The Context Window Problem
AI agents often struggle with remembering context across sessions. That's because most LLMs rely on massive token windows, which can fill up in seconds even for large codebases. A user's preferences or the structure of a codebase are lost when the session restarts – no matter how big your window is.
Building Persistent Memory
Tree-Sitter-based knowledge graphs offer a solution to this problem. By modeling relational dependencies and hierarchical information, you can create an efficient graph that stores context persistently.
Here's some sample code using MrMemory:
from mrmemory import MrMemory
client = MrMemory(api_key="your-key")
client.remember("user prefers dark mode", tags=["preferences"])
Tree-Sitter Knowledge Graphs
To build a persistent memory system, you create a Tree-Sitter-based knowledge graph that models the relationships between entities in your codebase or user preferences. When you need to retrieve context, you use GraphRAG to search and retrieve relevant information efficiently.
Hierarchical Memory Systems
Another approach is to organize information into a hierarchical structure. This allows for efficient retrieval of context across sessions.
client.remember("user's codebase architecture", tags=["codebases"])
results = client.recall("what is the user's preferred deployment method?")
Comparison with Alternatives
Other frameworks like Mem0, Zep, and MemGPT offer some benefits but also have limitations.
| Framework | Benefits | Limitations |
|---|---|---|
| Mem0 | Simple to implement | Limited scalability |
| Zep | Self-hosted solution | High maintenance costs |
| MemGPT | Large memory capacity | Limited search efficiency |
|---|