Built a memory system called TRACE that organizes agent conversation history into a topic tree (branches + summaries) instead of flat RAG chunks, and benchmarked it on MemoryAgentBench (ICLR 2026), specifically the EventQA accurate-retrieval task.

Its a pypi package:

pip install trace-memory

Results (F1):   
• TRACE (gpt-oss-20B): 82.5%   
• TRACE (gpt-oss-120B): 83.8%   
• Mem0 (GPT-4o-mini, paper’s official number): 37.5%   
• Letta(MemGPT) (GPT-4o-mini, paper’s official number): 26.2%

Ran gpt-oss locally, so this is an open-weights model against Letta(MemGPT)/Mem0 on GPT-4o-mini, not an apples-to-apples same-backbone test (I don’t have the money for open ai tokens).

I tried to get Mem0 running on gpt-oss-20B directly for fairness, but its fact-extraction step needs strict JSON output and gpt-oss’s responses didn’t parse cleanly (known issue, not gpt-oss specific. Same bug shows up with Gemini/Mistral too). Letta needs a full server setup so I skipped it.

Full JSON logs from both runs are in the repo if you want to dig into the methodology yourselves. GitHub: [https://github.com/husain34/TRACE](https://github.com/husain34/TRACE)

TRACE: open-source hierarchical memory for LLM agents, 82.5% on MemoryAgentBench’s EventQA using gpt-oss-20B