---
title: "mem0 vs planning-with-files"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/mem0ai-mem0-vs-othmanadi-planning-with-files"
tools: ["mem0ai-mem0", "othmanadi-planning-with-files"]
---

# mem0 vs planning-with-files

Neutral, constraint-first comparison with live GitHub stats.

| | [mem0](/tools/mem0ai-mem0.md) | [planning-with-files](/tools/othmanadi-planning-with-files.md) |
| --- | --- | --- |
| Tagline | Universal memory layer for AI Agents | Persistent file-based planning for AI coding agents |
| Stars | 60,369 | 25,031 |
| Forks | 7,008 | 2,122 |
| Open issues | 504 | 5 |
| Language | Python | Python |
| Adopt for | Mem0 is a comprehensive tool that optimizes token usage and reduces latency for efficient long-term memory management in AI agents. It has recently introduced significant improvements in its algorithm, boosting benchmark | Planning with Files is a persistent file-based planning tool designed for long-running and crash-proof AI coding agents. It employs markdown files (`task_plan.md`, `findings.md`, `progress.md`) as storage to ensure tasks |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | AI Agents, Data & Retrieval | AI Agents, Developer Tools |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [mem0](/tools/mem0ai-mem0.md) | [planning-with-files](/tools/othmanadi-planning-with-files.md) |
| --- | --- | --- |
| Open issues (now) | 504 | 5 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/mem0ai-mem0/trust.md) | [trust report](/tools/othmanadi-planning-with-files/trust.md) |

**Typed relationship:** mem0 _(alternative)_ planning-with-files

Planning with Files and mem0 both address the need for persistent context in AI agents but through different mechanisms: Planning with Files focuses on maintaining task plans via files while mem0 acts as a universal memory layer.

## Decision facts: mem0

- **Pricing:** unknown - The repository mentions an Apache-2.0 license but pricing information is not provided.
- **Requirements:** While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations.
- **Adopt for:** Mem0 is a comprehensive tool that optimizes token usage and reduces latency for efficient long-term memory management in AI agents. It has recently introduced significant improvements in its algorithm, boosting benchmark

## Decision facts: planning-with-files

- **Pricing:** freemium - Available under the MIT license, Planning with Files is free and open-source for download and use. There are no direct costs associated with using the tool itself, but it relies on third-party AI-codE
- **Requirements:** Min 2 GB RAM; - Requires Python to operate effectively.; - Works across a broad spectrum of AI coding agents including Claude Code, Codex CLI, and over sixty other compatible platforms via the SKILL.md standard.
- **Adopt for:** Planning with Files is a persistent file-based planning tool designed for long-running and crash-proof AI coding agents. It employs markdown files (`task_plan.md`, `findings.md`, `progress.md`) as storage to ensure tasks

## Choose when

### Choose mem0 if…

- License: mem0 is Apache-2.0, planning-with-files is MIT.
- Pricing: The repository mentions an Apache-2.0 license but pricing information is not provided..
- Requirements: While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations..
- Planning with Files and mem0 both address the need for persistent context in AI agents but through different mechanisms: Planning with Files focuses on maintaining task plans via files while mem0 acts as a universal memory layer.
- Tags unique to mem0: genai, agents, llm, python.
- Also covers Data & Retrieval.
- - When developing AI applications where enhancing the efficiency of memory retention is crucial.
- If your project requires state-of-the-art performance across various benchmarks like LoCoMo and Long

### Choose planning-with-files if…

- License: planning-with-files is MIT, mem0 is Apache-2.0.
- Pricing: Available under the MIT license, Planning with Files is free and open-source for download and use. There are no direct costs associated with using the tool itself, but it relies on third-party AI-codE.
- Requirements: Min 2 GB RAM; - Requires Python to operate effectively.; - Works across a broad spectrum of AI coding agents including Claude Code, Codex CLI, and over sixty other compatible platforms via the SKILL.md standard..
- Planning with Files and mem0 both address the need for persistent context in AI agents but through different mechanisms: Planning with Files focuses on maintaining task plans via files while mem0 acts as a universal memory layer.
- Tags unique to planning-with-files: agent-skills, long-running-tasks, file-based-planning, persistent-planning.
- Also covers Developer Tools.
- - When working on projects that require prolonged and uninterrupted AI-assisted development, such as large-scale software engineering or research initiatives.

