Fireworks's chat completions endpoint rejects unknown fields on tool
message content blocks — specifically the `id` key that LangChain
auto-generates on `TextContentBlock`. Add
`_sanitize_chat_completions_content` to strip those extra keys before
the payload hits the wire, preventing `Extra inputs are not permitted`
errors on tool message round-trips.
Automated refresh of model profile data for all in-monorepo partner
integrations via `langchain-profiles refresh`.
🤖 Generated by the `refresh_model_profiles` workflow.
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Sweep classic deprecations so every removal lands on `2.0.0`, runtime
warnings carry the auto-generated since/removal/alternative line, and
replacements steer at `langchain.agents.create_agent` and
`with_structured_output(...)` instead of pre-v1 LangGraph +
`python.langchain.com` links.
## Changes
- **Bump removal targets from `1.0` / `1.0.0` to `2.0.0`** across
agents, chains, memory, retrievers, structured-output, vectorstore
toolkits, and the `langchain_classic._api.module_import` shim — gives
users a real runway now that v1 has shipped.
- **Move bespoke `message=` strings onto `addendum=`** (or split into
`alternative=` + `addendum=`). `warn_deprecated` skips the
auto-generated since/removal/alternative line whenever `message=` is
set, so the prior pattern silently dropped that info from the runtime
`LangChainDeprecationWarning`. Matches the pattern already used in
`HTMLHeaderTextSplitter.split_text_from_url`, which is updated for
consistency.
- **Repoint `alternative=` at v1 replacements**: chains/memory/agent
toolkits → `langchain.agents.create_agent` (with checkpointer or
retrieval-tool guidance in the addendum); `openai_functions` and
`chains/structured_output` → `ChatModel.with_structured_output(...)`;
`openapi` chains → `ChatModel.bind_tools(...)` + HTTP client.
`ConversationChain` no longer points at `RunnableWithMessageHistory`.
- **Refresh `AGENT_DEPRECATION_WARNING`** in
`langchain_classic._api.deprecation` — drop stale LangGraph and
`python.langchain.com` links in favor of `langchain.agents.create_agent`
and the `docs.langchain.com/oss/python/migrate/langchain-v1` guide.
Propagates to all 13 caller sites in `agents/`.
- **Newly deprecate `langchain_classic.chat_models.init_chat_model` and
`langchain_classic.embeddings.init_embeddings`** with the framing
*"maintained in `langchain`; `langchain-classic` retains this entry
point for import-compatibility only"*. The classic docstring examples
and the warning admonition both point at `langchain.chat_models`.
- **Improve `init_chat_model` docstrings** in both `langchain_v1` and
the classic copy: clarify `provider:model` prefix vs. `model_provider=`,
recommend pinned IDs over moving aliases, add the `upstage` provider
row, and refresh examples to GA models (`gpt-5.5`, `claude-opus-4-7`).
- **Standardize partner Anthropic deprecations**: replace
`AnthropicLLM`'s `model_validator(raise_warning)` with
`@deprecated(since="0.1.0", removal="2.0.0",
alternative="ChatAnthropic")`, and pin the `ChatAnthropic`
`output_format` runtime warning at `langchain-anthropic 2.0.0` instead
of "a future version".
Re-enable the `[community]`, `[azure-ai]`, and `[cohere]` extras on
`langchain-classic`, and the `[cohere]` extra on `langchain` (v1). These
had been commented out as a temporary workaround during the `langchain`
-> `langchain-classic` rename so the renamed package could ship before
downstream partners were re-released against it. Now that
`langchain-community` 0.4.1, `langchain-cohere` 0.5.1, and
`langchain-azure-ai` 1.2.3 are published with the correct dependency
targets, the extras can be restored.
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Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
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## Description
Fixes#36400
During streaming, `AIMessageChunk.__add__` list-concatenates
`reasoning_details` in `additional_kwargs`, fragmenting a single entry
into many. When `_convert_message_to_dict()` serializes conversation
history back to the OpenRouter API for the next turn, these fragmented
entries cause `BadRequestResponseError`.
