Memory that forgets, like humans do.

A production-grade memory system for AI agents with intelligent forgetting.
Built on cognitive science principles: decay, rehearsal, consolidation, and importance-based retention.

Problem • Solution • Architecture • Quick Start • Docs • Contributing

Current agent memory solutions accumulate memories forever. This causes:

Humans don't work this way. We forget. And that's a feature, not a bug.

Cognitive Memory implements a biologically-inspired memory architecture:

┌─────────────────────────────────────────────────────────────────┐ │ HOW HUMAN MEMORY WORKS │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ 📥 Experience ──▶ 🧠 Working Memory ──▶ 💾 Long-term Memory │ │ │ │ │ │ │ ▼ │ │ │ ┌───────────────┐ │ │ │ │ Consolidation │ │ │ │ │ (during sleep)│ │ │ │ └───────┬───────┘ │ │ │ │ │ │ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ │ │ │ Decay │ │ Semantic │ │ │ │ (forgetting)│ │ Facts │ │ │ └─────────────┘ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘ 

┌─────────────────────────────────────────────────────────────────┐ │ MEMORY SYSTEM │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ WORKING │ │ EPISODIC │ │ SEMANTIC │ │ │ │ MEMORY │ │ MEMORY │ │ MEMORY │ │ │ │ (context) │ │ (events) │ │ (facts) │ │ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ RETRIEVAL ENGINE │ │ │ │ (decay-aware scoring + MMR diversity) │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ CONSOLIDATION ENGINE │ │ │ │ (clustering → summarization → fact extraction) │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘ 

pip install cognitive-memory

from cognitive_memory import MemoryManager # Initialize memory = MemoryManager() # Remember something memory.remember( content="User prefers dark mode and vim keybindings", source="conversation", ) # Recall relevant memories (decay-aware) results = memory.recall( query="What are the user's preferences?", k=5, ) # Build context for LLM context = memory.get_context( query="Help configure their editor", max_tokens=4000, )

from cognitive_memory.integrations import CognitiveCheckpointer checkpointer = CognitiveCheckpointer() graph = builder.compile(checkpointer=checkpointer)

from cognitive_memory.integrations import CognitiveMemory memory = CognitiveMemory() chain = ConversationChain(llm=llm, memory=memory)

Every memory has a strength that decays exponentially:

S(t) = S₀ × e^(-λ × Δt) 

Rehearsal Effect: When you retrieve a memory, its strength is boosted—just like how recalling something helps you remember it.

Periodically, weak episodic memories are:

1. Clustered by semantic similarity
2. Summarized into semantic facts
3. Stored in the knowledge graph
4. Pruned from episodic storage

This mimics how human memory consolidates during sleep.

🚧 In Active Development

This project is under active development. The core architecture is designed, and implementation is in progress.

• Core memory models and configuration
• Decay and importance engines
• Retrieval with decay-aware scoring
• Consolidation engine
• LangGraph integration
• LangChain integration
• REST API
• Benchmarks and evaluation
• v0.1.0 release

Want to contribute? Check out CONTRIBUTING.md or join the Discussions.

We welcome contributions! See CONTRIBUTING.md for guidelines.

# Clone and setup git clone https://github.com/NP-compete/cognitive-memory.git cd cognitive-memory pip install -e ".[dev]" # Run tests make test # Run linters make lint

If you use Cognitive Memory in your research, please cite:

@software{cognitive_memory, author = {Dutta, Soham}, title = {Cognitive Memory: Memory that forgets, like humans do}, url = {https://github.com/NP-compete/cognitive-memory}, year = {2026} }

MIT License - see LICENSE for details.

• Inspired by cognitive science research on human memory
• Built for the LangGraph and LangChain ecosystems
• Vector storage: Qdrant, Pinecone
• Knowledge graph: Neo4j

Memory that forgets, so your agents remember what matters.

⭐ Star us • 🐛 Report Bug • 💬 Discussions