**Locating the repository**

I need to find the repository, which might be located under /mnt/rpi/repos. It feels a bit broad, so I might want to narrow down my search. I should check there first, but there could be other locations I’m not thinking of. This could get a little complicated, so I'll take my time to ensure I'm looking in the right place. Let's see what I can uncover!

find /mnt/rpi/repos -maxdepth 3 -type d | grep 'agents' | sed -n '1,200p'


(no output)


**Exploring local directories**

I’m considering using the local repository in /mnt/rpi/pi-config. I think it could be helpful to inspect the files there, but I need to be sure I’m heading in the right direction. I'll take a closer look at what's inside. This approach might give me the information I need, so let’s see what I find when I check it out!

bash

cd /mnt/rpi/pi-config && git status --short && find . -maxdepth 2 -type f | grep -E 'agents-database|memory|schema|job' | sed -n '1,200p'


 M README.md
 M extensions/session-memory-dispatcher.ts
 M scripts/__pycache__/session-memory-analyzer.cpython-313.pyc
 M scripts/__pycache__/session-memory-dispatcher.cpython-313.pyc
 M scripts/session-memory-analyzer.py
 M scripts/session-memory-dispatcher.py
 M settings.json
./scripts/install-session-memory-dispatcher.service
./scripts/session-memory-dispatcher.py
./scripts/session-memory-analyzer.py
./extensions/session-memory-dispatcher.ts
./sessions/memory-techniques-review.html
./sessions/memory-techniques-review.md
./sessions/spec-memory-layer-agents.md
./sessions/RFC-memory-layer-agents.html
./sessions/RFC-memory-layer-agents.md


**Inspecting current files**

I need to inspect the current files that are relevant to my task. I want to make sure I have all the necessary information on hand to assist effectively. It's important to review the details and see what I can gather from them. I want to ensure I’m not missing anything vital that might help me in this process. Let’s take a closer look!


#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import subprocess
from datetime import datetime, timezone
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from pathlib import Path

BASE_DIR = Path(__file__).resolve().parent.parent
JOB_DIR = Path(os.environ.get("PI_SESSION_MEMORY_JOB_DIR", BASE_DIR / "sessions" / "session-memory-jobs"))
ANALYZER = Path(os.environ.get("PI_SESSION_MEMORY_ANALYZER", BASE_DIR / "scripts" / "session-memory-analyzer.py"))

def write_job(payload: dict[str, object]) -> Path:
    JOB_DIR.mkdir(parents=True, exist_ok=True)
    job_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ")
    job_file = JOB_DIR / f"{job_id}.json"
    job = {
        "job_id": job_id,
        "queued_at": datetime.now(timezone.utc).isoformat(),
        "status": "queued",
        "payload": payload,
    }
    job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
    return job_file

def launch_analyzer(job_file: Path) -> None:
    subprocess.Popen(
        ["python3", str(ANALYZER), "--job-file", str(job_file)],
        stdout=subprocess.DEVNULL,
        stderr=subprocess.DEVNULL,
        start_new_session=True,
        close_fds=True,
    )

class Handler(BaseHTTPRequestHandler):
    def do_POST(self):
        if self.path != "/api/session-memory":
            self.send_response(404)
            self.end_headers()
            return

        length = int(self.headers.get("Content-Length", "0") or "0")
        raw = self.rfile.read(length)
        try:
            payload = json.loads(raw.decode("utf-8"))
        except Exception as exc:
            self.send_response(400)
            self.send_header("Content-Type", "application/json")
            self.end_headers()
            self.wfile.write(json.dumps({"error": f"invalid json: {exc}"}).encode())
            return

        try:
            job_file = write_job(payload)
            launch_analyzer(job_file)
            self.send_response(202)
            self.send_header("Content-Type", "application/json")
            self.end_headers()
            self.wfile.write(json.dumps({"status": "queued", "job_file": str(job_file)}).encode())
        except Exception as exc:
            self.send_response(502)
            self.send_header("Content-Type", "application/json")
            self.end_headers()
            self.wfile.write(json.dumps({"error": str(exc)}).encode())

    def log_message(self, fmt, *args):
        return

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", default="127.0.0.1")
    parser.add_argument("--port", type=int, default=17882)
    args = parser.parse_args()

    server = ThreadingHTTPServer((args.host, args.port), Handler)
    print(f"session-memory-dispatcher listening on http://{args.host}:{args.port}/api/session-memory", flush=True)
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        return 0
    return 0

if __name__ == "__main__":
    raise SystemExit(main())


#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import subprocess
import sys
from pathlib import Path
from typing import Any
from urllib import request

AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
PI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4-mini")
PI_THINKING = os.environ.get("PI_SESSION_MEMORY_THINKING", "off")

def load_job(job_file: Path) -> dict[str, Any]:
    return json.loads(job_file.read_text(encoding="utf-8"))

def save_job(job_file: Path, payload: dict[str, Any]) -> None:
    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

def call_pi(prompt: str) -> str:
    cmd = [
        "pi",
        "-p",
        "--provider",
        "openai-codex",
        "--model",
        PI_MODEL,
        "--thinking",
        PI_THINKING,
        "--no-tools",
        "--no-session",
        prompt,
    ]
    proc = subprocess.run(cmd, capture_output=True, text=True)
    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()

def ingest(memory: dict[str, Any]) -> dict[str, Any]:
    req = request.Request(
        f"{AGENTS_DB_API}/api/ingest",
        method="POST",
        data=json.dumps(memory).encode(),
        headers={"Content-Type": "application/json"},
    )
    with request.urlopen(req, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def extract_json(text: str) -> dict[str, Any]:
    raw = text.strip()
    if raw.startswith("```"):
        raw = raw.strip("`")
        raw = raw.split("\n", 1)[-1] if "\n" in raw else raw
    start = raw.find("{")
    end = raw.rfind("}")
    if start >= 0 and end > start:
        raw = raw[start : end + 1]
    return json.loads(raw)

def session_meta(job: dict[str, Any]) -> dict[str, Any]:
    payload = job.get("payload")
    if isinstance(payload, dict):
        return payload
    return job

def build_prompt(job: dict[str, Any]) -> str:
    meta = session_meta(job)
    return f"""You are deciding whether a Pi session has durable value.

Rules:
- Prefer NOT saving anything unless it is durable and reusable.
- Session metadata alone is not enough.
- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, or a blocker that will matter in future unrelated sessions.
- If nothing durable exists, return save=false.
- Output JSON only. No markdown. No extra text.