### Changes
- Add `_merge_reasoning_details()` helper that merges consecutive
entries sharing the same `type` and `index` (streaming fragments) while
preserving distinct entries (legitimate non-streaming data)
- Metadata from later fragments (e.g. `signature`) is preserved in the
merged result
- Entries without `index` are never merged (safe for non-streaming
responses)
- Call `_merge_reasoning_details()` in `_convert_message_to_dict()`
before serializing `reasoning_details`
### Why merge instead of drop?
Non-streaming users (`invoke()`) rely on `reasoning_details` for
structured metadata (`type`, `signature`, `format`, `index`). Dropping
it entirely would be a regression. This approach fixes streaming while
preserving non-streaming functionality, similar to `langchain-openai`'s
`_implode_reasoning_blocks()`.
## Test plan
- [x] Fragmented entries (same type + same index) are merged into one
- [x] Distinct entries (different index) are preserved separately
- [x] Entries without index are never merged
- [x] Metadata from later fragments (e.g. signature) is preserved
- [x] Single-entry lists pass through unchanged
- [x] Round-trip (dict → message → dict) works correctly
- [x] All 210 unit tests pass
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Closes#37007
---
`ChatMistralAI` was POSTing `HumanMessage` content lists verbatim, so
canonical `ImageContentBlock` dicts (`{"type": "image", "url"/"base64":
...}`) reached the Mistral API unchanged and were rejected — the API
expects OpenAI-shape `{"type": "image_url", "image_url": {"url":
"..."}}`. Multimodal inputs failed for both URL and base64 images.
## Changes
- Introduce `_format_message_content` in
`langchain_mistralai.chat_models`, which delegates to
`is_data_content_block` and
`convert_to_openai_data_block(api="chat/completions")` from
`langchain-core`. Reuses the same translator `langchain-openai` and
`langchain-fireworks` (#37090) use, so v0 `source_type` blocks, v1
`url`/`base64` blocks, and `file_id` references are all handled by one
canonical path.
- Route `HumanMessage` content through `_format_message_content` in
`_convert_message_to_mistral_chat_message`. Strings, already-translated
`image_url` blocks, and Mistral-specific blocks (`document_url`,
`input_audio`) pass through unchanged; the API surfaces an error for
anything it doesn't recognize.
---------
Co-authored-by: Akash Choudhary <achoudhary@lenovo.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Add a `service_tier` init kwarg to `ChatFireworks`, mirroring the field
on `ChatOpenAI`. Forwards to the Fireworks chat completions API when
set, and echoes the response's tier back onto `response_metadata` and
`llm_output` so callbacks and consumers can read what the server
actually applied.
Add first-class `session_id` and `trace` constructor fields on
`ChatOpenRouter`, plumbed into the request payload alongside the
existing `route` / `plugins` / `openrouter_provider` knobs. Both
correspond to the OpenRouter
[broadcast](https://openrouter.ai/docs/guides/features/broadcast/overview)
feature for grouping requests under one logical workflow and attaching
per-request observability metadata. Previously these were only reachable
by stuffing them through `model_kwargs` or `.bind()`, neither of which
is discoverable.
[Docs](https://github.com/langchain-ai/docs/pull/3840)
## Changes
- New `session_id: str | None` field with a
`from_env("OPENROUTER_SESSION_ID", default=None)` factory, so a process
can group all requests via env var without threading the value through
application code. Truthy-guarded in `_default_params` so an explicit or
env-sourced empty string is treated as unset.
- New `trace: dict[str, Any] | None` field for per-request trace
metadata (`trace_id`, `trace_name`, `span_name`, `generation_name`,
`parent_span_id`, plus arbitrary extras forwarded as custom metadata).
No env fallback — set per-call or on the constructor.
- Per-call override (`model.invoke(..., session_id=..., trace=...)`)
works through the existing `**kwargs` flow into `_generate` / `_stream`,
with the constructor value preserved across calls.
- Updated the "Key init args — client params" docstring table on
`ChatOpenRouter` to surface both fields.
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Bumps `langchain` from 1.2.16 → 1.2.17.
Picks up:
- `respond` decision added to HITL middleware (#37095)
> This PR was opened with AI-agent assistance.
