Summary
Add module-level debug logging to track Agent canvas execution flow
(Closes#9306), enabling developers to diagnose component invocation,
input/output states, and variable resolution without modifying
production code.
Also fix related bugs in message.py: re.sub backreference issue and
unawaited _save_to_memory coroutine causing silent memory save failures.
Changes
agent/canvas.py: log workflow start, component invocation, and component
completion
agent/component/agent_with_tools.py: log Agent parameter resolution and
LLM invocation path; standardize json.dumps usage
agent/component/base.py: log get_input() variable resolution branches
agent/component/message.py: fix re.sub backreference issue; properly
await _save_to_memory coroutine
Design
Uses module-level loggers (logging.getLogger(__name__)) to support
selective debugging: LOG_LEVELS=agent=DEBUG
Zero performance impact in production (INFO level by default)
Works with existing PUT /system/config/log API for runtime level changes
Closes#9306
Note: While adding debug logging, I discovered and fixed two related
bugs in message.py:
- re.sub replacement value was interpreted as regex backreference
instead of literal string
- _save_to_memory coroutine was not properly awaited, causing silent
failures
---------
Co-authored-by: wills <willsgao@163.com>
## Summary
Closes#15425. The agent **Invoke** (HTTP Request) component now calls
`assert_url_is_safe` and `pin_dns` before `requests.*`, matching Crawler
and SearXNG.
## Changes
- `agent/component/invoke.py`: SSRF guard + DNS pinning on outbound
requests.
- `test_invoke_component_unit.py`: unit test blocks loopback URL without
calling `requests.get`.
## Test plan
- [x] `pytest
test/testcases/test_web_api/test_canvas_app/test_invoke_component_unit.py::test_invoke_blocks_loopback_url_with_ssrf_guard`
(requires project test env / `ZHIPU_AI_API_KEY` in CI)
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
This PR adds an Agent LLM setting to control thinking mode for official
providers that expose a thinking switch.
Related to #12842.
Closes#15445.
Some providers expose thinking controls through provider-specific
request fields, but Agent LLM settings did not have a unified option for
users to enable or disable thinking mode.
This PR adds a `Thinking` selector with:
- System default
- Enabled
- Disabled
<img width="452" height="278" alt="8566b0b4-0546-4c8a-913d-f9bbd38319f6"
src="https://github.com/user-attachments/assets/25b497f7-1ba0-4bfe-940d-6fe79287d6ab"
/>
<img width="471" height="971" alt="8a0a6bee-f45f-48d5-bd83-17af260de3db"
src="https://github.com/user-attachments/assets/41ad43c1-5087-48f1-bf37-f2ca14c2be2f"
/>
Initial support is limited to the verified official providers:
- Qwen / DashScope: `enable_thinking`
- Kimi / Moonshot: `thinking.type`
- GLM / ZHIPU-AI: `thinking.type`
For LiteLLM-based providers, provider-specific fields are forwarded
through `extra_body` before `drop_params` filtering so the request
parameters are preserved.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: jiashi <jiashi19@outlook.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
## Summary
#16332 fixed the missing `return` in DeepL's except branch, but
`ComponentBase.be_output` was removed during the agent refactor (#9113)
while several components still call it. DeepL (and other tools) would
raise `AttributeError` before any error message could be returned.
- Restore `ComponentBase.be_output` as `pd.DataFrame([{"content": v}])`
(same as pre-refactor behavior)
- Add regression test that `_run` returns the `**Error**:` message when
translation fails
Related to #16329
## Test plan
- [x] `test_run_returns_error_on_translation_failure`
- [x] Existing `test_deepl.py` check() tests still pass
---------
Co-authored-by: Harsh Kashyap <harshkashyap@Harshs-MacBook-Pro.local>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
Closes#14773.
Today, Pipeline (`rag/flow/`) chunking strategies only run as part of a
dataset ingestion that always embeds and indexes the result. There is no
way to drive Pipeline-style chunking from an Agent workflow without
paying that vectorization/persistence cost.
