rcortx/kiwiq

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

1276 stars · 241 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

KiwiQ is a production-grade multi-agent orchestration platform in Python that lets you define AI workflows as JSON/Python graph schemas compiled to LangGraph and executed via Prefect. It includes multi-provider LLM support, multi-tier memory (PostgreSQL, MongoDB, Weaviate, Redis), human-in-the-loop interactions, RAG pipelines, web scraping, and built-in observability.

Use cases

• orchestrate multiple AI agents in production workflows
• define multi-step LLM workflows as JSON graph schemas
• build RAG pipelines with vector search
• add human approval steps to AI agent workflows
• run AI-powered web scraping and lead research
• self-host a multi-agent AI platform with observability

When to choose

• you need production-tested multi-agent orchestration with memory and observability built in
• you want code/JSON-defined workflows rather than a visual builder
• you need multi-provider LLM support and human-in-the-loop approvals
• you want ready-made workflow templates for research, content, and lead scoring

When to avoid

• you need a lightweight single-agent library or simple LLM API wrapper
• you want a visual no-code workflow builder
• you cannot operate the required infrastructure stack (PostgreSQL, MongoDB, Redis, RabbitMQ, Weaviate, Prefect)
• you work outside Python 3.12

Facets

framework · maturity active

agent-framework workflow-automation rag web-scraping llm-inference chatbot monitoring api-framework websocket message-queue caching auth authorization artificial-intelligence large-language-models backend developer-tools self-hosted python self-hosted cloud multi-agent-orchestration langgraph prefect human-in-the-loop multi-provider-llm workflow-engine json-defined-agents fastapi rabbitmq weaviate ai-agents retrieval-augmented-generation automation docker web-server

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