anything-llm vs dify
dify is much bigger: 154.5k stars against 65.6k. Over the days we have tracked them anything-llm moved +3.5% and dify +3.6%, so dify is growing faster right now.
anything-llm leads on context depth and noise control. dify does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
An all-in-one, local-first no-code agent platform that bundles chat, agents, vector DBs, desktop and web deployments, and dynamic model routing—beyond libraries that only provide primitives.
- Stars
- 65.6k
- Tracked growth
- +3.5%
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- JavaScript
- License
- MIT
- Cost to run
- Self-hosted (free); external LLM API costs apply.
Integrated low-code workspace combining visual AI workflows, RAG pipelines, agent builders, and broad multi-provider model support that can be self-hosted.
- Stars
- 154.5k
- Tracked growth
- +3.6%
- Maturity
- ●●●●●
- Last commit
- 27m ago
- Language
- TypeScript
- License
- NOASSERTION
- Cost to run
- Free cloud sandbox (200 GPT-4 calls); self-hosted on your infra.
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | anything-llm | dify |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Whole-repo analysis | ●●●●● Diff/file only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Basic routing & filters | ●●●●● None mentioned |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Rich rules & plugins | ●●●●● Prompt IDE & tools |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Runs fully local | ●●●●● Self-hostable / local |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Any provider or local | ●●●●● Bring-your-key / any |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Quick start / one-click | ●●●●● Docker Compose quickstart |
Which one to pick
Pick anything-llm if…
Privacy-first — runs locally by default with support for local LLMs and full self-hosting while still integrating cloud providers when needed.
- Context depth: Whole-repo analysis (5/5 against 1/5)
- Noise control: Basic routing & filters (3/5 against 1/5)
Pick dify if…
Easy deployment — run a full-featured, visual LLM app and agent platform locally or in your VPC using Docker Compose (or use Dify Cloud for a sandbox with free GPT‑4 calls).
What people want from each one
Mintplex-Labs/anything-llm
Hacker News: Show HN: AnythingLLM – Open-Source, All-in-One Desktop AI Assistant drew 368 points and 77 comments.
langgenius/dify
Hacker News: Dify, a visual workflow to build/test LLM applications drew 185 points and 38 comments.
Questions people ask
Is anything-llm better than dify?
anything-llm leads on context depth and noise control. dify does not take any axis by a clear margin. anything-llm is worth picking when privacy-first — runs locally by default with support for local LLMs and full self-hosting while still integrating cloud providers when needed.
Which of anything-llm and dify keeps my code private?
anything-llm: Runs fully local (5/5). dify: Self-hostable / local (5/5).
What does each one cost to run?
anything-llm: Self-hosted (free); external LLM API costs apply.. dify: Free cloud sandbox (200 GPT-4 calls); self-hosted on your infra..
Full profiles: Mintplex-Labs/anything-llm and langgenius/dify. Everything else in Low-code builders.