wehub/simonw--llm
1
0
Fork 0
Code Issues Pull Requests Actions Deployments Models Agent Notes Packages Projects Releases Wiki Activity

chore: import upstream snapshot with attribution

Browse Source
This commit is contained in:
wehub-resource-sync
2026-07-13 12:48:46 +08:00
commit 6fdd9b4d7a
114 changed files with 40223 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/plugins/advanced-model-plugins.md
+614
View File
@@ -0,0 +1,614 @@
(advanced-model-plugins)=
# Advanced model plugins
The {ref}`model plugin tutorial <tutorial-model-plugin>` covers the basics of developing a plugin that adds support for a new model. This document covers more advanced topics.
Features to consider for your model plugin include:
- {ref}`Accepting API keys <advanced-model-plugins-api-keys>` using the standard mechanism that incorporates `llm keys set`, environment variables and support for passing an explicit key to the model.
- Including support for {ref}`Async models <advanced-model-plugins-async>` that can be used with Python's `asyncio` library.
- Support for {ref}`structured output <advanced-model-plugins-schemas>` using JSON schemas.
- Support for {ref}`tools <advanced-model-plugins-tools>`.
- Handling {ref}`attachments <advanced-model-plugins-attachments>` (images, audio and more) for multi-modal models.
- Tracking {ref}`token usage <advanced-model-plugins-usage>` for models that charge by the token.
(advanced-model-plugins-lazy)=
## Tip: lazily load expensive dependencies
If your plugin depends on an expensive library such as [PyTorch](https://pytorch.org/) you should avoid importing that dependency (or a dependency that uses that dependency) at the top level of your module. Expensive imports in plugins mean that even simple commands like `llm --help` can take a long time to run.
Instead, move those imports to inside the methods that need them. Here's an example [change to llm-sentence-transformers](https://github.com/simonw/llm-sentence-transformers/commit/f87df71e8a652a8cb05ad3836a79b815bcbfa64b) that shaved 1.8 seconds off the time it took to run `llm --help`!
(advanced-model-plugins-api-keys)=
## Models that accept API keys
Models that call out to API providers such as OpenAI, Anthropic or Google Gemini usually require an API key.
LLM's API key management mechanism {ref}`is described here <api-keys>`.
If your plugin requires an API key you should subclass the `llm.KeyModel` class instead of the `llm.Model` class. Start your model definition like this:
```python
import llm
class HostedModel(llm.KeyModel):
needs_key = "hosted" # Required
key_env_var = "HOSTED_API_KEY" # Optional
```
This tells LLM that your model requires an API key, which may be saved in the key registry under the key name `hosted` or might also be provided as the `HOSTED_API_KEY` environment variable.
Then when you define your `execute()` method it should take an extra `key=` parameter like this:
```python
def execute(self, prompt, stream, response, conversation, key=None):
# key= here will be the API key to use
```
LLM will pass in the key from the environment variable, key registry or that has been passed to LLM as the `--key` command-line option or the `model.prompt(..., key=)` parameter.
(advanced-model-plugins-async)=
## Async models
Plugins can optionally provide an asynchronous version of their model, suitable for use with Python [asyncio](https://docs.python.org/3/library/asyncio.html). This is particularly useful for remote models accessible by an HTTP API.
The async version of a model subclasses `llm.AsyncModel` instead of `llm.Model`. It must implement an `async def execute()` async generator method instead of `def execute()`.
This example shows a subset of the OpenAI default plugin illustrating how this method might work:
```python
from typing import AsyncGenerator
import llm
class MyAsyncModel(llm.AsyncModel):
# This can duplicate the model_id of the sync model:
model_id = "my-model-id"
async def execute(
self, prompt, stream, response, conversation=None
) -> AsyncGenerator[str, None]:
if stream:
completion = await client.chat.completions.create(
model=self.model_id,
messages=messages,
stream=True,
)
async for chunk in completion:
yield chunk.choices[0].delta.content
else:
completion = await client.chat.completions.create(
model=self.model_name or self.model_id,
messages=messages,
stream=False,
)
if completion.choices[0].message.content is not None:
yield completion.choices[0].message.content
```
If your model takes an API key you should instead subclass `llm.AsyncKeyModel` and have a `key=` parameter on your `.execute()` method:
```python
class MyAsyncModel(llm.AsyncKeyModel):
...
