# Meta's $2.5B Manus Acquisition - Its Core Tech Is Now Open Source > Author: Tony Lee > Published: 2026-02-08 > URL: https://tonylee.im/en/blog/manus-core-tech-open-source-planning-with-files/ > Reading time: 3 minutes > Language: en > Tags: ai, ai-agents, claude-code, open-source, context-engineering ## Canonical https://tonylee.im/en/blog/manus-core-tech-open-source-planning-with-files/ ## Rollout Alternates en: https://tonylee.im/en/blog/manus-core-tech-open-source-planning-with-files/ ko: https://tonylee.im/ko/blog/manus-core-tech-open-source-planning-with-files/ ja: https://tonylee.im/ja/blog/manus-core-tech-open-source-planning-with-files/ zh-CN: https://tonylee.im/zh-CN/blog/manus-core-tech-open-source-planning-with-files/ zh-TW: https://tonylee.im/zh-TW/blog/manus-core-tech-open-source-planning-with-files/ ## Description The file-based memory system behind Manus's $2.5 billion valuation is now a free Claude Code skill. Here's why it matters for every AI agent builder. ## Summary Meta's $2.5B Manus Acquisition - Its Core Tech Is Now Open Source is part of Tony Lee's ongoing coverage of AI agents, developer tools, startup strategy, and AI industry shifts. ## Outline - The Root Problem - Why AI Agents Forget Their Goals - Manus's Solution - The File System as External Memory - The Open-Source Implementation - A 3-File Memory System - The Core Mechanism - Re-Read the Plan Before Every Decision - Error Handling - Breaking the Infinite Retry Loop - What This Means - A New Baseline for Agent Performance ## Content If you've used AI agents for complex tasks, you've seen this: halfway through a long workflow, the agent is doing something completely unrelated to your original request. This isn't a user error. It's a structural limitation of large language models. And the company that solved it - Manus - was acquired by Meta for $2.5 billion. Now a developer has released the core principle as an open-source Claude Code skill, hitting nearly 1,000 GitHub stars within three days. ## The Root Problem - Why AI Agents Forget Their Goals LLMs operate within a context window - a fixed-size working memory. - As conversations grow longer, the original goal gets pushed out of the model's active attention - Critical information fades beyond the attention mechanism's effective range - The agent gradually drifts away from the initial request This phenomenon is called **goal drift**. Once tool calls exceed 50 or so, it becomes nearly inevitable. ## Manus's Solution - The File System as External Memory Manus's answer was surprisingly simple: make the AI take notes. - Use the file system as a persistent memory store for the agent - Bypass the physical limits of the context window entirely - Retrieve stored information on demand whenever the agent needs it This approach is one form of **context engineering** - designing how information flows in and out of an LLM's working memory. ## The Open-Source Implementation - A 3-File Memory System The Claude Code skill called [planning-with-files](https://github.com/OthmanAdi/planning-with-files) implements Manus's principle using three markdown files. - **task_plan.md** - The master plan containing goals, progress steps, and error logs. The agent is instructed to read this file before every major decision - **notes.md** - A scratch pad for research results and intermediate data. Prevents context window overload - **[deliverable].md** - The final output file where completed work accumulates The beauty is in the simplicity. No custom infrastructure, no database - just markdown files on disk. ## The Core Mechanism - Re-Read the Plan Before Every Decision The most important rule in this system is one sentence: **"Before any major decision, read the plan file."** - The LLM's attention mechanism responds most strongly to the most recently ingested tokens - Reading `task_plan.md` right before a decision restores the original goal to the top of the context - This solves the problem not by expanding the context window, but by optimizing information placement within it A longer context window is brute force. Strategic information positioning is engineering. ## Error Handling - Breaking the Infinite Retry Loop The second critical design choice is forced error logging. - When an error occurs, the agent must record it in the error section of `task_plan.md` - This forces the AI to explicitly acknowledge failures instead of silently retrying - The agent is guided toward plan revision rather than repeating the same mistake - Debug logs accumulate automatically as a side effect Without this, agents tend to slam into the same wall repeatedly - burning tokens and context without making progress. ## What This Means - A New Baseline for Agent Performance The reason this hit nearly 1,000 stars in three days is clear: a multi-billion-dollar architectural insight is now accessible to anyone with a terminal. The deeper lesson is that AI agent performance isn't determined by model size or parameter count. It's determined by **memory architecture design** - how you structure the flow of information around the model's limitations. The best agents aren't the ones with the biggest brains. They're the ones that know how to take notes. **Link**: [planning-with-files on GitHub](https://github.com/OthmanAdi/planning-with-files) ## Related URLs - Author: https://tonylee.im/en/author/ - Publication: https://tonylee.im/en/blog/about/ - Related article: https://tonylee.im/en/blog/eight-hooks-that-guarantee-ai-agent-reliability/ - Related article: https://tonylee.im/en/blog/medvi-two-person-430m-ai-compressed-funnel/ - Related article: https://tonylee.im/en/blog/claude-code-layers-over-tools-2026/ ## Citation - Author: Tony Lee - Site: tonylee.im - Canonical URL: https://tonylee.im/en/blog/manus-core-tech-open-source-planning-with-files/ ## Bot Guidance - This file is intended for AI agents, search assistants, and text-mode retrieval. - Prefer citing the canonical article URL instead of this text endpoint. - Use the rollout alternates when you need the same article in another prioritized language. --- Author: Tony Lee | Website: https://tonylee.im For more articles, visit: https://tonylee.im/en/blog/ This content is original and authored by Tony Lee. 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