Relevance
Relevance Knowledge Manager

Relevance Knowledge Manager

agent Agent

Tools

🗑️
🔍
🗂️
✏️

Description

Manages knowledge tables — adding, searching, updating, and deleting records. Designed to be called by master agents or directly. Always identifies which knowledge base is being used and confirms before destructive operations.

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Example Task

List all knowledge tables

Relevance Knowledge Manager
🗂️
Relevance Knowledge Manager

Relevance Knowledge Manager

provided an update

Available Knowledge Tables

I found 21 knowledge tables in your project. All are agent memory tables (system-registered):

Agent Memory Tables (21 total)

Table Name Records Type Last Updated
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Nov 6, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Feb 23, 2026
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Nov 24, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Oct 20, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Nov 27, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Feb 11, 2026
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Oct 30, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Oct 23, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Feb 18, 2026
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Sep 12, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Nov 10, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Oct 14, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Feb 23, 2026
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Dec 1, 2025
ID000000-0000-0000-0000-000000000000_memory 3 Agent Memory Nov 19, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Feb 12, 2026
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Oct 30, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Dec 8, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Nov 7, 2025
ID000000-0000-0000-0000-000000000000_memory 0 Agent Memory Dec 2, 2025

Notable Tables:

  • Only 1 table has data: ID000000-0000-0000-0000-000000000000_memory (3 records)
    • Description: "Instructions on how to avoid creating duplicate contacts and best practices for managing contact lists"
  • No user-created named tables found in the registry

Would you like me to explore the contents of the table with data, or help you work with any specific table?

Relevance AI

Give me the table with 3 records and list out the records

Relevance Knowledge Manager
🔍
Relevance Knowledge Manager

Relevance Knowledge Manager

provided an update

Table: ACCOUNT123_memory
Operation: Retrieved 3 records
Description: Instructions on avoiding duplicate contacts and contact management best practices

Records in the Table:

Record 1: Contact - John Doe

  • Document ID: ID456
  • Category: fact
  • Importance: high
  • Text: "Contact: John Doe, Chat ID: CHATID123, Telegram ID: ID789"
  • Reasoning: "Saving contact information from Get All Chats"
  • Last Modified: Nov 19, 2025 at 22:27:16
  • Memory ID: ID101

Record 2: Instruction - Avoid Duplicate Contacts

  • Document ID: ID457
  • Category: instruction
  • Importance: high
  • Text: "Instruction: Before saving any new contact information to memory, first check existing memories to avoid storing duplicate contacts. If the contact already exists, update the existing memory entry instead of creating a new one."
  • Reasoning: "To ensure memory efficiency and avoid duplicate contact entries."
  • Last Modified: Nov 19, 2025 at 22:28:33
  • Memory ID: ID456

Record 3: Contact - Jane Smith

  • Document ID: ID458
  • Category: fact
  • Importance: high
  • Text: "Contact: Jane Smith, Chat ID: CHATID124, Telegram ID: ID790"
  • Reasoning: "Saving contact information from Get All Chats"
  • Last Modified: Nov 19, 2025 at 22:27:16
  • Memory ID: ID102

Summary: This agent memory table contains 2 contact records (John Doe and Jane Smith) with their Telegram chat and user IDs, plus 1 instruction record about avoiding duplicate contacts when managing contact lists.

Relevance AI

Update the third record text "This is now Jane Smith's contact"

Relevance Knowledge Manager
✏️
Relevance Knowledge Manager

Relevance Knowledge Manager

provided an update

Table: TABLE_ID_0000000000000000000000000000000000
Operation: Updated 1 record
Record ID: RECORD_ID_00000000000000000000000000
Field Updated: text → "This is now Jane S contact"

The third record (previously Jane Smith's contact information) has been successfully updated. The text field now reads "This is now Jane S contact" while all other fields in the record remain unchanged.

A vectorization job has also been triggered (Job ID: JOB_ID_0000000000000000000000000000000) to update the search index with the new text content.

Relevance AI

Great, list all 3 records now

Relevance Knowledge Manager
🔍
Relevance Knowledge Manager

Relevance Knowledge Manager

provided an update

Table: TABLE123_memory
Operation: Retrieved all 3 records (showing current state after update)

All Records in the Table:

Record 1: Contact - John Doe

  • Document ID: ACCOUNT123
  • Category: fact
  • Importance: high
  • Text: "Contact: John Doe, Chat ID: CHATID123, Telegram ID: (555) 123-4567"
  • Reasoning: "Saving contact information from Get All Chats"
  • Last Modified: Nov 19, 2025 at 22:27:16
  • Memory ID: MEMORY123

Record 2: Instruction - Avoid Duplicate Contacts

  • Document ID: ID456
  • Category: instruction
  • Importance: high
  • Text: "Instruction: Before saving any new contact information to memory, first check existing memories to avoid storing duplicate contacts. If the contact already exists, update the existing memory entry instead of creating a new one."
  • Reasoning: "To ensure memory efficiency and avoid duplicate contact entries."
  • Last Modified: Nov 19, 2025 at 22:28:33
  • Memory ID: ID_INSTRUCTION_001

Record 3: Jane Smith's Contact ✅ UPDATED

  • Document ID: ACCOUNT789
  • Category: fact
  • Importance: high
  • Text: "This is now Jane Smith's contact" ← Updated successfully
  • Reasoning: "Saving contact information from Get All Chats"
  • Last Modified: Nov 19, 2025 at 22:27:16
  • Last Updated: Feb 26, 2026 at 00:47:21 ← Shows recent update
  • Memory ID: MEMORY789

Perfect! The third record has been successfully updated. You can see the text field now shows "This is now Jane Smith's contact" and the update_date_ reflects the recent change (Feb 26, 2026 at 00:47:21).

