Open Source Decagon Alternatives

Parlant is a Python harness for controlling how a customer-facing AI agent behaves. It targets the failure everyone meets in production: the more instructions you add to a system prompt, the faster the model stops paying attention to any of them, and routed graphs turn brittle the moment a real conversation wanders.

Instead of sending one large prompt followed by the raw conversation, the engine assembles a focused context on every turn. You define rules, knowledge and tools once, and the matching engine narrows what reaches the model down to what is relevant right now. Adding more rules therefore sharpens the agent rather than confusing it.

Behavior is defined in code through a handful of primitives.

• Guidelines: behavioral rules as condition and action pairs, evaluated fresh on each conversational turn.
• Relationships: dependencies and exclusions between guidelines, so one rule can suppress another and keep context tight.
• Journeys: multi-turn standard procedures that adapt to how a customer actually interacts.
• Canned responses: pre-approved templates that remove the chance of hallucination at the moments that matter.
• Glossary and tools: domain vocabulary the agent must know, plus external APIs called only when their observation matches.

Every guideline match and decision is traced through OpenTelemetry, which is what makes an answer explainable and auditable afterwards. Installation is a single pip install. The project aims at support, sales, onboarding and advisory agents in regulated domains such as finance, insurance, healthcare and telecom, and reports production deployments in banks.