## When NOT to use mem0

- - If your project does not require long-term memory management or advanced state management techniques.
- - In scenarios where the application's performance is already optimized for token usage and latency without needing external enhancements.
- - For applications that do not benefit from new features like entity linking, temporal reasoning, and multi-signal retrieval.

## When NOT to use planning-with-files

- - If your project involves small-scale or short-term coding tasks that do not require long-running, persistent planning.
- - When working in environments with high security constraints where file-based persistence is restricted and cloud-based solutions are more appropriate.
- - For projects that prioritize real-time interactions over long-term task continuity; Planning with Files may introduce additional latency due to file operations.

## Common questions

### What is the difference between mem0 and planning-with-files?

mem0: Universal memory layer for AI Agents. planning-with-files: Persistent file-based planning for AI coding agents. See the comparison table for live GitHub stats and shared categories.

### When should I choose mem0 over planning-with-files?

Choose mem0 over planning-with-files when License: mem0 is Apache-2.0, planning-with-files is MIT; Pricing: The repository mentions an Apache-2.0 license but pricing information is not provided.; Requirements: While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations.; Planning with Files and mem0 both address the need for persistent context in AI agents but through different mechanisms: Planning with Files focuses on maintaining task plans via files while mem0 acts as a universal memory layer; Tags unique to mem0: genai, agents, llm, python; Also covers Data & Retrieval; - When developing AI applications where enhancing the efficiency of memory retention is crucial.
- If your project requires state-of-the-art performance across various benchmarks like LoCoMo and Long.

### When should I choose planning-with-files over mem0?

Choose planning-with-files over mem0 when License: planning-with-files is MIT, mem0 is Apache-2.0; Pricing: Available under the MIT license, Planning with Files is free and open-source for download and use. There are no direct costs associated with using the tool itself, but it relies on third-party AI-codE; Requirements: Min 2 GB RAM; - Requires Python to operate effectively.; - Works across a broad spectrum of AI coding agents including Claude Code, Codex CLI, and over sixty other compatible platforms via the SKILL.md standard.; Planning with Files and mem0 both address the need for persistent context in AI agents but through different mechanisms: Planning with Files focuses on maintaining task plans via files while mem0 acts as a universal memory layer; Tags unique to planning-with-files: agent-skills, long-running-tasks, file-based-planning, persistent-planning; Also covers Developer Tools; - When working on projects that require prolonged and uninterrupted AI-assisted development, such as large-scale software engineering or research initiatives.

### When should I avoid mem0?

- If your project does not require long-term memory management or advanced state management techniques. - In scenarios where the application's performance is already optimized for token usage and latency without needing external enhancements. - For applications that do not benefit from new features like entity linking, temporal reasoning, and multi-signal retrieval.

### When should I avoid planning-with-files?

- If your project involves small-scale or short-term coding tasks that do not require long-running, persistent planning. - When working in environments with high security constraints where file-based persistence is restricted and cloud-based solutions are more appropriate. - For projects that prioritize real-time interactions over long-term task continuity; Planning with Files may introduce additional latency due to file operations.

### Is mem0 or planning-with-files more popular on GitHub?

mem0 has more GitHub stars (60,369 vs 25,031). Stars measure visibility, not whether either tool fits your constraints.

### Are mem0 and planning-with-files open source?

Yes - both are open-source projects on GitHub (mem0: Apache-2.0, planning-with-files: MIT).

### Where can I find alternatives to mem0 or planning-with-files?

GraphCanon lists graph-backed alternatives at /tools/mem0ai-mem0/alternatives and /tools/othmanadi-planning-with-files/alternatives (/tools/mem0ai-mem0/alternatives.md, /tools/othmanadi-planning-with-files/alternatives.md), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at /compare/mem0ai-mem0-vs-othmanadi-planning-with-files.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, mem0 or planning-with-files?

mem0: Very active. planning-with-files: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for mem0 and planning-with-files?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mem0: /tools/mem0ai-mem0/trust; planning-with-files: /tools/othmanadi-planning-with-files/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=mem0ai-mem0`](/api/graphcanon/graph?tool=mem0ai-mem0)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_