Parlant
Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions.
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What is Parlant?
Parlant is an open-source Agentic Behavior Modeling Engine for building customer-facing AI agents that behave exactly as your business requires. It is a conversational control layer that sits between your frontend and LLM provider, managing the entire lifecycle of every interaction to keep agent behavior focused, on-track, and auditable. The core innovation is itsManaged Guidelinesapproach, where developers define behavioral rules (guidelines) with conditions and actions, and Parlant dynamically matches relevant guidelines to each conversational context.
Key features include: Behavioral Guidelines that define rules triggering based on conversational context with intelligent merging when multiple apply; Observations that detect specific situations like customer interest in high-risk products; Tool Use for attaching external APIs and backend services to interaction events; Canned Responses/Utterance Templates that eliminate hallucinations and guarantee brand consistency; Domain Glossary for teaching agents domain-specific terminology; Explainability that shows why and when each guideline was matched; Built-in content filtering and jailbreak protection; An integrated sandbox UI at localhost:8800 for behavioral testing; and native Python and TypeScript/JavaScript client SDKs plus a React chat widget.
Parlant is designed for AI developers, machine learning engineers, and data scientists building customer-facing chat agents in regulated or brand-sensitive industries like financial services, healthcare, legal assistance, and compliance-focused use cases. It is ideal for teams that need maximum control over conversation experience, require maximum prevention of unwanted behaviors, and want the fastest path from product feedback to implementation without manual rewiring of graphs or fine-tuning models.
Parlant pricing
Pricing model: Freemium
Parlant is open-source software available under Apache 2.0 license, completely free to use. It is available on PyPI (pip install parlant) and GitHub. The framework includes all features: conversational AI server, behavioral guidelines, tool use, canned responses, sandbox UI, content filtering, jailbreak protection, Python and TypeScript SDKs, and React chat widget. No paid tiers or enterprise plans are mentioned on the website - the entire platform is free and open-source. Enterprise partnership options exist for collaborating with their team of open-source experts for development and deployment support.
Parlant pros
- Open-source with Apache 2.0 license
- Maximum control over conversation behavior with precision
- Structurally prevents unwanted agent behaviors
- Fast feedback cycle for product changes without rewiring graphs
- No prompt engineering needed for most use cases
- Guidelines are granular first-class entities, easy to maintain
- 200th guideline is as safe to add as the first
- Built-in explainability shows which rules fired
- Canned responses deterministically cap failure severity
- LLM-agnostic, works with OpenAI, Anthropic, Gemini, Llama 3
- Built-in content filtering and jailbreak protection
- Integrated sandbox UI for behavioral testing at localhost:8800
- Native Python and TypeScript client SDKs available
- Official React chat widget for easy frontend integration
- Detects guideline contradictions automatically
- Async Python architecture handles concurrent requests efficiently
- Trusted by enterprise teams at JPMorgan Chase, Oracle, Slice Bank
Parlant cons
- Requires Python 3.10 or higher
- Async programming paradigm has learning curve for beginners
- Only Python and TypeScript SDKs officially supported
- Other languages must use REST API directly
- Adds complexity compared to simple prompt engineering
- Best results with OpenAI or Anthropic models, not all models equal
- Steeper learning curve than flow-based chatbot builders
- Self-hosted server requires infrastructure management
- Newer framework with smaller community than LangChain
Frequently asked questions about Parlant
What is Parlant?
Parlant is an open-source Agentic Behavior Modeling Engine for LLM agents, built to help developers quickly create customer-engaging, business-aligned conversational agents with control, clarity, and confidence. It is the conversational control layer for customer-facing AI agents that gives you behavioral control structures you can mix, maintain, and reason about.
How does Parlant differ from prompt engineering?
Prompt engineering is about getting a model to behave correctly when it already has the right context-optimizing the template, tuning temperature, tweaking reasoning strategy. Context engineering (which Parlant focuses on) is about what goes into that template in the first place. A well-engineered prompt with wrong context will still produce wrong results. They are complementary: prompt engineering handles the how, context engineering handles the what and when.
How does Parlant compare to LangChain and LangGraph?
LangChain and LangGraph are workflow tools that manage how data flows between steps. Parlant works on a different level of abstraction: it manages behavior. Instead of building a graph of nodes and edges, you define individual behavioral rules (guidelines) and the relationships between them. The framework figures out which rules apply at each turn. This means changing agent behavior is a content change (add a guideline), not a structural change (restructure the graph). That is why the 200th rule is as safe to add as the first.
What are Guidelines in Parlant?
Guidelines are the core of Parlant's behavior model. They allow you to define how your agent should respond to specific user inputs or conditions with a condition (when to trigger) and an action (what to do). Parlant cleverly manages guideline context automatically, so you can add as many guidelines as needed without worrying about context overflow or scale issues. When multiple guidelines apply, Parlant merges them intelligently in-context with no rigid or manual routing required.
What are Observations in Parlant?
Observations detect specific situations in conversations. For example, you can create an observation to detect when a customer is discussing high-risk products like options, crypto, or leveraged ETFs. Guidelines can depend on observations, so risk disclosure guidelines only activate when the high-risk observation holds. Every matched context item is traced: what glossary terms were used, which rules fired.
Can I control exact wording of agent responses?
Yes, use canned responses with strict composition mode for control of wording. Parlant's response selection mechanism ensures that if a required field (from tool results, retrievers, or guidelines) is not present in current context, Parlant automatically disqualifies that response from being selected. This means the agent cannot claim something happened when it has not, or vice versa. The guardrails are structural and 100% deterministic, not just prompt-based.
Does Parlant support all LLM providers?
Parlant is LLM-agnostic, so you can use any provider and model, and many teams do. The officially recommended providers are Emcie, OpenAI (directly via OpenAI or via cloud providers like Azure), and Anthropic (via AWS Bedrock or others). Parlant also supports Gemini, Meta Llama 3 (via Together AI or Cerebras), and more are added regularly. However, for consistency and reliability, OpenAI and Anthropic models perform best as they generate high-quality completions with valid JSON schemas.
Is Parlant rigid like traditional chatbots?
No, the real rigidity of traditional chatbots comes from tree-based flows forcing conversations through predefined branches, not from controlled wording. Even when using optional strict canned responses mode (which lets you control exact wording when needed), the agent still chooses when to use them based on the fluid nature of interaction, just like call center reps do. The flow stays flexible, but you get precise wording where it matters.
Do I need to define rules for every scenario?
No, the LLM still handles general conversation naturally. Observations and guidelines define behavioral expectations for specific situations-everything else works as you would expect from an LLM. If you do not need special handling for a scenario, you do not need to define any rules for it.
Who is Parlant for?
Parlant is for developers and data scientists building LLM-based chat agents where: (1) behavioral precision and consistency are high importance, especially in customer-facing scenarios; (2) the agent is expected to undergo continuous behavioral refinements requiring efficient implementation; (3) you maintain a growing set of behavioral guidelines needing coherent management with version-tracking; (4) conversational UX and user engagement are important and you want to control flow and tone. It is used in regulated financial services, healthcare communications, legal assistance, compliance-focused use cases, brand-sensitive customer service, and personal advocacy.