# CogniHive: Transactive Memory System for Multi-Agent AI

## The World's First Implementation of Collective Agent Intelligence

---

## Executive Summary

**CogniHive** is the first open-source implementation of **Transactive Memory Systems (TMS)** for AI agents — a concept from cognitive science that has NEVER been implemented for LLMs.

While Mem0, Zep, and Letta focus on giving **one agent** memory, CogniHive solves a fundamentally different problem: **How do teams of agents remember collectively?**

The core innovation: **"Who Knows What"** — agents don't just share memories, they know which agent is the expert on which topic, and can route queries to the right specialist.

---

## Why This Is Groundbreaking (Not Just Another Memory Library)

### The Research Gap

From the latest survey on LLM Multi-Agent Memory (July 2025):

> "Just as human teams develop transactive memory systems, i.e., knowing 'who knows what', LLM-based agents require similar meta-memory capabilities to efficiently allocate cognitive resources and avoid redundant processing."

**Implementation status: ZERO.**

Every paper mentions this need. Nobody has built it.

### What Exists vs What We're Building

| Existing Solutions | What They Do | What's Missing |
|-------------------|--------------|----------------|
| **Mem0** | Single agent memory | No multi-agent coordination |
| **Zep** | Knowledge graphs | No "who knows what" routing |
| **Letta/MemGPT** | Virtual memory paging | Single agent only |
| **LangChain Memory** | Conversation buffers | No cross-agent sharing |
| **AutoGen/CrewAI** | Multi-agent orchestration | No shared memory layer |

| **CogniHive** | What It Does | Why It's Novel |
|--------------|--------------|----------------|
| **Transactive Memory** | Tracks which agent knows what | First implementation ever |
| **Expertise Routing** | Routes queries to expert agents | No manual configuration |
| **Memory Consensus** | Resolves conflicting memories | Voting + confidence scoring |
| **Access Control** | Fine-grained permissions | Privacy-preserving collaboration |
| **Memory Provenance** | Tracks where knowledge came from | Auditability for enterprise |

---

## The Core Innovation: Transactive Memory

### What is Transactive Memory? (Cognitive Science Background)

In human teams, not everyone remembers everything. Instead, teams develop a **shared awareness of who knows what**:

- "Sarah handles the legal stuff"
- "Mike knows all the technical details"
- "Ask Jennifer about customer history"

This is called a **Transactive Memory System (TMS)** — coined by psychologist Daniel Wegner in 1985. Research shows TMS is the #1 predictor of team performance.

### Why AI Agents Need This

Current multi-agent systems fail because:

1. **Redundant Work**: Multiple agents research the same thing
2. **Lost Expertise**: Agent A learns something, Agent B doesn't know to ask
3. **Coordination Chaos**: "Early agents made errors like spawning 50 subagents for simple queries" — Anthropic Research
4. **Token Explosion**: "Multi-agent systems use about 15× more tokens than chats" — Anthropic Research

### CogniHive's Solution

```
┌─────────────────────────────────────────────────────────────────────┐
│                      CogniHive Core                                  │
│  ┌─────────────────────────────────────────────────────────────┐    │
│  │               TRANSACTIVE MEMORY INDEX                       │    │
│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐          │    │
│  │  │ Agent: Coder│  │ Agent: Docs │  │Agent: Data  │          │    │
│  │  │ Knows:      │  │ Knows:      │  │ Knows:      │          │    │
│  │  │ - Python    │  │ - API specs │  │ - SQL       │          │    │
│  │  │ - FastAPI   │  │ - Tutorials │  │ - Analytics │          │    │
│  │  │ - Testing   │  │ - Examples  │  │ - Metrics   │          │    │
│  │  └─────────────┘  └─────────────┘  └─────────────┘          │    │
│  └─────────────────────────────────────────────────────────────┘    │
│                                                                      │
│  ┌─────────────────────────────────────────────────────────────┐    │
│  │                    EXPERTISE ROUTER                          │    │
│  │  Query: "How do I optimize the database queries?"            │    │
│  │  ──────────────────────────────────────────────────────────  │    │
│  │  Routing Decision:                                           │    │
│  │    • Primary Expert: Data Agent (0.92 confidence)            │    │
│  │    • Secondary: Coder Agent (0.67 confidence)                │    │
│  │    • Action: Route to Data Agent, CC Coder Agent             │    │
│  └─────────────────────────────────────────────────────────────┘    │
│                                                                      │
│  ┌─────────────────────────────────────────────────────────────┐    │
│  │                   SHARED MEMORY POOL                         │    │
│  │  (Private memories) ←→ (Shared memories) ←→ (Team memories)  │    │
│  │       Agent-only         Selectively shared    All agents    │    │
│  └─────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────┘
```