async def execute(
self, prompt, stream, response, conversation=None, key=None
) -> AsyncGenerator[str, None]:
```
This async model instance should then be passed to the `register()` method in the `register_models()` plugin hook:
```python
@hookimpl
def register_models(register):
register(
MyModel(), MyAsyncModel(), aliases=("my-model-aliases",)
)
```
The `prompt` object passed to your `execute()` method is an instance of {class}`~llm.Prompt`:
```{eval-rst}
.. autoclass:: llm.Prompt
:members: prompt, system
:exclude-members: model, options
```
(advanced-model-plugins-schemas)=
## Supporting schemas
If your model supports {ref}`structured output <schemas>` against a defined JSON schema you can implement support by first adding `supports_schema = True` to the class:
```python
class MyModel(llm.KeyModel):
...
support_schema = True
```
And then adding code to your `.execute()` method that checks for `prompt.schema` and, if it is present, uses that to prompt the model.
`prompt.schema` will always be a Python dictionary representing a JSON schema, even if the user passed in a Pydantic model class.
Check the [llm-gemini](https://github.com/simonw/llm-gemini) and [llm-anthropic](https://github.com/simonw/llm-anthropic) plugins for example of this pattern in action.
(advanced-model-plugins-tools)=
## Supporting tools
Adding {ref}`tools support <tools>` involves several steps:
1. Add `supports_tools = True` to your model class.
2. If `prompt.tools` is populated, turn that list of `llm.Tool` objects into the correct format for your model.
3. Look out for requests to call tools in the responses from your model. Call `response.add_tool_call(llm.ToolCall(...))` for each of those. This should work for streaming and non-streaming and async and non-async cases. Pass the provider's tool call ID as `tool_call_id=` if there is one; if you omit it LLM synthesizes a unique `tc_`-prefixed id, since consumers rely on every tool call having one.
4. If your prompt has a `prompt.tool_results` list, pass the information from those `llm.ToolResult` objects to your model.
5. Include `prompt.tools` and `prompt.tool_results` and tool calls from `response.tool_calls_or_raise()` in the conversation history constructed by your plugin.
6. Make sure your code is OK with prompts that do not have `prompt.prompt` set to a value, since they may be carrying exclusively the results of a tool call.
This [commit to llm-gemini](https://github.com/simonw/llm-gemini/commit/a7f1096cfbb733018eb41c29028a8cc6160be298) implementing tools helps demonstrate what this looks like for a real plugin.
Here are the relevant dataclasses:
```{eval-rst}
.. autoclass:: llm.Tool
.. autoclass:: llm.ToolCall
.. autoclass:: llm.ToolResult
```
(advanced-model-plugins-attachments)=
## Attachments for multi-modal models
Models such as GPT-4o, Claude 3.5 Sonnet and Google's Gemini 1.5 are multi-modal: they accept input in the form of images and maybe even audio, video and other formats.
LLM calls these **attachments**. Models can specify the types of attachments they accept and then implement special code in the `.execute()` method to handle them.
See {ref}`the Python attachments documentation <python-api-attachments>` for details on using attachments in the Python API.
### Specifying attachment types
A `Model` subclass can list the types of attachments it accepts by defining a `attachment_types` class attribute:
```python
class NewModel(llm.Model):
model_id = "new-model"
attachment_types = {
"image/png",
"image/jpeg",
"image/webp",
"image/gif",
}
```
These content types are detected when an attachment is passed to LLM using `llm -a filename`, or can be specified by the user using the `--attachment-type filename image/png` option.
**Note:** MP3 files will have their attachment type detected as `audio/mpeg`, not `audio/mp3`.
LLM will use the `attachment_types` attribute to validate that provided attachments should be accepted before passing them to the model.
### Handling attachments
The `prompt` object passed to the `execute()` method will have an `attachments` attribute containing a list of `Attachment` objects provided by the user.
An `Attachment` instance has the following properties:
- `url (str)`: The URL of the attachment, if it was provided as a URL
- `path (str)`: The resolved file path of the attachment, if it was provided as a file
- `type (str)`: The content type of the attachment, if it was provided
- `content (bytes)`: The binary content of the attachment, if it was provided
Generally only one of `url`, `path` or `content` will be set.
