# Hive + Bodai Integration Plan

**Analysis Date:** 2026-02-21
**Status:** ⚠️ **SUPERSEDED BY TRIO REVIEW**
**Overall Compatibility:** ~~8.2/10~~ **REJECTED**

---

> **⚠️ CRITICAL UPDATE (2026-02-21)**
>
> Three independent power trios reviewed this integration plan and unanimously recommended **AGAINST** Hive integration.
>
> **Key Findings:**
> - Hive is "conceptware with stubs" - not production-ready
> - Bodai already has 70% of what Hive offers (StatisticalRouter, AgentTeamManager, DependencyGraph)
> - Only gap: Natural language goal parsing (implementable in ~100 lines, 2-3 days)
>
> **Recommendation:** Build `GoalDrivenTeamFactory` natively. Skip Hive integration.
>
> **See:** [Hive Integration Trio Synthesis](./HIVE_INTEGRATION_TRIO_SYNTHESIS.md) for full analysis.

---

## Executive Summary

This document synthesizes findings from 6 specialized AI agents evaluating the integration of Hive's goal-driven agent generation with the Bodai ecosystem. The integration opportunity is:

1. **Hive as Agent Builder** - Use Hive's goal-driven agent generation
2. **Bodai as Runtime** - Execute Hive-generated agents in Mahavishnu pools
3. **Bodai Quality/Memory as Enhancement** - Replace Hive's stub systems with Crackerjack/Session-Buddy

### Agent Evaluation Scores

| Agent | Focus Area | Score | Key Finding |
|-------|-----------|-------|-------------|
| AI Engineer | AI/ML Integration | 8.5/10 | Strong LLM provider compatibility |
| Security Auditor | Security Risks | HIGH | Code validation layer required |
| Backend Developer | Runtime Integration | 8/10 | Existing infrastructure leveragable |
| Architect Reviewer | Architecture Fit | 8.5/10 | Seamless dependency graph mapping |
| WebSocket Engineer | Real-Time Integration | 8.5/10 | 85% infrastructure reuse |

---

## 1. Core Integration Architecture

### 1.1 Goal → Graph → Evolution Loop

```
┌─────────────────────────────────────────────────────────────────────┐
│                    HIVE-STYLE EVOLUTION LOOP                        │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐          │
│  │    GOAL     │────▶│    GRAPH    │────▶│  EXECUTION  │          │
│  │ (Natural    │     │ (Agent DAG  │     │ (Pool       │          │
│  │  Language)  │     │  Generation)│     │  Routing)   │          │
│  └─────────────┘     └─────────────┘     └──────┬──────┘          │
│         ▲                                       │                  │
│         │                                       ▼                  │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐          │
│  │   LEARNING  │◀────│   QUALITY   │◀────│   OUTCOME   │          │
│  │ (Session-   │     │ (Crackerjack│     │ (Success/   │          │
│  │  Buddy)     │     │  Gates)     │     │  Failure)   │          │
│  └─────────────┘     └─────────────┘     └─────────────┘          │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘
```

### 1.2 Component Mapping

| Hive Component | Bodai Equivalent | Integration Approach |
|----------------|------------------|---------------------|
| Goal Parser | GoalDrivenTeamFactory (NEW) | Use existing LLMProviderFactory |
| Graph Execution | DependencyGraph + GraphExecutor | Direct mapping to existing DAG |
| Memory System | Session-Buddy | Replace Hive stub |
| Quality Gates | Crackerjack | Replace Hive stub |
| Analytics | Akosha | Pattern detection across executions |
| Worker Pools | MahavishnuPool | Low-latency execution |

---

## 2. Security Requirements (P0)

### 2.1 Critical Security Controls

**Code Validation Layer** (Required before any production use):

- AST-level code analysis before execution
- Semantic validation of generated agent logic
- Block dangerous operations: exec, eval, compile, dynamic imports
- Import whitelist for allowed packages

**Evolution Guardrails**:

- Immutable security constraints
- Evolution rate limiting (max 3 per hour)
- Human approval for significant changes
- Audit trail via Session-Buddy

### 2.2 Security Implementation Priority

| Priority | Risk | Control | Effort |
|----------|------|---------|--------|
| P0 | Malicious code in generated agents | AST-level code validation | 2-3 weeks |
| P0 | Evolution bypasses constraints | Immutable constraint enforcement | 1-2 weeks |
| P0 | Unbounded agent execution | Stricter sandbox with seccomp | 1 week |
| P1 | Goal injection attacks | Goal sanitization | 1-2 weeks |

---

## 3. Implementation Roadmap

### Phase 1: Foundation (Weeks 1-2)

**Deliverables:**
- Create mahavishnu/hive/ module structure
- Implement HiveCodeValidator (P0 security)
- Implement EvolutionGuardrails (P0 security)
- Create GoalDrivenTeamFactory class
- Create GraphParser for Hive graph format
- Create GraphCompiler to DependencyGraph