---

## Technical Architecture

### Core Components

```
cognihive/
├── src/cognihive/
│   ├── __init__.py
│   ├── core/
│   │   ├── hive.py              # Main CogniHive orchestrator
│   │   ├── agent_registry.py    # Agent registration and capabilities
│   │   └── memory_types.py      # Memory dataclasses
│   │
│   ├── transactive/             # THE NOVEL COMPONENT
│   │   ├── expertise_index.py   # "Who knows what" index
│   │   ├── expertise_router.py  # Routes queries to experts
│   │   ├── capability_tracker.py # Learns agent capabilities over time
│   │   └── meta_memory.py       # Memory about memories
│   │
│   ├── memory/
│   │   ├── private.py           # Agent-private memories
│   │   ├── shared.py            # Selectively shared memories
│   │   ├── team.py              # Team-wide memories
│   │   └── provenance.py        # Track memory origins
│   │
│   ├── consensus/               # NOVEL: Conflict resolution
│   │   ├── conflict_detector.py # Detect contradicting memories
│   │   ├── resolution.py        # Voting, recency, confidence
│   │   └── merge.py             # Merge compatible memories
│   │
│   ├── access/                  # NOVEL: Fine-grained permissions
│   │   ├── policies.py          # Access control policies
│   │   ├── permissions.py       # Read/write/share permissions
│   │   └── audit.py             # Access audit log
│   │
│   ├── storage/
│   │   ├── base.py              # Abstract storage interface
│   │   ├── chroma.py            # ChromaDB backend
│   │   └── qdrant.py            # Qdrant backend
│   │
│   └── integrations/
│       ├── crewai.py            # CrewAI adapter
│       ├── autogen.py           # AutoGen adapter
│       ├── langgraph.py         # LangGraph adapter
│       └── swarms.py            # Swarms adapter
```

### The Transactive Memory Index (Novel Component)

This is what makes CogniHive different from everything else:

```python
@dataclass
class ExpertiseProfile:
    """What an agent knows and how well they know it."""
    agent_id: str
    agent_name: str
    
    # Domain expertise (learned automatically)
    expertise_domains: Dict[str, float]  # {"python": 0.95, "sql": 0.87, ...}
    
    # Topic-level knowledge
    topics: Dict[str, TopicExpertise]  # Detailed topic breakdowns
    
    # Learning trajectory
    expertise_history: List[ExpertiseEvent]  # How expertise evolved
    
    # Reliability metrics
    accuracy_score: float  # How often this agent's memories are correct
    citation_count: int    # How often other agents reference this agent
    
    # Metadata
    last_active: datetime
    total_memories: int
    
@dataclass
class TopicExpertise:
    """Detailed expertise on a specific topic."""
    topic: str
    confidence: float           # 0-1 self-assessed confidence
    validated_confidence: float # 0-1 externally validated
    memory_count: int           # How many memories on this topic
    last_updated: datetime
    sample_memories: List[str]  # Representative memory IDs
```

### The Expertise Router (Novel Component)

```python
class ExpertiseRouter:
    """Routes queries to the right agent based on expertise."""
    
    def route(self, query: str, available_agents: List[str]) -> RoutingDecision:
        """
        Determine which agent(s) should handle a query.
        
        Returns:
            RoutingDecision with:
            - primary_agent: Best expert for this query
            - secondary_agents: Others who might help
            - confidence: How confident we are in routing
            - reasoning: Why we chose this routing
        """
        # 1. Embed the query
        query_embedding = self.embed(query)
        
        # 2. Find matching expertise domains
        domain_matches = self.match_domains(query_embedding)
        
        # 3. Score each agent's expertise
        agent_scores = {}
        for agent_id in available_agents:
            profile = self.get_expertise_profile(agent_id)
            score = self.compute_expertise_score(
                profile, 
                domain_matches,
                query_embedding
            )
            agent_scores[agent_id] = score
        
        # 4. Return routing decision
        ranked = sorted(agent_scores.items(), key=lambda x: x[1], reverse=True)
        
        return RoutingDecision(
            primary_agent=ranked[0][0],
            primary_confidence=ranked[0][1],
            secondary_agents=[a for a, s in ranked[1:3] if s > 0.5],
            reasoning=self.explain_routing(ranked, domain_matches)
        )
    
    def learn_from_outcome(self, query: str, agent_id: str, success: bool):
        """Update expertise profiles based on query outcomes."""
        # Reinforcement learning on expertise accuracy
        pass
```

