Mirrors_Framework/awesome-python
1
0
Fork 0
mirror of https://github.com/vinta/awesome-python.git synced 2026-10-02 08:23:10 +08:00
Code Issues Packages Projects Releases Wiki Activity

docs: name Mem0's license by family in ai-and-agents intro

Browse Source 
The intro named Mem0's license with a version (Apache 2.0) where intros name only the family.

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Vinta Chen
2026-09-27 09:10:23 +08:00
co-authored by Claude
parent a942b13d06
commit ffcdff3053
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files
website/data/category_intros/ai-and-agents.md
+1 -1
View File
@@ -29,7 +29,7 @@ DSPy has you [write signatures, not prompts](https://dspy.ai/). Give it examples
LlamaIndex is a [data framework](https://github.com/run-llama/llama_index) for LLM apps: it loads, indexes, and queries your documents. Install `llama-index` to start, or `llama-index-core` plus only the integrations you need. LlamaIndex is a [data framework](https://github.com/run-llama/llama_index) for LLM apps: it loads, indexes, and queries your documents. Install `llama-index` to start, or `llama-index-core` plus only the integrations you need.
Mem0 adds [memory that persists across sessions](https://docs.mem0.ai/). Self-host the open-source version, or use the managed platform. OpenViking is [AGPL-licensed](https://github.com/volcengine/OpenViking/blob/main/LICENSE), where Mem0 is Apache 2.0. Mem0 adds [memory that persists across sessions](https://docs.mem0.ai/). Self-host the open-source version, or use the managed platform. OpenViking is [AGPL-licensed](https://github.com/volcengine/OpenViking/blob/main/LICENSE), where Mem0 is Apache-licensed.
Transformers runs pre-trained models from the Hugging Face Hub. Start with [`pipeline()`](https://huggingface.co/docs/transformers/pipeline_tutorial): pick a task and a model, and it handles preprocessing and output. Diffusers works the same way for [diffusion models](https://huggingface.co/docs/diffusers/index). Transformers runs pre-trained models from the Hugging Face Hub. Start with [`pipeline()`](https://huggingface.co/docs/transformers/pipeline_tutorial): pick a task and a model, and it handles preprocessing and output. Diffusers works the same way for [diffusion models](https://huggingface.co/docs/diffusers/index).