GitHub - neo4j-labs/create-context-graph: AI agents with graph based reasoning memory, scaffolded in seconds

# Python — NAMS-default (hosted memory)
uvx create-context-graph my-app --domain healthcare --framework strands --nams-api-key sk-nams-...

# Self-hosted Neo4j with full demo fixtures
uvx create-context-graph my-app --domain healthcare --framework pydanticai --self-hosted --demo

# Node.js / interactive wizard
npx create-context-graph

  Creating context graph application...

  Domain:     Wildlife Management
  Framework:  PydanticAI
  Data:       Demo (synthetic)
  Neo4j:      Docker (neo4j://localhost:7687)

  [1/6] Generating domain ontology...          ✓
  [2/6] Creating project scaffold...           ✓
  [3/6] Configuring agent tools & system prompt...  ✓
  [4/6] Generating synthetic documents (25 docs)... ✓
  [5/6] Writing fixture data...                ✓
  [6/6] Bundling project...                    ✓

  Done! Your context graph app is ready.

  cd my-app
  make install && make start

uvx create-context-graph my-app \
  --domain healthcare \
  --framework strands \
  --nams-api-key sk-nams-...

cd my-app
echo 'ANTHROPIC_API_KEY=sk-ant-...' >> .env   # Strands needs Anthropic
make install
make start

uvx create-context-graph my-app \
  --domain healthcare \
  --framework pydanticai \
  --self-hosted \
  --demo

cd my-app
echo 'ANTHROPIC_API_KEY=sk-ant-...' >> .env
make install
make docker-up   # or: make neo4j-start  (for neo4j-local)
make seed
make start

# Interactive wizard (asks 6 prompts, autocomplete domain picker)
uvx create-context-graph

# Custom domain from description
uvx create-context-graph my-app \
  --custom-domain "veterinary clinic management" \
  --framework pydanticai \
  --self-hosted --demo \
  --anthropic-api-key $ANTHROPIC_API_KEY

# Import real data from SaaS services
uvx create-context-graph my-app \
  --domain personal-knowledge --framework pydanticai --self-hosted \
  --connector github --connector slack

# With MCP server for Claude Desktop (works on either backend)
uvx create-context-graph my-app \
  --domain healthcare --framework strands \
  --nams-api-key sk-nams-... --with-mcp

# Pick a specific LiteLLM provider for memory entity extraction
uvx create-context-graph my-app \
  --domain healthcare --framework strands --nams-api-key sk-nams-... \
  --memory-llm bedrock/anthropic.claude-3-haiku-20240307-v1:0 \
  --memory-embedding sentence-transformers/all-MiniLM-L6-v2

# List all available domains
create-context-graph --list-domains

cd my-app
make import            # Re-import from connected services
make import-and-seed   # Import and seed into Neo4j

