langchain-content-normalizer 0.1.0

Normalize LangChain, MCP, and multimodal content blocks into provider-ready text and image payloads.

langchain-content-normalizer

Normalize the messy content shapes produced by LangChain, MCP tools, Anthropic content blocks, and multimodal chat APIs.

The package has no runtime dependencies. It works by duck typing instead of importing LangChain or MCP classes.

What it solves

LLM agent stacks often receive content as one of many incompatible shapes:

Source	Example shape	Output
Classic chat	"plain text"	"plain text"
Anthropic blocks	[{"type": "text", "text": "hi"}]	"hi"
Tool calls	[{"type": "tool_use", ...}]	skipped by default
MCP tool results	[{"type": "tool_result", "content": [...]}]	flattened text
MCP objects	objects exposing .text	extracted text
Message wrappers	objects exposing .content	recursively normalized

Install

uv add langchain-content-normalizer

Text normalization

from lc_content_normalizer import extract_text_content, normalize_tool_output

content = [
    {"type": "text", "text": "Reading logs..."},
    {"type": "tool_use", "name": "tail_logs", "input": {"service": "api"}},
]

assert extract_text_content(content) == "Reading logs..."
assert "tail_logs" in extract_text_content(content, skip_tool_use=False)

safe_output = normalize_tool_output(huge_tool_payload, max_chars=50_000)

Vision format routing

from lc_content_normalizer import build_human_message_content, detect_vision_format

vision_format = detect_vision_format("anthropic", "claude-3-5-sonnet")
content = build_human_message_content(
    "Explain this alert screenshot",
    images=[{"data_url": "data:image/png;base64,...", "mime_type": "image/png"}],
    vision_format=vision_format,
)

detect_vision_format() returns:

Provider/model	Format
anthropic	native Anthropic image block with source.base64
ollama + llava/vision model name	OpenAI-compatible image_url block
ollama text-only model	none, images are dropped
OpenAI-compatible providers	OpenAI-compatible image_url block

Examples

• examples/normalize_mcp_output.py shows how MCP-style tool results are flattened.
• examples/build_vision_content.py shows provider-aware image block generation.

Roadmap

• Add strict mode for unknown content blocks.
• Add more MCP fixture coverage.
• Add provider-specific adapters as content formats evolve.
• Keep runtime dependencies at zero.

Development

uv sync --dev
uv run ruff check .
uv run pytest
uv run python scripts/smoke.py

License

MIT