To fix nondeterministic results causing integration testing to sometimes
fail
Also speeds up from 10s to 0.5
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- use latest models in examples to highlight support
- standardize on using IDs in examples - no more aliases to improve
determinism in future tests
- bump lock
- in integration tests, fix stale casettes and use `MODEL_NAME`
uniformly where possible
- add case for default max tokens for sonnet-4-5 (was missing)
Moving all `ToolNode` related improvements back to LangGraph and
importing them in LC!
pairing w/ https://github.com/langchain-ai/langgraph/pull/6321
this fixes a couple of things:
1. `InjectedState`, store etc will continue to work as expected no
matter where the import is from
2. `ToolRuntime` is now usable w/in langgraph, woohoo!
- Both middleware share the same implementation, the only difference is
one uses Claude's server-side tool definition, whereas the other one
uses a generic tool definition compatible with all models
- Implemented 3 execution policies (responsible for actually running the
shell process)
- HostExecutionPolicy runs the shell as subprocess, appropriate for
already sandboxed environments, eg when run inside a dedicated docker
container
- CodexSandboxExecutionPolicy runs the shell using the sandbox command
from the Codex CLI which implements sandboxing techniques for Linux and
Mac OS.
- DockerExecutionPolicy runs the shell inside a dedicated Docker
container for isolation.
- Implements all behaviours described in
https://docs.claude.com/en/docs/agents-and-tools/tool-use/bash-tool#handle-large-outputs
including timeouts, truncation, output redaction, etc
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Middleware Classes
Text Editor Tools
- StateClaudeTextEditorToolMiddleware: In-memory text editor using agent
state
- FilesystemClaudeTextEditorToolMiddleware: Text editor operating on
real filesystem
Implementing Claude's text editor tools
https://docs.claude.com/en/docs/agents-and-tools/tool-use/text-editor-tool
Operations: view, create, str_replace, insert
Memory Tools
- StateClaudeMemoryToolMiddleware: Memory persistence in agent state
- FilesystemClaudeMemoryToolMiddleware: Memory persistence on filesystem
Implementing Claude's memory tools
https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool
Operations: Same as text editor plus delete and rename
File Search Tools
- StateFileSearchMiddleware: Search state-based files
Provides Glob and Grep tools with same schema as used by Claude Code
(but compatible with any model)
- Glob: Pattern matching (e.g., **/*.py, src/**/*.ts), sorted by
modification time
- Grep: Regex content search with output modes (files_with_matches,
content, count)
Usage
``` from langchain.agents import create_agent from langchain.agents.middleware import (
StateTextEditorToolMiddleware, StateFileSearchMiddleware, )
agent = create_agent( model=model, tools=[], middleware=[
StateTextEditorToolMiddleware(), StateFileSearchMiddleware(), ], ) ```
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Largely:
- Remove explicit `"Default is x"` since new refs show default inferred
from sig
- Inline code (useful for eventual parsing)
- Fix code block rendering (indentations)
**Description:**
This PR updated links to the latest Anthropic documentation. Changes
include revised links for model overview, tool usage, web search tool,
text editor tool, and more.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
N/A
- Beta isn't needed for search result tests anymore
- Add TODO for other tests to come back when generally available
- Regenerate remote MCP snapshot after some testing (now the same, but
fresher)
- Bump deps
* Create usage metadata on
[`message_delta`](https://docs.anthropic.com/en/docs/build-with-claude/streaming#event-types)
instead of at the beginning. Consequently, token counts are not included
during streaming but instead at the end. This allows for accurate
reporting of server-side tool usage (important for billing)
* Add some clarifying comments
* Fix some outstanding Pylance warnings
* Remove unnecessary `text` popping in thinking blocks
* Also now correctly reports `input_cache_read`/`input_cache_creation`
as a result
Supersedes #32461
Fixed incorrect input token reporting during streaming when tools are
used. Previously, input tokens were counted at `message_start` before
tool execution, leading to inaccurate counts. Now input tokens are
properly deferred until `message_delta` (completion), aligning with
Anthropic's billing model and SDK expectations.
**Before Fix:**
- Streaming with tools: Input tokens = 0 ❌
- Non-streaming with tools: Input tokens = 472 ✅
**After Fix:**
- Streaming with tools: Input tokens = 472 ✅
- Non-streaming with tools: Input tokens = 472 ✅
Aligns with Anthropic's SDK expectations. The SDK handles input token
updates in `message_delta` events:
```python
# https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py
if event.usage.input_tokens is not None:
current_snapshot.usage.input_tokens = event.usage.input_tokens
```
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
fix#30146
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
```python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
caching_llm = llm.bind(cache_control={"type": "ephemeral"})
caching_llm.invoke(
[
HumanMessage("..."),
AIMessage("..."),
HumanMessage("..."), # <-- final message / content block gets cache annotation
]
)
```
Potentially useful given's Anthropic's [incremental
caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#continuing-a-multi-turn-conversation)
capabilities:
> During each turn, we mark the final block of the final message with
cache_control so the conversation can be incrementally cached. The
system will automatically lookup and use the longest previously cached
prefix for follow-up messages.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Multiple models were
[retired](https://docs.anthropic.com/en/docs/about-claude/model-deprecations#model-status)
yesterday.
Tests remain broken until we figure out what to do with the legacy
Anthropic LLM integration— currently uses their (legacy) text
completions API, for which there appear to be no remaining supported
models.
