agentscope vs langchain

langchain is much bigger: 145.7k stars against 30.7k. Over the days we have tracked them agentscope moved +16.3% and langchain +2.6%, so agentscope is growing faster right now.

They split the axes: agentscope leads on context depth and noise control and customization, langchain on model freedom and setup ease.

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 production-ready agent framework offering multi-tenancy, fine-grained permission controls, sandboxed workspaces, and an extensible middleware/event system for building trustworthy agent services.

Stars
30.7k
Tracked growth
+16.3%
Maturity
Last commit
1d ago
Language
Python
License
Apache-2.0
Cost to run
Requires a model provider API key (provider-dependent).

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
145.7k
Tracked growth
+2.6%
Maturity
Last commit
2h ago
Language
Python
License
MIT
Cost to run
Your API key, per-provider pricing
0%+16%90 tracked days
agentscope-ai/agentscopelangchain-ai/langchain

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.

Axisagentscopelangchain
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 + related files
Diff/file only
Noise control
How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only.
Human-in-the-loop
None mentioned
Customization
How far it bends to your team: custom rules, prompts, style guides, per-path config.
Extensible middleware & tools
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.
Provider-specific
Multiple providers
Setup ease
What it takes to get a first useful run out of it.
API key + config
One-command start

Which one to pick

Pick agentscope if…

Production-ready — choose AgentScope when you need a deployable, multi-tenant agent platform with sandboxed workspaces and fine-grained permission controls.

  • Context depth: Diff + related files (3/5 against 1/5)
  • Noise control: Human-in-the-loop (3/5 against 1/5)
  • Customization: Extensible middleware & tools (5/5 against 3/5)
Runs in cli, web-app. Works with other-fixed.

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)
  • Setup ease: One-command start (5/5 against 3/5)
Runs in cli, ide, ci, web-app. Works with byok, openai, anthropic, gemini.

What people want from each one

Questions people ask

Is agentscope better than langchain?

They split the axes: agentscope leads on context depth and noise control and customization, langchain on model freedom and setup ease. agentscope is worth picking when production-ready — choose AgentScope when you need a deployable, multi-tenant agent platform with sandboxed workspaces and fine-grained permission controls.

Which of agentscope and langchain keeps my code private?

agentscope: Self-hostable (4/5). langchain: Cloud-key usage (3/5).

What does each one cost to run?

agentscope: Requires a model provider API key (provider-dependent).. langchain: Your API key, per-provider pricing.

Full profiles: agentscope-ai/agentscope and langchain-ai/langchain. Everything else in Agent SDKs & frameworks.