This PR adds a single new Agent component, `PipelineChunker`, that:
- Takes one or more file references (from `Begin` / `UserFillUp`
uploads) as input.
- Runs the existing `rag.app.*` chunking strategies (`naive`, `paper`,
`qa`, `manual`, `book`, `presentation`, `laws`, `table`, `one`, `email`,
`picture`, `audio`, `resume`, `tag`) against each file.
- Emits the resulting chunks as `chunks: list[str]` and `chunks_full:
list[dict]` for downstream Agent nodes.
- Performs **no embedding and no persistence** — chunks live only in
canvas variables for the duration of the run, exactly as requested in
the issue.
The component is auto-discovered by `agent/component/__init__.py`; no
registry edits required. Chunker functions are imported lazily so the
component itself does not pull `deepdoc` / OCR / VLM at
component-discovery time. File resolution mirrors the existing
`ExcelProcessor` convention.
Out of scope for this PR (potential follow-ups):
- Vectorization / KB persistence (explicit ask in the issue).
- Frontend canvas UI for the new component.
- Bridging to the newer Pydantic-based `rag/flow/chunker/TokenChunker`
(consumes a parser node's structured output rather than a raw file — a
separate, larger feature).
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---
## Files changed
- `agent/component/pipeline_chunker.py` — new component (~180 lines)
- `test/unit_test/agent/test_pipeline_chunker.py` — unit tests (~120
lines)
## Test plan
- [x] `ruff check` on changed files — clean.
- [x] `ruff format` applied to the new component file.
- [x] `python -m py_compile` on both new files — both compile.
- [x] New unit test file carries `pytestmark = pytest.mark.p2` so it
runs under marker-filtered CI.
- [x] Every new function, method, and class has a docstring (CodeRabbit
80% docstring-coverage gate).
- [x] `python -m pytest test/unit_test/agent/test_pipeline_chunker.py -x
-q` — **7 passed in 1.95s** locally. Tests stub
`api.db.services.file_service` and `rag.app.*` so they exercise the
parameter validation and parser-id lookup table without requiring the
full backend / model stack.
## Manual integration plan (post-merge)
1. Drop the component into an Agent canvas after a `Begin` node with a
file input.
2. Set `parser_id = "naive"` (or any other strategy) and reference the
file input in `inputs`.
3. Wire the `chunks` output into a downstream `LLM` / `Message` /
`Iteration` node — chunks are available as plain text without any
embedding or KB write.
Co-authored-by: John Baillie <johnbaillie2007@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
## Summary
- harden reopened advisory fixes across REST connector, invoke, document
downloads, and markdown rendering
- add targeted regression coverage for redirect-safe SSRF handling,
invoke SSRF checks, document access control, and markdown sanitization
- verify each referenced GHSA against the original GitHub advisory text
and align the closed-advisory plan with the implemented remediation
## What changed
- add tenant access checks to document download endpoints to avoid
cross-tenant document disclosure
- add per-hop SSRF validation, DNS pinning, redirect handling, and
redirect limits to the REST API connector
- ensure invoke requests validate and pin the resolved host and never
follow redirects implicitly
- keep the generic rate-limited request path wrapped, not just GET and
POST helpers
- sanitize markdown HTML before rendering in the highlight markdown
component
## Validation
- `cd web && npm test -- --runInBand
src/components/highlight-markdown/__tests__/index.test.tsx`
- `.venv/bin/python -m pytest -q
test/unit_test/data_source/test_rest_api_connector.py`
- targeted `test/testcases/test_web_api/...` unit additions were
reviewed, but the suite cannot be executed end-to-end in this
environment because parent `test/testcases/conftest.py` requires a local
service on `127.0.0.1:9380`
## Notes
- all GHSA entries referenced by the plan were checked against the
original GitHub advisory text, not sampled
- the closed-advisory plan document was updated locally during review,
but is intentionally not included in this PR
### What problem does this PR solve?
Two bugs in the Agent Categorize component:
1. The backend rejected `message_history_window_size = 0` while frontend
allowed it, causing API errors.