## Summary
`langchain_fireworks._convert_message_to_dict` ships LangChain canonical
v0/v1 multimodal content blocks (e.g. `{"type": "image", "base64": ...,
"mime_type": ...}`) on the wire unchanged. Fireworks' OpenAI-compatible
chat completions API rejects the unknown `base64`/`mime_type` keys and
the list shape on roles that expect a string, returning HTTP 422 — so
any image upload, including via tools that return image content blocks,
fails for Kimi K2.6 and other Fireworks vision models.
This change mirrors
`langchain_openai.chat_models.base._format_message_content`:
- Walk `content` blocks.
- Drop block types the chat-completions wire doesn't carry (`tool_use`,
`thinking`, `reasoning_content`, `function_call`,
`code_interpreter_call`).
- Detect v0/v1 multimodal data blocks via
`langchain_core.messages.is_data_content_block`, and translate them via
`convert_to_openai_data_block(..., api="chat/completions")`.
- Strings and non-list content pass through unchanged.
Applied in the `ChatMessage`, `HumanMessage`, `SystemMessage`, and
`ToolMessage` paths of `_convert_message_to_dict`. `AIMessage` already
routes through `_convert_from_v1_to_chat_completions` for v1 output and
assistant content is text-only on the way out, so it is left untouched.
## Why this approach
Fireworks is OpenAI-compatible. The canonical → OpenAI translator
already exists in `langchain_core.messages.block_translators.openai` and
is the same one `langchain-openai` uses. Reusing it (rather than
inventing a Fireworks-specific translator) gives:
- v0 (`source_type`-based) and v1 (`base64`/`url`-based) data block
coverage for free.
- Consistent behavior with `langchain-openai` for image, file, and any
future canonical data block.
- A small, focused diff (≈30 lines of new code, plus tests).
## Test plan
- [x] `make test` passes (64/64 unit tests, including 9 new ones for the
new helper and translation paths).
- [x] `make lint` passes (ruff check, ruff format, mypy, lint_imports).
- [ ] End-to-end: image upload to a Kimi K2.6 (Fireworks) agent
translates to `{"type": "image_url", "image_url": {"url":
"data:image/png;base64,..."}}` on the wire and the model returns a
coherent description (validated locally against
`langchain-fireworks==1.0.0` site-packages with the same patch).
---------
Co-authored-by: murugand23 <murugand23@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Tool runs in `_TracerCore._create_tool_run` were discarding the
structured `inputs` dict that `BaseTool.run` passes to `on_tool_start`,
replacing it with `{"input": str(filtered_tool_input)}`. Consequently,
every multi-arg tool (e.g. ones in `deepagents` like `execute`,
`edit_file`, `write_file`, `grep`, ...) appeared in LangSmith with a
stringified, escaped dump of its arguments — multi-line bash commands
rendered with `\n` and were effectively unreadable. Chain runs already
preserved dicts via `_get_chain_inputs`; tool runs are now symmetric.
## Changes
- Preserve `inputs` when it is already a `dict` in the `original` /
`original+chat` branch of `_TracerCore._create_tool_run`, falling back
to `{"input": input_str}` only when no structured payload was provided
- Add regression tests in the sync and async base-tracer suites that
pass a structured `inputs` to `on_tool_start` and assert the dict
survives onto the resulting `Run`
## Breaking change
Custom `BaseTracer` subclasses that parsed `Run.inputs["input"]` as a
stringified dict for tool runs will need to read the structured fields
directly. The shape now matches what `on_tool_start(inputs=...)` has
always received — introduced alongside `_schema_format` in the
`astream_events` work — and what `streaming_events` consumers already
see.
Extends `HumanInTheLoopMiddleware` with a new `respond` decision type
for "ask user" style tools — tools whose real implementation is the
human's response. The interrupt is raised with the tool call as usual;
the resume payload becomes the body of a synthetic `ToolMessage` with
`status="success"`, and the tool itself is not executed.
This complements `reject` (which produces a synthetic `ToolMessage` with
`status="error"`) by enabling the symmetric success path: a reviewer can
answer on the tool's behalf without invoking it.
## Changes
- New `RespondDecision` `TypedDict` with a required `message: str`
field; added to the `Decision` union.
- `"respond"` added to the `DecisionType` literal.
- `_process_decision` handles `"respond"` by emitting a `ToolMessage`
with `status="success"` and preserving the original tool call on the
`AIMessage` so provider-required tool-call/tool-message pairing is
maintained.
- The `True` shortcut in `interrupt_on` now expands to `["approve",
"edit", "reject", "respond"]`, so existing callers that opted into "all
decisions" pick up the new capability without code changes. The `reject`
decision already permits a reviewer to inject arbitrary `ToolMessage`
content, so `respond` extends the same trust model — not a new
capability class.
## Example
```python
from langchain.agents.middleware import HumanInTheLoopMiddleware
middleware = HumanInTheLoopMiddleware(
interrupt_on={"ask_user": {"allowed_decisions": ["respond"]}}
)
# Resume payload: {"decisions": [{"type": "respond", "message": "blue"}]}
# → synthetic ToolMessage(content="blue", status="success") for `ask_user`.
```
---
*Implementation drafted with AI-agent assistance.*
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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## Description
This PR adds a new `PerplexityEmbeddings` class to the
`langchain-perplexity` partner package, providing first-class support
for the Perplexity Embeddings API alongside the existing
`ChatPerplexity`, `PerplexitySearchRetriever`, and
`PerplexitySearchResults` integrations.
### What was added
- `langchain_perplexity/embeddings.py` — `PerplexityEmbeddings` class
implementing `langchain_core.embeddings.Embeddings` with sync
(`embed_documents`, `embed_query`) and async (`aembed_documents`,
`aembed_query`) methods. Defaults to model `pplx-embed-v1-4b` and reuses
the existing `_utils.initialize_client` helper for API key resolution
(`PPLX_API_KEY` / `PERPLEXITY_API_KEY`).
- `__init__.py` exports `PerplexityEmbeddings` and adds it to `__all__`.
- Unit tests under `tests/unit_tests/test_embeddings.py` covering
sync/async paths with mocked clients (no network).
- Integration tests under `tests/integration_tests/test_embeddings.py`,
gated on `PPLX_API_KEY` (matches the pattern in `test_search_api.py`).
- README updated to advertise the new component.
### Why
LangChain users already get chat, search, and tool wrappers from
`langchain-perplexity`, but had to drop down to the raw Perplexity SDK
to use embeddings. This closes that gap.
### References
- Perplexity Embeddings docs: https://docs.perplexity.ai/docs/embeddings
- Perplexity Embeddings API reference:
https://docs.perplexity.ai/api-reference/embeddings-post
### Issue
Closes#36726
## Testing
- `cd libs/partners/perplexity && make lint` — passes (ruff, format,
mypy).
- `cd libs/partners/perplexity && make test` — all unit tests pass (59
passed, 1 skipped).
- Integration tests will run in CI with secrets; they exercise real
`embed_documents` / `embed_query` / async variants against the live API
and assert vector dimensionality consistency.
---------
Co-authored-by: Claude Agent <agent@anthropic.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Description
Updates package metadata and README badges so LangChain social links
point to the new `@langchain_oss` X handle. This was completed with
AI-agent assistance.
## Test Plan
- [ ] Validate README badges and package metadata links point to
`https://x.com/langchain_oss`
_Opened collaboratively by Mason Daugherty and open-swe._
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Co-authored-by: Mason Daugherty <61371264+mdrxy@users.noreply.github.com>
Automated refresh of model profile data for all in-monorepo partner
integrations via `langchain-profiles refresh`.
🤖 Generated by the `refresh_model_profiles` workflow.
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Closes#37042
---
`AnthropicPromptCachingMiddleware` was unconditionally setting top-level
`cache_control` in `model_settings` for any `ChatAnthropic` subclass.
That field is direct-Anthropic-API only — `ChatAnthropicBedrock` (which
subclasses `ChatAnthropic` and passed the existing `isinstance` gate)
errored with `cache_control: Extra inputs are not permitted`.
