Borrowing it
Nothing to install: this file belongs to Vedant9500/OpenLMlib. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Vedant9500/OpenLMlib/master/.github/prompts/collab_sessions_plan_v2.mdgit clone --depth 1 https://github.com/Vedant9500/OpenLMlibWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/vedant9500/openlmlib/collab_sessions_plan_v2)<a href="https://agentmods.dev/commands/vedant9500/openlmlib/collab_sessions_plan_v2"><img src="https://agentmods.dev/badge/commands/vedant9500/openlmlib/collab_sessions_plan_v2/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/vedant9500/openlmlib/collab_sessions_plan_v2"><img src="https://agentmods.dev/badge/commands/vedant9500/openlmlib/collab_sessions_plan_v2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.07836 |
| Opus 5 | $0.00000 | $0.03918 |
| Sonnet 5 | $0.00000 | $0.01567 |
| Haiku 4.5 | $0.00000 | $0.00784 |
Grade A, and why
collab_sessions_plan_v2 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 855 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Collaboration Feature: Research-Backed Implementation Plan (v2)
Executive Summary
This plan outlines CollabSessions — a local-first, hybrid SQLite+file multi-agent collaboration system for OpenLMLib. After extensive research across academic papers (ICLR 2026, arXiv), production case studies (Oracle, Google ADK, Anthropic, Zylos), and coordination pattern analysis (Tacnode, Fazm, ElectricSQL), this revised plan adopts a hybrid architecture: SQLite for the session registry, message bus, and state tracking; files for artifacts and research outputs. This converges with what the industry's best systems are doing in 2026.
Key research sources informing this plan:
- Oracle: "Comparing File Systems and Databases for AI Agent Memory" (Feb 2026) — benchmarked FSAgent vs MemAgent
- Zylos Research: "AI Agent Memory Architectures for Multi-Agent Systems" (Mar 2026) — framework comparison
- Google ADK: "Architecting efficient context-aware multi-agent framework" (Dec 2025) — context engineering
- Anthropic: "How We Built Our Multi-Agent Research System" — artifact-based memory
- Tacnode: "8 Coordination Patterns That Actually Work" (Jan 2026) — production patterns
- arXiv 2601.13671: "The Orchestration of Multi-Agent Systems" (Jan 2026) — academic framework
1. Core Architecture — REVISED: Hybrid Approach
1.1 Design Philosophy (Updated)
Research finding: Oracle's benchmark showed MemAgent (SQLite) beat FSAgent (files) on both latency AND quality. Zylos Research: "Shared state demands a database." But files win as an interface — LLMs already know how to use them.
Decision: Hybrid SQLite + Files
- SQLite (
collab_sessions.db): Session registry, message bus (append-only table), agent registry, state tracking. Gives us ACID transactions, FTS5 search, row-level locking, zero platform-specific code. - Files (
sessions/{id}/artifacts/): Research outputs, agent working notes, large artifacts. LLM-native interface, human-readable, versionable. - JSONL shadow log (
sessions/{id}/messages.jsonl): Human-readable copy of the messages table for debugging. Written alongside SQLite inserts.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 855 lines · 0 tokens per session scan A 59d89ee3ce12
collab_sessions_plan_v2 is a command published in the GitHub repository Vedant9500/OpenLMlib (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 7,836 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.
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