You should usually access the type and the content through one of these methods:
- `attachment.resolve_type() -> str`: Returns the `type` if it is available, otherwise attempts to guess the type by looking at the first few bytes of content
- `attachment.content_bytes() -> bytes`: Returns the binary content, which it may need to read from a file or fetch from a URL
- `attachment.base64_content() -> str`: Returns that content as a base64-encoded string
A `id()` method returns a database ID for this content, which is either a SHA256 hash of the binary content or, in the case of attachments hosted at an external URL, a hash of `{"url": url}` instead. This is an implementation detail which you should not need to access directly.
Note that it's possible for a prompt with an attachments to not include a text prompt at all, in which case `prompt.prompt` will be `None`.
Here's how the OpenAI plugin handles attachments, including the case where no `prompt.prompt` was provided:
```python
if not prompt.attachments:
messages.append({"role": "user", "content": prompt.prompt})
else:
attachment_message = []
if prompt.prompt:
attachment_message.append({"type": "text", "text": prompt.prompt})
for attachment in prompt.attachments:
attachment_message.append(_attachment(attachment))
messages.append({"role": "user", "content": attachment_message})
# And the code for creating the attachment message
def _attachment(attachment):
url = attachment.url
base64_content = ""
if not url or attachment.resolve_type().startswith("audio/"):
base64_content = attachment.base64_content()
url = f"data:{attachment.resolve_type()};base64,{base64_content}"
if attachment.resolve_type().startswith("image/"):
return {"type": "image_url", "image_url": {"url": url}}
else:
format_ = "wav" if attachment.resolve_type() == "audio/wav" else "mp3"
return {
"type": "input_audio",
"input_audio": {
"data": base64_content,
"format": format_,
},
}
```
As you can see, it uses `attachment.url` if that is available and otherwise falls back to using the `base64_content()` method to embed the image directly in the JSON sent to the API. For the OpenAI API audio attachments are always included as base64-encoded strings.
### Attachments from previous conversations
Conversation history — including attachments from prior turns — is available on the canonical `prompt.messages` list. See the [next section](#structured-messages-streaming) for how that works.
(structured-messages-streaming)=
## Structured messages and streaming events
The 0.32 alpha introduced a richer contract for plugins than "yield strings":
1. **`execute()` yields `StreamEvent` objects** (or plain `str`, still supported) so text, reasoning (thinking tokens), tool calls, and server-side tool results each surface as their own event type. The framework assembles these into typed `Part` objects.
2. **`build_messages` (or equivalent) reads `prompt.messages`** — a `list[llm.Message]` that is the complete input chain for this turn.
3. **Opaque provider tokens round-trip via `provider_metadata`** — Anthropic thinking signatures, Gemini thought signatures, OpenAI Responses API encrypted reasoning blobs. Plugins stash whatever the API returns, then echo it back on the next request.
**Older plugins still work.** A plugin that still yields plain `str` from `execute()` works unchanged — each string is wrapped as a `StreamEvent(type="text", chunk=...)` internally.
### Yielding StreamEvent from execute()
```python
from llm.parts import StreamEvent
def execute(self, prompt, stream, response, conversation, key=None):
messages = self.build_messages(prompt, conversation)
...
for chunk in provider_sdk.stream(...):
if chunk.type == "text":
yield StreamEvent(type="text", chunk=chunk.text)
elif chunk.type == "thinking":
yield StreamEvent(type="reasoning", chunk=chunk.text)
```
A `StreamEvent` has four frequently-used fields:
- **`type`** — one of `"text"`, `"reasoning"`, `"tool_call_name"`, `"tool_call_args"`, `"tool_result"`.
- **`chunk`** — the text fragment. For tool calls this is the tool name (for `tool_call_name`) or a partial JSON string (for `tool_call_args`).
- **`tool_call_id`** — the provider's id for the tool call, set on `tool_call_name` / `tool_call_args` / `tool_result` events. Also the signal the framework uses to group tool-call events into one `ToolCallPart`.
- **`provider_metadata`** — an optional `dict[str, dict]` namespaced by provider name. Carries opaque data (signatures, encrypted blobs) that must be echoed back on future requests.
Three additional fields exist for special cases:
- **`server_executed: bool`** — set `True` for server-side tool calls (for example, Anthropic web search) and their results. This means the model ran the tool internally as part of responding to the prompt.
