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c99773b652
Easiest to review side by side (not inline)
* Adding `dict` type requests + responses so that we can ship config w/
interrupts. Also more extensible.
* Keeping things generic in terms of `interrupt_on` rather than
`tool_config`
* Renaming allowed decisions -- approve, edit, reject
* Draws differentiation between actions (requested + performed by the
agent), in this case tool calls, though we generalize beyond that and
decisions - human feedback for said actions
New request structure
```py
class Action(TypedDict):
"""Represents an action with a name and arguments."""
name: str
"""The type or name of action being requested (e.g., "add_numbers")."""
arguments: dict[str, Any]
"""Key-value pairs of arguments needed for the action (e.g., {"a": 1, "b": 2})."""
DecisionType = Literal["approve", "edit", "reject"]
class ReviewConfig(TypedDict):
"""Policy for reviewing a HITL request."""
action_name: str
"""Name of the action associated with this review configuration."""
allowed_decisions: list[DecisionType]
"""The decisions that are allowed for this request."""
description: NotRequired[str]
"""The description of the action to be reviewed."""
arguments_schema: NotRequired[dict[str, Any]]
"""JSON schema for the arguments associated with the action, if edits are allowed."""
class HITLRequest(TypedDict):
"""Request for human feedback on a sequence of actions requested by a model."""
action_requests: list[Action]
"""A list of agent actions for human review."""
review_configs: list[ReviewConfig]
"""Review configuration for all possible actions."""
```
New response structure
```py
class ApproveDecision(TypedDict):
"""Response when a human approves the action."""
type: Literal["approve"]
"""The type of response when a human approves the action."""
class EditDecision(TypedDict):
"""Response when a human edits the action."""
type: Literal["edit"]
"""The type of response when a human edits the action."""
edited_action: Action
"""Edited action for the agent to perform.
Ex: for a tool call, a human reviewer can edit the tool name and args.
"""
class RejectDecision(TypedDict):
"""Response when a human rejects the action."""
type: Literal["reject"]
"""The type of response when a human rejects the action."""
message: NotRequired[str]
"""The message sent to the model explaining why the action was rejected."""
Decision = ApproveDecision | EditDecision | RejectDecision
class HITLResponse(TypedDict):
"""Response payload for a HITLRequest."""
decisions: list[Decision]
"""The decisions made by the human."""
```
User facing API:
NEW
```py
HumanInTheLoopMiddleware(interrupt_on={
'send_email': True,
# can also use a callable for description that takes tool call, state, and runtime
'execute_sql': {
'allowed_decisions': ['approve', 'edit', 'reject'],
'description': 'please review sensitive tool execution'},
}
})
Command(resume={"decisions": [{"type": "approve"}, {"type": "reject": "message": "db down"}]})
```
OLD
```py
HumanInTheLoopMiddleware(interrupt_on={
'send_email': True,
'execute_sql': {
'allow_accept': True,
'allow_edit': True,
'allow_respond': True,
description='please review sensitive tool execution'
},
})
Command(resume=[{"type": "approve"}, {"type": "reject": "message": "db down"}])
```
The platform for reliable agents.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
Documentation: To learn more about LangChain, check out the docs.
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Note
Looking for the JS/TS library? Check out LangChain.js.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.
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