OpenClaw + Todoist: Sync Agent Reasoning to Tasks

AI Task Manager Automation: The Agent Black Box Problem

An OpenClaw agent runs a 12-step research and drafting task overnight. The output file is there in the morning. But when a colleague asks "what did it actually do in step 4?" or "why did it take that approach?" — there is no answer. The reasoning happened inside the context window and was never written anywhere durable.

This is the agent black box problem. It makes agents hard to trust and difficult to debug when something goes wrong.

The Todoist sync pattern solves it by writing the agent's reasoning trail to a place you already check: your task manager. One Todoist task per agent run. One comment per reasoning step. Updated in real time as the agent works. This pattern is one of the most cross-applicable OpenClaw use cases in the library — it adds an audit trail to any agent you've already configured, without changing how that agent operates.

Get a full audit trail for every OpenClaw agent run — no architecture changes, just four Todoist API calls wrapped around your existing agent. Test the log formatting prompt at wisgate.ai/studio/image before the first live run. Get your WisGate key at wisgate.ai/hall/tokens.

WisGate API and Model Specifications

This tutorial uses Claude Opus 4.6 via WisGate for the log formatting step — condensing verbose agent output into a clean 120-word comment before each Todoist write. The 256,000-token context window means Opus can read a full execution history and produce a precise summary for each step.

Step-by-Step Setup: Configuring OpenClaw for WisGate

Step 1 — Open the configuration file

nano ~/.openclaw/openclaw.json

Step 2 — Add the WisGate provider

Paste the following into your models section:

"models": {
  "mode": "merge",
  "providers": {
    "moonshot": {
      "baseUrl": "https://api.wisgate.ai/v1",
      "apiKey": "WISGATE-API-KEY",
      "api": "openai-completions",
      "models": [
        {
          "id": "claude-opus-4-6",
          "name": "Claude Opus 4.6",
          "reasoning": false,
          "input": ["text"],
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "contextWindow": 256000,
          "maxTokens": 8192
        }
      ]
    }
  }
}

Replace WISGATE-API-KEY with your key from wisgate.ai/hall/tokens. The "mode": "merge" setting adds WisGate alongside existing providers. The cost fields are zero because WisGate billing is tracked on their dashboard, not inside OpenClaw.

Step 3 — Save and restart

• Ctrl + O → Enter to save, then Ctrl + X to exit
• Ctrl + C to stop OpenClaw, then run openclaw tui

Note: OpenClaw was previously known as ClawdBot and MoltBot. These steps apply to all versions.

Designing the Reasoning Log Format for Todoist

Raw agent output is too verbose for a task comment thread. The log formatting prompt condenses each step to 120 words before writing to Todoist.

Log formatting system prompt (Opus — one call per reasoning step):

You are an agent transparency logger.
INPUT: a single reasoning step output from an OpenClaw agent.

FORMAT as a Todoist comment:
- Step: [NUMBER] — [TYPE: plan | research | draft | evaluate | decide | execute]
- Action: one sentence — what the agent did
- Outcome: one sentence — what was produced or concluded
- Next: one sentence — what happens next (if known)

Rules:
- Maximum 120 words total
- No raw JSON, no code blocks, no markdown headers
- If a judgment call was made, add: Decision: [one sentence]
- Return plain text only. No preamble.

Validate before the first live run:

curl -s -X POST "https://api.wisgate.ai/v1/chat/completions" \
  -H "Authorization: Bearer $WISDOM_GATE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-6",
    "messages": [
      {"role": "system", "content": "[PASTE LOG FORMATTING PROMPT]"},
      {"role": "user",   "content": "Format this step:\n\n[SAMPLE REASONING OUTPUT]"}
    ],
    "max_tokens": 300
  }' | jq -r '.choices[0].message.content'

Or test at wisgate.ai/studio/image with claude-opus-4-6 selected.

LLM Agent Transparency Configuration: Syncing Task Completion

Four Todoist API calls cover the full agent run lifecycle. All four reference current_task_id.txt as the coordination file.

1 — Create task when the agent run starts:

curl -s -X POST "https://api.todoist.com/rest/v2/tasks" \
  -H "Authorization: Bearer $TODOIST_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "[AGENT_NAME] — Run [RUN_ID] — [TIMESTAMP]",
    "project_id": "'$TODOIST_PROJECT_ID'",
    "description": "Status: running\nStarted: [TIMESTAMP]"
  }' | jq -r '.id' > current_task_id.txt

2 — Add comment per reasoning step:

TASK_ID=$(cat current_task_id.txt)
curl -s -X POST "https://api.todoist.com/rest/v2/comments" \
  -H "Authorization: Bearer $TODOIST_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"task_id": "'"$TASK_ID"'", "content": "[FORMATTED LOG ENTRY]"}'

3 — Update description at milestones:

curl -s -X POST "https://api.todoist.com/rest/v2/tasks/$TASK_ID" \
  -H "Authorization: Bearer $TODOIST_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"description": "Status: running\nSteps: [N]\nMilestone: [NAME]"}'

4 — Close on completion (or label FAILED on error):

# Success
curl -s -X POST "https://api.todoist.com/rest/v2/tasks/$TASK_ID/close" \
  -H "Authorization: Bearer $TODOIST_TOKEN"

# Failure — preserve the task, add label
curl -s -X POST "https://api.todoist.com/rest/v2/tasks/$TASK_ID" \
  -H "Authorization: Bearer $TODOIST_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"labels": ["FAILED"], "description": "Error: [SUMMARY]"}'

The failure path matters: the audit trail is most useful when a run does not complete successfully. Keeping the task open with a FAILED label — rather than closing it — makes it visible in Todoist's default task views.

OpenClaw Use Cases: Cost Per Run and Next Steps

Per-run token estimate (10-step agent):

Confirm claude-opus-4-6 pricing at wisgate.ai/models. At ~7,600 tokens per 10-step run, the overhead is modest against the debugging time saved when any run needs investigation.

The configuration is complete and all four API calls are ready to copy. Browse the WisGate model catalog at wisgate.ai/models, generate your key at wisgate.ai/hall/tokens, and validate the log formatting prompt at wisgate.ai/studio/image. Start with one agent — once the comment format looks right, apply the same four calls to every agent in your stack.