botserver/docs/src/chapter-11-features/whats-new.md

What's New: Multi-Agent Features

General Bots has been enhanced with powerful multi-agent orchestration capabilities. This document summarizes the new features, keywords, and configuration options.

Overview

The multi-agent update introduces:

• Agent-to-Agent (A2A) Protocol - Bots communicate and delegate tasks
• Cross-Session User Memory - User data persists across bots and sessions
• Dynamic Model Routing - Switch LLM models based on task requirements
• Hybrid RAG Search - Combined semantic + keyword search with RRF
• Code Sandbox - Safe Python/JavaScript/Bash execution
• Agent Reflection - Self-analysis for continuous improvement
• SSE Streaming - Real-time response streaming

New BASIC Keywords

Multi-Agent Keywords

Keyword	Description
ADD BOT	Add a bot with triggers, tools, or schedules
DELEGATE TO BOT	Send task to another bot and get response
BROADCAST TO BOTS	Send message to all session bots
TRANSFER CONVERSATION	Hand off conversation to another bot
BOT REFLECTION	Enable agent self-analysis
BOT REFLECTION INSIGHTS	Get reflection analysis results

Memory Keywords

Keyword	Description
SET USER MEMORY	Store data at user level (cross-bot)
GET USER MEMORY	Retrieve user-level data
SET USER FACT	Store a fact about the user
USER FACTS	Get all stored user facts

Model Routing Keywords

Keyword	Description
USE MODEL	Switch LLM model (fast/quality/code/auto)

Code Execution Keywords

Keyword	Description
RUN PYTHON	Execute Python in sandbox
RUN JAVASCRIPT	Execute JavaScript in sandbox
RUN BASH	Execute Bash script in sandbox
RUN ... WITH FILE	Run script from file

Quick Examples

Multi-Agent Routing

' Router bot directing queries to specialists
HEAR userquery

category = LLM "Classify into billing, technical, sales: " + userquery

SWITCH category
    CASE "billing"
        result = DELEGATE userquery TO BOT "billing-bot"
    CASE "technical"
        result = DELEGATE userquery TO BOT "tech-bot"
    CASE "sales"
        result = DELEGATE userquery TO BOT "sales-bot"
END SWITCH

TALK result

Cross-Bot User Memory

' Store user preference (accessible from any bot)
SET USER MEMORY "language", "pt-BR"
SET USER MEMORY "timezone", "America/Sao_Paulo"

' In another bot - retrieve preference
language = GET USER MEMORY("language")
IF language = "pt-BR" THEN
    TALK "Olá! Como posso ajudar?"
END IF

Dynamic Model Selection

' Use fast model for simple queries
USE MODEL "fast"
greeting = LLM "Say hello"

' Switch to quality model for complex analysis
USE MODEL "quality"
analysis = LLM "Analyze market trends and provide recommendations"

' Let system decide automatically
USE MODEL "auto"

Code Sandbox

' Execute Python for data processing
code = "
import json
data = [1, 2, 3, 4, 5]
print(json.dumps({'sum': sum(data), 'avg': sum(data)/len(data)}))
"
result = RUN PYTHON code
TALK "Statistics: " + result

Agent Reflection

' Enable self-analysis
BOT REFLECTION true
BOT REFLECTION ON "conversation_quality"

' Later, check insights
insights = BOT REFLECTION INSIGHTS()
IF insights.qualityScore < 0.7 THEN
    SEND MAIL admin, "Low Quality Alert", insights.summary
END IF

New Configuration Options

Add these to your config.csv:

Multi-Agent (A2A)

name,value
a2a-enabled,true
a2a-timeout,30
a2a-max-hops,5
a2a-retry-count,3

User Memory

name,value
user-memory-enabled,true
user-memory-max-keys,1000
user-memory-default-ttl,0

Model Routing

name,value
model-routing-strategy,auto
model-default,fast
model-fast,DeepSeek-R1-Distill-Qwen-1.5B-Q3_K_M.gguf
model-quality,gpt-4
model-code,codellama-7b.gguf

Hybrid RAG Search

name,value
rag-hybrid-enabled,true
rag-dense-weight,0.7
rag-sparse-weight,0.3
rag-reranker-enabled,true

Code Sandbox

name,value
sandbox-runtime,lxc
sandbox-timeout,30
sandbox-memory-mb,512
sandbox-cpu-percent,50
sandbox-network,false
sandbox-python-packages,numpy,pandas,pillow

Bot Reflection

name,value
reflection-enabled,true
reflection-interval,10
reflection-min-messages,3
reflection-model,quality

SSE Streaming

name,value
sse-enabled,true
sse-heartbeat,30
sse-max-connections,1000

Database Migrations

Run migrations to create the new tables:

cd botserver
cargo run -- migrate

New Tables

Table	Purpose
user_memory	Cross-session user preferences and facts
user_preferences	Per-session user settings
a2a_messages	Agent-to-Agent protocol messages
user_memory_extended	Enhanced memory with TTL
kg_relationships	Knowledge graph relationships
conversation_summaries	Episodic memory summaries
conversation_costs	LLM cost tracking
openapi_tools	OpenAPI tool tracking
agent_reflections	Agent self-analysis results
chat_history	Conversation history
session_bots	Multi-agent session tracking

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Multi-Agent System                        │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌──────────┐    A2A Protocol    ┌──────────┐               │
│  │ Router   │◄──────────────────►│ Billing  │               │
│  │ Bot      │                    │ Bot      │               │
│  └────┬─────┘    ┌──────────┐    └──────────┘               │
│       │          │ Support  │                                │
│       └─────────►│ Bot      │◄──────────────────┐           │
│                  └──────────┘                    │           │
│                                                  │           │
│  ┌──────────────────────────────────────────────┼──────┐    │
│  │              Shared Resources                 │      │    │
│  │  ┌────────────┐  ┌────────────┐  ┌──────────┴─┐    │    │
│  │  │ User       │  │ Hybrid     │  │ Model      │    │    │
│  │  │ Memory     │  │ RAG Search │  │ Router     │    │    │
│  │  └────────────┘  └────────────┘  └────────────┘    │    │
│  └───────────────────────────────────────────────────────┘  │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Design Principles

These features follow General Bots' core principles:

1. BASIC-First - All features accessible via simple BASIC keywords
2. KISS - Simple syntax, predictable behavior
3. Pragmatismo - Real-world utility over theoretical purity
4. No Lock-in - Local deployment, own your data

Performance Considerations

Feature	Impact	Mitigation
A2A Protocol	Adds network latency	Use timeouts, local bots
User Memory	Database queries	Caching, indexing
Hybrid Search	Dual search paths	Results cached
Code Sandbox	Container startup	Warm containers
Reflection	LLM calls	Run periodically, not per-message
SSE Streaming	Connection overhead	Connection pooling

Migration Guide

From Single-Bot to Multi-Agent

1. Identify Specializations - What tasks need dedicated bots?
2. Create Specialist Bots - Each with focused config
3. Build Router - Central bot to direct traffic
4. Share Memory - Move shared data to User Memory
5. Test Delegation - Verify communication paths

Upgrading Existing Bots

1. Run database migrations
2. Add new config options as needed
3. Existing keywords continue to work unchanged
4. Gradually adopt new features

See Also

• Multi-Agent Orchestration - Complete guide
• Memory Management - Memory deep dive
• Hybrid RAG Search - Search configuration
• Keywords Reference - All keywords
• Configuration Parameters - All config options