2. When calling the agent API without a `session_id`, a new session was
created but retained history from previous conversations.
### Type of change
- [x] Bug Fix
### How has this been tested?
- Issue 1: `CategorizeParam().check()` now accepts `0` and rejects
negative values.
- Issue 2: `canvas.clear_history()` is called for new sessions (no
`session_id`), ensuring fresh conversation state. Verified via UI and
API that a second call without `session_id` does not remember the first
conversation.
### Related Issue
Closes#15897
Co-authored-by: RAGFlow Dev <dev@ragflow.local>
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
Ensure agent components with image inputs route to `image2text` models
instead of staying on the chat path, so visual requests use the CV
wrapper when supported.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Browser parsed sys.query from prompts but never called set_input_value,
so node_finished inputs displayed null in the agent orchestration run
log.
Additionally, Browser’s tenant-model path could trigger unsupported
structured-output modes (response_format/tool_choice) for some
OpenAI-compatible providers (notably DeepSeek thinking models), causing
step failures.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
## What
- make `Switch` ignore conditions that have no evaluable items
- add a regression for blank `cpn_id` items falling through to the else
branch
- keep the existing non-empty `and` condition behavior covered
Fixes#15643.
## Verified
- `python -m py_compile agent\component\switch.py
test\unit_test\agent\component\test_switch.py`
- `python -m pytest test\unit_test\agent\component\test_switch.py -q` ->
`2 passed`
- `python -m ruff check agent\component\switch.py
test\unit_test\agent\component\test_switch.py`
- `git diff --check`
I also checked `python -m ruff format --check` on the touched files. It
would reformat pre-existing style in `agent/component/switch.py` beyond
this bug fix, so I kept the patch scoped instead of reformatting the
whole file.
### What problem does this PR solve?
Fix:
- Handle siliconflow and siliconflow_intl api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
Declare doc_id, filename, mime_type, and size as separate outputs on the
Document Generation component so downstream nodes (e.g., the Code
component) can consume them via the variable picker. The existing
download JSON blob is preserved unchanged for the Message component's
download-chip rendering.
### What problem does this PR solve?
The Document Generation component previously exposed only a single
`download` output —
a JSON-encoded blob containing the file's `doc_id`, `filename`,
`mime_type`, `size`,
and base64 payload. On top of that, the variable picker actively hides
this `download`
entry from every consumer except the Message component (because the
embedded base64 is
too heavy to splat into arbitrary downstream nodes).
The combined effect: users wiring the Doc Generator's output into a Code
component had
no way to retrieve basic file info such as `file_name` or `doc_id` from
the picker,
blocking workflows that need to post-process the generated file (e.g.,
registering it
elsewhere, custom delivery, follow-up API calls).
This PR declares `doc_id`, `filename`, `mime_type`, and `size` as
**discrete outputs**
on the Document Generation component, alongside the existing `download`
blob. The new
fields:
- Appear in the variable picker for **all** downstream nodes, including
the Code
component, so users can bind them directly to script arguments.
- Are cheap scalars only — no base64 payload leaks into other
components.
- Leave the existing `download` JSON blob completely untouched, so the
Message
component's download-chip rendering (which parses that blob via
`_is_download_info`)
keeps working with no behavior change.
Changes:
- `agent/component/docs_generator.py` — declare the four new outputs in
`DocGeneratorParam` and emit them via `set_output(...)` in `_invoke`.
- `web/src/pages/agent/constant/index.tsx` — extend
`initialDocGeneratorValues.outputs`
with the new keys.
- `web/src/pages/agent/form/doc-generator-form/index.tsx` — mirror the
new outputs in
the zod schema so the form is valid.
