langchain-ai/docs
1
0
Fork 0
mirror of https://github.com/langchain-ai/docs.git synced 2026-07-21 20:05:30 -04:00
Code Issues 490 Packages Projects Releases Wiki Activity

[GH-ISSUE #3455] Add example: AI triage workflow for inbound conversations #2728

New Issue
Open
opened 2026-06-05 17:26:30 -04:00 by yindo · 4 comments
yindo commented 2026-06-05 17:26:30 -04:00
Owner
Copy Link
Copy Source

Originally created by @iveteamorim on GitHub (Apr 1, 2026).
Original GitHub issue: https://github.com/langchain-ai/docs/issues/3455

Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

I’d like to propose adding an official end-to-end example for AI-powered inbound conversation workflows using LangChain.

The example could demonstrate how to:

  • classify user intent from an inbound message
  • assign a priority score
  • generate a suggested next response or action
  • return structured output for downstream logic

The goal is to provide a clear, production-relevant reference for building AI-driven inbound systems.

Use Case

This type of workflow is common in:

  • customer support automation
  • lead qualification
  • inbound operations systems

Currently, implementing this requires manually combining multiple LangChain components without a clear, production-oriented reference.

An official example would make it easier to adopt LangChain for real-world AI systems beyond simple chat demos.

Proposed Solution

This could be implemented as a simple example using existing LangChain components:

  • a prompt or chain for intent classification
  • a scoring step for priority
  • a response generation step

The output could be structured, for example:

{
  "intent": "pricing",
  "priority": "medium",
  "response": "Here is the information about our pricing..."
}

Alternatives Considered

I've tried building similar workflows manually by chaining prompts and model calls.

However:

  • there is no clear reference example for this use case
  • it requires designing the structure from scratch

A dedicated example would reduce friction and help users adopt LangChain for real-world applications.

Additional Context

This type of workflow is common in:

  • customer support automation
  • lead qualification systems
  • inbound operations tools

Providing this example would make LangChain more accessible for building practical AI systems beyond simple demos.

Originally created by @iveteamorim on GitHub (Apr 1, 2026). Original GitHub issue: https://github.com/langchain-ai/docs/issues/3455 ### Checked other resources - [x] This is a feature request, not a bug report or usage question. - [x] I added a clear and descriptive title that summarizes the feature request. - [x] I used the GitHub search to find a similar feature request and didn't find it. - [x] I checked the LangChain documentation and API reference to see if this feature already exists. - [x] This is not related to the langchain-community package. ### Package (Required) - [x] langchain - [ ] langchain-openai - [ ] langchain-anthropic - [ ] langchain-classic - [x] langchain-core - [ ] langchain-model-profiles - [ ] langchain-tests - [ ] langchain-text-splitters - [ ] langchain-chroma - [ ] langchain-deepseek - [ ] langchain-exa - [ ] langchain-fireworks - [ ] langchain-groq - [ ] langchain-huggingface - [ ] langchain-mistralai - [ ] langchain-nomic - [ ] langchain-ollama - [ ] langchain-openrouter - [ ] langchain-perplexity - [ ] langchain-qdrant - [ ] langchain-xai - [x] Other / not sure / general ### Feature Description I’d like to propose adding an official end-to-end example for AI-powered inbound conversation workflows using LangChain. The example could demonstrate how to: - classify user intent from an inbound message - assign a priority score - generate a suggested next response or action - return structured output for downstream logic The goal is to provide a clear, production-relevant reference for building AI-driven inbound systems. ### Use Case This type of workflow is common in: - customer support automation - lead qualification - inbound operations systems Currently, implementing this requires manually combining multiple LangChain components without a clear, production-oriented reference. An official example would make it easier to adopt LangChain for real-world AI systems beyond simple chat demos. ### Proposed Solution This could be implemented as a simple example using existing LangChain components: - a prompt or chain for intent classification - a scoring step for priority - a response generation step The output could be structured, for example: ```python { "intent": "pricing", "priority": "medium", "response": "Here is the information about our pricing..." } ``` ### Alternatives Considered I've tried building similar workflows manually by chaining prompts and model calls. However: - there is no clear reference example for this use case - it requires designing the structure from scratch A dedicated example would reduce friction and help users adopt LangChain for real-world applications. ### Additional Context This type of workflow is common in: - customer support automation - lead qualification systems - inbound operations tools Providing this example would make LangChain more accessible for building practical AI systems beyond simple demos.
yindo added the langchainexternal labels 2026-06-05 17:26:30 -04:00
yindo commented 2026-06-05 17:26:31 -04:00
Author
Owner
Copy Link
Copy Source

@lxrbox commented on GitHub (Apr 1, 2026):

I'd like to take this on.

