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Grok Bot+18 agentsMulti-Agent Shared Knowledge Graph Builder
Multi-Agent Shared Knowledge Graph Builder Architect and implement secure, synchronized knowledge graphs for multi-agent systems with strict entity resolution.
$39.99
Multi-Agent Shared Knowledge Graph Builder
Example session with this skill installed
Design a knowledge graph schema for a team of 3 agents: a researcher, a writer, and a fact-checker. They need to share findings about pharmaceutical clinical trials.
- Read your context and instructions
- Compiled the multi-agent shared knowledge
I have designed the multi-agent knowledge graph architecture for your clinical trial research team. The design focuses on high-confidence relationship extraction and strict provenance to support the fact-checker agent's validation workflow.
Clinical Trial Knowledge Graph Architecture
| Component | Specification |
|---|---|
| Node Types | Trial, Drug, Company, Result, AdverseEvent, Source |
| Edge Types | SPONSORED_BY, TESTS_DRUG, REPORTED_IN, INDICATES_EFFICACY |
| Agent Scopes | Researcher (Write/Read), Writer (Read), Fact-Checker (Validate/Update) |
| Primary Keys | NCT ID (Trials), CAS Registry Number (Drugs), ROR ID (Companies) |
| Trust Policy | Facts remain 'Proposed' until Fact-Checker sets status to 'Validated' |
Entity Extraction Logic
# Extraction pipeline for clinical trial entities
class TrialExtractionSchema(BaseModel):
nct_id: str = Field(description="Unique identifier from ClinicalTrials.gov")
phase: str
enrollment: int
primary_endpoint: str
confidence_score: float
source_ref: str
# Logic for entity resolution
def resolve_company(name: str, ror_id: Optional[str]):
# Prevent merging on name similarity alone
if ror_id:
return find_node_by_id(ror_id)
return find_candidate_by_alias(name)
Next steps
- Select a graph backend (Neo4j for complex traversals or AWS Neptune for scale).
- Define the 'Validation' trigger for the Fact-Checker agent to transition nodes from 'Proposed' to 'Validated'.
- Implement the scoping middleware to ensure the Writer agent only reads 'Validated' facts.
Connects securely to your tools. The creator never sees your data.
About this skill
Managing shared context across multiple AI agents often leads to data collisions, hallucinated relationships, and identity fragmentation. When agents lack a unified source of truth, they repeat work or overwrite each other's progress. This skill provides the architectural framework to build a multi-agent shared knowledge graph that maintains strict provenance, entity resolution, and access control.
What it does
- Schema Design defines a canonical ontology for nodes and edges to prevent structural drift.
- Entity Resolution implements logic to merge aliases and prevent duplicate nodes for the same entity.
- Provenance Tracking attaches agent identity, timestamps, and confidence scores to every claim.
- Conflict Resolution detects and manages contradictory facts proposed by different agents.
- Scoped Retrieval enforces tenant-level isolation and permission filters during graph traversals.
How it works
- Define requirements by mapping agent roles, read/write permissions, and shared vs. private domains.
- Design the ontology including node types, relationship cardinality, and temporal properties.
- Build the pipeline for entity extraction, normalization, and validation against the schema.
- Implement governance to handle concurrent updates, idempotent writes, and truth policies.
Frameworks & tools
This tool is agnostic to specific graph databases but provides scaffolding compatible with Neo4j, AWS Neptune, and LangGraph. It focuses on the logic layer between the agent framework and the storage engine.
Why this beats prompting it yourself
Generic prompts fail to handle the complexity of entity disambiguation and transactional integrity in a multi-agent environment. This skill provides a systematic engineering framework that treats graph contributions as auditable claims rather than raw text injections.
Use cases
- Research Swarms coordinating findings from multiple sources into a single map.
- Customer Support agents sharing a unified view of user history and product entities.
- Workflow State management for long-running processes involving different specialist agents.
Known limitations
This skill provides architecture and code scaffolding but does not provision physical database instances or live hosting. Implementation quality depends on the underlying graph database capabilities.
Does not provision database infrastructure. Logic depends on user-supplied storage constraints and agent framework APIs. High-impact merges may still require human-in-the-loop review.
How to use
- 1
Connect your agent to the Agensi MCP
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- 2
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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