my-app/
├── backend/
│   ├── app/
│   │   ├── main.py                # FastAPI application
│   │   ├── agent.py               # AI agent (framework-specific)
│   │   ├── config.py              # Settings from .env
│   │   ├── routes.py              # REST API endpoints
│   │   ├── models.py              # Pydantic models (from ontology)
│   │   ├── context_graph_client.py # Neo4j CRUD operations
│   │   ├── memory.py              # Memory integration (MemoryIntegration)
│   │   ├── gds_client.py          # Graph Data Science algorithms
│   │   ├── vector_client.py       # Vector search
│   │   └── connectors/            # SaaS connectors (if selected)
│   ├── scripts/
│   │   ├── generate_data.py       # Data seeding script
│   │   └── import_data.py         # SaaS import script (if connectors selected)
│   └── pyproject.toml
├── frontend/
│   ├── app/                       # Next.js pages
│   ├── components/
│   │   ├── ChatInterface.tsx      # Streaming AI chat (SSE) with real-time tool calls + graph data flow
│   │   ├── ContextGraphView.tsx   # Interactive NVL graph (schema view, expand, drag/zoom, properties)
│   │   ├── DecisionTracePanel.tsx  # Reasoning trace viewer with step details
│   │   ├── DocumentBrowser.tsx    # Document browser with template filtering
│   │   └── Provider.tsx           # Chakra UI v3 provider
│   ├── lib/config.ts              # Domain configuration
│   ├── theme/index.ts             # Chakra theme with domain colors
│   └── package.json
├── cypher/
│   ├── schema.cypher              # Constraints & indexes
│   └── gds_projections.cypher     # GDS algorithm config
├── data/
│   ├── ontology.yaml              # Domain ontology definition
│   └── fixtures.json              # Pre-generated sample data
├── .env                           # Neo4j + API key configuration
├── .env.example                   # Configuration template (tracked in git)
├── .dockerignore                  # Docker build context exclusions
├── docker-compose.yml             # Local Neo4j instance (Docker mode only)
├── Makefile                       # start, seed, reset, install, test, test-connection, lint
├── mcp/                           # MCP server config (only if --with-mcp)
└── README.md                      # Domain-specific documentation (with framework docs + troubleshooting)

create-context-graph [PROJECT_NAME] [OPTIONS]

Arguments:
  PROJECT_NAME              Project name (optional — auto-generated from domain+framework if omitted)

Options:
  --domain TEXT             Domain ID (e.g., healthcare, gaming)
  --framework TEXT          Agent framework (strands [default], pydanticai, claude-agent-sdk, openai-agents, langgraph, crewai, google-adk, anthropic-tools)
  --self-hosted             Use self-hosted Neo4j (bolt) instead of NAMS hosted memory
  --nams-api-key TEXT       NAMS API key [env: MEMORY_API_KEY] — obtain at https://memory.neo4jlabs.com
  --nams-endpoint TEXT      Override NAMS endpoint URL (default: https://memory.neo4jlabs.com/v1)
  --memory-llm TEXT         LiteLLM provider string for memory entity extraction (e.g. anthropic/claude-haiku-4-5)
  --memory-embedding TEXT   LiteLLM provider string for memory embeddings (e.g. sentence-transformers/all-MiniLM-L6-v2)
  --demo-data               Generate synthetic demo data
  --custom-domain TEXT      Generate custom domain from description (requires --anthropic-api-key)
  --connector TEXT          SaaS connector to enable; repeatable (github, slack, jira, notion, gmail, gcal, salesforce, linear, google-workspace, claude-code, claude-ai, chatgpt)
  --import-type TEXT        Chat history import: claude-ai or chatgpt (requires --import-file)
  --import-file PATH        Path to chat export file (.zip, .json, .jsonl)
  --import-depth TEXT       Import extraction depth: fast (default) or deep
  --import-filter-after TEXT Only import conversations after this date (ISO 8601)
  --import-filter-before TEXT Only import conversations before this date (ISO 8601)
  --import-filter-title TEXT Only import conversations matching title pattern (regex)
  --import-max-conversations INT Max conversations to import, 0=all (default: 0)
  --linear-api-key TEXT    Linear API key (required for --connector linear) [env: LINEAR_API_KEY]
  --linear-team TEXT       Linear team key to filter import (e.g., ENG) [env: LINEAR_TEAM]
  --gws-folder-id TEXT     Google Drive folder ID to scope import [env: GWS_FOLDER_ID]
  --gws-include-comments / --gws-no-comments  Import comment threads (default: on)
  --gws-include-revisions / --gws-no-revisions  Import revision history (default: on)
  --gws-include-activity / --gws-no-activity  Import Drive Activity (default: on)
  --gws-include-calendar   Import Calendar events (default: off)
  --gws-include-gmail      Import Gmail thread metadata (default: off)
  --gws-since TEXT         Import data since date (ISO format, default: 90 days ago)
  --gws-mime-types TEXT    MIME types to include (default: docs,sheets,slides)
  --gws-max-files INT      Maximum files to import (default: 500)
  --claude-code-scope TEXT Import current project or all (default: current)
  --claude-code-project TEXT Explicit project path to import sessions for
  --claude-code-since TEXT Import sessions since date (ISO format)
  --claude-code-max-sessions INT Max sessions to import, 0=all (default: 0)
  --claude-code-content TEXT Content mode: truncated, full, none (default: truncated)
  --with-mcp                Generate MCP server configuration for Claude Desktop
  --mcp-profile TEXT        MCP tool profile: core (6 tools) or extended (16 tools, default)
  --session-strategy TEXT   Memory session strategy: per_conversation (default), per_day, persistent
  --auto-extract/--no-auto-extract  Auto-extract entities from messages (default: on)
  --auto-preferences/--no-auto-preferences  Auto-detect user preferences (default: on)
  --ingest                  Ingest data into Neo4j after generation
  --neo4j-uri TEXT          Neo4j connection URI [env: NEO4J_URI] (--self-hosted only)
  --neo4j-username TEXT     Neo4j username [env: NEO4J_USERNAME] (--self-hosted only)
  --neo4j-password TEXT     Neo4j password [env: NEO4J_PASSWORD] (--self-hosted only)
  --neo4j-aura-env PATH    Path to Neo4j Aura .env file with credentials (--self-hosted only)
  --neo4j-local             Use @johnymontana/neo4j-local for local Neo4j (--self-hosted only)
  --anthropic-api-key TEXT  Anthropic API key for LLM generation [env: ANTHROPIC_API_KEY]
  --openai-api-key TEXT    OpenAI API key for LLM generation [env: OPENAI_API_KEY]
  --google-api-key TEXT    Google/Gemini API key (required for google-adk) [env: GOOGLE_API_KEY]
  --output-dir PATH         Output directory (default: ./<project-name>)
  --demo                    Shortcut for --reset-database --demo-data --ingest
  --reset-database          Clear all Neo4j data before ingesting
  --dry-run                 Preview what would be generated without creating files
  --verbose                 Enable verbose debug output
  --list-domains            List available domains and exit
  --version                 Show version and exit
  --help                    Show help and exit

