Programmatic memory for long-horizon LLM agents

PRO-LONG, a minimal memory addition for LLM agents that appends every observation, action, and outcome to a structured log.txt and retrieves it programmatically, improves performance on the ARC-AGI-3 public game set by 18 percentage points on average over the same coding agents without the log, matches or exceeds specialized harnesses at 4.2–5.8x fewer billed tokens, and reaches 97.4% best@2 with Fable 5 at a total cost of $1,750, according to a paper released by the developers on arXiv.

PRO-LONG is a minimal memory addition for LLM agents on long-horizon tasks. The harness appends every observation, action, and outcome to a single structured log.txt, and the agent retrieves and reasons over it programmatically grep, Python . There are no subagents or specialized retrieval mechanisms, and the system prompt is about 30 lines. On the full ARC-AGI-3 https://three.arcprize.org/ public game set, PRO-LONG improves over the same coding agents without the log by 18 percentage points on average, matches or exceeds specialized harnesses at 4.2–5.8x fewer billed tokens, and reaches 97.4% best@2 with Fable 5 at a total cost of $1,750. Paper: arxiv.org/abs/2607.20064 https://arxiv.org/abs/2607.20064 Requires Python 3.12 recommended and Docker. git clone git@github.com:alexisfox7/PRO-LONG.git cd PRO-LONG python -m venv .venv source .venv/bin/activate pip install -e . codex backend docker build -t prolong-agent/codex-sandbox:latest docker/codex-sandbox docker build -t prolong-openai-proxy docker/openai-proxy claude-code backend docker build -t prolong-agent/claude-sandbox:latest docker/claude-sandbox docker build -t prolong-anthropic-proxy docker/anthropic-proxy Create a .env file: ARC API KEY=... CODEX API KEY=... codex backend CLAUDE CODE OAUTH TOKEN=... claude-code backend default ANTHROPIC API KEY=... claude-code backend with --api-key The agent container only mounts the game workspace and, by default, has no network access except a proxy to the model API. prolong-swarm --suite all -m gpt-5.5 --max-actions 500 prolong-swarm --suite all --backend claude-code -m claude-opus-4-6 prolong-swarm --game ls20,ft09 -m gpt-5.5 Results are written to evaluation results/ . | Flag | Default | Description | |---|---|---| --backend | codex | codex OpenAI Codex CLI or claude-code Claude Code CLI | --suite | — | Game suite all only | --game | — | Comma-separated individual game names or full IDs | --max-actions | 500 | Max actions per game | --model , -m | Backend-specific | gpt-5.5 for Codex; claude-opus-4-6 for Claude Code | --effort | high | Effort level claude-code backend | --reasoning-effort | none | Reasoning effort codex backend | --operation-mode | online | online / offline / normal | The agent's access to game history is controlled by --log-window and --workspace . These are the ablation conditions from the paper: | Condition | Flags | History available | |---|---|---| | prolong | default | Full game log | | lw25 | --log-window 25 | Last 25 action sections of the log | | no-log in-prompt | --log-window -1 | No log file; the current board is added to the prompt | | stateless | --workspace stateless | Full log, but the workspace is wiped each call | scorecards/ contains the official online scorecards, including all 25 Fable 5 runs from the paper fable online scorecards.txt ; each can be verified on arcprize.org. release logs/ contains logs for the Fable 5 online runs: game logs, agent transcripts, and workspaces. Logs for the remaining ablations will be added. prolong agent/ ├── agent/ │ ├── base.py base architecture │ ├── codex agent.py Codex CLI backend │ ├── claude code agent.py Claude Code backend │ ├── swarm.py CLI entry point │ ├── action queue.py action execution │ ├── game state.py board/log formatting │ └── prompts.py prompts ~30 lines ├── environment/ │ ├── arcagi3.py ARC-AGI-3 API wrapper │ ├── runner.py per-game loop │ └── config.py ├── metrics/ └── utils/ This repo was formerly the Read-Grep-Bash RGB Agent, see our original blog post https://blog.alexisfox.dev/arcagi3 on the ARC-AGI-3 preview games. @misc{fox2026prolong, title={PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning}, author={Fox, Alexis and Wang, Junlin and Rosu, Paul and Dhingra, Bhuwan}, year={2026}, eprint={2607.20064}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2607.20064}, }