Investigating that surfaced a related regression: PR #35967 also deleted
the block-level `cache_control` injection in `_get_request_payload`,
which silently disabled caching entirely for non-direct subclasses
(Bedrock had been falling back to in-block breakpoints). This restores
both paths.
## Changes
- Add `_is_direct_anthropic_llm_type` predicate that allowlists
`_llm_type == "anthropic-chat"`. Both the middleware's
`_supports_automatic_caching` and the new branch in
`ChatAnthropic._get_request_payload` route through it, so any subclass
that overrides `_llm_type` (Bedrock today, future direct-API variants
tomorrow) is treated as non-direct by default. Replaces the prior
substring-matching denylist on `"bedrock"`/`"vertex"`.
- Restore `_collect_code_execution_tool_ids`,
`_is_code_execution_related_block`, and a new
`_apply_cache_control_to_last_eligible_block` helper in `chat_models`.
For non-direct subclasses, `_get_request_payload` now pops
`cache_control` from kwargs and walks messages newest-to-oldest,
attaching the breakpoint to the last block that isn't
`code_execution`-related (Anthropic forbids breakpoints on those).
- Emit `UserWarning` when `cache_control` is requested but every
candidate block is `code_execution`-related — previously a silent drop.
- `AnthropicPromptCachingMiddleware._apply_caching` now sets the
top-level `cache_control` only when
`_supports_automatic_caching(request.model)`. System-message and
tool-definition breakpoints continue to apply for all `ChatAnthropic`
subclasses, since those are accepted by every transport.
- Note: `ChatAnthropicVertex` does not subclass `ChatAnthropic` (it
lives in `langchain-google-vertexai` and ships its own
`_get_request_payload`), so the chat-models changes here only affect
Bedrock. The middleware-side gate covers Vertex implicitly via the
`isinstance(request.model, ChatAnthropic)` check that already excludes
it.
Drop the `NotImplementedError` branch in `warn_deprecated` so callers
can pass `pending=False` without specifying a `removal` version. The
previous behavior contradicted the docstring (which claimed an empty
default would auto-compute a removal version) — no such computation
existed; the function just raised a placeholder "Need to determine which
default deprecation schedule to use" error.
Automated refresh of model profile data for all in-monorepo partner
integrations via `langchain-profiles refresh`.
🤖 Generated by the `refresh_model_profiles` workflow.
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
## Summary
Fixes four issues in `get_separators_for_language()` in `character.py`:
- **Kotlin**: removed `"\ncase "` — `case` is not a Kotlin keyword.
Kotlin uses `when` expressions (already present in the list). This was
copied from Java/Swift.
- **Rust**: removed duplicate `"\nconst "` — appeared twice, once under
function definitions and again under control flow statements.
- **Haskell**: removed duplicate `"\n:: "` — appeared under function
definitions and again under type declarations.
- **Haskell**: removed duplicate `"\ndata "` — appeared under type
declarations and again under record field declarations.
All four are dead separators that never match or produce redundant
splits.
## Issue
Closes#37038
## Types of changes
- [x] Bug fix
## Checklist
- [x] I have read the CONTRIBUTING doc
- [x] Lint and unit tests pass locally with my changes
## Summary
Removes two incorrect separators from `get_separators_for_language()` in
`RecursiveCharacterTextSplitter`:
- **C#**: `"\nimplements "` is a Java keyword. C# uses `:` for interface
implementation. This separator never matches valid C# source code.
- **Elixir**: `"\nwhile "` does not exist in Elixir. The language uses
recursion and `Enum.reduce_while/3` instead of while loops.
Both are dead separators that silently degrade chunking quality by
occupying positions in the separator priority list without contributing
useful split points.
## Tests
Added two targeted tests:
- `test_csharp_separators_no_java_keywords`: verifies `"\nimplements "`
is not in the C# separator list
- `test_elixir_separators_no_while`: verifies `"\nwhile "` is not in the
Elixir separator list
Existing `test_csharp_code_splitter` continues to pass (no change to
expected output since `implements` never matched valid C# code).
Full suite: 129 passed, 0 failed.
Fixes#37030
## Summary
Stop inlining the full agent state into every tool-dispatch `Send` in
`create_agent`. Dispatch with the bare list form `Send("tools",
[tool_call])` and let `ToolNode` hydrate `ToolRuntime.state` from graph
channels at tool-execution time.
