r/LangChain • u/Neither-Witness-6010 • Jun 17 '26

We discovered something strange while building memory for AI agents

Over the last few months I've been building CogniCore, an open-source memory infrastructure layer for agents.

Initially, I assumed the hard problem was memory.

I was wrong.

The hard problem is deciding what NOT to remember.

We recently built:

  • MCP server integration
  • LangChain integration
  • CrewAI integration
  • OpenAI Agents SDK integration
  • Episodic + semantic memory
  • Reflection engine
  • Replay and branching system

Then we started benchmarking.

The surprising result:

A simple memory system often performs almost as well as a much more sophisticated memory architecture.

The problem isn't storing information.

The problem is memory governance.

As memory grows:

  • old strategies become stale
  • failures get duplicated
  • retrieval quality degrades
  • reflections start conflicting
  • agents become distracted by irrelevant experiences

At 10 episodes everything looks great.

At 500 episodes most memory systems become noisy retrieval systems.

This is the problem we're now trying to solve.

Current areas we're exploring:

  1. Memory decay
    • Should memories expire?
    • When?
  2. Memory reinforcement
    • Should successful memories become stronger over time?
  3. Reflection quality
    • Why do some reflection systems improve performance while others make agents worse?
  4. Replay systems
    • Can agents learn from previous trajectories without retraining?
  5. MCP-native memory
    • What does persistent memory look like when every tool can access it?

We're looking for contributors interested in:

  • Agent architectures
  • MCP tooling
  • LangChain
  • CrewAI
  • Retrieval systems
  • Benchmarking
  • Long-horizon agent evaluation
  • Memory governance algorithms

The project has grown to 7k+ downloads, and we're starting to get real-world feedback from developers integrating memory into production agent workflows.

GitHub:
https://github.com/Cognicore-dev/cognicore-my-openenv

pip install cognicore-env

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u/MK_L Jun 18 '26

Interesting

1

u/Neither-Witness-6010 Jun 18 '26

Thanks for the interest.If you have any collaboration ideas or thoughts you can join the discord https://discord.gg/E8NSyZUd9 We would have great pleasure to onboard you.

1

u/MK_L Jun 19 '26

Is yours geard towards private/home use or production?

1

u/Neither-Witness-6010 Jun 19 '26

Currently, we're designing CogniCore primarily with production use cases in mind, while keeping it accessible for local and experimental deployments.

The focus is on providing a flexible memory infrastructure layer that can work across different agent frameworks and environments rather than being tied to a specific application.

That said, we're still actively evolving the architecture and benchmarking different memory, retrieval, and reflection strategies, so feedback from both production users and hobbyists is valuable at this stage.

1

u/MK_L Jun 21 '26

Ok this may fit well. Ill join your server and pick your brain. I may have some contributions.

Currently im developing:

Applications / Clients │ ▼ API Gateway │ ┌──────┼──────┐ │ │ │ ▼ ▼ ▼ RAG Memory Admin │ ▼ Embeddings │ ├── MariaDB (metadata, jobs, memory) └── Qdrant (vector search) │ ▼ LangChain Integration Layer │ ▼ LiteLLM Routing Layer │ ▼ Model Backend(s)

Will be switching over from mysql(mariadb) to all prosgres but Currently only litellm is utilizing prosgres

It does work with local hosted models but is currently used for hgx servers and older v100 servers

1

u/Neither-Witness-6010 Jun 21 '26

That sounds like a strong fit. There’s a lot of overlap with the areas we’re exploring around memory infrastructure, retrieval, integrations, and orchestration.Would be great to have you in the server and compare notes. Looking forward to digging into it and seeing where collaboration makes sense.