@_avichawla: Build human-like memory for your Agents (open-source)! Every agentic and RAG system struggles with real-time knowledge …

Build human-like memory for your Agents (open-source)!

Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval.

Graphiti solves these issues with its continuously evolving and temporally-aware knowledge graph.

Like humans, Graphiti organizes an Agent’s memories into episodes, extracts entities and their relationships from these episodes, and stores them in a knowledge graph:

(refer to the image below as you read)

1. Episode subgraph: Captures raw data with timestamps, retaining every detail for easy historical lookup.

2. Semantic entity subgraph: Extracts entities (e.g., “Alice,” “Google”) and facts (“Alice works at Google”). Everything is versioned, so outdated info gets replaced.

3. Community subgraph: Groups related entities into clusters, with summaries for faster retrieval.

Graphiti delivers up to 18.5% higher accuracy with 90% lower latency when compared to tools like MemGPT.

It’s fully open-source with 26k+ stars.

I have shared the repo in the replies.

GitHub repo: https://github.com/getzep/graphiti

(don’t forget to star it )

Karpathy’s prediction about RL is coming true now!

He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what “good” means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel.

Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek).

And their key bottleneck has always been the reward functions.

GRPO by DeepSeek worked well for math and code because the environment gave a binary signal.

But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes.

RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified.

The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training.

I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow.

In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition.

You can see the full implementation on GitHub and try it yourself.

Here’s the ART Repo: http://github.com/OpenPipe/ART

(don’t forget to star it )

Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions.

RL reward engineering is now prompt engineering.

I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.