CoLong Idea Studio
A Dynamic-Memory-First Collaborative Agent Framework for Long-Form Creative Ideation and Story Generation
Overview
CoLong Idea Studio targets long-form, chaptered, and high-consistency creative writing tasks. The framework is organized around a dynamic-memory-first generation loop, where planning artifacts, chapter summaries, character settings, world settings, and fact cards are continuously written back and later retrieved as contextual constraints.
In addition to generation itself, the project emphasizes collaborative ideation. Before the drafting phase begins, the system engages the user in an iterative clarification process so that vague premises can be transformed into a more stable creative brief. This improves downstream coherence, reduces prompt underspecification, and better aligns the system with human-centered creative workflows.
Project Links
Core Contributions
Collaborative Ideation Agent
The idea-copilot procedure is implemented as a genuine agent loop that keeps asking targeted questions until the user explicitly confirms the concept is ready.
Dynamic Memory Priority
The system prioritizes dynamic memory over static retrieval, storing and recalling chapter text, outlines, fact cards, character settings, and world settings.
Progress-Log Observability
Runtime logs expose global outline creation, chapter plans, chapter outlines, inferred length targets, memory snapshots, and setting writes.
Completion-First Chapter Execution
The primary objective is to complete planned chapters rather than stopping early when an external evaluator score happens to cross a threshold.
System Workflow
The workflow figure below summarizes the full trajectory from idea refinement to outline planning, chapter execution, dynamic-memory writeback, and subsequent contextual reinjection.
Methodology
Outline-Grounded Length Steering
Chapter length is treated as a prompt-level target inferred from outline semantics, not as a simplistic hard cap detached from narrative structure.
Typed Dynamic Memory Assembly
Retrieved context is grouped by semantic role, including outlines, facts, characters, and world settings, before being injected back into generation prompts.
User-Confirmed Ideation Exit
The collaborative ideation loop terminates only when the user explicitly confirms readiness, making the transition into drafting both intentional and inspectable.
Dynamic Memory and Progress Log
Memory Buckets
textsoutlinescharactersworld_settingsplot_pointsfact_cards
These structured buckets are maintained in memory_index.json and serve as lightweight narrative anchors across chapters.
Representative Log Events
global_outlinechapter_outline_readychapter_planchapter_length_plancharacter_settingworld_settingmemory_snapshotThe logging layer reveals hidden planning and memory signals, making long-form generation easier to debug and inspect.
Evaluation Snapshot
The current project page retains an evaluation-style visualization for research presentation while the main system focus has shifted toward collaborative ideation quality, generation observability, and dynamic-memory consistency.
Deployment and Usage Notes
Runtime Entry Points
CLI: python main.py
Web Portal: python -m uvicorn local_web_portal.app.main:app --host 0.0.0.0 --port 8010
Portal Goal: support collaborative ideation, long-form planning, and chapter generation for multi-user deployment.
Recommended Packaging Principle
For server deployment, keep only runtime-required files and exclude historical outputs, local caches, transient vector data, and environment artifacts whenever possible.
This reduces repository noise, improves cold-start clarity, and better matches a reproducible research-demo workflow.
Star Growth
Open Full ChartRepository momentum is often a more useful signal here than a citation block, so the project page now highlights GitHub star growth instead.