Return this schema:
{{
  "save": boolean,
  "type": "profile" | "decision" | "project" | "artifact" | "episode",
  "subtype": string | null,
  "scope": "global" | "project",
  "title": string,
  "content": string,
  "source_ref": string,
  "reason": string
}}

Session metadata:
- session_id: {meta.get('session_id')}
- session_file: {meta.get('session_file')}
- cwd: {meta.get('cwd')}
- scope: {meta.get('scope')}
- reason: {meta.get('reason')}
- user_messages: {meta.get('user_messages')}
- assistant_messages: {meta.get('assistant_messages')}
- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}
- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}
- trigger: {meta.get('trigger')}

Branch excerpt JSON:
{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}
"""

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--job-file", required=True)
    args = parser.parse_args()

    job_file = Path(args.job_file)
    job = load_job(job_file)
    meta = session_meta(job)
    job.setdefault("analysis", {})
    job["analysis"]["status"] = "running"
    save_job(job_file, job)

    try:
        raw = call_pi(build_prompt(job))
        analysis = extract_json(raw)
        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"

        if bool(analysis.get("save")):
            memory = {
                "type": analysis.get("type") or "episode",
                "subtype": analysis.get("subtype"),
                "scope": analysis.get("scope") or meta.get("scope") or "global",
                "status": "active",
                "source_kind": "manual",
                "title": analysis.get("title") or "Session insight",
                "content": analysis.get("content") or "",
                "source_ref": analysis.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
                "origin_agent": "pi-session-memory-analyzer",
                "metadata": {
                    "job_file": str(job_file),
                    "session_id": meta.get("session_id"),
                    "session_file": meta.get("session_file"),
                    "cwd": meta.get("cwd"),
                    "reason": meta.get("reason"),
                    "user_messages": meta.get("user_messages"),
                    "assistant_messages": meta.get("assistant_messages"),
                    "analysis_reason": analysis.get("reason"),
                    "trigger": meta.get("trigger"),
                },
            }
            saved = ingest(memory)
            job["analysis"]["memory_id"] = saved.get("id")
            job["analysis"]["saved"] = True
        else:
            job["analysis"]["saved"] = False
            job["analysis"]["memory_id"] = None

        save_job(job_file, job)
        print(json.dumps(job["analysis"], ensure_ascii=False))
        return 0
    except Exception as exc:
        job["analysis"]["status"] = "failed"
        job["analysis"]["error"] = str(exc)
        save_job(job_file, job)
        print(json.dumps(job["analysis"], ensure_ascii=False), file=sys.stderr)
        return 1

if __name__ == "__main__":
    raise SystemExit(main())


import path from "node:path";
import type { ExtensionAPI } from "@mariozechner/pi-coding-agent";

const DISPATCH_URL = process.env.PI_SESSION_MEMORY_DISPATCH_URL || "http://127.0.0.1:17882/api/session-memory";
const PROMPT_SAVE = "Yes";
const PROMPT_SKIP = "No";
const PROMPT_CANCEL = "Cancel";

function textFromMessage(message: any): string {
	if (!message || !Array.isArray(message.content)) return "";
	return message.content
		.filter((part: any) => part?.type === "text" && typeof part.text === "string")
		.map((part: any) => part.text)
		.join("\n")
		.trim();
}

function truncate(text: string | undefined, max = 220): string {
	const value = (text || "").replace(/\s+/g, " ").trim();
	if (!value) return "";
	return value.length > max ? value.slice(0, max - 1) + "…" : value;
}

function inferScope(cwd: string): "global" | "project" {
	const home = path.resolve(process.env.HOME || "/home/sebas");
	const resolved = path.resolve(cwd);
	if (resolved === home) return "global";
	if (resolved.startsWith(path.join(home, ".pi"))) return "global";
	return "project";
}

function getSessionStats(ctx: any) {
	const branch = ctx.sessionManager.getBranch();
	let userCount = 0;
	let assistantCount = 0;
	let lastUserText = "";
	let lastAssistantText = "";

	for (const entry of branch) {
		if (entry?.type !== "message") continue;
		const message = entry.message;
		if (!message || typeof message !== "object" || !("role" in message)) continue;
		if (message.role === "user") {
			userCount += 1;
			lastUserText = textFromMessage(message) || lastUserText;
		}
		if (message.role === "assistant") {
			assistantCount += 1;
			lastAssistantText = textFromMessage(message) || lastAssistantText;
		}
	}

	return {
		branch,
		userCount,
		assistantCount,
		lastUserText,
		lastAssistantText,
		shouldPrompt: userCount > 0 && assistantCount > 0,
	};
}

function buildHookPayload(ctx: any, stats: ReturnType<typeof getSessionStats>, reason: string) {
	return {
		session_id: ctx.sessionManager.getLeafId?.() || null,
		session_file: ctx.sessionManager.getSessionFile?.() || null,
		reason,
		cwd: ctx.cwd,
		scope: inferScope(ctx.cwd),
		user_messages: stats.userCount,
		assistant_messages: stats.assistantCount,
		last_user_text: truncate(stats.lastUserText, 2000),
		last_assistant_text: truncate(stats.lastAssistantText, 2000),
		branch: stats.branch,
	};
}

async function dispatchMemory(payload: any) {
	const response = await fetch(DISPATCH_URL, {
		method: "POST",
		headers: { "Content-Type": "application/json" },
		body: JSON.stringify(payload),
	});
	if (!response.ok) {
		throw new Error(`session memory dispatcher failed: ${response.status} ${response.statusText}`);
	}
	return response.json();
}

export default function (pi: ExtensionAPI) {
	let promptHandledForCurrentSession = false;

	async function runPrompt(ctx: any, reason: string, options?: { allowCancel?: boolean; title?: string; timeoutMs?: number }) {
		if (promptHandledForCurrentSession) return { cancel: false, skipped: true };
		if (!ctx.hasUI) return { cancel: false, skipped: true };

		const stats = getSessionStats(ctx);
		if (!stats.shouldPrompt) return { cancel: false, skipped: true };

		const allowCancel = options?.allowCancel ?? false;
		const timeoutMs = options?.timeoutMs;
		const title = options?.title || "Before closing this session";
		const choices = allowCancel ? [PROMPT_SAVE, PROMPT_SKIP, PROMPT_CANCEL] : [PROMPT_SAVE, PROMPT_SKIP];
		const dialogOptions = timeoutMs ? { timeout: timeoutMs } : undefined;
		const choice = await ctx.ui.select(title, choices, dialogOptions);

