mirror of
https://github.com/hwchase17/langchain.git
synced 2026-07-01 22:59:06 +00:00
221f934f9d
`stream_events(version="v3")` / `astream_events(version="v3")` drops `input_token_details` and `output_token_details` from the usage metadata on the assembled message and the `on_llm_end` payload: the conversion to the protocol `UsageInfo` shape copied only the flat token counts. Providers fold cached tokens into `input_tokens` and break them out in `input_token_details`, so tracers (e.g. LangSmith) price every input token at the uncached rate on the v3 path, inflating reported cost for prompt-cached runs (cache reads bill at roughly a tenth of the base input rate). The v2 events path and `astream` aggregation preserve the details and report correctly; reasoning-token breakdowns in `output_token_details` are lost the same way. The detail breakdowns now live on the wire type itself: `input_token_details` / `output_token_details` were added to `UsageInfo` in `langchain-protocol` 0.0.17 (alongside `InputTokenDetails` / `OutputTokenDetails`), so core imports `UsageInfo` directly instead of carrying a local subclass. The v3 usage accumulator threads the details through end to end, shallow-copying the nested dicts (`_isolate_usage`) so later accumulator mutation cannot leak into already-emitted events. Since native provider converters share `build_message_finish`, this also covers provider-native v3 streams. Verified against a live claude-sonnet-4-6 call with a cached prompt: v3 `on_llm_end` usage now matches v2, with `cache_read` / `cache_creation` intact. Requires `langchain-protocol>=0.0.17` (core pin bumped accordingly).
The agent engineering platform.
LangChain is a framework for building agents and 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.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
For an equivalent JS/TS library, check out LangChain.js.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain 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.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- 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
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
- Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
- Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards