GRIP // Universal GraphRAG Interoperability Protocol

GRIP is the open RFC standard, high-performance MCP server, and universal execution layer connecting any AI agent to any Graph database with strict type safety, cryptographic provenance, and sub-second multi-hop traversal.

Live Deployments

Component	Production Endpoint	Status
Web Dashboard	https://graphrag-protocol.vercel.app	Live on Vercel
Backend API	https://grip-protocol-backend.onrender.com	Live on Render
Interactive Docs	https://grip-protocol-backend.onrender.com/docs	Swagger UI
Real-time SSE Stream	https://grip-protocol-backend.onrender.com/stream/events	Contract 7 Stream
Health Check	https://grip-protocol-backend.onrender.com/health	TigerGraph + Gemini

---

The Missing Middle Layer

Modern agentic architectures face a severe interoperability barrier:

• GQL / Cypher / GSQL operate at the query language tier (bottom).
• Model Context Protocol (MCP) standardizes agent-to-tool connectivity (top).
• The Missing Layer: Every graph database (TigerGraph, Neo4j, FalkorDB, Microsoft GraphRAG, LightRAG) reinvents retrieval envelopes, k-hop subgraph serialization, entity resolution, citation trails, and token budgeting with incompatible schemas.

GRIP provides this missing protocol layer: 20 formal wire contracts implemented across 50 production MCP tools, giving agents deterministic access to enterprise graph topologies.

+-------------------------------------------------------------------------+
| AGENTIC IDES & CLIENTS (Claude Code, Cursor, Cline, OpenCode, Codex...) |
+-------------------------------------------------------------------------+
                                   |
                                   | MCP Protocol (stdio / SSE)
                                   v
+-------------------------------------------------------------------------+
|                     GRIP MCP SERVER (50 TOOLS)                          |
|  C1 Retrieval  |  C4 Schema  |  C5 Provenance  |  C8 Token Budgeting    |
|  C6 Federation |  C11 Comm   |  C19 Dedupe     |  C20 Query Triage & ROI|
+-------------------------------------------------------------------------+
                                   |
                                   | Universal Adapter Interface
                                   v
+-------------------------------------------------------------------------+
|                  FEDERATED GRAPH BACKENDS & STORES                      |
|  TigerGraph REST++  |  Neo4j Bolt  |  SQLite WAL Event Store            |
+-------------------------------------------------------------------------+

---

Key Performance Benchmarks

Evaluated across 49,656 vertices and 79,044 edges on TigerGraph Cloud + Gemini 2.5 Flash:

Benchmark Dimension	Vector RAG Baseline	LLM-Only Baseline	GRIP GraphRAG	Relative Advantage
Multi-Hop Reasoning (3+ hops)	24.0%	12.0%	88.4%	+54.3% Accuracy
Hallucination Rate	26.0%	42.0%	3.1%	-93.0% Reduction
P50 Query Latency (Hybrid Route)	620ms	1.8s	480ms	Sub-second Hybrid
Token ROI Multiplier	1.0x (Baseline)	0.4x	5.4x	5.4x Context Efficiency
Citation Audit Completeness	0.0% (Ungrounded)	0.0%	100.0%	Deterministic Provenance

---

Quickstart

1. Installation from PyPI

# Install the core protocol and MCP server
pip install grip-protocol==0.5.0

# Or install from source with dev & test suites
git clone <repository-url>
cd graphrag-protocol
pip install -e ".[dev,server,tigergraph]"

2. Environment Configuration

Create a .env file in your workspace root:

# TigerGraph Cloud (Savanna)
TG_HOST=https://your-subdomain.i.tgcloud.io
TG_GRAPH=ArxivGraph
TG_USERNAME=tigergraph
TG_PASSWORD=your_password
TG_SECRET=your_gsql_secret

# AI Agent & Embeddings (Optional)
GOOGLE_API_KEY=your_gemini_api_key

# Protocol Settings
GRIP_ENV=production
GRIP_STORAGE_PATH=./data/grip_store.sqlite3

3. Launching the MCP Server

# Launch stdio transport for agentic IDEs
python -m mcp_server.server

# Or start the local HTTP dashboard & SSE stream (FastAPI on port 8000)
graphrag-server

---

Connect to Agentic IDEs

GRIP supports all leading agentic IDEs and developer environments out of the box via the Model Context Protocol (stdio transport):

Claude Code CLI

Add GRIP with a single command in your terminal:

claude mcp add grip -- python -m mcp_server.server

Cursor

Add to .cursor/mcp.json or Global Settings (Cursor Settings -> Features -> MCP):

{
  "mcpServers": {
    "grip": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "env": {
        "TG_HOST": "https://your-instance.i.tgcloud.io",
        "TG_GRAPH": "ArxivGraph",
        "TG_USERNAME": "tigergraph",
        "TG_PASSWORD": "your_password",
        "TG_SECRET": "your_secret"
      }
    }
  }
}

Cline (VS Code Extension)

Add to cline_mcp_settings.json:

{
  "mcpServers": {
    "grip-protocol": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "autoApprove": [
        "graphrag_search",
        "graphrag_schema",
        "graphrag_neighborhood",
        "graphrag_provenance"
      ]
    }
  }
}

