination of guidelines for every specific context.

When an agent needs to respond to a customer, Parlant's engine evaluates the situation, checks relevant guidelines, gathers necessary information through your tools, and continuously re-evaluates its approach based on your guidelines as new information emerges. When it's time to generate a message, Parlant implements self-critique mechanisms to ensure that the agent's responses precisely align with your intended behavior as given by the contextually-matched guidelines.

Parlant comes pre-built with responsive session (conversation) management, a detection mechanism for incoherence and contradictions in guidelines, content-filtering, jailbreak protection, an integrated sandbox UI for behavioral testing, native API clients in Python and TypeScript, and other goodies.

The entire source codeโ€”licensed under Apache 2.0โ€”can be found on GitHub. Parlant is developed and maintained primarily by Emcie, along with other contributors.

๐Ÿ™‹โ€โ™‚๏ธ Who Is Parlant For?โ€‹

Parlant is the right tool for the job if you're building an LLM-based chat agent, and:

  1. Your use case places a high importance on behavioral precision and consistency, particularly in customer-facing scenarios
  2. Your agent is expected to undergo continuous behavioral refinements and changes, and you need a way to implement those changes efficiently and confidently
  3. You're expected to maintain a large set of behavioral guidelines, and you need to maintain them coherently and with version-tracking
  4. Conversational UX and user-engagmeent is an important concern for your use case, and you want to easily control the flow and tone of conversations

๐ŸŒŸ What Makes Parlant Different?โ€‹

In a word: guidance. Parlant's engine revolves around solving one key problem: How can we reliably guide customer-facing agents to behave in alignment with our needs and intentions?

Hence Parlant's fundamentally different approach to agent building: Managed Guidelines.

$ parlant guideline create \
--agent-id MY_AGENT_ID \
--condition "the customer wants to return an item" \
--action "get the order number and item name and then help them return it"

By giving structure to behavioral guidelines, and granularizing guidelines (i.e. making each behavioral guideline a first-class entity in the engine), Parlant's engine is able to offer unprecedented control, quality, and efficiency in building LLM-based agents:

  1. Reliability: Running focused self-critique in real-time, per guideline, to ensure it is actually followed
  2. Explainability: Providing feedback around its interpretation of guidelines in each real-life context, which helps in troubleshooting and improvement
  3. Maintainability: Helping you maintain a coherent set of guidelines by detecting and alerting you to possible contradictions (gross or subtle) in your instructions

๐Ÿ˜ค The Pain Pointโ€‹

Think of an LLM like a stranger with an encyclopedic knowledge of different approaches to every possible situation.

Although incredibly powerful, this combination of versatility and lack of context is precisely why it so rarely behaves as we'd expectโ€”there are too many options.

This is why, without clear guidelines, an LLM will always try to draw optimistically from its vast but unfiltered set of observations, potentially using tones that are out of touch with the customer and the situation, making irrelevant offers, getting into loops, or just losing focus and going off on tangents.

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Parlant lets you step in and guide your agent easily, every time you see it missing the mark. You do this primarily by using guidelines, as well as other behavioral control structures that Parlant supports, like tools, glossary terms, and context variables.

Parlant is designed from the ground up to let you easily and quickly train and align your agent's behavior when you find issues or get feedback from customers and business experts. The result is an effective, controlled, and incremental process of improvement.

The premise behind Parlant is that unguided AI agents are a dead-end. Without guidance, an AI agent is bound to encounter numerous ambiguities, and end up trying to resolve them using many incorrect or even problematic approaches. Only we can authoritatively disambiguate the agent's choicesโ€”so we should be able to do so dynamically, thus gradually guiding them to become better performers.

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Parlant was designed to let you guide the behavior of customer-facing agents simply, quickly, and reliablyโ€”so you can feel confident to iterate on your agents' behavior whenever necessary, teaching them how to best serve your customers.

Instead of an agent that goes around the bush, meanders, and offers irrelevant solutions or answers, Parlant paves the way for you to build an agent that is guided, focused, and feels well-designed. A focused and guided agent is one your customers could actually use!

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So pack your bags and get ready to create some awesome customer-facing agents. Or crappy ones. You've got the controls now and the choice is yours. Let's start!