Parlant: LLM Agents You Can Actually Control
Building LLM agents that work reliably in production is still hard. Most frameworks either lock you into rigid workflows or require endless tweaking to get right. That’s where Parlant comes in—a Python library for building controllable, production-ready LLM agents that deploy fast.
What It Does
Parlant lets you design LLM agents with clear boundaries and predictable behavior. Unlike black-box chatbots, these agents follow strict guidelines (e.g., "never suggest unverified medical advice") and integrate with real-world APIs and data sources. The framework handles orchestration, so you focus on defining what the agent should do, not debugging recursive hallucinations.
Why It’s Cool
- Control-first: Set hard rules for agent behavior (like forcing fact-checking steps) without wrestling with prompt engineering.
- Real-world ready: Built-in tools for API calls, RAG, and state management mean you can ship agents that actually do things (e.g., customer support bots that pull order data).
- Fast iteration: Deploy locally or to Parlant’s cloud in minutes. The CLI even auto-generates Docker configs.
How to Try It
- Install:
pip install parlant - Run the quickstart example to spin up a weather-checking agent.
Or check out their live demo to see a prebuilt agent in action.
Final Thoughts
Parlant feels like a pragmatic middle ground between heavyweight platforms and DIY LangChain scripts. If you’ve ever wasted hours tuning prompts just to make an agent not say something stupid, this is worth a look. The Apache 2.0 license and active community (5.7k GitHub stars) make it easy to experiment without vendor lock-in.
Got a use case? Fork it, tweak it, and let us know what you build.