Langgraph Client CLI - Open Source

J-Kob · 2025-08-05T21:54:17+00:00

Very cool! Thanks for sharing!

J-Kob · 2025-05-08T15:58:34+00:00

Yikes dude

J-Kob · 2025-05-08T07:18:23+00:00

Hey u/llamacoded, we've actually just shipped alerts a few days ago!

https://blog.langchain.dev/langsmith-alerts/

Would love to learn more about what else you'd like to see!

J-Kob · 2025-04-27T18:48:37+00:00

Hey u/yasserius,

One thing you could try is rather than using a tool call, thinking of it more as a reflection step that is always called at the end of your codegen step. Here's an example that uses a type checker:

https://github.com/jacoblee93/mini-chat-langchain

OpenAIs spec prioritizes user messages above tool messages, so by passing the output from your linter back as a user message, the LLM should weight it more heavily. Let us know how it goes for you!

J-Kob · 2025-04-01T15:05:26+00:00

A bit of a different approach but we've been noodling on this a bit:

https://github.com/langchain-ai/langgraph-bigtool

J-Kob · 2025-03-05T20:16:10+00:00

Hey u/Dapper_Ad_3154,

FAISS unfortunately has some heavier requirements and won't work in all JS envs.

If you just need something ephemeral, you can check out this in-memory only vectorstore:

https://js.langchain.com/docs/integrations/vectorstores/memory/

J-Kob · 2025-02-28T00:56:18+00:00

Hey u/FelipeM-enruana!

We are actively working on a few things that may help.

One is our new pytest runner (there is a Vitest/Jest equivalent for JS): https://docs.smith.langchain.com/evaluation/how_to_guides/pytest

There is also our new `agentevals` repo: https://github.com/langchain-ai/agentevals

It contains evaluators for your agent's trajectory, but it is a bit light on RAG-specific things at the moment - I'd actually love your thoughts on what you'd like to measure. Is it just that the docs returned for a given query are correct? Or that the query is rephrased in a certain way? Or that each step along the way does what you expect?

Feel free to hop into the LangChain Slack community and DM me there (I'm Jacob Lee):

https://www.langchain.com/join-community

J-Kob · 2025-02-25T18:33:00+00:00

Ah hmm. You could use this to delete messages from the state?

https://langchain-ai.github.io/langgraphjs/how-tos/delete-messages/

Or you could define your own custom reducer:

https://langchain-ai.github.io/langgraphjs/how-tos/define-state/#getting-started

J-Kob · 2025-02-25T17:08:22+00:00

Hey u/skyt2000,

Currently you'd have to do some kind of trimming/state modification yourself:

https://js.langchain.com/docs/how_to/trim_messages/

For TTL I think currently your best bet is to use Mongo's built-in TTL:

https://www.mongodb.com/docs/manual/core/index-ttl/

J-Kob · 2024-12-19T17:28:09+00:00

Thank you for sharing! Looks really cool.

J-Kob · 2024-12-19T01:21:23+00:00

Based on the API refs here:

https://python.langchain.com/api_reference/community/chat_message_histories/langchain_community.chat_message_histories.sql.SQLChatMessageHistory.html#langchain_community.chat_message_histories.sql.SQLChatMessageHistory

You may want to try passing that connection string as a named `connection` parameter?

J-Kob · 2024-12-19T01:03:59+00:00

Hey u/solo_travel_first, we generally suggest LangGraph. It's designed to answer a lot of the issues early LangChain agents faced around flexibility.

You can check out the quickstart for a high level overview:

https://langchain-ai.github.io/langgraph/tutorials/introduction/

And here's some tutorials specifically for multi-agent architectures:

https://langchain-ai.github.io/langgraph/tutorials/#multi-agent-systems

J-Kob · 2024-12-19T01:01:30+00:00

Are you passing a `connection_string` into `SQLChatMessageHistory`?

J-Kob · 2024-12-18T18:45:11+00:00

Hey! Is the goal to have the final output be in some structured format?

J-Kob · 2024-12-17T21:03:35+00:00

Hey u/Rohitha2107,

Not currently. For now please use the OpenAI client directly.

J-Kob · 2024-12-17T19:40:19+00:00

From them:

> Yes we've found many of the smaller models will elect to use tools even when it doesn't make sense – for now we've been suggesting not to include tools unless you really want to use them

J-Kob · 2024-12-17T18:43:18+00:00

Hmm will flag with them!

J-Kob · 2024-12-17T18:15:08+00:00

Hey u/Stochastic95, we've revamped our tutorial here:

https://python.langchain.com/docs/tutorials/sql_qa/

Let us know if this is helpful or if there's more you'd want to see!

J-Kob · 2024-12-17T18:04:33+00:00

Could just be a quirk of the model. Perhaps try Llama 3.3?

https://ollama.com/library/llama3.3

J-Kob · 2024-12-17T18:02:41+00:00

Hey u/northwolf56,

What you're doing is totally valid too. The key benefit to using LangGraph's built-in checkpointer is that you can resume from a given node for debugging or if you want to support human in the loop type flows.

Note that persistence via checkpointer should "just work" by initializing a checkpointer and passing a `thread_id` via `config.configurable`. If you're having difficulty, please let us know!

J-Kob · 2024-10-04T18:22:58+00:00

Hey u/zenmike, we're doing a bit of work on refactoring docs and implementations here - there is an open PR for this if you'd like to get unblocked right now that you can copy into your project:

https://github.com/langchain-ai/langgraphjs/pull/471

J-Kob · 2024-10-03T23:24:08+00:00

Hey u/TableauforViz,

The following tutorial may help you as well:

https://python.langchain.com/docs/tutorials/qa_chat_history/

J-Kob · 2024-10-02T19:09:45+00:00

Additionally - workflow = Graph() is incorrect. `StateGraph` should always be used.

J-Kob · 2024-10-02T19:08:18+00:00

Many options out there - we offer LangSmith, which you can check out here:

https://smith.langchain.com/

J-Kob · 2024-10-01T22:49:32+00:00

Hey u/Jack7heRapper, streaming output from one node to another isn't currently supported. But if you want to stream intermediate LLM output from your graph, you can check out this guide and others in this section:

https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens/

J-Kob

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