		if (choice === PROMPT_CANCEL || (allowCancel && choice === undefined)) {
			promptHandledForCurrentSession = false;
			return { cancel: true, skipped: false };
		}

		if (choice !== PROMPT_SAVE) {
			promptHandledForCurrentSession = true;
			return { cancel: false, skipped: true };
		}

		const payload = buildHookPayload(ctx, stats, reason);
		try {
			const result = await dispatchMemory({
				...payload,
				trigger: "session-memory-dispatcher",
			});
			promptHandledForCurrentSession = true;
			ctx.ui.notify(`Session analysis queued${result?.job_file ? `: ${result.job_file}` : ""}`, "info");
			return { cancel: false, skipped: false, queued: true, jobFile: result?.job_file || null };
		} catch (error) {
			promptHandledForCurrentSession = false;
			const message = error instanceof Error ? error.message : String(error);
			ctx.ui.notify(message, "error");
			if (!allowCancel) return { cancel: false, skipped: true, error: message };
			const ok = await ctx.ui.confirm("Session analysis failed", "Continue closing this session without queueing?", dialogOptions);
			if (!ok) return { cancel: true, skipped: false, error: message };
			promptHandledForCurrentSession = true;
			return { cancel: false, skipped: true, error: message };
		}
	}

	pi.on("session_before_switch", async (event, ctx) => {
		const result = await runPrompt(ctx, `before_switch:${event.reason}`, { allowCancel: true, title: "Before closing this session" });
		if (result.cancel) return { cancel: true };
		return undefined;
	});

	pi.on("session_before_fork", async (_event, ctx) => {
		const result = await runPrompt(ctx, "before_fork", { allowCancel: true, title: "Before forking away from this session" });
		if (result.cancel) return { cancel: true };
		return undefined;
	});

	pi.on("session_before_shutdown", async (event, ctx) => {
		const result = await runPrompt(ctx, `before_shutdown:${event.reason}`, { allowCancel: true, title: "Guardar insights en memoria?", timeoutMs: 12000 });
		if (result.cancel) return { cancel: true };
		return undefined;
	});

	pi.on("session_shutdown", async (_event, ctx) => {
		await runPrompt(ctx, "shutdown_fallback", { allowCancel: false, title: "Guardar insights en memoria?", timeoutMs: 12000 });
	});

	pi.registerCommand("remember-session", {
		description: "Queue a session analysis from the current session",
		handler: async (_args, ctx) => {
			promptHandledForCurrentSession = false;
			await runPrompt(ctx, "manual_command", { allowCancel: false, title: "Queue session analysis from this session" });
		},
	});
}


**Planning schema changes**

I need to implement changes gradually, maybe starting with schema updates and dispatch payload fields. I'll consider whether to read through the rest of the analyzer documentation, but it might not be necessary. Do we need to ask questions? Probably not, so I can proceed with sensible defaults. I'll also think about inspecting the agents-database ingest schema to see if I can use the memory search, but maybe inspection isn't accessible. I'll work on including hierarchical fields in our scripts, ensuring the analyzer produces structured memory fields like confidence and freshness.

**Updating memory fields**

I want to patch the specifications and scripts to include candidate and promotion fields in outgoing memories. Storing in the agents-database might be sufficient for now, even if jobs aren’t included yet. I’ll check the settings or README to see if they mention the service. I’m thinking of gradually implementing changes in the agents-database through the ingest API. For now, I’ll update the analyzer prompt to include confidence, freshness, and evidence, while also enhancing the job payload and possibly adding new job types.

edit

def build_prompt(job: dict[str, Any]) -> str:
    meta = session_meta(job)
    return f"""You are deciding whether a Pi session has durable value.

Rules:
- Prefer NOT saving anything unless it is durable and reusable.
- Session metadata alone is not enough.
- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, or a blocker that will matter in future unrelated sessions.
- If nothing durable exists, return save=false.
- Output JSON only. No markdown. No extra text.

Return this schema:
{{
  "save": boolean,
  "type": "profile" | "decision" | "project" | "artifact" | "episode",
  "subtype": string | null,
  "scope": "global" | "project",
  "title": string,
  "content": string,
  "source_ref": string,
  "reason": string
}}

Session metadata:
- session_id: {meta.get('session_id')}
- session_file: {meta.get('session_file')}
- cwd: {meta.get('cwd')}
- scope: {meta.get('scope')}
- reason: {meta.get('reason')}
- user_messages: {meta.get('user_messages')}
- assistant_messages: {meta.get('assistant_messages')}
- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}
- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}
- trigger: {meta.get('trigger')}

Branch excerpt JSON:
{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}
"""

def build_prompt(job: dict[str, Any]) -> str:
    meta = session_meta(job)
    return f"""You are deciding whether a Pi session has durable value.

Rules:
- Prefer NOT saving anything unless it is durable and reusable.
- Session metadata alone is not enough.
- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.
- If nothing durable exists, return save=false.
- If the memory is likely durable, assign the best type and include evidence.
- Output JSON only. No markdown. No extra text.

Return this schema:
{{
  "save": boolean,
  "type": "profile" | "decision" | "project" | "artifact" | "episode" | "procedural" | "semantic",
  "subtype": string | null,
  "scope": "global" | "project",
  "title": string,
  "content": string,
  "source_ref": string,
  "evidence_ref": string | null,
  "confidence": number,
  "freshness": number,
  "importance": number,
  "reason": string
}}

Scoring guidance:
- confidence: 0 to 1, how well supported the memory is.
- freshness: 0 to 1, how current it is.
- importance: 0 to 1, how much future value it has.
- evidence_ref: cite the strongest source in the session or branch excerpt when possible.

Session metadata:
- session_id: {meta.get('session_id')}
- session_file: {meta.get('session_file')}
- cwd: {meta.get('cwd')}
- scope: {meta.get('scope')}
- reason: {meta.get('reason')}
- user_messages: {meta.get('user_messages')}
- assistant_messages: {meta.get('assistant_messages')}
- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}
- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}
- trigger: {meta.get('trigger')}

Branch excerpt JSON:
{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}
"""


Successfully replaced 1 block(s) in /mnt/rpi/pi-config/scripts/session-memory-analyzer.py.

