jbeasley/openclaw-workspace-2026

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JC Beasley 0bd719ab91 Add cross-project pattern registry for retrieval-augmented generalization

- Create patterns/ directory with README, manifest, and 10 initial patterns
  covering Ollama JSON fallback, API escaping, deprecation, PTY auth,
  queue-poll, LLM-as-parser, credential rotation, reverse proxy binding,
  human approval gates, and transient retry.
- Wire pattern loading into architecture/pipeline.js based on task tags.
- Update architecture/orchestrator.js to load patterns and surface them in
  the system prompt.
- Update MEMORY.md, ARCHITECTURE.md, and CONTEXT.md to document the registry
  and record the decision.

2026-08-06 12:46:09 -07:00

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Pattern: JSON Escaping for Downstream APIs

Symptom

An n8n workflow passes LLM-generated text into a downstream API call, and the request fails with a JSON parse error, malformed payload, or unexpected truncation. The generated text contains quotes, newlines, backslashes, emojis, or control characters that break JSON encoding.

Affected Projects

LinkedIn content automation (LinkedIn REST API posts)
AI video generation pipeline (ComfyUI / JSON2Video payloads)
Any n8n workflow that injects LLM output into an HTTP Request node body

Root Cause

LLMs produce human-readable text; downstream APIs consume machine-readable JSON. Naive string concatenation or weak JSON serialization allows unescaped characters to corrupt the payload. The failure often appears at the receiving API, making root-cause diagnosis slower.

Standard Fix

Treat LLM output as untrusted string data.
Always serialize it through a proper JSON encoder (JSON.stringify in JS, json.dumps in Python) before embedding in a payload.
If building a payload string manually, escape quotes, backslashes, newlines, and control characters; better, avoid manual string building entirely.
Add a validation step that parses the final payload with JSON.parse before sending.
For n8n, prefer expression mapping through structured fields rather than raw body strings.

When to Apply

Any new integration where LLM-generated content becomes part of an API request body.
Any HTTP Request node in n8n that builds a JSON body from expressions containing LLM output.

Verification

Test with adversarial LLM output containing quotes, newlines, unicode, and backslashes.
Confirm the receiving API parses the payload correctly.
Log payload shape (without secrets) for debugging.

ollama-structured-output-fallback
llm-as-parser-fallback

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