nango
gorilla-cli
nango | gorilla-cli | |
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33 | 11 | |
4,207 | 1,158 | |
5.0% | 3.5% | |
9.9 | 5.5 | |
3 days ago | 3 months ago | |
TypeScript | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nango
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Launch HN: Nango (YC W23) – Open-Source Unified API
2 min demo video: https://www.loom.com/share/d04c67b47e284e86b91b4b99fba548ec
SaaS engineering teams face a tough choice: they can build each integration in-house from scratch, which gives them full control but takes a lot of time and maintenance effort. Or they can use pre-built solutions, which are fast and easy but less flexible and might not fulfill all customer needs.
Nango combines the best of both worlds. We let you quickly ship custom integrations without building complex infrastructure or diving deep into the quirks of each API. You control the business logic, data models, and customer-specific configurations, like custom field mappings. We handle (O)Auth and run your integrations reliably in production.
Under the hood, your integrations run as typescript “lambdas” on Nango. A typical integration has 3-5 lambdas of 20-50 lines of code each. These lambdas live inside your git repo, are version-controlled with the rest of your app, and get deployed to Nango with a CLI (https://docs.nango.dev/understand/core-concepts).
Our runtime has a built-in scheduler for continuous background syncs, monitoring to know if your integrations run as expected, detailed logging of everything that happens in Nango, and pre-built infrastructure to deal with (O)auth, retries, rate-limit handling, webhook floods, data caching, de-duplication, etc. More here: https://docs.nango.dev/understand/architecture
We have found that ChatGPT and Copilot let you build integrations on Nango very fast without having to learn each API’s intricacies. LLMs are great at figuring out which endpoint to use, what parameters it takes, etc. Paired with our runtime, this lets you build complex, high-scale integrations in hours instead of weeks.
We’ve put a ton of effort into dealing with API complexities, so you don’t have to. Even integrations that looked simple at first ended up forcing us to extend our infra to deal with their quirks and gotchas.
For example, we had to figure out 100+ different OAuth implementations (see https://www.nango.dev/blog/why-is-oauth-still-hard and https://news.ycombinator.com/item?id=35713518). We had to deal with a half-dozen non-standard auth methods (Github apps, Stripe apps, Netsuite, etc.), expiring webhooks, ways to deal with data dependencies, weird pagination methods, API keys that change with every API call, dozens of different ways to register for webhooks, etc. It’s a constantly moving target, but it is a challenge we have come to love, and we think the approach makes sense: we specialize in finicky details that vary from API to API—you specialize in making your product great and offering more integrations to your users.
The fastest way to see Nango in action is with our interactive demo here (no signup required): https://app.nango.dev/hn-demo
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Show HN: Nango – Open unified API for product integrations
Back in August I queried [1] your usage of "open source" while not being an open source project (ELv2 licensed). It looks like you're no longer describing yourself as "100% Open Source" which is good but you still label yourself as open source in the repo readme and still refer to yourself as open source on the website. Do you intend to keep labelling yourself as open source or is that something you're moving away from?
[1] https://github.com/NangoHQ/nango/issues/900
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Show HN: Revert – open-source unified API for product integrations
https://www.nango.dev founder here.
I think the biggest difference is that Nango lets you customize & extend the unified APIs on the platform.
Usually unified APIs mitigate their limited catalog with passthrough/proxy requests. But this is a partial solution, since you go back to having a lot of integration logic in your code base.
With Nango these customizations live in the unified API itself and benefit from all the infrastructure available there (OAuth, rate-limit handling, pagination, de-duplication of records, etc.).
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Show HN: Poozle – open-source Plaid for LLMs
Definitely a difficult problem you're taking on here, but I don't see anything specific to LLMs here? How or why are you marketing towards LLMs?
How do you compare to the larger players here already Nango[0] and Merge[1] ?
I'm curious how you're thinking about data access / staleness? It's great that you're handling the oauth dance, but does that mean every end user of the product has to auth every product they interface with or are you handling this all at the super admin / enterprise level?
Right now I think there's too much emphasis on the "data loading" aspect of LLMs. I expect to see a swing back into using 3rd party API's SDKs. Interested to hear your thoughts on the Google API, it's absolutely massive and trying to shoehorn that into a unified API scares me.
The only real player that I could see to launch something like this and be successful is Okta.
[0] - https://github.com/NangoHQ/nango
- Ask HN: Suggest open source alternative to Unified API providers like Merge.dev
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Why is OAuth still hard in 2023?
What you describe is pretty much what we build at https://github.com/NangoHQ/nango, would be great to incorporate your learnings :)
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Buy vs. Build: Share your journey on choosing between purchasing or developing integration components
You can mix & match these components as needed to build your own custom integrations fast. In brief, we are building an open-source platform for product integrations.
- Nango
- Nango: Pre-built OAuth flows & token refreshes for 50+ APIs (open-source, written in TypeScript on node)
- Show HN: Open-source OAuth service for 40+ APIs
gorilla-cli
- FLaNK 15 Jan 2024
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Show HN: Shell-AI, run shell commands with natural language
Hello HN! I know this project is a super simple wrapper around LangChain/OpenAI but I just found myself wanting this badly myself: a super simple `pip install` package that I can use to get command suggestions within the terminal as I'm being productive doing other things.
The implementation is literally one short glue of LangChain and InquirerPy for interactive CLI.
I'm curious which ideas you all have to make this smarter/better. MIT licensed, if you're keen on contributing please feel free to do so. It's a pure hobby project for me.
Some key objectives: never automatically run shell code, I want to see what I run before I run it, present me with some alternatives, a simple path to using local models in the future (Llama 2 Code soon?).
Will add I was inspired by the great https://github.com/gorilla-llm/gorilla-cli project, but didn't like that it sent the prompt to some IP based endpoint.
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Show HN: Poozle – open-source Plaid for LLMs
Very cool product! Have you consider relying on Gorilla for integrations?
https://github.com/gorilla-llm/gorilla-cli
- FLaNK Stack Weekly for 07August2023
- Show HN: Lemon AI – open-source Zapier NLA to empower agents
- GitHub - gorilla-llm/gorilla-cli: LLMs for your CLI (cum să faci operations doar în limba engleză)
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30-Jun-2023
gorilla-cli: LLMs for your CLI (https://github.com/gorilla-llm/gorilla-cli)
- Gorilla-CLI: LLMs for CLI including K8s/AWS/GCP/Azure/sed and 1500 APIs
What are some alternatives?
vault-plugin-secrets-oauthapp - OAuth 2.0 secrets plugin for HashiCorp Vault supporting a variety of grant types
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
revert - Revert makes it incredibly easy to build integrations with any third party API
FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
feeds - Collection of Dash docset feeds
shell_gpt - A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.
afum - audio file upload manager gui for tttweb
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
sitcom-simulator-cli - A tool that combines GPT-3, Stable Diffusion, and FakeYou to create fully automated video. [Moved to: https://github.com/joshmoody24/sitcom-simulator]
CallCMLModel - An example on calling models deployed in CML
fatsecret-unofc-api - Food Bank HTTP API, provide you with food information, calorie, fat, etc. Powered by Fat Secret webpage.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.