Micro-RAG (mRAG); ~85% accuracy across Benchmarks at an avg. of 700 token per query

Micro-RAG or mRAG is a highly efficient AI Memory system designed to keep injections below 1k (generally an average of around 700 per query) while able to maintain a competitive 85+% accuracy in both LoCoMo and LongMemEval. Even at 100k+ dataset pools, the accuracy and token cost remain fairly constant at ~85% and ~710 tokens per query.
For comparison, alternative 'High efficiency' memory systems often have a low end of 1,200 tokens going as high as over 4,000 tokens per query before showing accuracy degradation. mRAG essentially never injects more than 1k tokens per query, offering, even against the most efficient alternatives, a 40-80% decrease in token cost per turn. Compared to less efficient systems, over longer context periods, token reduction can easily reach to 95-98% with an only marginal reduction in comparative accuracy (top tier systems generally score ~90-95% accuracy in LoCoMo and LongMemEval respectively).

Sample scores:

| Benchmark | Model | Evaluation Size | Total Questions | Correct | Missed | Accuracy | Avg Injected Tokens |

| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |

| **LoCoMo (Latest Run)** | `gemini-2.5-flash` | 3 Conversations | 60 | 51 | 9 | **85.0%** | **711.1** (Min: 457, Max: 734) |

| **LoCoMo (Best Run)** | `gemini-2.5-flash` | 3 Conversations | 60 | 52 | 8 | **86.7%** | **~711.0** |

| **LongMemEval_S** | `gemini-3.1-flash-lite` | 20 Questions (Stratified) | 20 | 17 | 3 | **85.0%** | **718.9** (Min: 630, Max: 759) |

mRAG is model and system agnostic and can be applied to your current running Agent, it requires a Vector database (default is set to numpy but recommended to use ChromaDB or Pinecone).

More information and Benchmark results are contained in the Repo at:
https://github.com/munch2u-a11y/mRAG.git