# CodeMem: Architecting Reproducible Agents via Dynamic MCP and Procedural Memory

arXiv: https://arxiv.org/abs/2512.15813 · HTML: https://mlsanity.com/paper/2512.15813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 4 of 4)

- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [DynaSaur: Large Language Agents Beyond Predefined Actions](https://mlsanity.com/paper/2411.01747/) (2024)
- [Dynamic ReAct: Scalable Tool Selection for Large-Scale MCP Environments](https://mlsanity.com/paper/2509.20386/) (2025)

## Built on by (most cited first, 3 of 3)

- [Code as Agent Harness](https://mlsanity.com/paper/2605.18747/) (2026)
- [From Tool Orchestration to Code Execution: A Study of MCP Design Choices](https://mlsanity.com/paper/2602.15945/) (2026)
- [Synthesizing Procedural Memory: Challenges and Architectures in Automated Workflow Generation](https://mlsanity.com/paper/2512.20278/) (2025)

## Abstract (arXiv)

Current tool-using AI agents suffer from limited action space, context inefficiency, and probabilistic instability that makes them unsuitable for handling repetitive tasks which are otherwise reliably and efficiently tackled by agentic workflows built on platforms like n8n and Zapier. Earlier works like CodeAct, DynaSaur, Code Mode have tried to tackle the first two issues by using the whole Python language as its action space: The number of tools that the agent can call becomes infinite. Python code blocks can execute complex actions into a single step and print only relevant results which helps in keeping the context lean. However, the probabilistic instability issue still remains, as for the same task in the same environment, the agent can follow different trajectories due to the probabilistic nature of LLMs. Therefore, we need procedural memory for consistency and reliability. This paper proposes CodeMem, an architecture to implement procedural memory via code which can be used to build and run reusable agentic workflows with deterministic reliability.