Parlant

Parlant is a self-hostable conversational AI server designed for teams building customer-facing agents that need to stay on-brand, on-policy, and predictable in production. It sits between your frontend and your LLM provider, managing every interaction through a structured control layer rather than relying on monolithic system prompts or rigid intent graphs.

The core idea is context engineering: instead of stuffing all your rules into one prompt and hoping the model follows them, Parlant dynamically assembles only the rules, data, and tools relevant to each turn. The result is that the 200th behavioral rule is as safe to add as the first, with no degradation in adherence.

Key capabilities:

• Guidelines define conditional behavioral rules that fire based on conversational context. When several apply at once, Parlant merges them intelligently without manual routing.
• Canned responses let you lock down exact wording for high-stakes moments. In strict mode, required fields must be present in context before a response can be selected, making hallucination prevention structural and deterministic.
• Journeys model multi-step interactions while preserving the fluid, non-linear nature of real dialogue. Users can mix topics and circle back without losing context.
• Full traceability on every turn: which guidelines fired, which glossary terms were matched, and why the agent responded the way it did.

This is a meaningful departure from tools like Rasa or flow-based builders. Changing agent behavior is a content change (add or edit a guideline), not a structural one (rewire a graph). That makes it practical for product teams to feed ongoing feedback directly into the agent without requiring engineering intervention for every adjustment.

Parlant is LLM-agnostic and works with OpenAI, Anthropic, Azure, and others. It's built for enterprise AI teams where tone, edge cases, and policy constraints aren't afterthoughts.