emcie-co/parlant
Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions. observed · 2026-08-28
Health v2 · maintenance only
83/100
- Activity 92
- Release rhythm 81
- Longevity 66
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 7.5
- age_days: 930
- days_rel: 127
- days_push: 52
- n_releases_24m: 33
Adoption not part of the score
18269 stars · 1554 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Parlant is an open-source Python framework that acts as an interaction control harness for building reliable, customer-facing LLM agents. It dynamically assembles conversation context using primitives like observations, guidelines, tools, journeys, and canned responses to keep agent behavior consistent, compliant, and traceable at scale.
Use cases
- build a customer support chatbot that follows company policies
- deploy an LLM agent that won't hallucinate in customer conversations
- control what my AI agent says with granular behavioral rules
- replace brittle system prompts with structured agent guidelines
- build a compliant customer-facing AI agent for banking or healthcare
- attach backend API tools to an agent that only fire when relevant
- define multi-turn SOPs for an AI agent without rigid flowcharts
- trace and debug why my conversational agent said something
When to choose
- you need production-grade, predictable agent behavior for customer-facing conversations
- system prompts degrade as rules grow and you need structured context control
- compliance, brand voice, and traceability matter (B2C or sensitive B2B)
- you want to iterate on agent behavior from product feedback quickly
- you want an open-source alternative to Ada, Decagon, or Sierra
When to avoid
- you need simple internal chatbots where strict behavior control is unnecessary
- you want fully autonomous agents exploring tasks without conversational constraints
- your use case is non-conversational workflow automation rather than dialogue
- you need a lightweight prompt-chaining library rather than a full harness
Facets
framework · maturity active
agent-framework chatbot llm-inference rag prompt-engineering web-framework large-language-models chatbots developer-tools python self-hosted cross-platform conversational-ai context-engineering customer-service guardrails guidelines journeys behavior-as-code llm-orchestration compliance ai-agents retrieval-augmented-generation natural-language-processing docker
6 sources
- readme: https://github.com/emcie-co/parlant · fetched 2026-08-28 · 41adabb6c5e6
- homepage: https://www.parlant.io · fetched 2026-08-29 · a9c2049ee688
- site_page: https://www.parlant.io/docs/quickstart/installation · fetched 2026-08-29 · 6538a61a5d55
- site_page: https://www.parlant.io/docs/quickstart/motivation · fetched 2026-08-29 · b93a718bb633
- site_page: https://www.parlant.io/docs/about · fetched 2026-08-29 · 165e005212a9
- site_page: https://www.parlant.io/blog/parlant-3-3-release · fetched 2026-08-29 · a3f98eb33a10
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| emcie-co/parlant | main | 83 |
For agents
markdown · JSON · MCP: product_card(name="emcie-co/parlant")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem