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chore: Apply binary size optimizations, update PROMPT standards and fix clippy warnings

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Rodrigo Rodriguez (Pragmatismo) 2026-01-25 08:36:36 -03:00
parent 4c8f828d41
commit cf7d1f9992
5 changed files with 27 additions and 631 deletions
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2
.cargo/config.toml
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@ -3,4 +3,4 @@ rustc-wrapper = "/home/rodriguez/.cargo/bin/sccache"
[target.x86_64-unknown-linux-gnu]
linker = "clang"
rustflags = ["-C", "link-arg=-fuse-ld=mold"]
# rustflags = ["-C", "link-arg=-fuse-ld=mold"]

11
Cargo.toml
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@ -36,7 +36,7 @@ tokio-stream = "0.1"
tokio-util = { version = "0.7", default-features = false }
tokio-rustls = "0.26"
tokio-test = "0.4"
tokio-tungstenite = { version = "0.21", default-features = false }
tokio-tungstenite = { version = "0.24", default-features = false }
async-trait = "0.1"
async-lock = "2.8.0"
async-stream = "0.3"
@ -62,7 +62,7 @@ uuid = { version = "1.11", default-features = false }
axum = { version = "0.7.5", features = ["ws", "multipart", "macros"] }
axum-server = { version = "0.8", features = ["tls-rustls"] }
tower = "0.4"
tower-http = { version = "0.5", default-features = false }
tower-http = { version = "0.6", default-features = false }
tower-cookies = "0.10"
hyper = { version = "1.4", default-features = false }
hyper-rustls = { version = "0.27", default-features = false }
@ -73,8 +73,8 @@ http-body-util = "0.1.3"
reqwest = { version = "0.12", default-features = false }
# ─── DATABASE ───
diesel = { version = "2.1" }
diesel_migrations = "2.1"
diesel = { version = "2.2" }
diesel_migrations = "2.2"
# ─── CACHING ───
redis = { version = "0.27", default-features = false }
@ -244,8 +244,7 @@ lto = true
opt-level = "z"
strip = true
panic = "abort"
codegen-units = 8
overflow-checks = true
codegen-units = 1
[profile.ci]
inherits = "release"

612
IMPROVE.md
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@ -1,612 +0,0 @@
# General Bots AI Orchestrator Operating System - Architectural Improvements
**Analysis Date:** January 24, 2026
**Analyzed Version:** 6.1.0
**Target Version:** 7.0.0
---
## Executive Summary
General Bots is a **folder-based AI orchestrator** where `.gbai` packages contain everything (BASIC scripts, documents, config). The system ALREADY has:
- **LXC container isolation** via `botserver install --container`
- **Visual designer** for BASIC scripts
- **Package manager** built-in (drop `.gbai` folder = deployed)
- **Multi-agent via ADD BOT** keyword in BASIC
- **Single binary** deployment model
This analysis focuses on ENHANCING what exists, not replacing it.
## Current Architecture Analysis
### What Already Exists (Don't Rebuild)
1. **Package System (.gbai)**: Folder-based deployment - copy folder = deployed bot
2. **LXC Container Isolation**: `botserver install vault --container` creates isolated services
3. **Visual Designer**: Already exists for BASIC script creation
4. **Multi-Agent**: `ADD BOT` keyword enables bot-to-bot delegation
5. **Single Binary**: One `botserver` binary manages everything
6. **Auto-Bootstrap**: Scans `templates/` for `.gbai` folders, auto-deploys
7. **BASIC Scripting**: Rhai-based interpreter with 80+ keywords
8. **Built-in Package Manager**: `botserver install <component>` handles dependencies
### Real Strengths
1. **Zero-Config Deployment**: Drop `.gbai` folder in `templates/`, restart = live
2. **BASIC Accessibility**: Non-programmers can create AI workflows
3. **Comprehensive Suite**: 50+ integrated apps (CRM, docs, video, etc.)
4. **Security-First**: No unwrap(), proper error handling, SafeCommand wrapper
5. **LXC Isolation**: Each service (PostgreSQL, Vault, VectorDB) in own container
6. **Feature Flags**: Granular control from minimal to full deployment
### Current Limitations (What to Actually Improve)
1. **Multi-Agent Coordination**: `ADD BOT` exists but lacks sophisticated orchestration patterns
2. **Workflow Visualization**: Designer exists but limited to linear BASIC scripts
3. **Agent Memory**: Basic episodic memory, lacks long-term agent learning
4. **Cross-Bot Communication**: Bots can delegate but no pub/sub or event bus
5. **LLM Routing**: Basic model selection, lacks intelligent routing based on task
6. **Plugin Ecosystem**: MCP support exists but no marketplace or discovery
---
## Proposed Architectural Improvements
### 1. Enhanced Multi-Agent Orchestration (Priority: Critical)
#### Current State
- `ADD BOT` keyword enables bot delegation
- Basic priority and trigger matching
- No sophisticated coordination patterns
#### Proposed Enhancement: Agent Collaboration Patterns
**Extend existing BASIC keywords, don't replace:**
```basic
' Current (works)
ADD BOT "specialist" WITH TRIGGER "technical questions"
' Enhanced (new patterns)
ADD BOT "analyst" WITH ROLE "data-processor"
ADD BOT "reviewer" WITH ROLE "quality-check"
' New coordination keyword
ORCHESTRATE WORKFLOW
STEP 1: BOT "researcher" GATHERS data
STEP 2: BOT "analyst" PROCESSES data
STEP 3: BOT "writer" CREATES report
STEP 4: HUMAN APPROVAL required
STEP 5: BOT "publisher" DISTRIBUTES report
END WORKFLOW
```
**Implementation:**
- Add `ORCHESTRATE WORKFLOW` keyword to `basic/keywords/`
- Store workflow state in existing PostgreSQL
- Use existing `ADD BOT` infrastructure
- Leverage existing session management
**Benefits:**
- Builds on existing `ADD BOT` system
- BASIC-accessible workflow definition
- No new infrastructure needed
### 2. Visual Workflow Designer Enhancement (Priority: High)
#### Current State
- Designer exists for BASIC scripts
- Linear script editing
- No visual workflow representation
#### Proposed Enhancement: Drag-and-Drop Workflow Canvas
**Extend existing designer, don't rebuild:**
```rust
// Add to existing botserver/src/designer/
pub struct WorkflowNode {
node_type: NodeType,
basic_code: String, // Generates BASIC
position: (f32, f32),
}
pub enum NodeType {
BotAgent { bot_name: String },
HumanApproval { approvers: Vec<String> },
Condition { expression: String },
Loop { iterations: String },
Parallel { branches: Vec<Branch> },
}
```
**Output:** Generates BASIC code using existing keywords
```basic
' Generated from visual designer
ORCHESTRATE WORKFLOW
STEP 1: BOT "support" HANDLES initial_request
IF priority = "high" THEN
STEP 2: HUMAN APPROVAL FROM "manager"
END IF
STEP 3: BOT "resolver" COMPLETES task
END WORKFLOW
```
**Benefits:**
- Visual design → BASIC code generation
- Uses existing BASIC interpreter
- No new runtime needed
### 3. Agent Memory & Learning (Priority: Medium)
#### Current State
- Episodic memory via `REMEMBER` keyword
- Session-based context
- No long-term agent learning
#### Proposed Enhancement: Agent Knowledge Evolution
**New BASIC keywords:**
```basic
' Agent learns from interactions
AGENT LEARN FROM conversation_id
PATTERN "customer prefers email"
CONFIDENCE 0.8
END LEARN
' Agent recalls learned patterns
preferences = AGENT RECALL "customer communication"
' Agent shares knowledge with other bots
AGENT SHARE KNOWLEDGE WITH "support-bot-2"
```
**Implementation:**
- Store in existing VectorDB (Qdrant)
- Use existing knowledge base infrastructure
- Extend `REMEMBER` keyword functionality
### 4. Event-Driven Bot Communication (Priority: Medium)
#### Current State
- Bots delegate via `ADD BOT`
- Direct invocation only
- No pub/sub pattern
#### Proposed Enhancement: Event Bus in BASIC
**New keywords:**
```basic
' Subscribe to events
ON EVENT "new_ticket" DO
priority = GET "priority" FROM EVENT
IF priority = "urgent" THEN
DELEGATE TO "escalation-bot"
END IF
END ON
' Publish events
PUBLISH EVENT "ticket_resolved"
WITH ticket_id = ticket_id
WITH resolution = "Fixed"
END PUBLISH
' Cross-bot coordination
WAIT FOR EVENT "approval_received" TIMEOUT 3600
```
**Implementation:**
- Use existing Redis for pub/sub
- Extend existing session management
- Add event handlers to BASIC interpreter
### 5. Intelligent LLM Routing (Priority: Medium)
#### Current State
- Basic model selection via config
- `MODEL ROUTING` keyword exists
- No automatic optimization
#### Proposed Enhancement: Smart Model Selection
**Enhanced BASIC keyword:**
```basic
' Current
result = LLM "Analyze this data" WITH MODEL "gpt-4"
' Enhanced with auto-routing
result = LLM "Analyze this data"
WITH OPTIMIZE FOR "speed" ' or "cost" or "quality"
WITH MAX_COST 0.01
WITH MAX_LATENCY 2000
' Learns from usage
AGENT LEARN MODEL PERFORMANCE
TASK "data analysis"
BEST_MODEL "claude-3"
REASON "faster and cheaper"
END LEARN
```
**Implementation:**
- Extend existing `llm/observability.rs`
- Use existing model routing infrastructure
- Add cost/latency tracking
### 6. Plugin Marketplace (Priority: Low)
#### Current State
- MCP server support exists
- Manual configuration via `mcp.csv`
- No discovery mechanism
#### Proposed Enhancement: Plugin Discovery
**New BASIC keywords:**
```basic
' Discover available plugins
plugins = SEARCH PLUGINS FOR "calendar integration"
' Install plugin
INSTALL PLUGIN "google-calendar-mcp"
WITH PERMISSIONS "read,write"
WITH SCOPE "current-bot"
' Use plugin tools
USE TOOL "create-calendar-event" FROM PLUGIN "google-calendar-mcp"
```
**Implementation:**
- Extend existing MCP infrastructure
- Add plugin registry (PostgreSQL table)
- Security scanning before installation
---
## Implementation Roadmap
### Phase 1: Enhanced Orchestration (Months 1-2)
**Goal:** Improve multi-agent coordination using existing infrastructure
1. **ORCHESTRATE WORKFLOW keyword**
- Add to `basic/keywords/orchestration.rs`
- Use existing session management
- Store workflow state in PostgreSQL
2. **Event Bus in BASIC**
- Leverage existing Redis pub/sub
- Add `ON EVENT`, `PUBLISH EVENT` keywords
- Extend existing message handling
3. **Agent Memory Enhancement**
- Extend `REMEMBER` keyword
- Use existing VectorDB (Qdrant)
- Add `AGENT LEARN`, `AGENT RECALL` keywords
**Deliverables:**
- 3 new BASIC keywords
- No new infrastructure
- Backward compatible
### Phase 2: Visual Workflow Designer (Months 3-4)
**Goal:** Enhance existing designer with workflow canvas
1. **Drag-and-Drop Canvas**
- Extend `botserver/src/designer/`
- Generate BASIC code from visual design
- Use existing HTMX architecture
2. **Workflow Templates**
- Pre-built workflow patterns
- Stored as `.gbai` packages
- Shareable via existing package system
**Deliverables:**
- Visual workflow editor
- Generates BASIC code
- Uses existing designer infrastructure
### Phase 3: Intelligence & Learning (Months 5-6)
**Goal:** Add agent learning and smart routing
1. **LLM Router Enhancement**
- Extend `llm/observability.rs`
- Add cost/latency tracking
- Automatic model selection
2. **Agent Knowledge Evolution**
- Pattern recognition from conversations
- Cross-bot knowledge sharing
- Stored in existing VectorDB
**Deliverables:**
- Smart LLM routing
- Agent learning system
- Uses existing infrastructure
### Phase 4: Plugin Ecosystem (Months 7-8)
**Goal:** Enable plugin discovery and marketplace
1. **Plugin Registry**
- PostgreSQL table for plugins
- Security scanning
- Version management
2. **Discovery Keywords**
- `SEARCH PLUGINS`, `INSTALL PLUGIN`
- Extend existing MCP support
- No new runtime needed
**Deliverables:**
- Plugin marketplace
- Discovery system
- Extends existing MCP infrastructure
---
## Technical Specifications
### New BASIC Keywords (Extend Existing Interpreter)
```rust
// Add to botserver/src/basic/keywords/
// orchestration.rs
pub fn register_orchestrate_workflow(engine: &mut Engine) { }
pub fn register_on_event(engine: &mut Engine) { }
pub fn register_publish_event(engine: &mut Engine) { }
// agent_learning.rs
pub fn register_agent_learn(engine: &mut Engine) { }
pub fn register_agent_recall(engine: &mut Engine) { }
pub fn register_agent_share(engine: &mut Engine) { }
// plugin_discovery.rs
pub fn register_search_plugins(engine: &mut Engine) { }
pub fn register_install_plugin(engine: &mut Engine) { }
```
### Database Schema Extensions (PostgreSQL)
```sql
-- Workflow state (uses existing session management)
CREATE TABLE workflow_executions (
id UUID PRIMARY KEY,
bot_id UUID REFERENCES bots(id),
workflow_definition TEXT,
current_step INTEGER,
state JSONB,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Agent learning (uses existing VectorDB for embeddings)
CREATE TABLE agent_knowledge (
id UUID PRIMARY KEY,
bot_id UUID REFERENCES bots(id),
pattern TEXT,
confidence FLOAT,
learned_from UUID REFERENCES conversations(id),
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Plugin registry (extends existing MCP support)
CREATE TABLE plugins (
id UUID PRIMARY KEY,
name TEXT UNIQUE,
mcp_server_url TEXT,
permissions TEXT[],
security_scan_result JSONB,
downloads INTEGER DEFAULT 0,
rating FLOAT,
created_at TIMESTAMPTZ DEFAULT NOW()
);
```
### Performance Targets (Realistic)
| Metric | Current | Target | How |
|--------|---------|--------|-----|
| Multi-agent coordination | Manual | Automated | ORCHESTRATE keyword |
| Workflow complexity | Linear | Branching/parallel | Visual designer |
| Agent learning | None | Pattern recognition | VectorDB + LEARN keyword |
| Plugin discovery | Manual CSV | Searchable | Plugin registry |
| LLM routing | Static | Dynamic | Cost/latency tracking |
### Resource Requirements (No Change)
**Development:**
- Same as current (16+ cores, 32GB RAM)
- No new infrastructure
**Production:**
- Same LXC container model
- Existing PostgreSQL, Redis, Qdrant
- No additional services needed
---
## Why NOT Microservices?
**You already have LXC containers for isolation.** The current architecture:
```
Host System
├── botserver (single binary)
├── LXC: default-tables (PostgreSQL)
├── LXC: default-drive (S3 storage)
├── LXC: default-cache (Redis)
├── LXC: default-vault (Secrets)
└── LXC: default-vectordb (Qdrant)
```
**This gives you:**
- ✅ Process isolation (LXC containers)
- ✅ Resource limits per service
- ✅ Independent service restarts
- ✅ Security boundaries
- ✅ Easy backup/restore (LXC snapshots)
**Microservices would add:**
- ❌ Network latency between services
- ❌ 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
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@ -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
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@ -1,11 +0,0 @@
pkill rustc -9
pkill botserver -9
pkill botui -9
cd botserver
cargo build
cargo run -- --noconsole &
cd ../botui
cargo run &