@people1/n8n-nodes-bedrock-advanced

People1 fork of [email protected] by Amir Souchami.

Why this fork?

The original node extracts Bedrock prompt cache metrics (cacheReadInputTokens, cacheWriteInputTokens) from the API response but does not propagate them to the N8N node output. They are only written to container debug logs, which requires admin SSH access to read.

This fork fixes that: cache metrics now appear in the standard N8N execution output and can be queried via the REST API (GET /api/v1/executions/{id}) by any developer.

Coexistence with the original

This fork uses different internal node type names so it can be installed alongside the original:

| | Original | Fork (P1) | |---|---|---| | Converse API node | AWS Bedrock Chat Model (Advanced) | AWS Bedrock Chat Model (Advanced P1) | | InvokeModel node | Bedrock Claude | Bedrock Claude (P1) | | Internal type | lmChatAwsBedrockAdvanced | lmChatAwsBedrockAdvancedP1 | | Internal type | lmChatBedrockClaude | lmChatBedrockClaudeP1 |

Both appear in the N8N node picker. Developers choose which to use per workflow.

What changed

Patches applied to both nodes:

1. Added cache fields to llmOutput.tokenUsage — so cache metrics travel through LangChain's callback chain
2. Custom tokensUsageParser for N8nLlmTracing — so N8N's tracing callback preserves cache fields instead of stripping them
3. Streaming path fix (BedrockClaude only) — reads cache metrics from response_metadata.usage for the streaming aggregation path
4. Renamed node types — P1 suffix to coexist with the original package

Output format (after fix)

{
  "tokenUsage": {
    "completionTokens": 500,
    "promptTokens": 150,
    "totalTokens": 650,
    "cacheReadInputTokens": 120,
    "cacheWriteInputTokens": 0
  }
}

When cache is disabled or not applicable, cacheReadInputTokens and cacheWriteInputTokens are 0.

Installation

On the N8N server (admin only)

# 1. Find the N8N main container
docker ps --format '{{.Names}}' | grep n8n

# 2. Install the fork alongside the original (replace CONTAINER)
docker exec CONTAINER sh -c "cd /home/node/.n8n/nodes && npm install github:franlealp1/n8n-nodes-bedrock-advanced-p1"

# 3. Restart N8N to pick up the new nodes
docker restart CONTAINER

Both the original and P1 nodes will appear in the node picker.

Persisting across Coolify deploys

Add to the N8N service's Custom Start Command in Coolify:

cd /home/node/.n8n/nodes && npm install github:franlealp1/n8n-nodes-bedrock-advanced-p1 && cd / && n8n start

Or in a post-deploy script if available.

Querying cache metrics (developers)

After installation, any developer with N8N API access can query cache metrics:

# Get execution details (replace ID with execution ID)
curl -s "$N8N_BASE_URL/api/v1/executions/724002" \
  -H "X-N8N-API-KEY: $N8N_API_KEY" | \
  python3 -c "
import json, sys
data = json.load(sys.stdin)
for node_name, node_data in data.get('data', {}).get('resultData', {}).get('runData', {}).items():
    for run in node_data:
        for output in run.get('data', {}).get('ai_languageModel', []):
            for item in output:
                tu = item.get('json', {}).get('tokenUsage', {})
                if tu.get('cacheReadInputTokens', 0) > 0 or tu.get('cacheWriteInputTokens', 0) > 0:
                    print(f'{node_name}: read={tu[\"cacheReadInputTokens\"]}, write={tu[\"cacheWriteInputTokens\"]}')
"

Original features (unchanged)

All features from the original node are preserved:

• AWS Bedrock Chat Model (Advanced P1) — Converse API, multi-model, prompt caching (system/tools/history), configurable TTL
• Bedrock Claude (P1) — InvokeModel API, Claude-specific features (web search, computer use, bash, text editor, tool search, programmatic tool calling, 1M context, context compaction)

License

MIT (same as original)