**Depends on**
[langchain-ai/langgraph#7594](https://github.com/langchain-ai/langgraph/pull/7594)
which teaches `ToolNode` to read channel state via `CONFIG_KEY_READ`
when given a bare tool-call list. `uv.lock` pins that branch for CI
while the langgraph PR is in flight — this pin will be reverted to a
published `langgraph` version before merge.
## What was happening
Before this change, every pending tool call produced a `Send` whose
payload was:
```python
ToolCallWithContext(
__type="tool_call_with_context",
tool_call=tool_call,
state=state, # ← the FULL agent state dict, including messages list
)
```
For any agent that runs many turns, `state["messages"]` grows linearly
with the conversation. Every super-step that dispatches tools serializes
that whole list into every `Send`, and those Sends live forever in the
checkpointer's `__pregel_tasks` writes. The result is **O(N²)
`__pregel_tasks` storage** across a run.
## What changed
- `libs/langchain_v1/langchain/agents/factory.py`:
- `_make_model_to_tools_edge` now returns `Send("tools", [tool_call])` —
no inlined state.
- Drops the `ToolCallWithContext` import.
- `libs/langchain_v1/pyproject.toml` + `libs/langchain_v1/uv.lock`:
- Temporary `[tool.uv.sources]` pin on `langgraph`,
`langgraph-prebuilt`, `langgraph-checkpoint` to the companion PR branch
so CI exercises both changes end-to-end. Revert after langgraph release.
## Why it's safe
- Same snapshot semantics as before. `Send` is emitted at the end of the
model super-step and consumed at the start of the tools super-step;
channels by that point reflect every write from the model super-step
(including the new AIMessage). Parallel tool tasks all see the same
values since sibling writes don't land until end-of-super-step.
- Legacy `ToolCallWithContext` input path is preserved in `ToolNode` —
no-op for any external caller still constructing it by hand.
## Test plan
- [x] `tests/unit_tests/agents/` — **738 passed, 2 skipped, 1 xfailed**
- [x] `ruff check .` / `ruff format .` — clean
- [x] `mypy langchain/agents/factory.py` — clean
- [x] Before/after benchmark (below)
## Benchmark
Script runs `create_agent` with a mock `GenericFakeChatModel` and two
tools (`write_file`, `edit_file`). Each of the N turns dispatches 2 tool
calls. After the run, the `InMemorySaver` is inspected for bytes stored
under `__pregel_tasks` — the channel that carries the tool-dispatch
`Send` payloads.
| N | TASKS before | TASKS after | ratio |
|---:|---:|---:|---:|
| 5 | 87.6 KB | **4.7 KB** | **18.6× smaller** |
| 10 | 335 KB | **9.4 KB** | **35.7× smaller** |
| 25 | 2.05 MB | **23.7 KB** | **86.5× smaller** |
| 50 | 8.14 MB | **47.6 KB** | **171× smaller** |
| 100 | 32.5 MB | **95.3 KB** | **341× smaller** |
| 200 | 130 MB | **192 KB** | **677× smaller** |
| 500 | 815 MB | **482 KB** | **1,691× smaller** |
**Growth shape:**
- **Before:** per-Send bytes scale with current `messages` length (full
state is inlined), so total TASKS across N turns = Σ(2 × k) for k=1..N ≈
O(N²).
- **After:** per-Send bytes are constant — just the `tool_call` dict.
Total TASKS is O(#dispatches), completely independent of conversation
length. In this bench with ~2 dispatches/turn: **940–964 bytes per turn
across N=5..500, essentially flat.**
An agent that makes 100 tool calls in a single turn pays the same TASKS
cost as one that makes 100 across 50 turns — which is the semantically
correct behavior.
Note: the `messages` channel is unchanged by this PR — it's still the
dominant storage term (growing O(N²) via `add_messages`). TASKS was a
second, compounding cost sitting on top of it; at N=100 it added 40% on
top of `messages`, at N=500 it added 67%. After the fix, TASKS is a
rounding error regardless of N.
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
