Welcome to PromptOrchestrator Discussions!

[VeryComplexAndLongName]

I’ve been working on a Python project called prompt_orchestrator focused on structured prompt orchestration for LLM systems.

The main idea is simple:

As AI applications grow, prompt management becomes infrastructure.

At some point, every serious LLM system needs:

• context compaction
• summaries
• token budgeting
• retrieval integration
• prompt safety checks
• provider abstraction
• prompt layout management

So I tried to build a lightweight orchestration layer around those problems.

Current features:

• static / semi-stable / dynamic prompt sections
• configurable summary providers
• OpenAI / Ollama / custom summary backends
• optional RAG integration
• injection & contradiction heuristics
• token-aware prompt fitting with tiktoken
• centralized mutable config
• simulations & replay tooling

It also integrates directly with rag_orchestrator through a compatible retrieval adapter layer.

One design goal was flexibility:
the module should integrate into existing systems instead of forcing an entire framework ecosystem.

Repository:
Reposiory

I’d love feedback and discussion about:

• prompt orchestration architectures
• long-context management
• memory compaction strategies
• safety heuristics
• lightweight alternatives to agent-heavy frameworks
• production prompt pipelines

Curious how others are solving these problems in real systems.