### Memory Consensus (Novel Component)

When agents have conflicting memories, CogniHive resolves them:

```python
class ConflictResolver:
    """Resolve conflicting memories between agents."""
    
    def detect_conflicts(self, memories: List[Memory]) -> List[Conflict]:
        """Find memories that contradict each other."""
        conflicts = []
        
        for i, mem_a in enumerate(memories):
            for mem_b in memories[i+1:]:
                # Check semantic contradiction
                if self.are_contradictory(mem_a, mem_b):
                    conflicts.append(Conflict(
                        memory_a=mem_a,
                        memory_b=mem_b,
                        contradiction_type=self.classify_contradiction(mem_a, mem_b),
                        severity=self.compute_severity(mem_a, mem_b)
                    ))
        
        return conflicts
    
    def resolve(self, conflict: Conflict) -> Resolution:
        """Resolve a conflict using multiple strategies."""
        
        strategies = [
            self.recency_resolution,      # Prefer newer information
            self.confidence_resolution,   # Prefer higher confidence
            self.source_resolution,       # Prefer authoritative sources
            self.consensus_resolution,    # Majority vote among agents
            self.human_escalation,        # Ask human if critical
        ]
        
        for strategy in strategies:
            resolution = strategy(conflict)
            if resolution.is_definitive:
                return resolution
        
        # If no definitive resolution, mark as uncertain
        return Resolution(
            winning_memory=None,
            status="uncertain",
            recommendation="flag_for_human_review"
        )
```

### Access Control (Novel Component)

Fine-grained permissions for enterprise use:

```python
@dataclass
class AccessPolicy:
    """Define who can access what memories."""
    
    # Memory visibility
    visibility: Literal["private", "team", "shared", "public"]
    
    # Specific permissions
    can_read: List[str]    # Agent IDs that can read
    can_write: List[str]   # Agent IDs that can modify
    can_share: List[str]   # Agent IDs that can re-share
    
    # Conditions
    expires_at: Optional[datetime]
    requires_audit: bool
    redact_fields: List[str]  # Fields to hide from certain agents
    
    # Provenance
    created_by: str
    created_at: datetime
    
class AccessController:
    """Enforce access policies on memory operations."""
    
    def check_access(
        self, 
        agent_id: str, 
        memory: Memory, 
        operation: Literal["read", "write", "share"]
    ) -> AccessDecision:
        """Check if an agent can perform an operation on a memory."""
        
        policy = memory.access_policy
        
        # Check basic visibility
        if policy.visibility == "private" and agent_id != memory.owner_id:
            return AccessDecision(allowed=False, reason="Private memory")
        
        # Check specific permissions
        permission_list = getattr(policy, f"can_{operation}")
        if agent_id not in permission_list and "*" not in permission_list:
            return AccessDecision(allowed=False, reason=f"Not in {operation} list")
        
        # Check expiration
        if policy.expires_at and datetime.now() > policy.expires_at:
            return AccessDecision(allowed=False, reason="Access expired")
        
        # Log if audit required
        if policy.requires_audit:
            self.audit_log.log(agent_id, memory.id, operation)
        
        return AccessDecision(allowed=True)
```

---

## The API: Simple Yet Powerful

### Basic Usage (3 lines to start)

```python
from cognihive import Hive

# Create a hive (multi-agent memory system)
hive = Hive()

# Register agents with their specializations
hive.register_agent("coder", expertise=["python", "javascript", "testing"])
hive.register_agent("analyst", expertise=["sql", "data", "metrics"])
hive.register_agent("writer", expertise=["docs", "tutorials", "api"])

# Add a memory (automatically indexed by expertise)
hive.remember(
    "The API rate limit is 1000 requests per minute",
    agent="writer",
    topics=["api", "limits"]
)

# Query with automatic routing
result = hive.ask("What's the API rate limit?")
# Returns: Memory from 'writer' agent with answer

# Query "who knows about..."
experts = hive.who_knows("database optimization")
# Returns: [("analyst", 0.92), ("coder", 0.45)]
```

### Multi-Agent Workflow

```python
from cognihive import Hive, Agent

hive = Hive()

# Agents can share knowledge selectively
coder = Agent("coder", hive=hive)
analyst = Agent("analyst", hive=hive)

# Coder learns something and shares with team
coder.remember(
    "Use connection pooling for better DB performance",
    visibility="team"  # All agents can see this
)

# Analyst can now find this knowledge
result = analyst.recall("database performance tips")
# Returns the coder's memory about connection pooling

# Analyst adds private note (only analyst sees this)
analyst.remember(
    "Current pool size is set to 20 connections",
    visibility="private"
)

# Check for conflicts
conflicts = hive.detect_conflicts(topic="database")
if conflicts:
    resolution = hive.resolve_conflicts(conflicts)
```

### Framework Integration (CrewAI Example)

```python
from crewai import Agent, Crew, Task
from cognihive.integrations import CrewAIHive

# Wrap CrewAI with CogniHive
hive = CrewAIHive()

researcher = Agent(
    role="Researcher",
    goal="Find information",
    memory=hive.agent_memory("researcher")  # CogniHive memory
)

writer = Agent(
    role="Writer", 
    goal="Write content",
    memory=hive.agent_memory("writer")  # CogniHive memory
)

# Now agents automatically:
# 1. Know what each other knows
# 2. Route questions to the right expert
# 3. Share learnings with access control
# 4. Resolve conflicting information

crew = Crew(
    agents=[researcher, writer],
    tasks=[...],
    memory=hive  # Shared transactive memory
)
```

---

## What Makes This Groundbreaking

### 1. First TMS Implementation for AI

No one has implemented Transactive Memory Systems for LLM agents. This is backed by 40 years of cognitive science research showing TMS is the #1 predictor of team performance.

### 2. Solves Real Multi-Agent Pain Points

| Problem | Current State | CogniHive Solution |
|---------|--------------|-------------------|
| "Which agent knows X?" | Manual orchestration | Automatic expertise routing |
| Agents doing redundant work | No coordination | "Who knows" queries prevent duplication |
| Conflicting information | Silent failures | Conflict detection + resolution |
| Security/privacy | All-or-nothing sharing | Fine-grained access control |
| Debugging multi-agent | Black box | Memory provenance + audit logs |

### 3. Works WITH Existing Solutions

CogniHive isn't competing with Mem0 — it's a layer ABOVE it:

```
┌─────────────────────────────────────┐
│           CogniHive                 │  ← Coordination layer
│    (Transactive Memory System)      │
└─────────────────────────────────────┘
              ↓ uses
┌─────────────────────────────────────┐
│      Mem0 / Zep / ChromaDB          │  ← Storage layer
│      (Individual agent memory)       │
└─────────────────────────────────────┘
```

### 4. Research-Backed Innovation

CogniHive implements concepts from:
- **Wegner (1985)**: Transactive Memory Systems
- **Anthropic (2025)**: Multi-agent coordination research
- **Stanford (2023)**: Generative Agents memory architecture
- **LLM-MAS Survey (2025)**: "Who knows what" as critical gap

---

## 7-Day Implementation Plan

### Day 1: Core Foundation
**Goal**: Basic hive + agent registration working

- [ ] Set up repository structure, pyproject.toml, CI/CD
- [ ] Implement `Agent` dataclass with expertise fields
- [ ] Implement `Hive` class with agent registration
- [ ] Create `ExpertiseProfile` dataclass
- [ ] Basic ChromaDB storage integration
- [ ] Unit tests for core components

**Deliverable**: `hive.register_agent("name", expertise=[...])` works

### Day 2: Transactive Memory Index
**Goal**: "Who knows what" queries working

- [ ] Implement `ExpertiseIndex` with domain embeddings
- [ ] Create expertise matching algorithm
- [ ] Implement `hive.who_knows("topic")` query
- [ ] Add expertise confidence scoring
- [ ] Automatic expertise extraction from memories
- [ ] Tests for expertise matching accuracy

**Deliverable**: `hive.who_knows("python")` returns ranked agents

### Day 3: Expertise Router
**Goal**: Automatic query routing to experts

- [ ] Implement `ExpertiseRouter` class
- [ ] Create routing decision logic
- [ ] Add primary/secondary expert selection
- [ ] Implement confidence thresholds
- [ ] Routing explanation generation
- [ ] Integration tests with multiple agents

**Deliverable**: Queries automatically route to best expert

### Day 4: Memory Tiers + Access Control
**Goal**: Private/shared/team memories with permissions

- [ ] Implement three-tier memory model
- [ ] Create `AccessPolicy` dataclass
- [ ] Implement `AccessController` with policy enforcement
- [ ] Add memory visibility controls
- [ ] Implement memory provenance tracking
- [ ] Permission inheritance logic

**Deliverable**: Agents can share memories selectively

### Day 5: Conflict Detection + Resolution
**Goal**: Handle contradicting memories

- [ ] Implement `ConflictDetector` for semantic contradictions
- [ ] Create resolution strategies (recency, confidence, voting)
- [ ] Implement `ConflictResolver` pipeline
- [ ] Add conflict severity scoring
- [ ] Human escalation path for critical conflicts
- [ ] Tests with intentionally conflicting data

**Deliverable**: `hive.detect_conflicts()` and `hive.resolve()` work

### Day 6: Framework Integrations + CLI
**Goal**: Works with CrewAI, AutoGen, LangGraph

- [ ] Create `CrewAIHive` adapter
- [ ] Create `AutoGenHive` adapter  
- [ ] Create `LangGraphHive` adapter
- [ ] Implement CLI tool (`cognihive init`, `cognihive status`)
- [ ] Create quickstart examples for each framework
- [ ] Integration tests with real framework workflows

**Deliverable**: Drop-in integration with major frameworks

### Day 7: Demo, Docs, Launch
**Goal**: Public release with compelling demo

- [ ] Build Gradio demo showing transactive memory in action
- [ ] Create interactive "Who Knows What" visualization
- [ ] Write comprehensive README with badges
- [ ] Generate API documentation
- [ ] Deploy demo to HuggingFace Spaces
- [ ] Publish to PyPI
- [ ] Create launch thread for Twitter/LinkedIn
- [ ] Submit to r/MachineLearning, r/LocalLLaMA

**Deliverable**: Live on PyPI, HuggingFace Hub, with viral demo

---

## HuggingFace Demo Concept

### Interactive "Team Brain" Visualization

The demo will show:

1. **Agent Network Graph**: Visual representation of agents and their expertise areas
2. **Live Query Routing**: Type a question, watch it route to the right expert
3. **"Who Knows What" Explorer**: Browse expertise by topic
4. **Conflict Resolution Demo**: Show how contradicting memories are resolved
5. **Memory Timeline**: Visualize how team knowledge evolves

### Viral Elements

- **Before/After**: Show multi-agent chaos → organized coordination
- **Token Savings Counter**: Show how much money you're saving
- **Expertise Heatmap**: Beautiful visualization of team knowledge
- **"Ask the Hive"**: Interactive Q&A with the agent team

---

## Success Metrics

| Metric | Week 1 Target | Month 1 Target |
|--------|---------------|----------------|
| GitHub Stars | 1,000+ | 5,000+ |
| PyPI Downloads | 2,000+ | 20,000+ |
| HuggingFace Demo Plays | 1,000+ | 10,000+ |
| Discord/Community | 200+ | 1,000+ |
| Framework Integrations | 3 | 5+ |
| Research Citations | 0 | 2+ |

---

## Positioning Statement

**For developers building multi-agent AI systems**
**Who struggle with agent coordination and knowledge sharing**
**CogniHive is the first Transactive Memory System for AI**
**That enables agents to know "who knows what" and route queries to experts**
**Unlike Mem0/Zep which only give individual agents memory**
**CogniHive gives your entire agent team a collective brain**

---

## The Tagline

> **"Mem0 gives one agent a brain. CogniHive gives your agent team a collective mind."**

---

## Why This Will Make You Famous

1. **First-Mover Advantage**: Nobody has implemented TMS for AI agents
2. **Research Backing**: 40 years of cognitive science + 2025 AI research papers
3. **Real Pain Point**: Multi-agent coordination is the #1 struggle
4. **Perfect Timing**: Multi-agent AI is THE hot topic in 2025
5. **Complementary, Not Competitive**: Works WITH existing tools
6. **Demo-able**: Beautiful visualizations that go viral
7. **Enterprise Value**: Access control + audit = enterprise-ready

---

## Next Steps

1. **Confirm the name**: "CogniHive" (need to verify PyPI availability)
2. **Start Day 1 implementation**: Core foundation
3. **Create the GitHub repo**: With compelling README
4. **Build as we go**: Ship incrementally

---

*This is not another memory library. This is the missing coordination layer that makes multi-agent AI actually work.*
