deepseek-harness vs langchain
The two are close in size: 223.4k stars for deepseek-harness, 146.3k for langchain. Over the days we have tracked them deepseek-harness moved +109.9% and langchain +3.1%, so deepseek-harness is growing faster right now.
They split the axes: deepseek-harness leads on customization, langchain on model freedom.
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
A plugin-first agent harness where every component is a plugin, enabling highly composable agent workflows.
- Stars
- 223.4k
- Tracked growth
- +109.9%
- Maturity
- ●●●●●
- Last commit
- 3d ago
- Language
- TypeScript
- License
- MIT
- Cost to run
- Free, self-hosted
A broad, production-ready agent framework with a large ecosystem of integrations and modular abstractions to swap models, tools, and retrievers without rewriting application code.
- Stars
- 146.3k
- Tracked growth
- +3.1%
- Maturity
- ●●●●●
- Last commit
- 2h ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key, per-provider pricing
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 | deepseek-harness | langchain |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff only | ●●●●● Diff/file only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● No controls | ●●●●● None mentioned |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Plugin-based | ●●●●● Config & prompts |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Cloud-key usage |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Fixed provider only | ●●●●● Multiple providers |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-command start | ●●●●● One-command start |
Which one to pick
Pick deepseek-harness if…
Extensible plugin architecture — pick this when you want a locally runnable, highly modular agent harness you can extend with custom plugins.
- Customization: Plugin-based (5/5 against 3/5)
Pick langchain if…
Model interoperability — Swap providers and integrations easily to prototype and productionize LLM applications with minimal code changes.
- Model freedom: Multiple providers (3/5 against 1/5)
What people want from each one
langchain-ai/langchain
Hacker News: Why we no longer use LangChain for building our AI agents drew 480 points and 297 comments.
Questions people ask
Is deepseek-harness better than langchain?
They split the axes: deepseek-harness leads on customization, langchain on model freedom. deepseek-harness is worth picking when extensible plugin architecture — pick this when you want a locally runnable, highly modular agent harness you can extend with custom plugins.
Which of deepseek-harness and langchain keeps my code private?
deepseek-harness: Self-hostable (4/5). langchain: Cloud-key usage (3/5).
What does each one cost to run?
deepseek-harness: Free, self-hosted. langchain: Your API key, per-provider pricing.
Full profiles: deepseek-ai/deepseek-harness and langchain-ai/langchain. Everything else in Agent SDKs & frameworks.