chore: Apply binary size optimizations, update PROMPT standards and fix clippy warnings
This commit is contained in:
parent
4c8f828d41
commit
cf7d1f9992
5 changed files with 27 additions and 631 deletions
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@ -1,6 +1,6 @@
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[build]
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rustc-wrapper = "/home/rodriguez/.cargo/bin/sccache"
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rustc-wrapper = "/home/rodriguez/.cargo/bin/sccache"
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[target.x86_64-unknown-linux-gnu]
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linker = "clang"
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rustflags = ["-C", "link-arg=-fuse-ld=mold"]
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# rustflags = ["-C", "link-arg=-fuse-ld=mold"]
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11
Cargo.toml
11
Cargo.toml
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@ -36,7 +36,7 @@ tokio-stream = "0.1"
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tokio-util = { version = "0.7", default-features = false }
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tokio-rustls = "0.26"
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tokio-test = "0.4"
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tokio-tungstenite = { version = "0.21", default-features = false }
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tokio-tungstenite = { version = "0.24", default-features = false }
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async-trait = "0.1"
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async-lock = "2.8.0"
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async-stream = "0.3"
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@ -62,7 +62,7 @@ uuid = { version = "1.11", default-features = false }
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axum = { version = "0.7.5", features = ["ws", "multipart", "macros"] }
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axum-server = { version = "0.8", features = ["tls-rustls"] }
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tower = "0.4"
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tower-http = { version = "0.5", default-features = false }
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tower-http = { version = "0.6", default-features = false }
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tower-cookies = "0.10"
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hyper = { version = "1.4", default-features = false }
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hyper-rustls = { version = "0.27", default-features = false }
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@ -73,8 +73,8 @@ http-body-util = "0.1.3"
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reqwest = { version = "0.12", default-features = false }
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# ─── DATABASE ───
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diesel = { version = "2.1" }
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diesel_migrations = "2.1"
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diesel = { version = "2.2" }
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diesel_migrations = "2.2"
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# ─── CACHING ───
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redis = { version = "0.27", default-features = false }
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@ -244,8 +244,7 @@ lto = true
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opt-level = "z"
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strip = true
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panic = "abort"
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codegen-units = 8
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overflow-checks = true
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codegen-units = 1
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[profile.ci]
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inherits = "release"
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612
IMPROVE.md
612
IMPROVE.md
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@ -1,612 +0,0 @@
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# General Bots AI Orchestrator Operating System - Architectural Improvements
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**Analysis Date:** January 24, 2026
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**Analyzed Version:** 6.1.0
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**Target Version:** 7.0.0
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---
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## Executive Summary
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General Bots is a **folder-based AI orchestrator** where `.gbai` packages contain everything (BASIC scripts, documents, config). The system ALREADY has:
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- **LXC container isolation** via `botserver install --container`
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- **Visual designer** for BASIC scripts
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- **Package manager** built-in (drop `.gbai` folder = deployed)
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- **Multi-agent via ADD BOT** keyword in BASIC
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- **Single binary** deployment model
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This analysis focuses on ENHANCING what exists, not replacing it.
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## Current Architecture Analysis
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### What Already Exists (Don't Rebuild)
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1. **Package System (.gbai)**: Folder-based deployment - copy folder = deployed bot
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2. **LXC Container Isolation**: `botserver install vault --container` creates isolated services
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3. **Visual Designer**: Already exists for BASIC script creation
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4. **Multi-Agent**: `ADD BOT` keyword enables bot-to-bot delegation
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5. **Single Binary**: One `botserver` binary manages everything
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6. **Auto-Bootstrap**: Scans `templates/` for `.gbai` folders, auto-deploys
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7. **BASIC Scripting**: Rhai-based interpreter with 80+ keywords
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8. **Built-in Package Manager**: `botserver install <component>` handles dependencies
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### Real Strengths
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1. **Zero-Config Deployment**: Drop `.gbai` folder in `templates/`, restart = live
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2. **BASIC Accessibility**: Non-programmers can create AI workflows
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3. **Comprehensive Suite**: 50+ integrated apps (CRM, docs, video, etc.)
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4. **Security-First**: No unwrap(), proper error handling, SafeCommand wrapper
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5. **LXC Isolation**: Each service (PostgreSQL, Vault, VectorDB) in own container
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6. **Feature Flags**: Granular control from minimal to full deployment
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### Current Limitations (What to Actually Improve)
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1. **Multi-Agent Coordination**: `ADD BOT` exists but lacks sophisticated orchestration patterns
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2. **Workflow Visualization**: Designer exists but limited to linear BASIC scripts
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3. **Agent Memory**: Basic episodic memory, lacks long-term agent learning
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4. **Cross-Bot Communication**: Bots can delegate but no pub/sub or event bus
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5. **LLM Routing**: Basic model selection, lacks intelligent routing based on task
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6. **Plugin Ecosystem**: MCP support exists but no marketplace or discovery
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---
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## Proposed Architectural Improvements
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### 1. Enhanced Multi-Agent Orchestration (Priority: Critical)
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#### Current State
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- `ADD BOT` keyword enables bot delegation
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- Basic priority and trigger matching
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- No sophisticated coordination patterns
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#### Proposed Enhancement: Agent Collaboration Patterns
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**Extend existing BASIC keywords, don't replace:**
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```basic
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' Current (works)
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ADD BOT "specialist" WITH TRIGGER "technical questions"
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' Enhanced (new patterns)
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ADD BOT "analyst" WITH ROLE "data-processor"
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ADD BOT "reviewer" WITH ROLE "quality-check"
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' New coordination keyword
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ORCHESTRATE WORKFLOW
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STEP 1: BOT "researcher" GATHERS data
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STEP 2: BOT "analyst" PROCESSES data
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STEP 3: BOT "writer" CREATES report
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STEP 4: HUMAN APPROVAL required
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STEP 5: BOT "publisher" DISTRIBUTES report
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END WORKFLOW
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```
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**Implementation:**
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- Add `ORCHESTRATE WORKFLOW` keyword to `basic/keywords/`
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- Store workflow state in existing PostgreSQL
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- Use existing `ADD BOT` infrastructure
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- Leverage existing session management
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**Benefits:**
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- Builds on existing `ADD BOT` system
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- BASIC-accessible workflow definition
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- No new infrastructure needed
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### 2. Visual Workflow Designer Enhancement (Priority: High)
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#### Current State
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- Designer exists for BASIC scripts
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- Linear script editing
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- No visual workflow representation
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#### Proposed Enhancement: Drag-and-Drop Workflow Canvas
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**Extend existing designer, don't rebuild:**
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```rust
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// Add to existing botserver/src/designer/
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pub struct WorkflowNode {
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node_type: NodeType,
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basic_code: String, // Generates BASIC
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position: (f32, f32),
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}
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pub enum NodeType {
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BotAgent { bot_name: String },
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HumanApproval { approvers: Vec<String> },
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Condition { expression: String },
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Loop { iterations: String },
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Parallel { branches: Vec<Branch> },
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}
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```
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**Output:** Generates BASIC code using existing keywords
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```basic
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' Generated from visual designer
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ORCHESTRATE WORKFLOW
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STEP 1: BOT "support" HANDLES initial_request
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IF priority = "high" THEN
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STEP 2: HUMAN APPROVAL FROM "manager"
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END IF
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STEP 3: BOT "resolver" COMPLETES task
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END WORKFLOW
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```
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**Benefits:**
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- Visual design → BASIC code generation
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- Uses existing BASIC interpreter
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- No new runtime needed
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### 3. Agent Memory & Learning (Priority: Medium)
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#### Current State
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- Episodic memory via `REMEMBER` keyword
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- Session-based context
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- No long-term agent learning
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#### Proposed Enhancement: Agent Knowledge Evolution
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**New BASIC keywords:**
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```basic
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' Agent learns from interactions
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AGENT LEARN FROM conversation_id
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PATTERN "customer prefers email"
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CONFIDENCE 0.8
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END LEARN
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' Agent recalls learned patterns
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preferences = AGENT RECALL "customer communication"
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' Agent shares knowledge with other bots
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AGENT SHARE KNOWLEDGE WITH "support-bot-2"
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```
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**Implementation:**
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- Store in existing VectorDB (Qdrant)
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- Use existing knowledge base infrastructure
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- Extend `REMEMBER` keyword functionality
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### 4. Event-Driven Bot Communication (Priority: Medium)
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#### Current State
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- Bots delegate via `ADD BOT`
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- Direct invocation only
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- No pub/sub pattern
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#### Proposed Enhancement: Event Bus in BASIC
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**New keywords:**
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```basic
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' Subscribe to events
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ON EVENT "new_ticket" DO
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priority = GET "priority" FROM EVENT
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IF priority = "urgent" THEN
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DELEGATE TO "escalation-bot"
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END IF
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END ON
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' Publish events
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PUBLISH EVENT "ticket_resolved"
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WITH ticket_id = ticket_id
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WITH resolution = "Fixed"
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END PUBLISH
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' Cross-bot coordination
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WAIT FOR EVENT "approval_received" TIMEOUT 3600
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```
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**Implementation:**
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- Use existing Redis for pub/sub
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- Extend existing session management
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- Add event handlers to BASIC interpreter
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### 5. Intelligent LLM Routing (Priority: Medium)
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#### Current State
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- Basic model selection via config
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- `MODEL ROUTING` keyword exists
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- No automatic optimization
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#### Proposed Enhancement: Smart Model Selection
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**Enhanced BASIC keyword:**
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```basic
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' Current
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result = LLM "Analyze this data" WITH MODEL "gpt-4"
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' Enhanced with auto-routing
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result = LLM "Analyze this data"
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WITH OPTIMIZE FOR "speed" ' or "cost" or "quality"
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WITH MAX_COST 0.01
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WITH MAX_LATENCY 2000
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' Learns from usage
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AGENT LEARN MODEL PERFORMANCE
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TASK "data analysis"
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BEST_MODEL "claude-3"
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REASON "faster and cheaper"
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END LEARN
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```
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**Implementation:**
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- Extend existing `llm/observability.rs`
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- Use existing model routing infrastructure
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- Add cost/latency tracking
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### 6. Plugin Marketplace (Priority: Low)
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#### Current State
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- MCP server support exists
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- Manual configuration via `mcp.csv`
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- No discovery mechanism
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#### Proposed Enhancement: Plugin Discovery
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**New BASIC keywords:**
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```basic
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' Discover available plugins
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plugins = SEARCH PLUGINS FOR "calendar integration"
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' Install plugin
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INSTALL PLUGIN "google-calendar-mcp"
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WITH PERMISSIONS "read,write"
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WITH SCOPE "current-bot"
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' Use plugin tools
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USE TOOL "create-calendar-event" FROM PLUGIN "google-calendar-mcp"
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```
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**Implementation:**
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- Extend existing MCP infrastructure
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- Add plugin registry (PostgreSQL table)
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- Security scanning before installation
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---
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## Implementation Roadmap
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### Phase 1: Enhanced Orchestration (Months 1-2)
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**Goal:** Improve multi-agent coordination using existing infrastructure
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1. **ORCHESTRATE WORKFLOW keyword**
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- Add to `basic/keywords/orchestration.rs`
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- Use existing session management
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- Store workflow state in PostgreSQL
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2. **Event Bus in BASIC**
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- Leverage existing Redis pub/sub
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- Add `ON EVENT`, `PUBLISH EVENT` keywords
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- Extend existing message handling
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3. **Agent Memory Enhancement**
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- Extend `REMEMBER` keyword
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- Use existing VectorDB (Qdrant)
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- Add `AGENT LEARN`, `AGENT RECALL` keywords
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**Deliverables:**
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- 3 new BASIC keywords
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- No new infrastructure
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- Backward compatible
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### Phase 2: Visual Workflow Designer (Months 3-4)
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**Goal:** Enhance existing designer with workflow canvas
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1. **Drag-and-Drop Canvas**
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- Extend `botserver/src/designer/`
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- Generate BASIC code from visual design
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- Use existing HTMX architecture
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2. **Workflow Templates**
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- Pre-built workflow patterns
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- Stored as `.gbai` packages
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- Shareable via existing package system
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**Deliverables:**
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- Visual workflow editor
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- Generates BASIC code
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- Uses existing designer infrastructure
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### Phase 3: Intelligence & Learning (Months 5-6)
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**Goal:** Add agent learning and smart routing
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1. **LLM Router Enhancement**
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- Extend `llm/observability.rs`
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- Add cost/latency tracking
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- Automatic model selection
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2. **Agent Knowledge Evolution**
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- Pattern recognition from conversations
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- Cross-bot knowledge sharing
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- Stored in existing VectorDB
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**Deliverables:**
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- Smart LLM routing
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- Agent learning system
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- Uses existing infrastructure
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### Phase 4: Plugin Ecosystem (Months 7-8)
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**Goal:** Enable plugin discovery and marketplace
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1. **Plugin Registry**
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- PostgreSQL table for plugins
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- Security scanning
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- Version management
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2. **Discovery Keywords**
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- `SEARCH PLUGINS`, `INSTALL PLUGIN`
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- Extend existing MCP support
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- No new runtime needed
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**Deliverables:**
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- Plugin marketplace
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- Discovery system
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- Extends existing MCP infrastructure
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---
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## Technical Specifications
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### New BASIC Keywords (Extend Existing Interpreter)
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```rust
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// Add to botserver/src/basic/keywords/
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// orchestration.rs
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pub fn register_orchestrate_workflow(engine: &mut Engine) { }
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pub fn register_on_event(engine: &mut Engine) { }
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pub fn register_publish_event(engine: &mut Engine) { }
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// agent_learning.rs
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pub fn register_agent_learn(engine: &mut Engine) { }
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pub fn register_agent_recall(engine: &mut Engine) { }
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pub fn register_agent_share(engine: &mut Engine) { }
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// plugin_discovery.rs
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pub fn register_search_plugins(engine: &mut Engine) { }
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pub fn register_install_plugin(engine: &mut Engine) { }
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```
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### Database Schema Extensions (PostgreSQL)
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```sql
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-- Workflow state (uses existing session management)
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CREATE TABLE workflow_executions (
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id UUID PRIMARY KEY,
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bot_id UUID REFERENCES bots(id),
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workflow_definition TEXT,
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current_step INTEGER,
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state JSONB,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Agent learning (uses existing VectorDB for embeddings)
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CREATE TABLE agent_knowledge (
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id UUID PRIMARY KEY,
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bot_id UUID REFERENCES bots(id),
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pattern TEXT,
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confidence FLOAT,
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learned_from UUID REFERENCES conversations(id),
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Plugin registry (extends existing MCP support)
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CREATE TABLE plugins (
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id UUID PRIMARY KEY,
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name TEXT UNIQUE,
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mcp_server_url TEXT,
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permissions TEXT[],
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security_scan_result JSONB,
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downloads INTEGER DEFAULT 0,
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rating FLOAT,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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```
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### Performance Targets (Realistic)
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| Metric | Current | Target | How |
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|--------|---------|--------|-----|
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| Multi-agent coordination | Manual | Automated | ORCHESTRATE keyword |
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| Workflow complexity | Linear | Branching/parallel | Visual designer |
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| Agent learning | None | Pattern recognition | VectorDB + LEARN keyword |
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| Plugin discovery | Manual CSV | Searchable | Plugin registry |
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| LLM routing | Static | Dynamic | Cost/latency tracking |
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### Resource Requirements (No Change)
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**Development:**
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- Same as current (16+ cores, 32GB RAM)
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- No new infrastructure
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**Production:**
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- Same LXC container model
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- Existing PostgreSQL, Redis, Qdrant
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- No additional services needed
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|
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---
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## Why NOT Microservices?
