| name | graphiti-temporal |
| description | Temporal context graph for agent memory — track entity relationships and state changes over time
|
Graphiti Temporal Context Skill
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.
What's Unique About Graphiti
Graphiti builds temporal context graphs (evolving knowledge graphs that track how facts change over time):
- Temporal awareness — knows what's true now vs what was true before
- Provenance tracking — maintains links to source data
- Hybrid retrieval — semantic + keyword + graph traversal
- Incremental updates — efficiently adds new information without full recomputation
Unlike traditional RAG (flat chunks), Graphiti gives agents rich, structured context that evolves with each interaction.
Integration Opportunities
1. Agent Memory as Temporal Graph
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"""
2. Multi-Agent Coordination
Track which agents worked on which tasks with temporal awareness.
3. Knowledge Queries
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;
Database Schema
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);
Files to Create
services/temporal_context.py — temporal graph implementation
db/temporal_store.py — SQLite storage
tests/test_temporal_context.py — tests
References