     ...
  80 Rules:
  81 - Prefer NOT saving anything unless it is durable and reusable.
  82 - Session metadata alone is not enough.
  83 - Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
- 84 - Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, or a blocker that will matter in future unrelated sessions.
+ 84 - Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.
  85 - If nothing durable exists, return save=false.
+ 86 - If the memory is likely durable, assign the best type and include evidence.
  86 - Output JSON only. No markdown. No extra text.
  87
  88 Return this schema:
  89 {{
  90   "save": boolean,
- 91   "type": "profile" | "decision" | "project" | "artifact" | "episode",
+ 92   "type": "profile" | "decision" | "project" | "artifact" | "episode" | "procedural" | "semantic",
  92   "subtype": string | null,
  93   "scope": "global" | "project",
  94   "title": string,
  95   "content": string,
  96   "source_ref": string,
+ 98   "evidence_ref": string | null,
+ 99   "confidence": number,
+100   "freshness": number,
+101   "importance": number,
  97   "reason": string
  98 }}
  99
+105 Scoring guidance:
+106 - confidence: 0 to 1, how well supported the memory is.
+107 - freshness: 0 to 1, how current it is.
+108 - importance: 0 to 1, how much future value it has.
+109 - evidence_ref: cite the strongest source in the session or branch excerpt when possible.
+110
 100 Session metadata:
 101 - session_id: {meta.get('session_id')}
 102 - session_file: {meta.get('session_file')}
 103 - cwd: {meta.get('cwd')}
     ...


        if bool(analysis.get("save")):
            memory = {
                "type": analysis.get("type") or "episode",
                "subtype": analysis.get("subtype"),
                "scope": analysis.get("scope") or meta.get("scope") or "global",
                "status": "active",
                "source_kind": "manual",
                "title": analysis.get("title") or "Session insight",
                "content": analysis.get("content") or "",
                "source_ref": analysis.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
                "origin_agent": "pi-session-memory-analyzer",
                "metadata": {
                    "job_file": str(job_file),
                    "session_id": meta.get("session_id"),
                    "session_file": meta.get("session_file"),
                    "cwd": meta.get("cwd"),
                    "reason": meta.get("reason"),
                    "user_messages": meta.get("user_messages"),
                    "assistant_messages": meta.get("assistant_messages"),
                    "analysis_reason": analysis.get("reason"),
                    "trigger": meta.get("trigger"),
                },
            }
            saved = ingest(memory)

        if bool(analysis.get("save")):
            memory = {
                "type": analysis.get("type") or "episode",
                "subtype": analysis.get("subtype"),
                "scope": analysis.get("scope") or meta.get("scope") or "global",
                "status": "active",
                "source_kind": "manual",
                "title": analysis.get("title") or "Session insight",
                "content": analysis.get("content") or "",
                "confidence": float(analysis.get("confidence") or 0.75),
                "freshness": float(analysis.get("freshness") or 0.9),
                "importance": float(analysis.get("importance") or 0.5),
                "source_ref": analysis.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
                "evidence_ref": analysis.get("evidence_ref"),
                "origin_agent": "pi-session-memory-analyzer",
                "metadata": {
                    "job_file": str(job_file),
                    "session_id": meta.get("session_id"),
                    "session_file": meta.get("session_file"),
                    "cwd": meta.get("cwd"),
                    "reason": meta.get("reason"),
                    "user_messages": meta.get("user_messages"),
                    "assistant_messages": meta.get("assistant_messages"),
                    "analysis_reason": analysis.get("reason"),
                    "trigger": meta.get("trigger"),
                    "promotion_candidate": bool(float(analysis.get("importance") or 0.5) >= 0.75),
                },
            }
            saved = ingest(memory)


     ...
 152                 "status": "active",
 153                 "source_kind": "manual",
 154                 "title": analysis.get("title") or "Session insight",
 155                 "content": analysis.get("content") or "",
+156                 "confidence": float(analysis.get("confidence") or 0.75),
+157                 "freshness": float(analysis.get("freshness") or 0.9),
+158                 "importance": float(analysis.get("importance") or 0.5),
 156                 "source_ref": analysis.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
+160                 "evidence_ref": analysis.get("evidence_ref"),
 157                 "origin_agent": "pi-session-memory-analyzer",
 158                 "metadata": {
 159                     "job_file": str(job_file),
 160                     "session_id": meta.get("session_id"),
     ...
 164                     "user_messages": meta.get("user_messages"),
 165                     "assistant_messages": meta.get("assistant_messages"),
 166                     "analysis_reason": analysis.get("reason"),
 167                     "trigger": meta.get("trigger"),
+172                     "promotion_candidate": bool(float(analysis.get("importance") or 0.5) >= 0.75),
 168                 },
 169             }
 170             saved = ingest(memory)
 171             job["analysis"]["memory_id"] = saved.get("id")
     ...


    job = {
        "job_id": job_id,
        "queued_at": datetime.now(timezone.utc).isoformat(),
        "status": "queued",
        "payload": payload,
    }

    job = {
        "job_id": job_id,
        "queued_at": datetime.now(timezone.utc).isoformat(),
        "status": "queued",
        "job_type": "session_memory_analysis",
        "payload": payload,
        "analysis": {
            "status": "queued",
            "save": None,
            "memory_id": None,
        },
    }


Successfully replaced 1 block(s) in /mnt/rpi/pi-config/scripts/session-memory-dispatcher.py.

    ...
 21     job = {
 22         "job_id": job_id,
 23         "queued_at": datetime.now(timezone.utc).isoformat(),
 24         "status": "queued",
+25         "job_type": "session_memory_analysis",
 25         "payload": payload,
+27         "analysis": {
+28             "status": "queued",
+29             "save": None,
+30             "memory_id": None,
+31         },
 26     }
 27     job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
 28     return job_file
 29
    ...


	return {
		session_id: ctx.sessionManager.getLeafId?.() || null,
		session_file: ctx.sessionManager.getSessionFile?.() || null,
		reason,
		cwd: ctx.cwd,
		scope: inferScope(ctx.cwd),
		user_messages: stats.userCount,
		assistant_messages: stats.assistantCount,
		last_user_text: truncate(stats.lastUserText, 2000),
		last_assistant_text: truncate(stats.lastAssistantText, 2000),
		branch: stats.branch,
	};
}

	return {
		session_id: ctx.sessionManager.getLeafId?.() || null,
		session_file: ctx.sessionManager.getSessionFile?.() || null,
		reason,
		cwd: ctx.cwd,
		scope: inferScope(ctx.cwd),
		user_messages: stats.userCount,
		assistant_messages: stats.assistantCount,
		last_user_text: truncate(stats.lastUserText, 2000),
		last_assistant_text: truncate(stats.lastAssistantText, 2000),
		branch: stats.branch,
		memory_hints: {
			observed_messages: stats.userCount + stats.assistantCount,
			has_latest_dialogue: Boolean(stats.lastUserText || stats.lastAssistantText),
		},
	};
}


Successfully replaced 1 block(s) in /mnt/rpi/pi-config/extensions/session-memory-dispatcher.ts.

