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Formatters API Reference

Formatters convert a ContextWindow into the format expected by a specific LLM provider. All formatters are importable from anchor:

from anchor import (
    Formatter, AnthropicFormatter, OpenAIFormatter, GenericTextFormatter,
)

Formatter (Protocol)

The protocol all formatters must satisfy. This is a PEP 544 structural protocol -- no inheritance required.

Definition

@runtime_checkable
class Formatter(Protocol):
    @property
    def format_type(self) -> str: ...

    def format(self, window: ContextWindow) -> str | dict[str, Any]: ...

Members

Member Type Description
format_type property -> str Identifier (e.g. "anthropic", "openai", "generic")
format(window) method Converts a ContextWindow into the target format

Note

BaseFormatter is a deprecated alias for Formatter. Use Formatter in new code.

AnthropicFormatter

Formats context for the Anthropic Messages API.

Constructor

class AnthropicFormatter:
    def __init__(self, *, enable_caching: bool = False) -> None

Parameters

Parameter Type Default Description
enable_caching bool False Enable Anthropic prompt caching markers

Properties

Property Type Value
format_type str "anthropic"

Methods

format

def format(self, window: ContextWindow) -> dict[str, Any]

Returns a dict with:

  • "system" -- list of content block dicts ({"type": "text", "text": "..."})
  • "messages" -- list of message dicts with role and content keys

When enable_caching=True, adds "cache_control": {"type": "ephemeral"} to the last system block and last context message.

The formatter enforces strict user/assistant alternation required by the Anthropic API.

Example

from anchor import ContextPipeline, AnthropicFormatter

pipeline = ContextPipeline(max_tokens=8192)
pipeline.with_formatter(AnthropicFormatter(enable_caching=True))
pipeline.add_system_prompt("You are helpful.")

result = pipeline.build("Hello")
output = result.formatted_output
# {"system": [...], "messages": [...]}

OpenAIFormatter

Formats context for the OpenAI Chat Completions API.

Constructor

class OpenAIFormatter:
    def __init__(self) -> None

No constructor parameters.

Properties

Property Type Value
format_type str "openai"

Methods

format

def format(self, window: ContextWindow) -> dict[str, Any]

Returns a dict with:

  • "messages" -- list of message dicts with role ("system", "user", "assistant") and content keys

System prompts use the "system" role. Retrieval content uses the "user" role to prevent privilege escalation from untrusted content.

Example

from anchor import ContextPipeline, OpenAIFormatter

pipeline = ContextPipeline(max_tokens=8192)
pipeline.with_formatter(OpenAIFormatter())
pipeline.add_system_prompt("You are helpful.")

result = pipeline.build("Hello")
output = result.formatted_output
# {"messages": [{"role": "system", "content": "..."}, ...]}

GenericTextFormatter

Formats context as structured plain text with section headers.

Constructor

class GenericTextFormatter:
    def __init__(self) -> None

No constructor parameters.

Properties

Property Type Value
format_type str "generic"

Methods

format

def format(self, window: ContextWindow) -> str

Returns a string with up to three sections separated by blank lines:

=== SYSTEM ===
System prompt text

=== MEMORY ===
Conversation history

=== CONTEXT ===
Retrieved content

Empty sections are omitted.

Example

from anchor import ContextPipeline, GenericTextFormatter

pipeline = ContextPipeline(max_tokens=8192)
pipeline.with_formatter(GenericTextFormatter())
pipeline.add_system_prompt("You are helpful.")

result = pipeline.build("Hello")
print(result.formatted_output)
# === SYSTEM ===
# You are helpful.
# ...

See Also