8 Best planning-with-files Alternatives in 2026 (Open Source)

planning-with-files — Persistent file-based planning for AI coding agents and long-running tasks. Crash-proof markdown plans, session recovery after /clear and compaction, per-turn r. File-based planning that persists on disk and re-injects every turn, unlike context-window-dependent to-do lists that disappear with memory resets.

These 8 open-source tools do the same job. They are ordered by how closely they match planning-with-files, with live GitHub data so you can see which projects are actively maintained.

1. 1. Claude-Mem

   Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context b

   What sets it apart: Provides a dedicated memory compression and injection system specifically for AI coding agents across multiple platforms.

   Best for: Maintaining project context across AI coding sessions; Teams using multiple AI coding agents; Long-term development projects requiring continuity

   ⭐ 95.0kplanning-with-files vs Claude-Mem →Full profile →

2. 2. memvid

   Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

   What sets it apart: Packages complete memory system into a single portable file without requiring databases or complex infrastructure.

   Best for: Long-running AI agents; Offline-first AI systems; Auditable AI workflows

   ⭐ 16.6kplanning-with-files vs memvid →Full profile →

3. 3. memU

   Personal memory across agents

   What sets it apart: Core memory logic is only 500 lines, making it compact enough to inspect, understand, and adapt.

   Best for: Users running multiple AI coding agents; Teams needing shared knowledge across agents; Maintaining consistent memory across development sessions

   ⭐ 14.5kplanning-with-files vs memU →Full profile →

4. 4. Context Mode

   Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via

   What sets it apart: Solves context window bloat by sandboxing tool output and maintaining session memory without re-injecting data into the context.

   Best for: Teams building AI coding agents; Developers needing to manage agent context and memory; Projects where tool output bloats the context window

   ⭐ 24.4kplanning-with-files vs Context Mode →Full profile →

5. 5. claude-context

   Code search MCP for Claude Code. Make entire codebase the context for any coding agent.

   What sets it apart: Uses semantic search to provide relevant code context to AI coding agents instead of loading entire codebases, making it cost-effective for large projects.

   Best for: Developers using Claude Code; Teams with large codebases; AI coding agent users needing code context

   ⭐ 12.6kplanning-with-files vs claude-context →Full profile →

6. 6. code-review-graph

   Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked contex

   What sets it apart: Provides AI coding tools with a local, persistent structural map of the codebase to drastically reduce the context they need to read.

   Best for: Reducing context size for AI code reviews; Improving AI coding tool performance on large repositories; Providing precise code change context to assistants

   ⭐ 31.9kplanning-with-files vs code-review-graph →Full profile →

7. 7. Graft

   Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase.

   What sets it apart: Builds a persistent, actionable knowledge graph from your codebase that coding agents use to avoid redundant exploration.

   Best for: Teams using coding agents for development; Reducing costs and latency of coding agents; Maintaining context across agent sessions

   ⭐ 9.4kplanning-with-files vs Graft →Full profile →

8. 8. CowAgent

   Open-source personal AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, multi-c

   What sets it apart: Combines multi-agent collaboration, three-tier memory architecture with automatic distillation, and self-evolution capabilities in a lightweight, extensible open-source framework.

   Best for: Developers building personal AI assistants; Teams implementing multi-agent systems; Projects requiring long-term memory and knowledge management

   ⭐ 47.2kplanning-with-files vs CowAgent →Full profile →