Formatters Guide

Formatters convert the internal ContextWindow into the format expected by a specific LLM provider. anchor ships with three built-in formatters and a Formatter protocol for building your own.

Overview

After the pipeline assembles system prompts, memory, and retrieval context into a ContextWindow, a formatter transforms that window into the final output. Different providers expect different structures:

Formatter	Provider	Output Type
`AnthropicFormatter`	Anthropic Messages API	`dict` with `system` + `messages`
`OpenAIFormatter`	OpenAI Chat Completions	`dict` with `messages`
`GenericTextFormatter`	Any (plain text)	`str` with section headers

AnthropicFormatter

Produces a dict ready for the Anthropic Messages API:

from anchor import ContextPipeline, AnthropicFormatter

pipeline = ContextPipeline(max_tokens=8192)
pipeline.with_formatter(AnthropicFormatter())
pipeline.add_system_prompt("You are a helpful assistant.")

result = pipeline.build("Hello!")
output = result.formatted_output
# output = {
#     "system": [{"type": "text", "text": "You are a helpful assistant."}],
#     "messages": [...]
# }

Prompt Caching

Enable Anthropic prompt caching to reduce latency and cost:

formatter = AnthropicFormatter(enable_caching=True)

When caching is enabled:

The last system content block gets "cache_control": {"type": "ephemeral"}
The last retrieval/context message also gets a cache control marker
Repeated prefixes (system prompt + static context) are cached by Anthropic

[!TIP] Prompt caching is most effective when the system prompt and retrieval context are stable across requests. Enable it for production workloads where you send many requests with the same prefix.

Output Structure

{
    "system": [
        {"type": "text", "text": "System prompt here"},
        # ... more system blocks, each from a separate system item
    ],
    "messages": [
        {"role": "user", "content": "Here is relevant context:\n\n..."},
        {"role": "user", "content": "Hello!"},
        {"role": "assistant", "content": "Hi there!"},
        # ... alternating user/assistant messages
    ]
}

The formatter enforces strict user/assistant alternation required by the Anthropic API. Consecutive messages with the same role are merged automatically.

OpenAIFormatter

Produces a dict with a flat messages list for the OpenAI Chat Completions API:

from anchor import ContextPipeline, OpenAIFormatter

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

result = pipeline.build("Hello!")
output = result.formatted_output
# output = {
#     "messages": [
#         {"role": "system", "content": "You are a helpful assistant."},
#         {"role": "user", "content": "Relevant context:\n\n..."},
#         {"role": "user", "content": "Hello!"},
#     ]
# }

Security

Retrieval content is placed in user role messages, not system. This prevents prompt injection from untrusted retrieval sources from gaining system-level authority.

[!CAUTION] Content from memory and retrieval items is inserted verbatim without sanitization. If these items originate from untrusted sources (user-supplied documents, web scrapes), they may contain prompt injection payloads. Implement content validation or filtering before items enter the pipeline.

GenericTextFormatter

Produces structured plain text with section headers -- useful for non-chat models, logging, or debugging:

from anchor import ContextPipeline, GenericTextFormatter

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

result = pipeline.build("Hello!")
print(result.formatted_output)
# === SYSTEM ===
# You are a helpful assistant.
#
# === MEMORY ===
# [user] Hello!
#
# === CONTEXT ===
# Retrieved document chunk...

The formatter produces up to three sections (SYSTEM, MEMORY, CONTEXT), each separated by blank lines. Empty sections are omitted.

Custom Formatters

Implement the Formatter protocol to create your own formatter:

from anchor import Formatter, ContextPipeline
from anchor.models import ContextWindow

class GeminiFormatter:
    """Formats context for the Google Gemini API."""

    @property
    def format_type(self) -> str:
        return "gemini"

    def format(self, window: ContextWindow) -> dict:
        contents = []
        for item in window.items:
            contents.append({
                "role": "user",
                "parts": [{"text": item.content}],
            })
        return {"contents": contents}

# Use it
pipeline = ContextPipeline(max_tokens=8192)
pipeline.with_formatter(GeminiFormatter())

The Formatter protocol requires:

Member	Type	Description
`format_type`	`property -> str`	Identifier (e.g. `"anthropic"`, `"openai"`)
`format(window)`	`method -> str \| dict`	Converts `ContextWindow` to output

[!NOTE] Formatter is a PEP 544 Protocol -- no inheritance required. Any class with matching format_type and format() signatures satisfies it via structural subtyping.

Choosing a Formatter

Use Case	Formatter	Why
Anthropic Claude API	`AnthropicFormatter`	Native system block format
OpenAI GPT API	`OpenAIFormatter`	System/user/assistant roles
Local models, logging	`GenericTextFormatter`	Plain text, no structure
Agent class	`AnthropicFormatter`	Agent uses it internally
Custom provider	Custom `Formatter`	Match your API format

Formatters Guide

Formatters Guide

Overview

AnthropicFormatter

Prompt Caching

Output Structure

OpenAIFormatter

Security

GenericTextFormatter

Custom Formatters

Choosing a Formatter

See Also

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