No changes needed to the picker's existing `download`-hiding filter — it
matches only
on the literal output name `download`, so the new metadata entries fall
through
naturally.
Reported in: https://github.com/infiniflow/ragflow/issues/14461.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The agent API currently does not pass chat_template_kwargs to the
underlying LLM call path, so clients cannot control template-level model
behavior (such as thinking-mode toggles) when invoking
/agents/chat/completion. This PR adds passthrough support for
chat_template_kwargs across agent execution flows (session and
non-session, streaming and non-streaming) by propagating it through
canvas runtime state and into LLM invocation kwargs. This addresses the
feature gap raised in [Issue
#14182](https://github.com/infiniflow/ragflow/issues/14182).
Closes#14182
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
This PR adds a new `Browser` operator to Agent workflows, enabling
prompt-driven browser automation in RAGFlow.Technically based
‘Browser-Use’
It includes:
- Backend browser component execution with tenant LLM integration
- Upload source support (file IDs, URLs, variables, CSV/JSON array)
- Downloaded file persistence to RAGFlow storage
- Frontend node/operator integration, form config, icon, and i18n
updates
- Unit tests for upload/download and ID parsing logic
- Dependency and Docker updates for browser-use runtime support
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
This PR fixes 3 bugs in agent components:
### Bug 1: `DataOperations._invoke()` dispatches `"literal_eval"` to
wrong handler
**File:** `agent/component/data_operations.py`, line 76
The `_invoke()` method compares `self._param.operations` against
`"recursive_eval"` (line 76), but the valid value defined in
`DataOperationsParam.__init__()` (line 29) and validated in `check()`
(line 43) is `"literal_eval"`. This means selecting the `literal_eval`
operation from the frontend would never match, and the method
`_literal_eval()` would never be called.
**Fix:** Change `"recursive_eval"` to `"literal_eval"` in the dispatch
condition.
### Bug 2: `VariableAssigner._clear()` — `bool` branch unreachable
**File:** `agent/component/variable_assigner.py`, lines 95–100
In Python, `bool` is a subclass of `int` (`True` is `isinstance(True,
int) == True`). The `isinstance(variable, int)` check on line 95 catches
boolean values before the `isinstance(variable, bool)` check on line 99,
making the bool branch unreachable. A boolean variable would be cleared
to `0` instead of `False`.
**Fix:** Move the `isinstance(variable, bool)` check before
`isinstance(variable, int)`.
### Bug 3: `LoopItem.evaluate_condition()` — `bool` branch unreachable
**File:** `agent/component/loopitem.py`, lines 67–93
Same issue as Bug 2: `isinstance(var, (int, float))` on line 67 catches
boolean values before `isinstance(var, bool)` on line 85. Boolean
variables would be evaluated with numeric operators (`=`, `≠`, `>`,
etc.) instead of boolean operators (`is`, `is not`).
**Fix:** Move the `isinstance(var, bool)` check before `isinstance(var,
(int, float))`.
## Test plan
- [ ] Verify `DataOperations` with `literal_eval` operation correctly
invokes `_literal_eval()`
- [ ] Verify `VariableAssigner._clear()` returns `False` for boolean
variables (not `0`)
- [ ] Verify `LoopItem.evaluate_condition()` uses boolean operators for
`True`/`False` values
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Bug Fixes**
* Fixed operation routing logic to correctly dispatch the "literal_eval"
operation to its handler.
* **Refactor**
* Reorganized conditional branch ordering in agent components to improve
code structure and maintainability without affecting functional
behavior.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
### What problem does this PR solve?
When multiple MCP servers expose tools with the same name, the agent
currently registers those tools using their original MCP names. This can
lead to two issues:
- later MCP tools may overwrite earlier ones in the agent tool map
- duplicate function names may be exposed to the LLM
This PR fixes duplicate MCP tool-name handling by applying the same
indexed naming strategy already used for native agent tools. Native
tools are exposed with generated names such as `<tool_name>_<index>` to
avoid collisions, and MCP tools now follow the same convention for
consistency.
