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🧠 What is Transactive Memory?

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) — a concept from cognitive science that has never been implemented for AI agents... until now.

💡 Why CogniHive?

Current multi-agent systems fail because:

Problem	Without CogniHive	With CogniHive
"Which agent knows X?"	Manual orchestration	hive.who_knows("topic")
Redundant work	Multiple agents research same thing	Expertise routing prevents duplication
Conflicting info	Silent failures	Conflict detection + resolution
Token explosion	15x more tokens (Anthropic's research)	Smart routing = massive savings

🚀 Installation

pip install cognihive

With framework integrations:

pip install cognihive[crewai]      # For CrewAI
pip install cognihive[autogen]     # For AutoGen
pip install cognihive[langchain]   # For LangChain
pip install cognihive[openai]      # For OpenAI Assistants
pip install cognihive[mcp]         # For Anthropic MCP (Claude)
pip install cognihive[all]         # Everything

⚡ Quick Start

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"])

# Agents store knowledge
hive.remember(
    "Use connection pooling for better DB performance",
    agent="analyst",
    topics=["database", "performance"]
)

# 🔍 THE KEY INNOVATION: "Who Knows What" queries
experts = hive.who_knows("database optimization")
# Returns: [("analyst", 0.92), ("coder", 0.45)]

# 🎯 Automatic query routing to the right expert
result = hive.ask("How do I optimize my queries?")
# Automatically routes to "analyst" and returns relevant memories

print(f"Expert: {result['expert']}")  # "analyst"
print(f"Answer: {result['memories'][0].content}")

🔗 Integrations

CrewAI

from crewai import Agent, Crew
from cognihive.integrations import CrewAIHive

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:
# ✓ Know what each other knows
# ✓ Route questions to the right expert
# ✓ Share learnings across the team

AutoGen

from autogen import AssistantAgent
from cognihive.integrations import AutoGenHive

hive = AutoGenHive()

# Create agents with shared transactive memory
coder = hive.create_memory_enhanced_agent(
    name="coder",
    system_message="You are an expert coder.",
    expertise=["python", "coding"]
)

reviewer = hive.create_memory_enhanced_agent(
    name="reviewer",
    system_message="You review code for quality.",
    expertise=["review", "testing"]
)

# Agents now have collective intelligence!

LangGraph

from cognihive.integrations import LangGraphHive, create_expert_routing_graph

hive = LangGraphHive()
hive.register_agent("researcher", expertise=["research"])
hive.register_agent("writer", expertise=["writing"])

# Create a graph with automatic expert routing
graph = create_expert_routing_graph(
    hive=hive,
    agent_nodes={
        "researcher": researcher_node,
        "writer": writer_node
    }
)

LangChain

from cognihive.integrations import LangChainHive
from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI

hive = LangChainHive()
hive.register_agent("assistant", expertise=["general"])

# Use CogniHive as LangChain memory
chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=hive.as_memory("assistant")
)

# Or as a retriever for RAG
retriever = hive.as_retriever(top_k=5)

OpenAI Assistants

from openai import OpenAI
from cognihive.integrations import OpenAIHive, create_tool_outputs

client = OpenAI()
hive = OpenAIHive()

# Create assistant with CogniHive tools
assistant = client.beta.assistants.create(
    name="Team Coordinator",
    tools=hive.get_tools(),  # who_knows, remember, recall, ask_expert
    model="gpt-4-turbo"
)

# Process tool calls
outputs = create_tool_outputs(hive, tool_calls)

Anthropic MCP (Claude)

// claude_desktop_config.json
{
  "mcpServers": {
    "cognihive": {
      "command": "python",
      "args": ["-m", "cognihive.integrations.mcp"]
    }
  }
}

Claude can now use:

• cognihive_who_knows - Find team experts
• cognihive_remember - Store knowledge
• cognihive_recall - Search memories
• cognihive_ask_expert - Route to expert

🛠️ CLI

# Initialize a hive
cognihive init --name my_project

# Register agents
cognihive register coder --expertise python javascript

# Store knowledge
cognihive remember "Important info" --agent coder

# Query "who knows what"
cognihive who-knows "python optimization"

# Search memories
cognihive recall "best practices"

# Run interactive demo
cognihive demo

📚 Documentation

Core Concepts

Concept	Description
Hive	The central coordinator for multi-agent memory
Agent	An entity with expertise that stores/retrieves memories
Memory	A piece of knowledge with provenance and access control
ExpertiseProfile	Tracks "who knows what" for each agent
ExpertiseRouter	Routes queries to the best expert

Key Methods

# Agent management
hive.register_agent(name, expertise, role)
hive.get_agent(name)
hive.list_agents()

# Memory operations
hive.remember(content, agent, topics, visibility)
hive.recall(query, top_k=5)

# Transactive memory (THE INNOVATION)
hive.who_knows(topic)          # Find experts
hive.get_expert(topic)         # Get best expert
hive.expertise_matrix()        # Get full expertise map

# Query routing
hive.ask(query)                # Auto-route + retrieve
hive.route(query)              # Get routing decision

Memory Visibility

# Private - only the owner sees it
hive.remember("Secret notes", agent="coder", visibility="private")

# Shared - specific agents can see
hive.remember("For the team lead", agent="coder", visibility="shared")

# Team - all agents in the hive can see
hive.remember("Team announcement", agent="coder", visibility="team")

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                      CogniHive Core                                  │
│  ┌─────────────────────────────────────────────────────────────┐    │
│  │               TRANSACTIVE MEMORY INDEX                       │    │
│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐          │    │
│  │  │ Agent: Coder│  │ Agent: Docs │  │Agent: Data  │          │    │
│  │  │ Knows:      │  │ Knows:      │  │ Knows:      │          │    │
│  │  │ - Python    │  │ - API specs │  │ - SQL       │          │    │
│  │  │ - FastAPI   │  │ - Tutorials │  │ - Analytics │          │    │
│  │  └─────────────┘  └─────────────┘  └─────────────┘          │    │
│  └─────────────────────────────────────────────────────────────┘    │
│                                                                      │
│  ┌─────────────────────────────────────────────────────────────┐    │
│  │                    EXPERTISE ROUTER                          │    │
│  │  Query: "How do I optimize the database queries?"            │    │
│  │  Routing: Data Agent (0.92) > Coder Agent (0.67)            │    │
│  └─────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────┘

🎯 Use Cases

• Multi-Agent Software Teams - Coder, reviewer, tester, writer working together
• Research Workflows - Researcher, analyst, writer with shared findings
• Customer Support - Specialists routing questions to the right expert
• Enterprise Knowledge - Departments sharing institutional knowledge

📊 Comparison

Feature	Mem0	Zep	Letta	CogniHive
Single-agent memory	✅	✅	✅	✅
"Who Knows What"	❌	❌	❌	✅
Expert routing	❌	❌	❌	✅
Conflict resolution	❌	❌	❌	✅
Access control	❌	❌	❌	✅
CrewAI integration	❌	❌	❌	✅
AutoGen integration	❌	❌	❌	✅

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

• Daniel Wegner - Transactive Memory Systems theory (1985)
• Anthropic - Multi-agent coordination research
• Stanford - Generative Agents memory architecture

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