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Our resulting RBF models are tiny and fit in L1 cache, with microsecond inference latency.
Graphiti MCP has tens of thousands of users. They deploy it to their desktops, servers, you name it. And for many different use cases: B2B, B2C, and personal use.
More here: https://github.com/getzep/graphiti
Source: me, one of the authors of Graphiti :-)
There's also a Docker Compose setup: https://github.com/getzep/graphiti/tree/main/mcp_server#runn...
The Cursor MCP setup is also simple:
```{ "mcpServers": { "Graphiti": { "url": "http://localhost:8000/sse" } } }```
https://github.com/getzep/graphiti
I’m one of the authors. Happy to answer any questions.
"Facts" shared across all users will also be updated universally. Alongside Graphiti's search, you'd be able to use cypher to query Neo4j to, for example, find hub nodes (aka highly-connected nodes), identifying common beliefs.
More here: https://help.getzep.com/graphiti/graphiti/adding-episodes
Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context.
At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire chat history in a prompt. Accurate context is crucial for LLM applications. If an AI agent doesn't remember that you've changed jobs or confuses the chronology of events, its responses can be jarring or irrelevant, or worse, inaccurate.
## Zep’s Suboptimal Fact Pipeline
Before Graphiti, our approach to storing and retrieving user “memory” was, in effect, a specialized RAG pipeline. An LLM extracted “facts” from a user’s chat history. Semantic search, reranking, and other techniques then surfaced facts relevant to the current conversation back to a developer for inclusion in their prompt.
We attempted to reconcile how new information may change our understanding of existing facts:
Fact: “Kendra loves Adidas shoes”
User message: “I’m so angry! My favorite Adidas shoes fell apart! Puma’s are my new favorite shoes!”
Facts:
- “Kendra used to love Adidas shoes but now prefers Puma.”
- “Kendra’s Adidas shoes fell apart.”
Unfortunately, this approach became problematic. Reconciling facts from increasingly complex conversations challenged even frontier LLMs such as gpt-4o. We saw incomplete facts, poor recall, and hallucinations. Our RAG search also failed at times to capture the nuanced relationships between facts, leading to irrelevant or contradictory information being retrieved.
We tried fixing these issues with prompt optimization but saw diminishing returns on effort. We realized that a graph would help model a user’s complex world, potentially addressing these challenges.
We were intrigued by Microsoft’s GraphRAG, which expanded on RAG text chunking with a graph to better model a document corpus. However, it didn't solve our core problem: GraphRAG is designed for static documents and doesn't natively handle temporality.
So, we built Graphiti, which is designed from the ground up to handle constantly changing information, hybrid semantic and graph search, and scale:
- Temporal Awareness: Tracks changes in facts and relationships over time. Graph edges include temporal metadata to record relationship lifecycles.
- Episodic Processing: Ingests data as discrete episodes, maintaining data provenance and enabling incremental processing.
- Hybrid Search: Semantic and BM25 full-text search, with the ability to rerank results by distance from a central node.
- Scalable: Designed for large datasets, parallelizing LLM calls for batch processing while preserving event chronology.
- Varied Sources: Ingests both unstructured text and structured data.
Graphiti has significantly improved our ability to maintain accurate user context. It does a far better job of fact reconciliation over long, complex conversations. Node distance reranking, which places a user at the center of the graph, has also been a valuable tool. Quantitative data evaluation results may be a future ShowHN.
Work is ongoing, including:
1. Improving support for faster and cheaper small language models.
2. Exploring fine-tuning to improve accuracy and reduce latency.
3. Adding new querying capabilities, including search over neighborhood (sub-graph) summaries.
## Getting Started
Graphiti is open source and available on GitHub: https://github.com/getzep/graphiti.
If you try it, we'd love to hear your thoughts, questions, and experiences. Please also consider contributing!
As a result, we host embedding models on rented GPUs and get a P90 latency of 10ms.
Cursor is a purpose-built IDE for software development. The Cursor team has put a lot of research and sweat into providing the used LLMs (also from OpenAI/Anthropic) with:
- the right parts of your code
- relevant code/dependency documentation
- and, importantly, the right prompts.
to successfully complete coding tasks. It's an apple and oranges situation.
# Very Limited Field Typing
OpenAI offers a very limited set of types[2] (String, Number, Boolean, Object, Array, Enum, anyOf) without the ability to define patterns and max/min lengths. Outlines supports defining arbitrary RegEx patterns, making extracting currencies, phone numbers, zip codes, comma-separated lists, and more a trivial exercise.
# High Schema Setup Cost / Latency
vLLM and Outlines offer near-zero cost schema setup: RegEx finite state machine construction is extremely cheap on the first inference call. While OpenAI's context-free grammar generation has a significant latency penalty of "under ten seconds to a minute". This may not impact "warmed-up" inference but could present issues if schemas are more dynamic in nature.
Right now, this feels like a good first step, focusing on ensuring the right fields are present in schema-ed output. However, it doesn't yet offer the functionality to ensure the format of field contents beyond a primitive set of types. It will be interesting to watch where OpenAI takes this.
[0] https://help.getzep.com/structured-data-extraction
[1] https://help.getzep.com/dialog-classification
[2] https://platform.openai.com/docs/guides/structured-outputs/s...