- **`tool_name`** — set on `tool_result` events to identify which tool this result came from.
- **`part_index: int | None`** — defaults to `None`, which means "let the framework decide which Part this event belongs to." Pass an explicit integer only when you need to override the default grouping (see [below](#part-index-overrides)).
### How events group into Parts
When you leave `part_index` as `None` (the default), the framework groups events using these rules:
- **Consecutive same-family events concatenate.** Two `text` events in a row become one `TextPart`. Two `reasoning` events in a row become one `ReasoningPart`. A family transition (text → reasoning, or reasoning → text) starts a new Part.
- **Tool calls group by `tool_call_id`.** A `tool_call_name` and any number of `tool_call_args` events sharing a `tool_call_id` combine into one `ToolCallPart` — even if they're interleaved with other events (parallel tool calls).
- **`tool_result` is always its own Part**, paired to the originating call by `tool_call_id`.
| Stream | Resulting Parts |
|-------------------------------------------|----------------------------------------------------------|
| `text` × N | one `TextPart` |
| `reasoning` × N, then `text` × N | `ReasoningPart`, `TextPart` |
| `text`, `tool_call_name`+`args`, `text` | `TextPart`, `ToolCallPart`, `TextPart` |
| Parallel tool calls (interleaved by id) | one `ToolCallPart` per distinct `tool_call_id` |
| `reasoning`, tool call, `reasoning` | `ReasoningPart`, `ToolCallPart`, `ReasoningPart` |
(part-index-overrides)=
### Setting `part_index` explicitly
In rare cases you'll want to override the default grouping:
- **Forcing a single TextPart across non-adjacent text bursts.** If your provider interleaves text deltas with tool calls but you want all the text concatenated into one `TextPart`, pass `part_index=0` on every text event. (The default behavior produces separate `TextPart`s on each side of the tool calls — usually what you want, but not always.)
- **Tool-call args arriving before the id.** If your provider streams args before the `tool_call_id` is known, assign your own index per logical tool call and pass it on each event of that call.
You can mix explicit indices with `None` in the same stream — the framework reserves your explicit values and decides the rest.
(advanced-model-plugins-reasoning-tokens)=
### Reasoning tokens
For streamed reasoning text:
```python
yield StreamEvent(type="reasoning", chunk=text_chunk)
```
Reasoning events that appear before/after text events become distinct `ReasoningPart` and `TextPart` entries in `response.messages` automatically. If your provider emits two thinking blocks separated by a tool call, you'll get two `ReasoningPart`s.
Plugins should respect `prompt.hide_reasoning`. This is set when the caller passes `hide_reasoning=True` to `model.prompt()`, `conversation.prompt()`, `model.chain()`, `conversation.chain()`, or their async counterparts. It is also set by the CLI `-R/--hide-reasoning` option.
`prompt.hide_reasoning` means "hide visible reasoning output", not "disable model reasoning". If your provider requires an explicit request for visible reasoning summaries, do not request those summaries when `prompt.hide_reasoning` is true:
```python
kwargs = {}
if not prompt.hide_reasoning:
kwargs["reasoning"] = {"summary": "auto"}
```
If your provider emits reasoning blocks regardless of request parameters, keep yielding those reasoning events as usual:
```python
if chunk.type == "thinking":
yield StreamEvent(type="reasoning", chunk=chunk.text)
```
LLM's display layers use `prompt.hide_reasoning` to avoid showing those events to the user, while still allowing the framework to persist `ReasoningPart` objects and provider metadata for logs, serialization, and future turns.
### Tool calls
Each tool call emits two event types sharing a `tool_call_id`:
```python
yield StreamEvent(
type="tool_call_name",
chunk=tool_name,
tool_call_id=tool_call_id,
)
# then, as the provider streams JSON args:
yield StreamEvent(
type="tool_call_args",
chunk=partial_json_fragment,
tool_call_id=tool_call_id,
)
```
The framework groups them by `tool_call_id` — so parallel tool calls (where args for tool A and tool B interleave on the wire) work without any per-call index tracking. Some providers (Gemini) emit the complete tool call in one chunk — it's OK to emit both events back-to-back with the full name and full JSON.
For client-side tool calls — tools that LLM should execute locally in a chain — **also call `response.add_tool_call()`**. The chain-execution path (`response.tool_calls()` → `execute_tool_calls()`) reads from the explicitly-added list, not from the StreamEvent buffer.
