mcp vs ray
mcp and ray share 22 technologies: pymongo, pyarrow, MongoDB, pyiceberg, daft, FastAPI, starlette, uvicorn, sqlglot, jinja2, beautifulsoup4, defusedxml, lxml, ruamel-yaml, openapi-spec-validator, Pydantic, docker, python-json-logger, botocore, boto3, kubernetes, and psutil. Both are written in Python.
What they share and what sets them apart
mcp
Open source MCP Servers for AWS
Python9.9kRead on November 14, 2026, at commit 5d58a6aWhat is mcp built with?Only in mcp
- cassandra-driver
psycopg
- valkey-glide
- Postgres
- asyncmy
- ibm-db
oracledb
pika
- pymssql
- InfluxDB
fastmcp
authlib
- bcrypt
- langdetect
OpenAI- MCP
- bedrock-agentcore
- python-frontmatter
- readabilipy
- markdownify
- markitdown
- pdf2image
- pdfplumber
prance
- coloredlogs
loguru
In common
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
- gitdb
google-cloud-core
h5py
- huey
jupyter-cache
kombu
- partd
petastorm
pickleshare
- py-partiql-parser
- pylance
- qpd
- s3torchconnectorclient
- s3transfer
- semidbm
- sqlitedict
taskiq
tensorboard-data-server
- zict
mcap
- aioboto3
- Redis
- adlfs
- smart-open
clickhouse-connect
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
werkzeug
- Ray
- AIOHTTP
pyspark
- Lightning
raydp
- anyio
tornado
- React
react-router-dom
- accessible-pygments
- alabaster
- cycler
fonttools
jupyterlab-pygments
pydata-sphinx-theme
- soupsieve
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
- Rich
- pydot
- alembic
peewee
- adal
- argon2-cffi
- argon2-cffi-bindings
- azure-identity
- flask-basicauth
- gcs-oauth2-boto-plugin
google-auth-oauthlib
- google-oauth
- google-reauth
- id
itsdangerous
joserfc
- jwcrypto
- keyring
- msal
- msal-extensions
- oauth2client
oauthlib
- pyjwt
- 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
- LangChain
- 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
- tiktoken
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
- markdown
markdown-it-py
markupsafe
- mdit-py-plugins
- mdurl
mistune
- moviepy
myst-parser
- nbconvert
- nh3
- openpyxl
- pandocfilters
- pytablewriter
- readme-renderer
ruamel-yaml-clib
- strictyaml
- tinyscaler
webencodings
- xlrd
- xmltodict
pillow
- docutils
- decord
- nbformat
- torchcodec
- annotated-types
- fastjsonschema
- fqdn
- jsonschema-path
- jsonschema-specifications
- pathvalidate
- referencing
- rfc3339-validator
- rfc3986
- rfc3986-validator
- typepy
- validators
- jsonschema
- 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
opentelemetry-instrumentation-asgi
opentelemetry-semantic-conventions
opentelemetry-util-http
- supervisor
prometheus-client
- pybuildkite
- OpenTelemetry
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, mcp or ray?
The proof: ray-project/ray's code at commit de4c576
Which has more GitHub stars, mcp or ray?
The proof: ray-project/ray on GitHub
Which has more tests, mcp or ray?
The proof: ray-project/ray's code at commit de4c576
Which was updated more recently, mcp or ray?
The proof: awslabs/mcp's commits on GitHub
Which has more commits signed by AI, mcp or ray?
See inside your code, today.
Paste your repo and see it drawn, part by part: a medium one in under a minute.
Free, no account.

