AI tool comparison
Plain vs SMF (Semantic Memory Filesystem)
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Plain
A Django fork rebuilt for AI agents — typed, predictable, agent-readable
75%
Panel ship
—
Community
Free
Entry
Plain is a full-stack Python web framework that forks Django with one overriding goal: make the codebase maximally readable and understandable by AI coding agents. Built by Dropseed (Adam Engebretson), it started in 2023 and has quietly matured into a production-ready framework — today's Show HN submission (93 points) brought it to wider attention. The design philosophy is radical clarity over magic. Plain eliminates Django's more implicit behaviors, adds strict typing throughout, and includes built-in AI integration hooks: a `.claude/rules/` directory for Claude Code context, a CLI command for on-demand documentation retrieval, and OpenTelemetry instrumentation out of the box. The idea is that when a coding agent touches your codebase, it should be able to understand what's happening without fighting through Django's layers of metaclass magic. This represents a genuine philosophical bet: as AI agents write more of our code, the framework's readability to machines matters as much as its readability to humans. Plain is ahead of the curve on this — most frameworks were designed for human ergonomics first. The Show HN traction suggests senior engineers are taking the concept seriously, even if migration from Django remains a real cost.
Developer Tools
SMF (Semantic Memory Filesystem)
Your filesystem IS the vector database for AI agents
75%
Panel ship
—
Community
Paid
Entry
SMF (Semantic Memory Filesystem) is an open-source Python library that treats the POSIX filesystem as the native memory infrastructure for AI agents. The core bet: instead of standing up a vector database, embedding service, and retrieval pipeline, you model your agent's memory as ordinary directories, files, and symlinks — then use the OS's own tools for retrieval. Entities are directories, relationships are symlinks, metadata is file attributes, and search is built on grep and find. The appeal is radical simplicity. Every developer already understands the filesystem. Memory built on top of it is inspectable with any editor, versionable with git, and portable across machines with rsync. There's no new query language to learn, no vector index to maintain, and no external service to keep running. Dynamis-Labs argues that for many agent memory use cases, semantic similarity search is overkill — you need entity graphs and efficient lookup, which the filesystem already provides. With only 7 stars and created yesterday (April 14), SMF is in very early stages. But the approach has attracted immediate discussion from developers frustrated with the operational overhead of vector databases for relatively structured memory tasks. It's a contrarian bet that's worth watching.
Reviewer scorecard
“The `.claude/rules/` integration and typed APIs are exactly what you want when you're letting agents modify your codebase. OTel built-in is a legitimate win — no more strapping on tracing as an afterthought. If you're starting a new Python project in 2026, Plain is worth serious consideration.”
“I've been burned too many times by embedding pipelines that drift when models update and vector indexes that mysteriously degrade. Filesystem-native memory is zero-dependency, trivially inspectable, and you can version it with git. For structured agent memory this is genuinely compelling.”
“Django's 'magic' is also its ecosystem — 20 years of packages, tutorials, and institutional knowledge. Plain's ecosystem is tiny. For any non-trivial project, you'll hit the ecosystem wall fast. 'Designed for agents' is a compelling narrative but the migration cost from Django is real and steep.”
“The filesystem approach breaks down the moment you need fuzzy semantic matching — 'find memories related to customer churn' doesn't map to a grep. For anything beyond exact lookup, you're going to bolt on a vector DB anyway and now you have two systems. This is clever for toy agents, not production.”
“The question 'is this codebase understandable to an AI agent?' is going to be central to framework design by 2027. Plain is three years ahead of that conversation. Frameworks that don't add agent-readability features will be retrofitting them later at significant cost.”
“The insight that the filesystem is a perfectly good entity-relationship store is underappreciated. As agents move toward local-first architectures, having memory that's portable, inspectable, and git-versionable becomes a serious advantage over cloud-hosted vector DBs.”
“As someone who ships products, not just writes code, I care about the full stack being coherent. Plain's opinionated structure means less time arbitrating between packages and more time building. The built-in OTel means I can debug AI-assisted changes without adding another tool.”
“I love tools that demystify AI plumbing. The idea that agent memory could just be files I can open in a text editor makes the whole system feel less like a black box. This is the kind of transparency that builds trust.”
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