```python
response.add_tool_call(
llm.ToolCall(
tool_call_id=tool_id,
name=tool_name,
arguments=parsed_args,
)
)
```
### Server-side tool calls
For tools the API executes internally, set `server_executed=True` on the events. Anthropic web search is an example: the API returns a `server_tool_use` block for the search request, followed by a `web_search_tool_result` block containing the result payload.
```python
yield StreamEvent(
type="tool_call_name",
chunk="web_search",
tool_call_id=tool_id,
server_executed=True,
)
yield StreamEvent(
type="tool_call_args",
chunk=json.dumps(query_args),
tool_call_id=tool_id,
server_executed=True,
)
```
The tool *result* (for example, the search hits) is also emitted as an event:
```python
yield StreamEvent(
type="tool_result",
chunk=human_readable_summary,
tool_call_id=tool_id,
server_executed=True,
tool_name="web_search",
provider_metadata={"myprovider": {"raw_content": full_payload}},
)
```
For providers that don't stream server-tool-result contents (Anthropic's `web_search_tool_result` blocks only arrive in the final message), emit those results as a post-stream step. After the main iteration loop completes, inspect the final message and emit tool_result events for any server-side results.
Do **not** call `response.add_tool_call()` for server-side tool calls. This method should only be used for tool calls that need to be executed locally by the framework.
### Opaque provider metadata
Some providers require you to echo back opaque fields on the next request for multi-turn continuity to work:
- **Anthropic** — `signature` on each thinking block; `encrypted_content` inside web_search_tool_result items.
- **Google Gemini** — `thoughtSignature` on `functionCall` parts when thinking is active.
- **OpenAI Responses API** — `encrypted_content` on reasoning items in stateless mode.
These values are attached to a `StreamEvent` via its `provider_metadata` field. The framework merges metadata across events that group into the same Part (last non-None wins per top-level key) and persists it on the finalized Part.
Namespace under your provider's name so transcripts that mix providers don't collide:
```python
# Anthropic signature arrives at the end of a thinking block.
yield StreamEvent(
type="reasoning",
chunk="",
provider_metadata={"anthropic": {"signature": sig}},
)
```
```python
# Gemini attaches thoughtSignature to a functionCall part.
yield StreamEvent(
type="tool_call_name",
chunk=name,
tool_call_id=tc_id,
provider_metadata={"gemini": {"thoughtSignature": sig}},
)
```
The framework round-trips the value verbatim via JSON, so use JSON-safe primitives (string, int, bool, dict, list) for provider metadata - use base64 encoding if you need to store binary data.
### Non-streaming path
When `stream=False` (or the provider returns a complete message at once), emit one event per content block.
```python
else:
completion = client.messages.create(**kwargs)
response.response_json = completion.model_dump()
for block in completion.content:
if block.type == "thinking":
yield StreamEvent(
type="reasoning",
chunk=block.thinking,
provider_metadata={"anthropic": {"signature": block.signature}},
)
elif block.type == "text":
yield StreamEvent(type="text", chunk=block.text)
elif block.type == "tool_use":
yield StreamEvent(
type="tool_call_name",
chunk=block.name,
tool_call_id=block.id,
)
yield StreamEvent(
type="tool_call_args",
chunk=json.dumps(block.input),
tool_call_id=block.id,
)
```
## Consuming prompt.messages in build_messages
`prompt.messages` is an `list[llm.Message]` that is always **the complete input chain for this turn** — whether the caller supplied it explicitly via `model.prompt(messages=[...])`, or it was synthesized from kwargs (`prompt=`, `system=`, `attachments=`, `tool_results=`), or it was pre-built by a `Conversation` or by `response.reply()`.
**Do not also walk `conversation.responses`.** History is already baked into `prompt.messages`; walking the conversation would double-emit.