Specifically, this PR:
- assigns unique indexed function names to MCP tools exposed to the LLM
- preserves each MCP tool's original server-side name in an
`MCPToolBinding`
- dispatches MCP calls using the original MCP tool name while keeping
the indexed name in the agent tool map
- allows MCP metadata conversion to override only the OpenAI function
name without modifying the original MCP tool metadata
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Validation
The validation was performed using two MCP servers. Both servers exposed
a tool with the same name: `mcp0`. Both tools take no input parameters.
**MCP Server One:**
<img width="1780" height="625" alt="ONE"
src="https://github.com/user-attachments/assets/801a2654-fc10-4b71-b31c-81841fd40c55"
/>
**MCP Server Two:**
<img width="1777" height="624" alt="Second"
src="https://github.com/user-attachments/assets/c095151d-7bdf-47c8-9bfe-6aaf4a01b944"
/>
**Before the fix:**
When invoking `mcp0`, only the `mcp0` tool from the MCP server injected
later could be called successfully. As shown below, both `mcp0` tools
were present, but only the later-registered one was actually invokable.
<img width="694" height="935" alt="Three"
src="https://github.com/user-attachments/assets/3b9d7ab2-1765-492c-b8e0-bf05a69933ca"
/>
**After the fix:**
Both `mcp0` tools can now be invoked correctly.
<img width="737" height="1095" alt="F"
src="https://github.com/user-attachments/assets/6e896627-2b7f-41bb-becc-daa0c73ff58f"
/>
<img width="730" height="1090" alt="six"
src="https://github.com/user-attachments/assets/aba75593-26ae-4e3b-951d-b45ff177fd32"
/>
\`assert \"string\"\` always passes in Python because non-empty strings
are truthy. This silently skips input validation:
- **variable_assigner.py line 51**: \`assert \"Variable is not
complete.\"\` → \`raise ValueError(\"Variable is not complete.\")\`
- **loop.py line 59**: \`assert \"Loop Variable is not complete.\"\` →
\`raise ValueError(\"Loop Variable is not complete.\")\`
Without this fix, incomplete variables pass validation silently and
cause a confusing KeyError on the next line.
### What problem does this PR solve?
fix:
update null checks to use 'is None' for better clarity
replace RAGFlowSelect with SelectWithSearch in DebugContent
add max height and overflow to DialogContent in ParameterDialog
remove unused types from DataOperationsForm
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
- Tool-type components (Email, Invoke, etc.) fail to resolve template
strings that mix variable references with literal text in their
parameters.
- This adds template string resolution to `get_input()` in
`ComponentBase`, reusing existing `get_input_elements_from_text()` and
`string_format()` methods.
## Problem
`get_input()` in `ComponentBase` handles two cases:
1. **Pure reference** (`{Component:ID@field}`) — resolved via
`is_reff()` + `get_variable_value()`
2. **Literal value** — passed through as-is
But template strings like `{UserFillUp:X@name}@duke.edu` or `Question
from {Agent:Y@topic}` fall through to the literal branch because
`is_reff()` returns `False` (it expects the entire string to be a single
reference). The unresolved template is passed directly to the tool.
This affects **all** tool components (Email, Invoke, etc.) that need
mixed reference + text parameters — for example, constructing email
addresses or subjects dynamically.
## Fix
```python
# In get_input(), between is_reff check and literal fallback:
elif isinstance(v, str) and re.search(self.variable_ref_patt, v):
elements = self.get_input_elements_from_text(v)
kv = {k: e.get('value', '') for k, e in elements.items()}
self.set_input_value(var, self.string_format(v, kv))
```
This reuses `get_input_elements_from_text()` and `string_format()` which
are already used by `Message` components for the same purpose. The fix
only activates when the string contains at least one variable reference
pattern but is not a pure reference.