**Key Files:**
```
mahavishnu/
├── hive/
│   ├── __init__.py
│   ├── graph_parser.py       # Parse Hive graph JSON
│   ├── graph_compiler.py     # Convert to DependencyGraph
│   ├── execution_planner.py  # Generate parallel stages
│   ├── graph_executor.py     # Execute nodes via pools
│   └── goal_factory.py       # Goal → Team config
├── security/
│   ├── hive_code_validator.py
│   └── evolution_guardrails.py
└── engines/
    └── goal_factory.py       # GoalDrivenTeamFactory
```

### Phase 2: Execution Engine (Weeks 3-4)

**Deliverables:**
- Implement GraphExecutor with pool routing
- Add WebSocket streaming for execution events
- Implement state management across nodes
- Integrate with existing MemoryAggregator

**Pool Strategy:**
- MahavishnuPool (Primary) - Agent nodes, low latency
- SessionBuddyPool - Memory-intensive operations
- KubernetesPool (Future) - Production workloads

### Phase 3: API Layer (Week 5)

**Deliverables:**
- MCP tools for graph submission
- Status streaming endpoint
- Evolution hook for graph modifications
- Checkpoint/restore functionality

### Phase 4: Ecosystem Integration (Week 6)

**Deliverables:**
- Session-Buddy memory integration
- Crackerjack quality gates
- Akosha analytics integration
- Monitoring dashboards

---

## 4. Technical Architecture

### 4.1 Graph-to-Execution Pipeline

```
[ Hive Graph JSON ]
        │
        ▼
[ GraphParser ]          # Parse and validate Hive format
        │
        ▼
[ GraphCompiler ]        # Convert to Mahavishnu DependencyGraph
        │
        ▼
[ ExecutionPlanner ]     # Generate execution plan with parallel stages
        │
        ▼
[ GraphExecutor ]        # Execute nodes respecting dependencies
        │
        ▼
[ ResultAggregator ]     # Collect results, update state
```

### 4.2 LLM Coordination Strategy

**Three-Tier Model Strategy:**

| Tier | Purpose | Model | Temperature | Cost |
|------|---------|-------|-------------|------|
| 1 | Goal Parsing | Ollama qwen2.5:7b | 0.3 | Free |
| 2 | Reasoning | Anthropic Claude | 0.7 | Premium |
| 3 | Execution | Ollama qwen2.5:7b | 0.5 | Free |

### 4.3 Evolution Learning Pipeline

**Three-Layer Knowledge Architecture:**

- **Layer 1 (Hot)**: Akosha HotStore - Recent execution outcomes (24h TTL)
- **Layer 2 (Warm)**: Session-Buddy - Session memory (30d TTL)
- **Layer 3 (Cold)**: Dhruva - Persistent knowledge (infinite TTL)

---

## 5. API Specification

### 5.1 MCP Tools Summary

| Tool | Purpose | Priority |
|------|---------|----------|
| hive_submit_graph | Submit graph for execution | P0 |
| hive_get_status | Query execution status | P0 |
| hive_evolve_graph | Modify running graph | P1 |
| hive_list_executions | List active executions | P2 |
| hive_cancel_execution | Cancel running execution | P1 |
| hive_checkpoint | Create execution checkpoint | P2 |

### 5.2 WebSocket Channels

| Channel | Events |
|---------|--------|
| workflow:{workflow_id} | stage_started, node_completed, execution_completed |
| pool:{pool_id} | worker_status_changed, pool_status_changed |
| global | system-wide orchestration events |

---

## 6. Risk Assessment

### 6.1 Security Risks

| Risk | Severity | Mitigation |
|------|----------|------------|
| Code generation risk | Critical | AST-level code validation |
| Evolution safety | Critical | Immutable constraint enforcement |
| Data flow security | High | Data classification, encryption |
| Authentication | Medium | Service account with mTLS |

### 6.2 Technical Risks

| Risk | Impact | Mitigation |
|------|--------|------------|
| Hive graph format incompatible | High | Add format adapter layer |
| Parallel execution exceeds capacity | Medium | Dynamic pool scaling |
| WebSocket connection drops | Medium | Reconnect with replay |
| Evolution causes inconsistent state | High | Version-controlled state updates |

---

## 7. Success Metrics

### 7.1 Integration KPIs

| Metric | Target | Measurement |
|--------|--------|-------------|
| Goal parsing success rate | > 90% | Valid team configs generated |
| Execution latency | < 100ms | Per-node overhead |
| Evolution quality improvement | > 5% | Quality score delta |
| Security validation pass rate | 100% | No blocked code executed |

---

## 8. Conclusion

The integration of Hive's goal-driven agent generation with the Bodai ecosystem is technically feasible with strong compatibility (8.2/10). Key strengths:

1. **LLM Provider Compatibility** - Same providers (Anthropic, OpenAI, Ollama)
2. **Existing Infrastructure** - DependencyGraph, PoolManager, WebSocket already implemented
3. **Memory/Quality Enhancement** - Session-Buddy + Crackerjack provide superior alternatives to Hive stubs

**Critical Path**: Security controls (P0) must be implemented before any production deployment.

**Recommended Next Step**: Implement Phase 1 core infrastructure to validate the integration concept.

---

**Document Version:** 1.0
**Generated By:** Multi-Agent Evaluation (6 specialized agents)
**Next Review:** 2026-03-21
