context-graph

name	context-graph
description	Use when storing decision traces, querying past precedents, or implementing learning loops. Load in COMPLETE state or when needing to learn from history. Covers semantic search with Voyage AI embeddings, ChromaDB for cross-platform vector storage, and pattern extraction from history.
keywords	traces, learning, memory, precedent, semantic-search, vector-embeddings, chromadb

Context Graph

Living records of decision traces with semantic search. Find similar past decisions by meaning, not keywords.

Setup

MCP Server (recommended):

The context-graph MCP server provides the same functionality via tools:

• context_store_trace - Store decisions with embeddings
• context_query_traces - Semantic search
• context_get_trace - Get by ID
• context_update_outcome - Mark success/failure
• context_list_traces - List with pagination
• context_list_categories - Category breakdown

Configure in .claude/mcp.json:

{
  "mcpServers": {
    "context-graph": {
      "command": "uv",
      "args": ["--directory", "context-graph-mcp", "run", "python", "server.py"],
      "env": {"VOYAGE_API_KEY": "your_key_here"}
    }
  }
}

CLI Scripts (alternative):

# 1. Install dependencies
pip install voyageai chromadb

# 2. Set Voyage AI key
export VOYAGE_API_KEY="your_key_here"

# 3. Store/query traces
python scripts/store-trace.py "DECISION"
python scripts/query-traces.py "similar situation"

Instructions

1. Store trace after decisions with category + outcome
2. Query precedents when facing similar situations
3. Update outcome to success/failure after validation

Quick Commands (MCP)

context_store_trace(decision="Chose FastAPI for async", category="framework")
context_query_traces(query="web framework choice", limit=5)
context_update_outcome(trace_id="trace_abc...", outcome="success")

Quick Commands (CLI)

# Store a decision trace
python scripts/store-trace.py "Chose FastAPI over Flask for async support" --category framework

# Find similar past decisions
python scripts/query-traces.py "web framework selection"

# Query by category
python scripts/query-traces.py "database choice" --category architecture --limit 3

# Output JSON for parsing
python scripts/query-traces.py "error handling" --json

Trace Schema

Field	Description
id	Unique trace identifier
timestamp	When stored
category	Grouping (framework, api, error, etc.)
decision	What was decided (text)
outcome	pending / success / failure
state	State machine state when decided
feature_id	Related feature (if any)
embedding	1024-dim vector (Voyage AI)

Categories

• framework - Tech stack choices
• architecture - Design patterns, structure
• api - Endpoint design, contracts
• error - Failure modes, fixes
• testing - Test strategies
• deployment - Infra decisions

When to Use

Situation	Action
Made a technical decision	Store trace with category
Facing similar problem	Query traces before deciding
Session complete	Query category → extract patterns
Repeating error	Query traces for that error