I can work on an end-to-end example for AI-powered inbound conversation workflows using LangChain, covering intent classification, priority scoring, response/action
suggestion, and structured output.

If this direction sounds good, I'll put together a PR. Please let me know if there's a preferred location or format for this example.

<!-- gh-comment-id:4194995227 --> @lxrbox commented on GitHub (Apr 1, 2026): I'd like to take this on. I can work on an end-to-end example for AI-powered inbound conversation workflows using LangChain, covering intent classification, priority scoring, response/action suggestion, and structured output. If this direction sounds good, I'll put together a PR. Please let me know if there's a preferred location or format for this example.
yindo commented 2026-06-05 17:26:31 -04:00
Author
Owner
Copy Link
Copy Source

@iveteamorim commented on GitHub (Apr 2, 2026):

This sounds like a great direction.

One thing I’d highlight to make it more useful in practice is keeping the output operational, not just descriptive. From what I’ve seen, what tends to matter most is:

  • priority tied to estimated value, not just urgency
  • some notion of “at risk” (value + time without response)
  • outputs focused on what to do next, not only classification

I’ve been working on a similar inbound triage workflow in a production-oriented setup, happy to share more context if helpful.

<!-- gh-comment-id:4194995246 --> @iveteamorim commented on GitHub (Apr 2, 2026): This sounds like a great direction. One thing I’d highlight to make it more useful in practice is keeping the output operational, not just descriptive. From what I’ve seen, what tends to matter most is: - priority tied to estimated value, not just urgency - some notion of “at risk” (value + time without response) - outputs focused on what to do next, not only classification I’ve been working on a similar inbound triage workflow in a production-oriented setup, happy to share more context if helpful.
yindo commented 2026-06-05 17:26:31 -04:00
Author
Owner
Copy Link
Copy Source

@lxrbox commented on GitHub (Apr 2, 2026):

Thanks, this is very helpful.

I agree the example should focus on operational output, not just classification.
I’m thinking of a structured result along the lines of:

  • intent
  • estimated_value
  • priority
  • at_risk
  • next_best_action
  • suggested_response

with priority tied to value, and at_risk based on value plus time since last
response.

If you’re willing to share more production context, that would be very useful and
I’d be happy to incorporate it.

Also, could you confirm the preferred home for this example before I start the PR:

  1. a docs/tutorial example, or
  2. an example in the langchain-ai/langchain repo itself?
<!-- gh-comment-id:4194995278 --> @lxrbox commented on GitHub (Apr 2, 2026): Thanks, this is very helpful. I agree the example should focus on operational output, not just classification. I’m thinking of a structured result along the lines of: - `intent` - `estimated_value` - `priority` - `at_risk` - `next_best_action` - `suggested_response` with `priority` tied to value, and `at_risk` based on value plus time since last response. If you’re willing to share more production context, that would be very useful and I’d be happy to incorporate it. Also, could you confirm the preferred home for this example before I start the PR: 1. a docs/tutorial example, or 2. an example in the `langchain-ai/langchain` repo itself?
yindo commented 2026-06-05 17:26:31 -04:00
Author
Owner
Copy Link
Copy Source

@iveteamorim commented on GitHub (Apr 3, 2026):

That structure looks good to me.

A concrete production case would be an inbound inbox for leads or support: a message comes in, the system classifies intent, estimates value, flags whether it is at risk (value × delay), and suggests the next action.

What makes it useful is that the output is operational, not just descriptive.

I’d probably lean towards a docs/tutorial example since this pattern benefits from a bit of explanation.

I also put together a small demo to illustrate it:
https://ai-inbound-triage.vercel.app

<!-- gh-comment-id:4194995301 --> @iveteamorim commented on GitHub (Apr 3, 2026): That structure looks good to me. A concrete production case would be an inbound inbox for leads or support: a message comes in, the system classifies intent, estimates value, flags whether it is at risk (value × delay), and suggests the next action. What makes it useful is that the output is operational, not just descriptive. I’d probably lean towards a docs/tutorial example since this pattern benefits from a bit of explanation. I also put together a small demo to illustrate it: https://ai-inbound-triage.vercel.app
Sign in to join this conversation.
No Branch/Tag Specified
Branches Tags
Labels
Clear labels
bug
deepagents
docs-infra
external
external
help wanted
integration
internal
internal
javascript
js/ts
langchain
langgraph
langsmith
needs-title
pull-request
Mirrored from GitHub Pull Request
 python
reference
No Label external langchain
Milestone
No items
No Milestone
Projects
Clear projects
No project
Assignees
Clear assignees
yindo
No Assignees
1 Participants
Notifications
Due Date
No due date set.
Dependencies

No dependencies set.

Reference: langchain-ai/docs#2728
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?