OthmanAdi/planning-with-files
planning-with-files
Persistent file-based planning for AI coding agents and long-running agentic tasks. Crash-proof markdown plans that survive context loss and /clear, plus a deterministic completion gate and multi-agent shared state on disk. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard.
Usage guide
planning-with-files is an open-source project around agent-skills, agentic-ai, ai-agents with 24,073 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating planning-with-files for Python AI workflows.
- Comparing a GitHub project with 24,073 stars and current repository activity.
Pros
- planning-with-files has visible GitHub traction with 24,073 stars. Topics: agent-skills, agentic-ai, ai-agents.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
planning-with-files should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
planning-with-files architecture preview
planning-with-files's main path starts at the entry surface, runs through Coding agent runtime, combines OpenAI / Claude, Files / repository context, GitHub, and returns Code changes / developer feedback.
Entry
CLI / terminal entry
planning-with-files is primarily entered through a developer command or terminal workflow.
git clone https://github.com/OthmanAdi/planning-with-files.git
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
OpenAI / Claude
Model calls are likely routed through OpenAI, Claude based on README and topic signals.
OpenAI, Claude
Context
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Code changes / developer feedback
The final result is code edits, explanations, repository actions, or developer-facing feedback.
coding output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
planning-with-files depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/OthmanAdi/planning-with-files.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Persistent file-based planning for AI coding agents and long-running a
This is one of the documented reasons to evaluate planning-with-files before choosing a stack.
Focus area: agent-skills
This is one of the documented reasons to evaluate planning-with-files before choosing a stack.
AI Agents project comparison
Compare planning-with-files with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official planning-with-files setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented planning-with-files example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is planning-with-files?
planning-with-files is an open-source ai agents project. Persistent file-based planning for AI coding agents and long-running agentic tasks. Crash-proof markdown plans that survive context loss and /clear, plus a deterministic completion gate and multi-agent shared state on disk. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard.
How do I install planning-with-files?
Start with the official README. The first detected setup step is: git clone https://github.com/OthmanAdi/planning-with-files.git.
Is planning-with-files beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can planning-with-files be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does planning-with-files need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt planning-with-files?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.