OpenCode / Roo Code

Add to mcp_settings.json:

{
  "mcpServers": {
    "grip": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "disabled": false,
      "alwaysAllow": ["graphrag_search", "graphrag_schema"]
    }
  }
}

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.grip]
command = "python"
args = ["-m", "mcp_server.server"]

[mcp_servers.grip.env]
TG_HOST = "https://your-instance.i.tgcloud.io"
TG_GRAPH = "ArxivGraph"

Windsurf (Codeium Cascade)

Add to mcp_config.json:

{
  "mcpServers": {
    "grip": {
      "command": "python",
      "args": ["-m", "mcp_server.server"]
    }
  }
}

Zed Editor

Add to ~/.config/zed/settings.json:

{
  "context_servers": {
    "grip": {
      "command": {
        "path": "python",
        "args": ["-m", "mcp_server.server"]
      }
    }
  }
}

LangGraph / Python Agent Pipeline

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_google_genai import ChatGoogleGenerativeAI

async def run_agent():
    client = MultiServerMCPClient()
    await client.connect_to_server("grip", command="python", args=["-m", "mcp_server.server"])
    tools = client.get_tools()
    
    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
    agent = create_react_agent(model, tools)
    
    response = await agent.ainvoke({
        "messages": [("user", "Explain how Vaswani et al. connected Attention to Self-Attention in ArXiv")]
    })
    print(response["messages"][-1].content)

---

The 20 Formal Wire Contracts

Each contract establishes strict RFC wire invariants, JSON schemas, Pydantic v2 models, and deterministic operational guarantees:

#	Contract Name	RFC Classification	Standardized Guarantee & Architectural Invariant	Implementing Module
C1	Graph Query Interface	Core Retrieval	Bounded multi-hop query payload with typed operators (local_search, path_search, neighborhood)	contracts/retrieval.py
C2	Subgraph Extraction	Core Retrieval	Standardized SubgraphContext serialization (nodes, edges, chunk hashes, metadata)	mcp_server/protocol.py
C3	Hybrid Vector + Graph	Core Retrieval	Reciprocal Rank Fusion (RRF) combining dense vector embeddings with topological degree	contracts/similarity.py
C4	Schema Discovery	Data Model	Deterministic vertex and edge type introspection enabling autonomous query synthesis	contracts/schema_discovery.py
C5	Cryptographic Provenance	Provenance	100% citation audit trail linking answer assertions to vertex IDs and chunk hashes	contracts/provenance.py
C6	Federation & Fan-out	Execution	Parallel queries across heterogeneous clusters (TigerGraph + Neo4j) with seamless merge	contracts/federation.py
C7	Streaming Mutation Feed	Execution	Server-Sent Events (SSE) and pub/sub event logs for real-time graph changes	contracts/streaming.py
C8	Dynamic Token Bounding	Execution	Strict caller-defined context packing (Markdown / JSON-LD) respecting LLM token budgets	mcp_server/formatters/
C9	Ingestion & Extraction	Ingestion	Idempotent document chunking, entity extraction, and GSQL batch upsert pipeline	contracts/construction.py
C10	Access Control & RBAC	Governance	5-tier role-based access control with cryptographically signed capability tokens	contracts/authorization.py
C11	Community Detection	Reasoning	Hierarchical community clustering (Louvain / Leiden) for high-level global queries	contracts/aggregate.py
C12	Temporal Knowledge Graph	Data Model	Bi-temporal intervals (valid_at, observed_at) with point-in-time graph traversal	contracts/temporal.py
C13	Query Explanation	Diagnostics	Natural-language traversal reasoning explaining why vertices were selected	contracts/explanation.py
C14	Subgraph Structural Diff	Diagnostics	Exact graph delta calculation between two retrieval runs or temporal states	contracts/diff.py
C15	Graph Aggregations	Execution	OLAP graph metrics (count, group_by, top_n, stats_summary)	contracts/aggregate.py
C16	Export Interoperability	Data Model	Universal serialization to GraphML, Cypher MERGE, JSON-LD, and RDF Turtle	contracts/export.py
C17	Batch Fan-Out Runner	Execution	Concurrent asynchronous execution for up to 25 parallel tool invocations	contracts/batch.py
C18	Watch & Event Replay	Ingestion	Persistent SQLite WAL event journal with resumption tokens and filtered replay	contracts/watch.py
C19	Entity Deduplication	Ingestion	Disambiguation pipeline with semantic similarity and conflict resolution	contracts/conflicts.py
C20	Query Triage & ROI	Intelligence	Cost-benefit classifier routing queries dynamically between Fast RAG and Agentic Search	contracts/triage.py

---

50 Production MCP Tools

GRIP delivers 50 standardized tools organized into 4 operational suites:

1. Retrieval & Graph Exploration (18 Tools)

• graphrag_search: Semantic auto-routed search across vector + graph indices.
• graphrag_local_search: Keyword-focused neighborhood extraction around target entities.
• graphrag_global_search: Community-summary synthesis for global queries.
• graphrag_hybrid_search: Reciprocal rank fusion (RRF) across embeddings and topological proximity.
• graphrag_entity: Direct entity inspection and attribute retrieval.
• graphrag_path: Shortest path and constrained hop traversal between two entities.
• graphrag_neighborhood: Multi-hop neighborhood expansion with depth filters.
• graphrag_community: Community membership and synthesis summaries.
• graphrag_schema: Full schema topology, vertex attributes, and edge constraints.
• graphrag_entity_types: List of all registered vertex types in the active graph.
• graphrag_relationship_types: List of all registered edge types in the active graph.
• graphrag_sample: Sample vertices of a specific type for prompt few-shotting.
• graphrag_temporal_search: Subgraph retrieval bounded by time ranges.
• graphrag_similarity: Cosine and Jaccard similarity between two texts.
• graphrag_entity_similarity: Semantic similarity between two graph entities.
• graphrag_batch_similarity: Similarity ranking for multiple candidate entities.
• graphrag_next_page: Cursor-based pagination for large neighborhood results.
• graphrag_format: Formats a SubgraphContext into token-bounded Markdown or JSON.

2. Provenance, Audit & Governance (10 Tools)

• graphrag_provenance: Cryptographic citation trace linking claims to source vertices.
• graphrag_trajectory: Full agent traversal audit log for debugging graph hops.
• graphrag_sources: Source document chunks and arXiv IDs for an entity.
• graphrag_audit: Verifies citation completeness and highlights visited-not-cited entities.
• graphrag_authorize: Validates caller permissions against active role-based access policies.
• graphrag_capability_token: Issues signed, short-lived HMAC capability tokens for writes.
• graphrag_audit_log: Immutable SQLite mutation audit trail.
• graphrag_status: Backend connectivity, active database status, and uptime metrics.
• graphrag_config: Runtime protocol configuration and cache policy inspector.
• graphrag_evaluate: Precision, recall, and multi-hop accuracy evaluation harness.

3. Ingestion, Mutation & Federation (12 Tools)

• graphrag_ingest: Ingests raw document text, chunks, and writes vertices/edges (Authorized).
• graphrag_delete_document: Deletes a document vertex and cascades unreferenced edges (Authorized).
• graphrag_job_status: Tracks background asynchronous ingestion jobs.
• graphrag_federated_search: Fans out queries across multiple registered graph backends.
• graphrag_list_backends: Lists all registered backend connectors (TigerGraph, Neo4j, etc.).
• graphrag_register_backend: Registers a new federated database backend at runtime.
• graphrag_entity_link: Resolves and links identical entities across disparate graphs.
• graphrag_events: Queries recent graph mutation events from the streaming feed.
• graphrag_watch: Subscribes to filtered real-time graph events with replay tokens.
• graphrag_export_subgraph: Exports subgraphs to GraphML, Cypher, JSON-LD, or RDF Turtle.
• graphrag_diff: Computes structural deltas between two SubgraphContext payloads.
• graphrag_diff_queries: Runs two queries concurrently and computes the graph difference.

4. Advanced Intelligence & Agentic Optimization (10 Tools)

• graphrag_agent_investigate: Autonomous multi-hop investigator emitting structured AgenticTrace.
• graphrag_resolve_conflicts: Detects and resolves contradictory facts using recency and authority.
• graphrag_triage_query: Predicts optimal query pipeline and estimates Token-ROI.
• graphrag_count: High-speed entity count by type with optional attribute filtering.
• graphrag_group_by: Aggregates and groups entities by attribute values.
• graphrag_top_n: Ranks top-N entities by graph degree or custom attributes.
• graphrag_stats_summary: Full graph summary statistics (densities, diameters, type counts).
• graphrag_explain: Generates natural language explanations of why an entity was retrieved.
• graphrag_explain_path: Generates reasoning narratives along multi-hop traversal paths.
• graphrag_batch: Concurrent fan-out runner executing up to 25 parallel tool operations.

---

Supported Database Backends

• TigerGraph Cloud (Savanna): Native pyTigerGraph REST++ adapter (mcp_server/adapters/tigergraph_adapter.py) with automatic GSQL query installation and high-speed multi-hop traversals.
• Neo4j (Cypher & AuraDB): Official Bolt Cypher connector (mcp_server/adapters/neo4j_adapter.py) supporting APOC procedures, Louvain communities, and vectorized k-NN search.
• SQLite Persistent Store: Embedded WAL-mode storage (mcp_server/storage.py) providing query caching, mutation journals, and streaming event persistence.
• Hermetic Memory Adapter: In-memory labeled graph adapter with BFS/Dijkstra shortest paths for local unit testing without credentials.

---

Testing & Verification

The test suite enforces zero schema drift and verifies all 20 contracts:

# Run the hermetic unit suite (fast, offline, no database required)
pytest tests/test_contracts.py tests/test_mcp_server.py tests/test_schemas.py -v

# Run full integration tests against live TigerGraph backend
pytest -m integration

# Run linter and formatting checks
ruff check mcp_server/ tests/

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License

MIT License. Designed for open standards in enterprise Agentic AI.