     ...
  71 		assistant_messages: stats.assistantCount,
  72 		last_user_text: truncate(stats.lastUserText, 2000),
  73 		last_assistant_text: truncate(stats.lastAssistantText, 2000),
  74 		branch: stats.branch,
+ 75 		memory_hints: {
+ 76 			observed_messages: stats.userCount + stats.assistantCount,
+ 77 			has_latest_dialogue: Boolean(stats.lastUserText || stats.lastAssistantText),
+ 78 		},
  75 	};
  76 }
  77
  78 async function dispatchMemory(payload: any) {
     ...


**Checking syntax and running tests**

I think I need to verify the syntax and maybe check for any trailing import types. It might be a good idea to run some checks, such as using `py_compile` or `tsc`. I'm curious if those will help catch any potential issues. Let’s go ahead and run it to ensure everything is working correctly. It’s always better to double-check and avoid surprises later on!

bash

cd /mnt/rpi/pi-config && python3 -m py_compile scripts/session-memory-dispatcher.py scripts/session-memory-analyzer.py

cd /mnt/rpi/pi-config && node -e "require('typescript')" >/dev/null 2>&1; echo ok


ok


cd /mnt/rpi/pi-config && git diff -- scripts/session-memory-dispatcher.py scripts/session-memory-analyzer.py extensions/session-memory-dispatcher.ts | sed -n '1,260p'


diff --git a/extensions/session-memory-dispatcher.ts b/extensions/session-memory-dispatcher.ts
index be2b637..6063e7b 100644
--- a/extensions/session-memory-dispatcher.ts
+++ b/extensions/session-memory-dispatcher.ts
@@ -2,8 +2,8 @@ import path from "node:path";
 import type { ExtensionAPI } from "@mariozechner/pi-coding-agent";

 const DISPATCH_URL = process.env.PI_SESSION_MEMORY_DISPATCH_URL || "http://127.0.0.1:17882/api/session-memory";
-const PROMPT_SAVE = "Save durable memory";
-const PROMPT_SKIP = "Continue without saving";
+const PROMPT_SAVE = "Yes";
+const PROMPT_SKIP = "No";
 const PROMPT_CANCEL = "Cancel";

 function textFromMessage(message: any): string {
@@ -72,6 +72,10 @@ function buildHookPayload(ctx: any, stats: ReturnType<typeof getSessionStats>, r
 		last_user_text: truncate(stats.lastUserText, 2000),
 		last_assistant_text: truncate(stats.lastAssistantText, 2000),
 		branch: stats.branch,
+		memory_hints: {
+			observed_messages: stats.userCount + stats.assistantCount,
+			has_latest_dialogue: Boolean(stats.lastUserText || stats.lastAssistantText),
+		},
 	};
 }

@@ -118,17 +122,17 @@ export default function (pi: ExtensionAPI) {
 		try {
 			const result = await dispatchMemory({
 				...payload,
-				trigger: "session-memory-analyzer",
+				trigger: "session-memory-dispatcher",
 			});
 			promptHandledForCurrentSession = true;
-			ctx.ui.notify(`Memory saved: ${result.id}`, "info");
-			return { cancel: false, skipped: false, memoryId: result.id };
+			ctx.ui.notify(`Session analysis queued${result?.job_file ? `: ${result.job_file}` : ""}`, "info");
+			return { cancel: false, skipped: false, queued: true, jobFile: result?.job_file || null };
 		} catch (error) {
 			promptHandledForCurrentSession = false;
 			const message = error instanceof Error ? error.message : String(error);
 			ctx.ui.notify(message, "error");
 			if (!allowCancel) return { cancel: false, skipped: true, error: message };
-			const ok = await ctx.ui.confirm("Memory save failed", "Continue closing this session without saving?", dialogOptions);
+			const ok = await ctx.ui.confirm("Session analysis failed", "Continue closing this session without queueing?", dialogOptions);
 			if (!ok) return { cancel: true, skipped: false, error: message };
 			promptHandledForCurrentSession = true;
 			return { cancel: false, skipped: true, error: message };
@@ -148,20 +152,20 @@ export default function (pi: ExtensionAPI) {
 	});

 	pi.on("session_before_shutdown", async (event, ctx) => {
-		const result = await runPrompt(ctx, `before_shutdown:${event.reason}`, { allowCancel: true, title: "Before exit: save durable memory?", timeoutMs: 12000 });
+		const result = await runPrompt(ctx, `before_shutdown:${event.reason}`, { allowCancel: true, title: "Guardar insights en memoria?", timeoutMs: 12000 });
 		if (result.cancel) return { cancel: true };
 		return undefined;
 	});

 	pi.on("session_shutdown", async (_event, ctx) => {
-		await runPrompt(ctx, "shutdown_fallback", { allowCancel: false, title: "Before exit: save durable memory?", timeoutMs: 12000 });
+		await runPrompt(ctx, "shutdown_fallback", { allowCancel: false, title: "Guardar insights en memoria?", timeoutMs: 12000 });
 	});

 	pi.registerCommand("remember-session", {
-		description: "Save a durable memory from the current session",
+		description: "Queue a session analysis from the current session",
 		handler: async (_args, ctx) => {
 			promptHandledForCurrentSession = false;
-			await runPrompt(ctx, "manual_command", { allowCancel: false, title: "Save durable memory from this session" });
+			await runPrompt(ctx, "manual_command", { allowCancel: false, title: "Queue session analysis from this session" });
 		},
 	});
 }
diff --git a/scripts/session-memory-analyzer.py b/scripts/session-memory-analyzer.py
index ef80b93..f4b2d16 100644
--- a/scripts/session-memory-analyzer.py
+++ b/scripts/session-memory-analyzer.py
@@ -8,75 +8,38 @@ import subprocess
 import sys
 from pathlib import Path
 from typing import Any
-from urllib import request, error
+from urllib import request

 AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
-OPENAI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4")
-
-
-def load_session_file(session_file: Path) -> list[dict[str, Any]]:
-    entries: list[dict[str, Any]] = []
-    with session_file.open("r", encoding="utf-8") as fh:
-        for raw in fh:
-            raw = raw.strip()
-            if not raw:
-                continue
-            try:
-                entries.append(json.loads(raw))
-            except Exception:
-                continue
-    return entries
-
-
-def summarize_branch(branch: list[Any]) -> dict[str, Any]:
-    user_messages = 0
-    assistant_messages = 0
-    last_user = ""
-    last_assistant = ""
-    for entry in branch:
-        if not isinstance(entry, dict):
-            continue
-        if entry.get("type") != "message":
-            continue
-        message = entry.get("message") or {}
-        if not isinstance(message, dict):
-            continue
-        role = message.get("role")
-        content = message.get("content") or []
-        text = []
-        if isinstance(content, list):
-            for part in content:
-                if isinstance(part, dict) and part.get("type") == "text" and isinstance(part.get("text"), str):
-                    text.append(part["text"])
-        joined = "\n".join(text).strip()
-        if role == "user":
-            user_messages += 1
-            if joined:
-                last_user = joined
-        elif role == "assistant":
-            assistant_messages += 1
-            if joined:
-                last_assistant = joined
-    return {
-        "user_messages": user_messages,
-        "assistant_messages": assistant_messages,
-        "last_user": last_user,
-        "last_assistant": last_assistant,
-    }
-
-
-def call_llm(prompt: str) -> str:
+PI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4-mini")
+PI_THINKING = os.environ.get("PI_SESSION_MEMORY_THINKING", "off")
+
+
+def load_job(job_file: Path) -> dict[str, Any]:
+    return json.loads(job_file.read_text(encoding="utf-8"))
+
+
+def save_job(job_file: Path, payload: dict[str, Any]) -> None:
+    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+
+
+def call_pi(prompt: str) -> str:
     cmd = [
         "pi",
-        "run",
+        "-p",
+        "--provider",
+        "openai-codex",
         "--model",
-        OPENAI_MODEL,
-        "--prompt",
+        PI_MODEL,
+        "--thinking",
+        PI_THINKING,
+        "--no-tools",
+        "--no-session",
         prompt,
     ]
     proc = subprocess.run(cmd, capture_output=True, text=True)
     if proc.returncode != 0:
-        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi run failed ({proc.returncode})")
+        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
     return proc.stdout.strip()

@@ -91,83 +54,140 @@ def ingest(memory: dict[str, Any]) -> dict[str, Any]:
         return json.loads(resp.read().decode("utf-8"))

-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--session-file", required=True)
-    parser.add_argument("--session-id", default="")
-    parser.add_argument("--cwd", default="")
-    parser.add_argument("--reason", default="")
-    parser.add_argument("--scope", default="global")
-    parser.add_argument("--user-messages", type=int, default=0)
-    parser.add_argument("--assistant-messages", type=int, default=0)
-    parser.add_argument("--last-user-text", default="")
-    parser.add_argument("--last-assistant-text", default="")
-    parser.add_argument("--branch-json", default="")
-    parser.add_argument("--trigger", default="session-memory-analyzer")
-    args = parser.parse_args()
+def extract_json(text: str) -> dict[str, Any]:
+    raw = text.strip()
+    if raw.startswith("```"):
+        raw = raw.strip("`")
+        raw = raw.split("\n", 1)[-1] if "\n" in raw else raw
+    start = raw.find("{")
+    end = raw.rfind("}")
+    if start >= 0 and end > start:
+        raw = raw[start : end + 1]
+    return json.loads(raw)

-    session_path = Path(args.session_file)
-    entries = load_session_file(session_path)
-    branch = json.loads(args.branch_json) if args.branch_json else entries
-    stats = summarize_branch(branch)
-    if args.user_messages:
-        stats["user_messages"] = args.user_messages
-    if args.assistant_messages:
-        stats["assistant_messages"] = args.assistant_messages
-    if args.last_user_text:
-        stats["last_user"] = args.last_user_text
-    if args.last_assistant_text:
-        stats["last_assistant"] = args.last_assistant_text
-
-    prompt = f"""You are summarizing a completed Pi coding session into one durable memory.
+
+def session_meta(job: dict[str, Any]) -> dict[str, Any]:
+    payload = job.get("payload")
+    if isinstance(payload, dict):
+        return payload
+    return job
+
+
+def build_prompt(job: dict[str, Any]) -> str:
+    meta = session_meta(job)
+    return f"""You are deciding whether a Pi session has durable value.

 Rules:
-- Keep only durable, reusable information.
-- Prefer one concise episode memory.
-- Do not mention raw transcript mechanics.
-- Focus on stable preferences, decisions, project facts, blockers, or next steps.
-- Output plain text only, 1-3 short paragraphs max.
-
-Context:
-- session_id: {args.session_id}
-- session_file: {session_path}
-- cwd: {args.cwd}
-- reason: {args.reason}
-- scope: {args.scope}
-- trigger: {args.trigger}
-- user_messages: {stats['user_messages']}
-- assistant_messages: {stats['assistant_messages']}
-- last_user: {stats['last_user'][:4000]}
-- last_assistant: {stats['last_assistant'][:4000]}
-
-Session transcript JSONL:
-{session_path.read_text(encoding='utf-8')[:50000]}
+- Prefer NOT saving anything unless it is durable and reusable.
+- Session metadata alone is not enough.
+- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
+- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.
+- If nothing durable exists, return save=false.
+- If the memory is likely durable, assign the best type and include evidence.
+- Output JSON only. No markdown. No extra text.
+


**Considering implementation steps**

I need to note any previous changes that might affect the current state, especially since they weren't made by us. It might be useful to use memory-writing for my decision after implementing changes. The user wants me to implement gradually in the agents database, so maybe that's enough for now. I should ensure that our changes don’t break anything, possibly running a quick check. After this, I’ll mention that the implementation is complete and suggest the next steps.

~/.agents/skills/memory-write/memory-write --type decision --scope project --title "Session memory pipeline now emits hierarchical scoring metadata" --content "Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confidence, freshness, importance, evidence_ref and broader durable types; emitted memories now include confidence/freshness/importance plus evidence_ref and promotion_candidate metadata; extension hook payload now carries memory_hints. This is the first step toward hierarchical memory promotion in agents-database." --origin-agent "personal-agent" --source-ref "conversation:2026-04-22"


Session memory pipeline now emits hierarchical scoring metadata

Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confidence, freshness, importance, evidence_ref and broader durable types; emitted memories now include confidence/freshness/importance plus evidence_ref and promotion_candidate metadata; extension hook payload now carries memory_hints. This is the first step toward hierarchical memory promotion in agents-database.

Summary: Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confiden…


Question: fijate que agents-database no tenga cambios pendientes. si tiene hace el mejor esfuerzo para commit con mensaje descriptivo (a menos que sean artifacts basura)

**Crafting a concise response**

I need to respond briefly in Spanish. It's important to mention that being committed means handling leftovers as backup or recovery artifacts. These are crucial for ensuring we don't lose any important information. I'll make sure the message is clear and straightforward so that the user understands the importance of these aspects without overwhelming them with too much detail. Keeping it simple is the goal here!

Answer:
Hecho.

- `agents-database` tenía cambios.
- Dejé commit: `6a12638 feat: add HTTP API and maintenance daemon support`
- Quedó limpio salvo artifacts basura de recovery/backups:
  - `data/shared-agent-memory.pre-repair-20260419-185338.sqlite3`
  - `data/shared-agent-memory.rebuild-20260419.sqlite3`
  - `data/shared-agent-memory.recovered-20260419-184949.sqlite3`
  - `data/shared-agent-memory.recovery-dump.sql`
  - `data/shared-agent-memory.recovery-work.sqlite3`
  - `data/shared-agent-memory.sqlite3.bak-20260419-184938`

Si querés, los agrego a `.gitignore` en otro commit.


Hecho el primer paso.

Cambios:
- `scripts/session-memory-dispatcher.py`
  - ahora crea jobs con `job_type` y estado inicial de análisis
- `scripts/session-memory-analyzer.py`
  - ahora pide al modelo `confidence`, `freshness`, `importance` y `evidence_ref`
  - las memorias guardadas ya salen con esos campos
  - agregué `promotion_candidate` en metadata
- `extensions/session-memory-dispatcher.ts`
  - payload ahora incluye `memory_hints`

Verificación:
- `python3 -m py_compile scripts/session-memory-dispatcher.py scripts/session-memory-analyzer.py` OK

Memoria durable guardada:
- `mem_281fcf2fcb3e4f4782b08caac664619b`

Siguiente paso útil:
- agregar un **consolidator batch** que lea memorias candidatas y haga:
  - dedup
  - merge
  - promoción `episodic -> semantic`
  - scoring/ranking refresh

Si querés, hago ese siguiente slice ahora.


Question: borra artifacts

**Considering database changes**

I need to mention that the database might be modified due to writing, especially since the user mentioned deleting artifacts. It seems the data in SQLite has changed from a commit. I should check if the modifications come from the deletion — but no, the tracked database is definitely modified. I need to decide if it’s considered an artifact or not. I could ask the user if they want to commit the changes or revert, but they specifically asked to delete artifacts.

Answer:
Borré los artifacts basura.

Quedó un cambio pendiente más:
- `data/shared-agent-memory.sqlite3` modificado

Si querés, lo reviso o lo dejo fuera con otro commit.


Question: hace bck en /mnt/rpi nomas que hace mucho no hacemos

Answer:
Listo.

Hice backup en `/mnt/rpi/service-backups/agents-database/`:
- `agents-database-20260422-112632.tar.gz`
- `agents-database-20260422-112632.tar.gz.sha256`

Si querés, te dejo además un symlink `latest.tar.gz` o un backup más chico solo del estado git.


si. segui hasta que no tengas nada para hacer / necesites input


**Considering implementation steps**

I need to keep moving forward. Maybe I should inspect the agents-database API to check if there’s no repository available, and consider implementing more in the config. Also, it could be helpful to create a todo list for the next tasks. I might need to read the session memory jobs directory to see existing outputs. Additionally, inspecting the status of the git diff and current files could be important for our next steps.


[Unit]
Description=Pi session memory dispatcher
After=network-online.target
Wants=network-online.target

[Service]
Type=simple
ExecStart=/usr/bin/env python3 /mnt/rpi/pi-config/scripts/session-memory-dispatcher.py --host 127.0.0.1 --port 17882
Restart=always
RestartSec=2
Environment=AGENTS_DB_API=http://100.116.176.16:8091
WorkingDirectory=/mnt/rpi/pi-config

[Install]
WantedBy=default.target


# pi-config

Personal Pi configuration for **sebasfavaron**.

## Included
- `AGENTS.md`
- `MIGRATION.md`
- `settings.json`
- `extensions/subagent/`
- `extensions/questionnaire.ts`
- `extensions/session-memory-prompt.ts`
- `scripts/apply-pi-shutdown-hook-patch.py`
- `scripts/session-memory-analyzer.py`
- `scripts/session-memory-dispatcher.py`
- `scripts/install-session-memory-dispatcher.service`
- `TMP_CONTRACT.md`
- `bin/agent-tmp-run` managed TMPDIR runner
- `bin/agent-tmp-gc` contract cleanup tool
- `scripts/install-agent-tmp-gc-cron.sh` cron installer for tmp cleanup
- `bin/laptop-*` remote laptop helper scripts
- `bin/laptop-write` atomic remote file writer for laptop files

## Goal

Make this repo both the source of truth **and the live Pi config directory**.

## Notes

- Direct, compressed, low-noise communication is the default style.
- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.
- The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.
- Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`
- **Pending validation:** confirm the default communication mode auto-loads in every new Pi session.
- Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-write`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.

## Local server routes on `ballbox-first`

Current nginx portal on `:80` exposes:
- `/` → portal home from `/var/www/portal`
- `/portal/` → proxied app on `127.0.0.1:18789`
- `/calistenia/` → static files from `/home/sebas/clawd/web/calistenia/`
- `/mp3-share/` → static directory listing from `/home/sebas/downloads/mp3-share/`

Implementation note for `/mp3-share/`:
- nginx route lives in `/etc/nginx/sites-available/kiosk`
- portal home link lives in `/var/www/portal/index.html`
- nginx access to `/home/sebas/downloads/mp3-share/` depends on ACLs that allow user `www-data` to traverse `/home/sebas` and read that subtree

## Remote laptop helpers

Default host is `sebastians-macbook-pro-2`. Override with `LAPTOP_HOST` if needed.

Examples:
- `bin/laptop-run 'uname -a'`
- `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`
- `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`
- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
- `printf 'new contents\n' | bin/laptop-write /Users/sebas/Downloads/test.txt`
- `bin/laptop-write --from ./localfile /Users/sebas/Downloads/test.txt`
- `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`

## Agent tmp contract (portable root)

The repo includes tools + policy so agent workloads use a managed temp root instead of Linux `/tmp`:

- `bin/agent-tmp-run --scope scratch|logs|handoff <cmd>`
- `bin/agent-tmp-gc [--dry-run]`
- `scripts/install-agent-tmp-gc-cron.sh`
- Contract file: `TMP_CONTRACT.md` (includes root resolution order)

Default deletion policy:
- `agent-scratch`: older than 24h
- `agent-logs`: older than 72h
- `agent-handoff`: older than 168h

Target cadence: every 6h.


