> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anyformat.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Agentic Parse to Markdown
> End-to-end recipe — create a parse-only workflow with agentic routing, upload a document, retrieve markdown with per-block confidence scores
Each block in the document is routed through a typed strategy (text / table / figure / dense-table-agent) instead of a single batched call — better tables and richer figure descriptions.
## What is agentic parsing?
Standard parsing sends every block to one LLM call with one prompt. Agentic parsing first **routes** each block (using a fast LLM with the page image) to the right specialised strategy:
| Block type | Strategy | Why |
| ---------------- | -------------------- | ---------------------------------------------------------------------- |
| Plain text | `text-bytes-first` | Cheap, fast — bypasses LLM entirely when source bytes are extractable |
| Tables | `dense-table-agent` | Tool-calling agent that handles spans, merged cells, multi-page tables |
| Figures / charts | `single-call vision` | LLM with image input for structured figure descriptions |
## End-to-end
```bash theme={null}
# 1. Create an agentic parse-only workflow
curl -X POST 'https://api.anyformat.ai/v2/workflows/' \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $ANYFORMAT_API_KEY" \
-d '{
"name": "Agentic parse-only",
"nodes": [{"id": "parse_1", "type": "parse", "mode": "agentic"}],
"edges": []
}'
# 2. Submit a document
curl -X POST 'https://api.anyformat.ai/v2/workflows/WORKFLOW_ID/run/' \
-H "Authorization: Bearer $ANYFORMAT_API_KEY" \
-F 'file=@document.pdf'
# 3. Poll for results (agentic mode takes 30–90s for a typical 3-page document)
curl -H "Authorization: Bearer $ANYFORMAT_API_KEY" \
'https://api.anyformat.ai/v2/workflows/WORKFLOW_ID/files/COLLECTION_ID/results/'
```
```typescript theme={null}
import { Anyformat } from "@anyformat/sdk";
const af = new Anyformat({ apiKey: process.env.ANYFORMAT_API_KEY! });
const file: File = /* a File with .name set */;
const workflow = await af
.workflow("Agentic parse-only")
.parse({ mode: "agentic" })
.create();
const run = await workflow.run(file);
const result = await run.wait({ timeoutMs: 180_000, pollMs: 3_000 }); // agentic runs are slower
// Agentic mode doesn't emit parseConfidence — fall back to layoutConfidence.
const confidence = result.parse?.parseConfidence ?? result.parse?.layoutConfidence;
console.log(`document confidence: ${confidence}`);
console.log(result.parse?.markdown?.slice(0, 500) ?? "");
```
```python theme={null}
import os
from anyformat.sdk import Client
client = Client(api_key=os.environ["ANYFORMAT_API_KEY"])
workflow = (
client.workflow("Agentic parse-only")
.parse(mode="agentic")
.create()
)
result = workflow.run("document.pdf").wait(timeout=180) # agentic runs are slower
# Agentic mode doesn't emit parse_confidence — fall back to layout_confidence.
# Explicit `is not None` check (not `or`) so a real 0.0 confidence still wins.
confidence = (
result.parse.parse_confidence
if result.parse.parse_confidence is not None
else result.parse.layout_confidence
)
print(f"document confidence: {confidence}")
print((result.parse.markdown or "")[:500])
```
Agentic mode takes longer than standard mode — typically 30–90s for a 3-page document — because each block hits its own LLM strategy. Use [webhooks](/api-reference/webhooks/overview) instead of polling for production workloads.
### Sample response
```json theme={null}
{
"collection_id": "069dcc2c-e14c-7606-8000-2ee4fb17b4e1",
"verification_url": "https://app.anyformat.ai/workflows/.../files/...",
"parse": {
"markdown": "...",
"text": "...",
"parse_confidence": null,
"layout_confidence": 47.8,
"blocks": [/* … */]
},
"classifications": [],
"splits": [],
"extractions": []
}
```
## Per-block vs. document confidence
The response carries two document-level rollups plus a per-block `data-confidence` attribute inside the markdown.
```html theme={null}
```
| Field | Use for |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| **`parse.parse_confidence`** — char-weighted mean of per-block LLM logprobs (typical 80–99); `null` when no block had logprob-based confidence | Triage: "is this doc worth processing further?" |
| **`parse.layout_confidence`** — char-weighted mean of YOLO layout-segmentation scores (typical 30–60); always present when blocks exist | Fallback when `parse_confidence` is null. Measures "is this region a table?", not "is the parsed content accurate?" |
| **`data-confidence`** per `` | UI highlighting: dim or flag low-confidence regions inline |
The document-level values are **character-weighted means** — a 500-char paragraph at 80% counts \~100× more than a 5-char header at 99%.
**Mode caveat:** in agentic mode, per-block strategies don't always populate parser logprobs (e.g. the fast `text-bytes-first` strategy never calls an LLM). When logprobs are absent, `parse_confidence` is `null` and callers fall back to `layout_confidence`. For calibrated parser confidence comparable to extraction confidence (80–99 range), use `mode="standard"`.
## Use with AI coding agents
Building an integration on top of anyformat? Install the **anyformat Claude Code skill** so Claude (or any compatible agent) knows the right endpoints, payloads, and gotchas out of the box. See [Coding assistant](/guides/coding-assistant) for installation and example prompts.
## Next steps
Full reference for the typed-graph endpoint
Standard parse-only cookbook with the same endpoint
Add an extract node after the parse step to pull structured fields
Skip polling — receive `extraction.completed` events