OpenClaw Skill: glmocr
Parses documents (images, PDFs, scans) via the GLM-OCR SDK.
📌 On-demand: This skill requires only ZHIPU_API_KEY in the environment. No YAML config files or GPU needed.
⚡ Quick Start
# Install
pip install glmocr
# Set API key (once)
export ZHIPU_API_KEY=sk-xxx
# or add to .env file in working directory:
echo "ZHIPU_API_KEY=sk-xxx" >> .env
# One-liner
import glmocr
result = glmocr.parse("document.pdf")
print(result.markdown_result)
print(result.to_dict())
# CLI — pass API key directly (no env setup needed)
glmocr parse image.png --api-key sk-xxx
# Or load from a specific .env file
glmocr parse image.png --env-file /path/to/.env
# Or rely on env var / auto-discovered .env (set once, then omit)
glmocr parse image.png
glmocr parse ./scans/ --output ./output/ --stdout
Configuration Priority
Constructor kwargs > os.environ > .env file > config.yaml > built-in defaults
Agents override everything via constructor kwargs or env vars — no YAML editing needed.
Key Environment Variables
| Variable | Description | Example |
|---|
ZHIPU_API_KEY | API key (required for MaaS) | sk-abc123 |
GLMOCR_MODEL | Model name | glm-ocr |
GLMOCR_TIMEOUT | Request timeout (seconds) | 600 |
GLMOCR_ENABLE_LAYOUT | Layout detection on/off | true |
GLMOCR_LOG_LEVEL | DEBUG / INFO / WARNING / ERROR | INFO |
Python API
Convenience function (single call)
import glmocr
# Single file → PipelineResult
result = glmocr.parse("invoice.png")
# Multiple files → list[PipelineResult]
results = glmocr.parse(["page1.png", "page2.png", "report.pdf"])
Class-based (multiple calls / resource reuse)
from glmocr import GlmOcr
parser = GlmOcr(api_key="sk-xxx") # mode auto-set to "maas"
parser = GlmOcr(mode="maas") # reads ZHIPU_API_KEY from env
# Always use as context manager or call .close()
with GlmOcr(api_key="sk-xxx") as parser:
result = parser.parse("document.png")
print(result.markdown_result)
parser.close() # if not using `with`
Constructor Parameters
| Parameter | Type | Description |
|---|
api_key | str | API key. Providing this auto-enables MaaS mode. |
api_url | str | Override MaaS endpoint URL |
model | str | Model name override |
timeout | int | Request timeout in seconds (default: 600) |
enable_layout | bool | Enable layout detection |
log_level | str | Logging level |
Working with PipelineResult
Fields
result.markdown_result # str — full document as Markdown
result.json_result # list[list[dict]] — structured regions per page
result.original_images # list[str] — absolute paths of input images
json_result structure
List of pages → list of regions per page:
[
[
{
"index": 0,
"label": "title",
"content": "Annual Report 2024",
"bbox_2d": [100, 50, 900, 120]
},
{
"index": 1,
"label": "table",
"content": "| Q1 | Q2 |\n|---|---|\n| 120 | 145 |",
"bbox_2d": [100, 140, 900, 400]
}
]
]
Bounding boxes (bbox_2d): [x1, y1, x2, y2] normalised to 0–1000 scale.
Region labels: title, text, table, figure, formula, header, footer, page_number, reference, seal
Serialization
# Dict (JSON-serializable, for passing to other tools)
d = result.to_dict()
# Keys: json_result, markdown_result, original_images, usage (MaaS), data_info (MaaS)
# JSON string
json_str = result.to_json() # pretty-printed, ensure_ascii=False
json_str = result.to_json(indent=None) # compact single line
# Save to disk: writes <stem>/<stem>.json + <stem>/<stem>.md + layout_vis/
result.save(output_dir="./output")
result.save(output_dir="./output", save_layout_visualization=False)
Error Handling
The SDK does not raise on MaaS errors — check to_dict() for an "error" key:
result = parser.parse("image.png")
d = result.to_dict()
if "error" in d:
# Handle failure
print("OCR failed:", d["error"])
else:
print(d["markdown_result"])
CLI Reference
Agent-preferred interface: use the CLI for most operations. Set ZHIPU_API_KEY in env once, then invoke as needed.