Relevance AI

For a multi-agent flow, how would you work well with them?

Relevance Knowledge Manager
Relevance Knowledge Manager

Relevance Knowledge Manager

provided an update

Multi-Agent Collaboration Strategy

As the Knowledge Manager, I'm designed to be the central data hub for multi-agent workflows. Here's how I integrate seamlessly:

🔄 Structured Agent-to-Agent Communication

I accept structured parameters from other agents, eliminating back-and-forth clarification:

{
  "operation": "add|search|update|delete|list_tables",
  "knowledge_set": "contacts", 
  "data": [...],
  "message": "optional context"
}

Example Flow:

  1. Lead Agent → calls me with structured params
  2. I execute → return results with document IDs
  3. Lead Agent → uses results for next workflow step

🎯 Role-Based Integration Patterns

Agent Type How I Support Them Example
Contact Manager Store/retrieve contact data with deduplication "Before adding John Doe, search existing contacts"
Task Orchestrator Maintain workflow state and task history "Update task status to 'completed'"
Data Collector Centralize gathered information from multiple sources "Add survey responses to customer_feedback table"
Analytics Agent Provide clean datasets for analysis "Get all sales records from Q4 2025"

🛡️ Built-in Safety for Multi-Agent Ops

  • Preview Before Delete: Always show what will be affected
  • Document ID Tracking: Return IDs for audit trails
  • Table Identification: Always state which table is being used
  • Bulk Operation Confirmation: Report counts for large changes

🔗 Common Multi-Agent Workflows

1. Data Collection → Processing → Storage

Web Scraper Agent → Data Cleaner Agent → Knowledge Manager (me)
                                      ↓
                              Store in structured tables

2. Search → Analyze → Update

Query Agent → Knowledge Manager (search) → Analytics Agent → Knowledge Manager (update)

3. Validation → Deduplication → Storage

Validator Agent → Knowledge Manager (check existing) → Decision Agent → Knowledge Manager (add/update)

📋 Best Practices for Agent Integration

For Calling Agents:

  • Use structured params when possible
  • Include knowledge_set name explicitly
  • Pass document IDs for updates/deletes
  • Handle my response data structure: row.data.field_name

For Workflow Orchestrators:

  • Call me early to establish available tables
  • Use my document IDs as workflow state keys
  • Leverage my search filters for conditional logic
  • Use my bulk operations for efficiency

🚀 Advanced Multi-Agent Scenarios

Distributed Data Collection

Multiple agents collect data → I consolidate into unified tables → Analytics agent processes

Real-time Collaboration

Agent A updates record → I return new state → Agent B uses updated data immediately

Cross-Agent Memory

I serve as shared memory between agents that don't directly communicate

💡 Key Advantages in Multi-Agent Systems

  1. Single Source of Truth: All agents use same data store
  2. Atomic Operations: Consistent state across agent interactions
  3. Audit Trail: Every change tracked with document IDs
  4. Schema Flexibility: Tables adapt to agent needs automatically
  5. Performance: Optimized for high-frequency agent calls

🎪 Example Multi-Agent Conversation

Lead Agent: "We need to process new customer data"
├── Calls me: {"operation": "search", "knowledge_set": "customers", "filters": [...]}
├── I return: existing customer records
├── Lead Agent: decides to update vs create new
└── Calls me: {"operation": "update", "knowledge_set": "customers", "updates": [...]}

The key: I eliminate data management complexity so other agents can focus on their specialized logic while maintaining perfect data consistency across the entire system.

Would you like me to demonstrate any specific multi-agent integration pattern?

The Relevance Knowledge Manager is your AI-powered assistant for managing knowledge tables with ease. No coding skills needed—just use natural language to add, search, update, or delete records.

Key features:
• Safety-first: Always previews and confirms before deleting records
• Dual interface: Works for humans (chat) and master agents (structured commands)
• Transparent: Clearly states which table is affected and what changes are made
• Reliable: Built-in error handling and audit trails for every action

🛠️ Tools included:
• List all knowledge tables
• Search records with filters and pagination
• Add new records (auto-formatted)
• Update existing records (partial updates supported)
• Delete records (with preview and confirmation)

👥 Perfect for:
• Sales & marketing teams
• Operations & data teams
• Customer support
• Developers & automation engineers
• Business analysts

✨ Try it now - manage your data smarter and safer!

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