# 2514 Processors 1f5dbcf0

> Processors API

- Skill: `tools-only/2514-processors-1f5dbcf0` (Agent Skill, multi-file: 3 files)
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- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/2514-processors-1f5dbcf0

---

# Processors API

History processors for managing conversation context. These are re-exported from [summarization-pydantic-ai](https://github.com/vstorm-co/summarization-pydantic-ai).

## create_summarization_processor

Factory function for creating a summarization processor with sensible defaults.

### Signature

```python
def create_summarization_processor(
    model: str = "openai:gpt-4.1",
    trigger: ContextSize | list[ContextSize] | None = ("tokens", 170000),
    keep: ContextSize = ("messages", 20),
    max_input_tokens: int | None = None,
    token_counter: TokenCounter | None = None,
    summary_prompt: str | None = None,
) -> SummarizationProcessor
```

### Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `model` | `str` | `"openai:gpt-4.1"` | Model for generating summaries |
| `trigger` | `ContextSize \| list[ContextSize] \| None` | `("tokens", 170000)` | When to trigger summarization |
| `keep` | `ContextSize` | `("messages", 20)` | How much context to keep |
| `max_input_tokens` | `int \| None` | `None` | Max tokens (required for fraction triggers) |
| `token_counter` | `TokenCounter \| None` | `None` | Custom token counting function |
| `summary_prompt` | `str \| None` | `None` | Custom summarization prompt |

### Returns

`SummarizationProcessor` - Configured processor instance.

### Example

```python
from pydantic_deep import create_deep_agent, create_summarization_processor

processor = create_summarization_processor(
    trigger=("tokens", 100000),
    keep=("messages", 20),
)

agent = create_deep_agent(history_processors=[processor])
```

---

## SummarizationProcessor

Dataclass for LLM-based conversation summarization.

### Definition

```python
@dataclass
class SummarizationProcessor:
    model: str
    trigger: ContextSize | list[ContextSize] | None = None
    keep: ContextSize = ("messages", 20)
    token_counter: TokenCounter = count_tokens_approximately
    summary_prompt: str = DEFAULT_SUMMARY_PROMPT
    max_input_tokens: int | None = None
    trim_tokens_to_summarize: int | None = 4000
```

### Attributes

| Attribute | Type | Description |
|-----------|------|-------------|
| `model` | `str` | Model to use for generating summaries |
| `trigger` | `ContextSize \| list[ContextSize] \| None` | Threshold(s) that trigger summarization |
| `keep` | `ContextSize` | How much context to keep after summarization |
| `token_counter` | `TokenCounter` | Function to count tokens in messages |
| `summary_prompt` | `str` | Prompt template for generating summaries |
| `max_input_tokens` | `int \| None` | Maximum input tokens (required for fraction triggers) |
| `trim_tokens_to_summarize` | `int \| None` | Maximum tokens to include when generating summary |

### Methods

#### \_\_call\_\_

```python
async def __call__(self, messages: list[ModelMessage]) -> list[ModelMessage]
```

Process messages and summarize if needed. This is called automatically by pydantic-ai's history processor mechanism.

### Example

```python
from pydantic_deep import SummarizationProcessor

processor = SummarizationProcessor(
    model="openai:gpt-4.1",
    trigger=[
        ("messages", 50),
        ("tokens", 100000),
    ],
    keep=("messages", 10),
    trim_tokens_to_summarize=4000,
)
```

---

## create_sliding_window_processor

Factory function for creating a sliding window processor with sensible defaults.

### Signature

```python
def create_sliding_window_processor(
    trigger: ContextSize | list[ContextSize] | None = ("messages", 100),
    keep: ContextSize = ("messages", 50),
    max_input_tokens: int | None = None,
    token_counter: TokenCounter | None = None,
) -> SlidingWindowProcessor
```

### Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `trigger` | `ContextSize \| list[ContextSize] \| None` | `("messages", 100)` | When to trigger trimming |
| `keep` | `ContextSize` | `("messages", 50)` | How much context to keep |
| `max_input_tokens` | `int \| None` | `None` | Max tokens (required for fraction triggers) |
| `token_counter` | `TokenCounter \| None` | `None` | Custom token counting function |

### Returns

`SlidingWindowProcessor` - Configured processor instance.

