`PIIMiddleware` previously scrubbed detected PII only at the state level via its `after_model` / `before_model` hooks. Consumers reading the live stream — `astream_events(version="v3")` or `run.messages` / `run.tool_calls` / `run.values` — saw the raw model text, the raw tool-call args, the raw tool outputs, and the raw state snapshots until the run finished and the canonical conversation history was written. This change registers a stream transformer ahead of `MessagesTransformer` that redacts every wire surface of an agent run. The transformer holds a sliding lookback buffer (default 128 characters) per `(run_id, content-block index)` so PII patterns that straddle delta boundaries are caught before the safe prefix is released downstream. Anything older than the lookback is run through the configured detector and emitted; the trailing tail stays buffered until a later delta extends it past the cap or the block finishes. `_finalize_block` always re-runs detection over the full block snapshot so the finalized content lands fully redacted even when the in-flight buffer never released a tail (short responses, or PII arriving in the final delta). The `block` strategy is now supported on the streaming path via a buffering mode that withholds every delta until the block resolves — clean blocks release the full text at finalize, PII-bearing blocks zero the wire and let `after_model` / `apply_to_tool_results` raise `PIIDetectionError` on the original state message. Activation is gated on `apply_to_output=True`, matching the existing post-hoc semantics. The middleware's transformer factory is cloned by `StreamMux._make_child` into every subgraph scope, so attaching `PIIMiddleware` at the outer agent also redacts streamed deltas from sub-agents invoked inside tools. ## Tool-call and tools-channel coverage The transformer covers every wire surface of an agent run, not just AI message text: - **Streamed AI text deltas** (`content-block-delta` of type `text-delta`) — lookback machinery, redacted in place. - **Streamed tool-call args** (`content-block-delta` with `tool_call_chunk` / `server_tool_call_chunk` fields) — each delta carries the full cumulative args string; detection runs on the field directly and redacts in place. Verified empirically against `_compat_bridge.py` and the consumer-side `_merge_block_delta_into_store` snapshot-replace semantics. - **Finalized tool-call blocks** (`content-block-finish` with `tool_call` / `server_tool_call` / `invalid_tool_call`) — `args` dict walked recursively and each string leaf redacted. - **Tool execution events on the `tools` channel** — `tool-started.input`, `tool-output-delta`, `tool-finished.output`, `tool-error.message` all run through detection. String deltas use the same lookback machinery as text-deltas keyed by `tool_call_id`; structured payloads walk recursively. - **State snapshots on the `values` channel** — message lists are walked and each message's `.content` is redacted on a fresh copy. Graph state itself stays intact for the state-level enforcer (`apply_to_tool_results` via `before_model`) to act on independently. - **Legacy `(BaseMessage, metadata)` payloads** on the `messages` channel (Python 3.10 path, where `langgraph`'s `ASYNCIO_ACCEPTS_CONTEXT = sys.version_info >= (3, 11)` falls back to a code path that doesn't propagate the streaming callback into the chat model) — `.content` and `AIMessage.tool_calls[*].args` are scrubbed. For `block`, the event's `data` tuple is replaced with an empty-content copy so the original message stays in state for `after_model` to raise on. ## Worth a careful look - `_PIIStreamTransformer._mutate_text_delta` — lookback partition. Anything older than `lookback` characters is released after redaction; the tail stays buffered. Bulletproof against whitespace-permissive detectors (notably `credit_card`, whose regex matches across spaces). - `_PIIStreamTransformer._mutate_tool_call_chunk_delta` — direct in-place redaction of the cumulative args string. No buffer; the wire shape is cumulative-snapshot, the consumer-side merge is replace-not-append. - `_PIIStreamTransformer._mutate_legacy_payload` — the dual path: mutate-in-place for non-`block` (idempotent with `after_model`), replace-with-empty-copy for `block` (keeps original in graph state for `after_model` to raise on). - `_PIIStreamTransformer._redact_value` — the recursive walker. `BaseMessage` branch returns a fresh `.content`-redacted copy via `model_copy(update=...)` — never mutates in place — so tool-output payloads that wrap a `ToolMessage` and message lists in state snapshots flow through cleanly. - The new `transformers` attribute on `PIIMiddleware`: this is what makes `create_agent` pick the factory up. Multiple `PIIMiddleware` instances each register one transformer; ordering is preserved within the `before_builtins` lane. ## Compatibility Bumps `langgraph` to `>=1.2.1` for the `before_builtins` opt-in on `StreamTransformer`.
🦜️🔗 LangChain
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
pip install langchain
🤔 What is this?
LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more. LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.
We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use LangGraph, our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
LangChain agents are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs. You can also chat with the docs using Chat LangChain.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.