local-deep-research vs ray
local-deep-research and ray share 44 technologies: anyio, FastAPI, gitdb, s3transfer, starlette, uvicorn, werkzeug, AIOHTTP, soupsieve, Rich, peewee, alembic, id, keyring, pyjwt, tiktoken, LangChain, docutils, markdown-it-py, mdurl, nh3, readme-renderer, ruamel-yaml, ruamel-yaml-clib, xmltodict, beautifulsoup4, defusedxml, jinja2, lxml, markupsafe, markdown, annotated-types, jsonschema, jsonschema-specifications, referencing, rfc3986, Pydantic, boto3, botocore, docker, OpenTelemetry, opentelemetry-instrumentation, opentelemetry-semantic-conventions, and opentelemetry-util-http. Both are written in Python.
What they share and what sets them apart
local-deep-research
~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
Python9.6kRead on November 14, 2026, at commit 3d6e615What is local-deep-research built with?Only in local-deep-research
OpenAI- Bootstrap
- Chart.js
chartjs-adapter-date-fns
chartjs-plugin-annotation
- bootstrap-icons
- Font Awesome
- SQLAlchemy
- ecdsa
langchain-openai
- faiss-cpu
langchain-ollama
langchain-anthropic
crawl4ai
- MCP
sentence-transformers
- pdfplumber
pypdf
- dompurify
- extruct
highlight.js
- html2canvas
jspdf
- justext
katex
marked
- marked-katex-extension
- readabilipy
unstructured
beartype
- schema
opentelemetry-instrumentation-requests
opentelemetry-instrumentation-threading
structlog
In common
- anyio
- FastAPI
- gitdb
- s3transfer
starlette
uvicorn
werkzeug
- AIOHTTP
- soupsieve
- Rich
peewee
- alembic
- id
- keyring
- pyjwt
- tiktoken
- LangChain
- docutils
markdown-it-py
- mdurl
- nh3
- readme-renderer
ruamel-yaml
ruamel-yaml-clib
- xmltodict
- beautifulsoup4
- defusedxml
- jinja2
lxml
markupsafe
- markdown
- annotated-types
- jsonschema
- jsonschema-specifications
- referencing
- rfc3986
- Pydantic
- boto3
- botocore
- docker
- OpenTelemetry
opentelemetry-instrumentation
opentelemetry-semantic-conventions
opentelemetry-util-http
ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Python44.3kRead on November 13, 2026, at commit de4c576What is ray built with?Only in ray
- aimrecords
- aimrocks
- amqp
- array-record
- arro3-core
- async-lru
- azure-common
- azure-datalake-store
- azure-storage-blob
confluent-kafka
- delta-kernel-rust-sharing-wrapper
- diskcache
expiringdict
- feather-format
google-cloud-core
h5py
- huey
jupyter-cache
kombu
- partd
petastorm
pickleshare
- py-partiql-parser
- pylance
- pymongo
- qpd
- s3torchconnectorclient
- semidbm
- sqlitedict
taskiq
tensorboard-data-server
- zict
pyarrow
mcap
- aioboto3
- MongoDB
- Redis
- adlfs
pyiceberg
- smart-open
clickhouse-connect
daft
deltalake
- gcsfs
lance-namespace
- obstore
- Celery
delta-sharing
- fastavro
hudi
- pyzmq
- s3fs
- s3torchconnector
- taskiq-redis
zarr
- Hugging Face
- Google Cloud Storage
- Kafka
- Databricks
- adagio
- asgiref
- Flask
- flask-cors
- graphene
- graphql-core
- graphql-relay
- jupyter-core
jupyterlab-server
pygame
pymars
- Ray
pyspark
- Lightning
raydp
tornado
- React
react-router-dom
- accessible-pygments
- alabaster
- cycler
fonttools
jupyterlab-pygments
pydata-sphinx-theme
sphinx-design
- tinycss2
- aim-ui
- alive-progress
bokeh
contourpy
- glfw
halo
- jupyter-server-terminals
- jupyterlab-widgets
- matplotlib-inline
- Plotly
prompt-toolkit
pyglet
pyopengl
- spinners
- Textual
- widgetsnbextension
- xyzservices
- Gradio
- ipywidgets
- pydot
sqlglot
- adal
- argon2-cffi
- argon2-cffi-bindings
- azure-identity
- flask-basicauth
- gcs-oauth2-boto-plugin
google-auth-oauthlib
- google-oauth
- google-reauth
itsdangerous
joserfc
- jwcrypto
- msal
- msal-extensions
- oauth2client
oauthlib
- pyu2f
requests-oauthlib
- cryptography
google-auth-httplib2
- aws-requests-auth
- albucore
- autograd
ax-platform
- bayesian-optimization
- bitsandbytes
botorch
- cma
cmdstanpy
- crcmod
cupy-cuda100
cupy-cuda12x
cupy-cuda13x
- einops
- fairscale
- fbgemm-gpu
- gpytorch
gradio-client
- gym
jaxlib
- labmaze
- lightning-utilities
- linear-operator
- llvmlite
lm-eval
- minigrid
- ml-dtypes
- moocore
mosaicml
nltk
- ONNX
onnx-ir
- onnxruntime
onnxscript
openai-whisper
- opt-einsum
optax
- orbax-checkpoint
peft
prophet
pymoo
pyro-ppl
- pytorch-ranger
rouge-score
- sacrebleu
safetensors
scikit-image
- sentencepiece
skops
stanio
statsforecast
tf-keras
timm
tokenizers
- torch-geometric
- torch-optimizer
- torch-scatter
- torch-sparse
torchtext
- triton
- yahp
- gymnasium
- torch
- transformers
- TensorFlow
jax
torchvision
- xgboost
lightgbm
- scikit-learn
- MLflow
- datasets
- pytorch-lightning
- comet-ml
- dm-env
flax
- Keras
- torchmetrics
- aim
ale-py
configspace
deepspeed
diffusers
- dm-control
- hpbandster
- hyperopt
- nevergrad
- open-spiel
- Optuna
tensorflow-probability
- zoopt
- bleach
et-xmlfile
imageio
imageio-ffmpeg
- imagesize
- jsonlines
- linkify-it-py
- mako
- mdit-py-plugins
mistune
- moviepy
myst-parser
- nbconvert
- openpyxl
- pandocfilters
- pytablewriter
- strictyaml
- tinyscaler
webencodings
- xlrd
pillow
- decord
- nbformat
- torchcodec
- fastjsonschema
- fqdn
- jsonschema-path
- openapi-spec-validator
- pathvalidate
- rfc3339-validator
- rfc3986-validator
- typepy
- validators
- aiobotocore
- applicationinsights
- aws-sam-translator
aws-xray-sdk
- azure-cli-core
- azure-cli-telemetry
- azure-core
- azure-mgmt-compute
- azure-mgmt-core
- azure-mgmt-network
- azure-mgmt-resource
- azure-mgmt-resource-deployments
- boto
- cloud-tpu-diagnostics
- colorlog
- execnet
google-cloud-audit-log
- Gunicorn
- log-symbols
- ml-goodput-measurement
- msrestazure
- opencensus-context
opentelemetry-instrumentation-asgi
- python-json-logger
- supervisor
psutil
prometheus-client
- pybuildkite
kubernetes
opentelemetry-exporter-prometheus
opentelemetry-exporter-otlp-proto-grpc
opentelemetry-instrumentation-fastapi
- ray-haproxy
- typeface-roboto
- MUI
usehooks-ts
- lowlight
react-window
react-icons
- @mui/icons-material
- js-yaml
Side by side
Frequently asked questions
Which is bigger, local-deep-research or ray?
The proof: LearningCircuit/local-deep-research's code at commit 3d6e615
Which has more GitHub stars, local-deep-research or ray?
The proof: ray-project/ray on GitHub
Which has more tests, local-deep-research or ray?
The proof: LearningCircuit/local-deep-research's code at commit 3d6e615
Which was updated more recently, local-deep-research or ray?
The proof: LearningCircuit/local-deep-research's commits on GitHub
Which has more commits signed by AI, local-deep-research or ray?
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