### Example

```python
from pydantic_deep import create_deep_agent, create_sliding_window_processor

processor = create_sliding_window_processor(
    trigger=("messages", 100),
    keep=("messages", 50),
)

agent = create_deep_agent(history_processors=[processor])
```

---

## SlidingWindowProcessor

Dataclass for zero-cost message trimming without LLM calls.

### Definition

```python
@dataclass
class SlidingWindowProcessor:
    trigger: ContextSize | list[ContextSize] | None = None
    keep: ContextSize = ("messages", 50)
    token_counter: TokenCounter = count_tokens_approximately
    max_input_tokens: int | None = None
```

### Attributes

| Attribute | Type | Description |
|-----------|------|-------------|
| `trigger` | `ContextSize \| list[ContextSize] \| None` | Threshold(s) that trigger trimming |
| `keep` | `ContextSize` | How much context to keep after trimming |
| `token_counter` | `TokenCounter` | Function to count tokens in messages |
| `max_input_tokens` | `int \| None` | Maximum input tokens (required for fraction triggers) |

### Methods

#### \_\_call\_\_

```python
def __call__(self, messages: list[ModelMessage]) -> list[ModelMessage]
```

Process messages and trim if needed. Note: This is a synchronous method (no LLM calls).

### Example

```python
from pydantic_deep import SlidingWindowProcessor

processor = SlidingWindowProcessor(
    trigger=("tokens", 100000),
    keep=("messages", 50),
)
```

---

## Type Aliases

### ContextSize

```python
ContextFraction = tuple[Literal["fraction"], float]
ContextTokens = tuple[Literal["tokens"], int]
ContextMessages = tuple[Literal["messages"], int]

ContextSize = ContextFraction | ContextTokens | ContextMessages
```

Specifies context size thresholds:

- `("messages", N)` - Number of messages
- `("tokens", N)` - Number of tokens
- `("fraction", F)` - Fraction of `max_input_tokens` (0 < F <= 1)

### TokenCounter

```python
TokenCounter = Callable[[Sequence[ModelMessage]], int]
```

Function type for custom token counting.

---

---

## EvictionProcessor

History processor that evicts large tool outputs to files. See [Eviction](../advanced/eviction.md).

### Definition

```python
@dataclass
class EvictionProcessor:
    backend: BackendProtocol
    token_limit: int = 20_000
    eviction_path: str = "/large_tool_results"
    head_lines: int = 5
    tail_lines: int = 5
```

### Factory

```python
from pydantic_deep import create_eviction_processor

processor = create_eviction_processor(
    backend=StateBackend(),
    token_limit=20000,
)
```

---

## patch_tool_calls_processor

History processor that fixes orphaned tool calls in message history.

```python
from pydantic_deep.processors.patch import patch_tool_calls_processor

# Use as history processor
agent = Agent("openai:gpt-4.1", history_processors=[patch_tool_calls_processor])

# Or via create_deep_agent
agent = create_deep_agent(patch_tool_calls=True)
```

---

## ContextManagerMiddleware

Dual-protocol component from summarization-pydantic-ai. Acts as both history processor and AgentMiddleware.

### Factory

```python
from pydantic_ai_summarization import create_context_manager_middleware

middleware = create_context_manager_middleware(
    max_tokens=200_000,
    compress_threshold=0.9,
    on_usage_update=lambda pct, cur, mx: print(f"{pct:.0%}"),
)
```

See [History Processors](../advanced/processors.md#context-manager-middleware) for details.

---

## Next Steps

- [Agent API](agent.md) - Agent factory and configuration
- [Types API](types.md) - Type definitions