## Test plan
- [x] Pure references (`{Component:ID@field}`) still resolve correctly
via `is_reff()` path
- [x] Literal values without references pass through unchanged
- [x] Template strings like `{ref}@duke.edu` resolve the reference and
keep the literal suffix
- [x] Template strings like `Question from {ref}` resolve correctly
- [x] Multiple references in one string (`{ref1} and {ref2}`) both
resolve
- [x] Message components unaffected (they use their own template
resolution in `_run`)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: wanghualoong <wanghualoong@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
preserve doc generator download metadata
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
agent toolcall null response & schema validation & DeepSeek think
history
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
The POST /upload_info?url=<url> endpoint accepted a user-supplied URL
and passed it directly to AsyncWebCrawler without any validation. There
were no restrictions on URL scheme, destination hostname, or resolved IP
address. This allowed any authenticated user to instruct the server to
make outbound HTTP requests to internal infrastructure — including RFC
1918 private networks, loopback addresses, and cloud metadata services
such as http://169.254.169.254 — effectively using the server as a proxy
for internal network reconnaissance or credential theft.
This PR adds an SSRF guard (_validate_url_for_crawl) that runs before
any crawl is initiated. It enforces an allowlist of safe schemes
(http/https), resolves the hostname at validation time, and rejects any
URL whose resolved IP falls within a private or reserved network range.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Sandbox don't attach attachment metadata
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
- Implemented a helper function to convert markdown cell text to native
numeric types for Excel output.
- Ensured that leading zeros are preserved and handled various numeric
formats, including those with thousand separators and scientific
notation.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
\`switch.py\` line 137 concatenates the operator directly after the text
without separator:
\`'Not supported operator' + operator\` → produces \`"Not supported
operatorXXX"\`
Changed to: \`f'Not supported operator: {operator}'\`
Two small fixes:
1. **iterationitem.py line 72**: Typo "interationitem" → "iterationitem"
(missing 't'). The component name check never matched IterationItem
components.
2. **raptor.py line 94**: Error message "Embedding error: " had a
trailing colon with no details. Changed to "Embedding error: empty
embeddings returned".
### What problem does this PR solve?
Get user_id from canvas variable when input a {} pattern value.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Closes#13277
### What problem does this PR solve?
Adds `{variable_name}` (and `{component@variable}`) interpolation
support to HTTP header values in the `Invoke` component, matching the
existing URL interpolation behavior.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
<img width="1280" height="867" alt="image"
src="https://github.com/user-attachments/assets/8ab7b4e9-7cc0-4a7f-8a5f-f838a15a5fda"
/>
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
### What problem does this PR solve?
Get user_id from canvas and record it.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
This PR fixes two runtime bugs in agent components:
**Bug 1: `agent/component/invoke.py` — `NameError` in POST +
`clean_html` path**
The POST method's `clean_html` branch uses the variable `sections`
without ever defining it. Both the GET and PUT branches correctly call
`sections = HtmlParser()(None, response.content)` before referencing
`sections`, but this line was missing from the POST branch (copy-paste
omission). This causes a `NameError` whenever a user configures an
Invoke component with `method="post"` and `clean_html=True`.
**Bug 2: `agent/component/data_operations.py` — `AttributeError` in
`_recursive_eval`**
The `_recursive_eval` method recursively calls `self.recursive_eval()`
(without the leading underscore) instead of `self._recursive_eval()`.
Since the method is defined as `_recursive_eval`, this causes an
`AttributeError` at runtime when the `literal_eval` operation processes
nested dicts or lists.
## Test plan
- [ ] Configure an Invoke node with `method=post` and `clean_html=True`,
verify HTML is parsed correctly without `NameError`
- [ ] Configure a DataOperations node with `operations=literal_eval` on
nested data, verify no `AttributeError`
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Signed-off-by: JiangNan <1394485448@qq.com>
