بنقرة واحدة
graphiti-temporal
Temporal context graph for agent memory — track entity relationships and state changes over time
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Temporal context graph for agent memory — track entity relationships and state changes over time
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Onboard a new client/company onto this platform's real Agentic OS: create the company record, scan its websites/repos, auto-provision specialist agents, activate its 24x7 agency runtime, and know exactly which "OS" building blocks (memory, integrations, dashboard) already exist versus which are roadmap gaps. ADAPTED FROM: a third-party giveaway skill ("agentic-os-installer" by Gennaro Santoro / Operations Heroes) that described a generic vault + Google-suite + skill-pack installer. That skill's product (Obsidian vault, Gmail/Calendar/ Drive wiring, "skill packs") does not exist in this repo and its promotional content (Skool community link) does not belong here. This is a clean-room rewrite that keeps the useful idea — "stand up a working agency OS for a client from a short checklist" — and maps every step to the real module that already implements it in this codebase, per CLAUDE.md architecture rules.
Agile sprint planning, velocity tracking, and burndown metrics for agent-managed projects
Initiative-level portfolio management with dependency tracking and milestone coordination
AI-assisted engineering impact analysis — productivity metrics and code quality insights
Cross-harness agent patterns — standardize agent execution across different coding assistants
Andrej Karpathy's coding guidelines for AI agents — concise, correct, and well-tested code
| name | graphiti-temporal |
| description | Temporal context graph for agent memory — track entity relationships and state changes over time |
Inspired by: Graphiti — temporal context graphs for AI agents
Purpose: Integrate Graphiti's temporal knowledge graph patterns into local-llm-server's agent memory and context management.
Graphiti builds temporal context graphs (evolving knowledge graphs that track how facts change over time):
Unlike traditional RAG (flat chunks), Graphiti gives agents rich, structured context that evolves with each interaction.
Track agent decisions and outcomes:
class AgentContextGraph:
def add_interaction(self, timestamp, agent_id, action):
"""Record agent action with temporal metadata"""
def query_at_time(self, entity, timestamp):
"""Query what was true at specific time"""
Track which agents worked on which tasks with temporal awareness.
Query across relationships with SQL:
SELECT entity, fact, timestamp FROM context_graph
WHERE entity LIKE 'test_%'
AND fact LIKE 'status:failed'
ORDER BY timestamp DESC;
CREATE TABLE temporal_context (
id TEXT PRIMARY KEY,
entity TEXT NOT NULL,
fact TEXT NOT NULL,
timestamp DATETIME NOT NULL,
provenance TEXT,
agent_id TEXT,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX idx_entity_time ON temporal_context(entity, timestamp DESC);
services/temporal_context.py — temporal graph implementationdb/temporal_store.py — SQLite storagetests/test_temporal_context.py — tests