Research: AI Agent Grounding — Ontology-Backed Tool Selection

Scope: Capability → tool ontologies, Cypher selection, forge.find recall, agent-loop ergonomics.

Executive summary

GraphForge works as an embedded tool registry for LLM agents: model capabilities, tools, parameters, dependencies, and permissions as a graph, then select with Cypher and/or forge.find.

What holds up:

Capability hierarchy + HAS_TOOL / REQUIRES / PRODUCES patterns for planning
Permission and deprecation filters expressed as ordinary graph structure
to_dicts() / Arrow tables as the ergonomic boundary into agent loops
Sub-millisecond to low-millisecond lookup latency at tens–hundreds of tools
forge.find on tool name + description for paraphrase and CamelCase-adjacent recall

Design around:

Exact-match Cypher on tool names is brittle when models invent casing or paraphrases — index descriptions and search with forge.find
Split multi-statement create/link sequences when a single execute() would rebind the same variable incorrectly
Prefer MERGE for shared ontology hubs (DataModel, Capability) so tools link to one node rather than duplicates

Selection sketch

from graphforge import GraphForge

forge = GraphForge("tools/")

# Structured filter
allowed = forge.execute("""
    MATCH (r:Role {name: $role})-[:MAY_USE]->(t:Tool)
    WHERE coalesce(t.deprecated, false) = false
    RETURN t.name AS name, t.description AS description
""", {"role": "analyst"})

# Fuzzy intent
candidates = forge.find("search inventory stock", label="Tool", limit=5)

No major agent framework ships graph-native tool planning with dependency and permission traversal. GraphForge fills that gap as a local registry beside LangGraph, LlamaIndex, or custom loops.

Recommendations

Index tool name and description for forge.find.
Model dependencies as edges; plan with path queries, not flat ranking alone.
Persist the registry as GraphForge("tools/") so agents reopen without rebuild.