# Clone and install
git clone https://github.com/neo4j-labs/create-context-graph.git
cd create-context-graph
uv venv && uv pip install -e ".[dev]"

# Run tests (no Neo4j or API keys required)
source .venv/bin/activate
pytest tests/ -v               # Fast: 1,177 tests
pytest tests/ -v --slow        # Full: 1,398 tests (includes domain x framework matrix + perf + generated project tests)
pytest tests/ --integration    # Integration tests (requires running Neo4j)

# Test a specific scaffold
create-context-graph /tmp/test-app --domain software-engineering --framework pydanticai --demo-data

# Run all 3 smoke tests (requires Neo4j + at least one LLM API key)
make smoke-test

# Or run individual framework tests directly (default: --backend bolt)
python scripts/e2e_smoke_test.py --domain financial-services --framework pydanticai --quick
python scripts/e2e_smoke_test.py --domain real-estate --framework google-adk --quick
python scripts/e2e_smoke_test.py --domain trip-planning --framework strands --quick

# Test against NAMS hosted backend (requires MEMORY_API_KEY env)
python scripts/e2e_smoke_test.py --domain healthcare --framework strands --backend nams --quick

# Test all 27 domains with one framework
python scripts/e2e_smoke_test.py --all-domains --framework pydanticai --quick

# Full mode (all prompts per scenario, not just first)
python scripts/e2e_smoke_test.py --domain healthcare --framework claude-agent-sdk

# 1. Update version in pyproject.toml and npm-wrapper/package.json
# 2. Commit the version bump (and any CHANGELOG.md update)
# 3. Tag and push
git tag v0.13.0
git push origin v0.13.0