Supported input formats: .jpg, .jpeg, .png, .bmp, .gif, .webp, .pdf
Basic usage
# Parse a single file → saves to ./output/<stem>/
# MaaS mode is the default; ZHIPU_API_KEY must be set (or use --api-key)
glmocr parse image.png
# Pass API key directly without any env setup
glmocr parse image.png --api-key sk-xxx
# Parse a directory → saves each file to ./output/<stem>/
glmocr parse ./scans/
# Use self-hosted vLLM/SGLang instead of cloud
glmocr parse image.png --mode selfhosted
# Specify output directory
glmocr parse image.png --output ./results/
Read results in the terminal (agent-friendly)
# Print Markdown + JSON to stdout (and still save to disk)
glmocr parse image.png --stdout
# Print to stdout ONLY — do not write any files
glmocr parse image.png --stdout --no-save
# JSON only (no Markdown output)
glmocr parse image.png --stdout --json-only
# Pipe JSON into jq for structured extraction
glmocr parse image.png --stdout --json-only --no-save | jq '.[0] | map(select(.label=="table"))'
Save control
# Skip layout visualization images (faster, smaller output)
glmocr parse image.png --no-layout-vis
# Parse and save only JSON + Markdown, skip layout vis
glmocr parse image.png --no-layout-vis --output ./results/
Batch processing
# All images in a folder
glmocr parse ./invoice_scans/ --output ./parsed/ --no-layout-vis
# With progress visible in logs
glmocr parse ./docs/ --output ./parsed/ --log-level INFO
Debugging
glmocr parse image.png --log-level DEBUG
Full flag reference
| Flag | Default | Description |
|---|
--api-key / -k | env var | API key for MaaS mode (overrides ZHIPU_API_KEY) |
--mode | maas | maas (cloud, default) or selfhosted (local GPU) |
--env-file | auto | Path to .env file (default: auto-discover from cwd) |
--output / -o | ./output | Output directory |
--stdout | off | Print JSON + Markdown to stdout |
--no-save | off | Skip writing files (use with --stdout) |
--json-only | off | stdout JSON only, no Markdown |
--no-layout-vis | off | Skip layout visualization images |
--config / -c | none | Path to YAML config override |
--log-level | INFO | DEBUG / INFO / WARNING / ERROR |
Typical Agent Workflow
receive document path / URL
│
▼
glmocr.parse(path) ← single call, handles PDF/image
│
▼
result.to_dict() ← safe to pass as tool output
│
├── markdown_result → hand to LLM for reading / summarization
└── json_result → structured extraction (tables, formulas, regions by label)
Filter by label
result = glmocr.parse("report.png")
regions = result.json_result[0] # first page
tables = [r for r in regions if r["label"] == "table"]
formulas = [r for r in regions if r["label"] == "formula"]
body_text = [r for r in regions if r["label"] == "text"]
Multi-page PDF → iterate pages
with GlmOcr(api_key="sk-xxx") as parser:
result = parser.parse("document.pdf") # all pages in one PipelineResult
for page_idx, page_regions in enumerate(result.json_result):
print(f"Page {page_idx + 1}: {len(page_regions)} regions")
for region in page_regions:
print(f" [{region['label']}] {region['content'][:60]}")
Programmatic config (no env vars)
from glmocr.config import GlmOcrConfig
cfg = GlmOcrConfig.from_env(
api_key="sk-xxx",
mode="maas",
timeout=600,
log_level="DEBUG",
)
Output Directory Layout
After result.save(output_dir):
output_dir/
<image_stem>/
<image_stem>.json ← structured regions
<image_stem>.md ← full Markdown (with cropped figure images)
imgs/ ← cropped figures referenced in Markdown
layout_vis/ ← layout detection overlay images (if enabled)
<image_stem>.jpg
Common Pitfalls
ZHIPU_API_KEY not set: SDK defaults to MaaS mode. Without a key, parse() will fail with a clear error message and quick-fix instructions. Set via export ZHIPU_API_KEY=sk-xxx, add to a .env file, or pass --api-key sk-xxx to the CLI.- Large PDFs: Default timeout is 600s. For very long documents increase with
timeout=1200.
result.json_result is a string: Happens when the model returns malformed JSON. The SDK preserves the raw string — parse or log it manually.