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|
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**You already have LXC containers for isolation.** The current architecture:
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|
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```
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Host System
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├── botserver (single binary)
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├── LXC: default-tables (PostgreSQL)
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├── LXC: default-drive (S3 storage)
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├── LXC: default-cache (Redis)
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├── LXC: default-vault (Secrets)
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└── LXC: default-vectordb (Qdrant)
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```
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|
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**This gives you:**
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- ✅ Process isolation (LXC containers)
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- ✅ Resource limits per service
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||||
- ✅ Independent service restarts
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- ✅ Security boundaries
|
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- ✅ Easy backup/restore (LXC snapshots)
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|
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**Microservices would add:**
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- ❌ Network latency between services
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- ❌ Complex service discovery
|
||||
- ❌ Distributed tracing overhead
|
||||
- ❌ More failure points
|
||||
- ❌ Deployment complexity
|
||||
|
||||
**Keep the single binary + LXC model.** It's simpler and faster.
|
||||
|
||||
---
|
||||
|
||||
## Why NOT Separate Workflow Engine?
|
||||
|
||||
**You already have:**
|
||||
- BASIC interpreter (Rhai)
|
||||
- Visual designer
|
||||
- Session management
|
||||
- State persistence
|
||||
|
||||
**Just add keywords:**
|
||||
```basic
|
||||
ORCHESTRATE WORKFLOW
|
||||
STEP 1: BOT "researcher" GATHERS data
|
||||
STEP 2: BOT "analyst" PROCESSES data
|
||||
END WORKFLOW
|
||||
```
|
||||
|
||||
**This generates workflow state in PostgreSQL, executes via existing BASIC interpreter.**
|
||||
|
||||
No separate workflow engine needed. BASIC IS the workflow engine.
|
||||
|
||||
---
|
||||
|
||||
## Why NOT Plugin Marketplace Infrastructure?
|
||||
|
||||
**You already have:**
|
||||
- MCP server support
|
||||
- `mcp.csv` configuration
|
||||
- Tool registration system
|
||||
|
||||
**Just add:**
|
||||
- PostgreSQL table for plugin registry
|
||||
- `SEARCH PLUGINS` keyword
|
||||
- `INSTALL PLUGIN` keyword (writes to `mcp.csv`)
|
||||
|
||||
**No separate marketplace service needed.** It's just a searchable table + BASIC keywords.
|
||||
|
||||
---
|
||||
|
||||
## Migration Strategy
|
||||
|
||||
### There Is No Migration
|
||||
|
||||
**Everything is additive:**
|
||||
1. Add new BASIC keywords to `basic/keywords/`
|
||||
2. Add new database tables (migrations)
|
||||
3. Extend existing designer UI
|
||||
4. No breaking changes
|
||||
|
||||
**Existing `.gbai` packages continue working.**
|
||||
|
||||
### Backward Compatibility
|
||||
|
||||
- Old BASIC scripts: ✅ Work unchanged
|
||||
- Old `.gbai` packages: ✅ Deploy unchanged
|
||||
- Old API endpoints: ✅ Function unchanged
|
||||
- Old LXC containers: ✅ No changes needed
|
||||
|
||||
**Version 7.0 = Version 6.1 + new keywords**
|
||||
|
||||
---
|
||||
|
||||
## Business Impact
|
||||
|
||||
### Immediate Benefits (Phase 1-2, Months 1-4)
|
||||
|
||||
**Enhanced Multi-Agent Coordination:**
|
||||
- Users can create complex workflows in BASIC
|
||||
- No coding required for orchestration patterns
|
||||
- Builds on familiar `ADD BOT` keyword
|
||||
|
||||
**Visual Workflow Designer:**
|
||||
- Drag-and-drop workflow creation
|
||||
- Generates BASIC code automatically
|
||||
- Non-programmers can build AI workflows
|
||||
|
||||
**ROI:**
|
||||
- 50% faster workflow creation
|
||||
- 80% reduction in training time
|
||||
- No new infrastructure costs
|
||||
|
||||
### Long-term Benefits (Phase 3-4, Months 5-8)
|
||||
|
||||
**Agent Learning:**
|
||||
- Bots improve from interactions
|
||||
- Knowledge sharing between bots
|
||||
- Reduced manual configuration
|
||||
|
||||
**Plugin Ecosystem:**
|
||||
- Community-contributed integrations
|
||||
- Faster feature delivery
|
||||
- Revenue from marketplace (optional)
|
||||
|
||||
**ROI:**
|
||||
- 10x more integrations via community
|
||||
- 30% reduction in support costs
|
||||
- Potential marketplace revenue
|
||||
|
||||
### Cost Analysis
|
||||
|
||||
**Development Cost:** $200-300K over 8 months
|
||||
- 2 senior Rust developers
|
||||
- No new infrastructure
|
||||
- Extends existing codebase
|
||||
|
||||
**Infrastructure Cost:** $0 additional
|
||||
- Uses existing PostgreSQL, Redis, Qdrant
|
||||
- Same LXC container model
|
||||
- No new services
|
||||
|
||||
**Revenue Impact:** $1-2M additional ARR
|
||||
- Faster enterprise adoption
|
||||
- Plugin marketplace potential
|
||||
- Reduced implementation time
|
||||
|
||||
**Total ROI:** 5-10x return in first year
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The proposed improvements **extend** the existing architecture, not replace it:
|
||||
|
||||
1. **Keep `.gbai` package system** - It's brilliant
|
||||
2. **Keep LXC containers** - Better than microservices
|
||||
3. **Keep BASIC scripting** - Core differentiator
|
||||
4. **Keep single binary** - Simpler deployment
|
||||
5. **Keep visual designer** - Just enhance it
|
||||
|
||||
**Add:**
|
||||
- New BASIC keywords for orchestration
|
||||
- Agent learning capabilities
|
||||
- Plugin discovery system
|
||||
- Enhanced workflow visualization
|
||||
|
||||
**Result:** More powerful AI orchestration while maintaining simplicity.
|
||||
|
||||
### Key Success Factors
|
||||
|
||||
1. **BASIC-first design** - Everything accessible via BASIC keywords
|
||||
2. **No breaking changes** - Existing `.gbai` packages work unchanged
|
||||
3. **Extend, don't replace** - Build on existing infrastructure
|
||||
4. **Keep it simple** - Folder-based deployment stays
|
||||
5. **Community-driven** - Plugin ecosystem enables innovation
|
||||
|
||||
This approach maintains General Bots' unique position: **The only AI platform where non-programmers can create sophisticated multi-agent workflows by dropping folders and writing BASIC.**
|
||||
20
PROMPT.md
20
PROMPT.md
|
|
@ -112,6 +112,26 @@ When a file grows beyond this limit:
|
|||
|
||||
---
|
||||
|
||||
## 🚀 PERFORMANCE & SIZE STANDARDS
|
||||
|
||||
### Binary Size Optimization
|
||||
- **Release Profile**: Always maintain `opt-level = "z"`, `lto = true`, `codegen-units = 1`, `strip = true`, `panic = "abort"`.
|
||||
- **Dependencies**:
|
||||
- Run `cargo tree --duplicates` weekly to find and resolve duplicate versions.
|
||||
- Run `cargo machete` to remove unused dependencies.
|
||||
- Use `default-features = false` and explicitly opt-in to needed features.
|
||||
|
||||
### Memory Optimization
|
||||
- **Strings**: Prefer `&str` over `String` where possible. Use `Cow<str>` for conditional ownership.
|
||||
- **Collections**: Use `Vec::with_capacity` when size is known. Consider `SmallVec` for hot paths.
|
||||
- **Allocations**: Minimize heap allocations in hot paths.
|
||||
|
||||
### Linting & Code Quality
|
||||
- **Clippy**: Code MUST pass `cargo clippy --all-targets --all-features` with **0 warnings**.
|
||||
- **No Allow**: Do not use `#[allow(clippy::...)]` unless absolutely necessary and documented. Fix the underlying issue.
|
||||
|
||||
---
|
||||
|
||||
## 🔐 SECURITY DIRECTIVES - MANDATORY
|
||||
|
||||
### Error Handling - NO PANICS IN PRODUCTION
|
||||
|
|
|
|||
11
restart.sh
11
restart.sh
|
|
@ -1,11 +0,0 @@
|
|||
pkill rustc -9
|
||||
pkill botserver -9
|
||||
pkill botui -9
|
||||
cd botserver
|
||||
cargo build
|
||||
cargo run -- --noconsole &
|
||||
cd ../botui
|
||||
cargo run &
|
||||
|
||||
|
||||
|
||||
Loading…
Add table
Reference in a new issue