A plugin's `build_messages` (or equivalent) iterates `prompt.messages` and dispatches per `Part` subtype:
```python
from llm.parts import (
TextPart,
ReasoningPart,
ToolCallPart,
ToolResultPart,
AttachmentPart,
)
def build_messages(self, prompt, conversation):
messages = []
for msg in prompt.messages:
if msg.role == "system":
# Some APIs put system on a separate kwarg (Anthropic, Gemini).
# OpenAI-style APIs emit it as a message; handle accordingly.
continue
self._append_message(messages, msg)
return messages
def _append_message(self, out, msg):
# Map llm's role to the provider's (assistant→model for Gemini,
# tool→user for Anthropic/Gemini tool_result convention, etc.)
role = self._provider_role(msg.role)
parts = []
for part in msg.parts:
if isinstance(part, TextPart):
parts.append({"type": "text", "text": part.text})
elif isinstance(part, ReasoningPart):
# Skip redacted reasoning (no content to echo back).
if part.redacted or not part.text:
continue
block = {"type": "thinking", "thinking": part.text}
# Restore the signature from provider_metadata.
sig = (part.provider_metadata or {}).get("anthropic", {}).get("signature")
if sig:
block["signature"] = sig
parts.append(block)
elif isinstance(part, ToolCallPart):
parts.append({
"type": "tool_use",
"id": part.tool_call_id,
"name": part.name,
"input": part.arguments,
})
elif isinstance(part, ToolResultPart):
parts.append({
"type": "tool_result",
"tool_use_id": part.tool_call_id,
"content": part.output,
})
elif isinstance(part, AttachmentPart) and part.attachment:
parts.append(self._attachment_block(part.attachment))
# Merge with the previous message if roles match (some providers
# require strict alternation between user and assistant).
if out and out[-1]["role"] == role:
out[-1]["content"].extend(parts)
else:
out.append({"role": role, "content": parts})
```
## Restoring opaque metadata on subsequent requests
When a conversation continues, your `build_messages` walks prior-turn Parts via `prompt.messages`. Each Part's `provider_metadata` is a `dict[str, dict]` keyed by provider name — extract your namespace and fold the fields back into the outgoing request body:
```python
if isinstance(part, ReasoningPart):
block = {"type": "thinking", "thinking": part.text}
pm = (part.provider_metadata or {}).get("anthropic", {})
if "signature" in pm:
block["signature"] = pm["signature"]
parts.append(block)
if isinstance(part, ToolCallPart):
fc_part = {"function_call": {"name": part.name, "args": part.arguments}}
pm = (part.provider_metadata or {}).get("gemini", {})
if "thoughtSignature" in pm:
# Gemini expects thoughtSignature beside function_call,
# not nested inside it.
fc_part["thoughtSignature"] = pm["thoughtSignature"]
parts.append(fc_part)
```
If the key is missing (an older transcript that pre-dates your plugin's support), fall through — don't fail. Treat other providers' entries as opaque; don't parse them.
(advanced-model-plugins-usage)=
## Tracking token usage
Models that charge by the token should track the number of tokens used by each prompt. The ``response.set_usage()`` method can be used to record the number of tokens used by a response - these will then be made available through the Python API and logged to the SQLite database for command-line users.
`response` here is the response object that is passed to `.execute()` as an argument.
Call ``response.set_usage()`` at the end of your `.execute()` method. It accepts keyword arguments `input=`, `output=` and `details=` - all three are optional. `input` and `output` should be integers, and `details` should be a dictionary that provides additional information beyond the input and output token counts.
This example logs 15 input tokens, 340 output tokens and notes that 37 tokens were cached:
```python
response.set_usage(input=15, output=340, details={"cached": 37})
```
(advanced-model-plugins-resolved-model)=
## Tracking resolved model names
In some cases the model ID that the user requested may not be the exact model that is executed. Many providers have a `model-latest` alias which may execute different models over time.
If those APIs return the _real_ model ID that was used, your plugin can record that in the `resources.resolved_model` column in the logs by calling this method and passing the string representing the resolved, final model ID:
```bash
response.set_resolved_model(resolved_model_id)
```
This string will be recorded in the database and shown in the output of `llm logs` and `llm logs --json`.
(tutorial-model-plugin-raise-errors)=
## LLM_RAISE_ERRORS
While working on a plugin it can be useful to request that errors are raised instead of being caught and logged, so you can access them from the Python debugger.
Set the `LLM_RAISE_ERRORS` environment variable to enable this behavior, then run `llm` like this:
```bash
LLM_RAISE_ERRORS=1 python -i -m llm ...
```
The `-i` option means Python will drop into an interactive shell if an error occurs. You can then open a debugger at the most recent error using:
```python
import pdb; pdb.pm()
```