**Implementing memory consolidation**

I'm considering implementing a consolidator for scripts. First, I want to inspect the memory database APIs and see if there’s an existing search or ingest function. I think I should use HTTP claims and maybe leverage current records. Checking if the agents-db has a list endpoint would be helpful, and if not, I could use a search endpoint. I’m thinking about creating a script session-memory-consolidator.py that reads existing memories with a query for session memory.

**Creating a job output consolidator**

I want to implement a light consolidator for job outputs and handle promotions. My first step is to use a database search for recent memories and group similar titles, even if it's a bit rudimentary. I might also consider setting up a cron job or a service. I could write a script that queries the agents database for recent terms, perhaps using some common keywords. However, I'll also read job directories to collect saved memory IDs from analysis results, focusing on those created by the session analyzer.

**Considering candidate registration**

I want to ensure we're promoting correctly, so I think it might be better to implement a local candidate registry file in the pi-config/sessions directory, using session-memory-candidates.jsonl that derives from job outputs. However, since the user asked for this to be in the agents-database, I might explore using a search with a blank `q=` to see if it lists recent candidates. I'll also consider querying `q=pi-session`. Maybe I should inspect the API status docs for more details, but for now, a quick test search for my current title could work.

curl -fsSL 'http://100.116.176.16:8091/api/search?q=Session%20memory%20pipeline%20now%20emits%20hierarchical%20scoring%20metadata&scope=global&scope=project&limit=5' | jq '.results|length, .[0].memory.title'


5
"Session memory pipeline now emits hierarchical scoring metadata"


**Designing a consolidation script**

I'm thinking about how to create a consolidator based on broad queries, like listing some topical titles. I could search using `q=memory` and limit results to 100. My plan is to script something that takes candidate queries—memory, session, decision, etc.—and removes duplicates. It would then propose merges and write a consolidation report to a file. Additionally, it might build a new memory artifact to summarize promotions, while implementing a scoring system over the retrieved results. Let's get coding!

**Implementing the script**

I'm thinking about implementing the script and considering whether to set it up as a systemd service or a cron entry. I'm not entirely sure if that's necessary. My next steps will be to create the new script and possibly extend the README file to include more information about its functionality. I want to ensure everything is clear so that users can easily understand how to use it. Let's get started on these tasks!

write

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import re
from collections import defaultdict
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from urllib import parse, request

AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
OUTPUT_DIR = Path(os.environ.get("PI_SESSION_MEMORY_CONSOLIDATOR_DIR", Path(__file__).resolve().parent.parent / "sessions" / "session-memory-jobs"))
SEARCH_QUERIES = [
    "memory",
    "session",
    "decision",
    "project",
    "semantic",
    "procedural",
    "episode",
    "principle",
    "promotion",
]

def fetch_search(query: str, limit: int = 50) -> dict[str, Any]:
    url = f"{AGENTS_DB_API}/api/search?" + parse.urlencode({"q": query, "scope": "global", "limit": str(limit)})
    with request.urlopen(url, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def normalize(text: str) -> str:
    text = text.lower().strip()
    text = re.sub(r"[^a-z0-9\s]+", " ", text)
    text = re.sub(r"\s+", " ", text)
    return text

def tokenize(text: str) -> set[str]:
    return {tok for tok in normalize(text).split() if len(tok) > 2}

def jaccard(a: set[str], b: set[str]) -> float:
    if not a or not b:
        return 0.0
    return len(a & b) / len(a | b)

def collect_memories() -> list[dict[str, Any]]:
    seen: dict[str, dict[str, Any]] = {}
    for query in SEARCH_QUERIES:
        try:
            payload = fetch_search(query)
        except Exception:
            continue
        for result in payload.get("results", []):
            memory = result.get("memory") or {}
            mem_id = memory.get("id")
            if not mem_id:
                continue
            seen[mem_id] = {
                "memory": memory,
                "score": result.get("score", 0.0),
                "explanation": result.get("explanation"),
            }
    return list(seen.values())

def build_groups(items: list[dict[str, Any]]) -> list[dict[str, Any]]:
    groups: list[dict[str, Any]] = []
    for item in sorted(items, key=lambda x: (x["memory"].get("title") or "", x["memory"].get("created_at") or "")):
        memory = item["memory"]
        title = memory.get("title") or ""
        tokens = tokenize(f"{title} {memory.get('content') or ''}")
        placed = False
        for group in groups:
            if normalize(group["canonical_title"]) == normalize(title):
                group["members"].append(item)
                group["title_tokens"] |= tokens
                placed = True
                break
            if jaccard(group["title_tokens"], tokens) >= 0.45:
                group["members"].append(item)
                group["title_tokens"] |= tokens
                placed = True
                break
        if not placed:
            groups.append({
                "canonical_title": title,
                "title_tokens": set(tokens),
                "members": [item],
            })
    return groups

def summarize_group(group: dict[str, Any]) -> dict[str, Any]:
    members = group["members"]
    canonical = max(members, key=lambda x: float((x["memory"].get("confidence") or 0) or 0))
    memory = canonical["memory"]
    duplicates = [m["memory"].get("id") for m in members if m["memory"].get("id") != memory.get("id")]
    return {
        "canonical_id": memory.get("id"),
        "canonical_title": memory.get("title"),
        "canonical_type": memory.get("type"),
        "canonical_scope": memory.get("scope"),
        "duplicate_ids": duplicates,
        "member_count": len(members),
        "promotion_candidate": len(members) >= 2 and float(memory.get("confidence") or 0) >= 0.75,
        "evidence": [m["memory"].get("source_ref") for m in members if m["memory"].get("source_ref")],
        "signals": {
            "confidence": memory.get("confidence"),
            "freshness": memory.get("freshness"),
            "importance": memory.get("importance"),
        },
    }

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--write-report", action="store_true")
    args = parser.parse_args()

    items = collect_memories()
    groups = build_groups(items)
    summaries = [summarize_group(group) for group in groups if len(group["members"]) > 1]
    report = {
        "generated_at": datetime.now(timezone.utc).isoformat(),
        "queried": SEARCH_QUERIES,
        "memory_count": len(items),
        "group_count": len(groups),
        "duplicate_groups": len(summaries),
        "groups": summaries,
    }

    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    out_file = OUTPUT_DIR / f"consolidation-{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ')}.json"
    out_file.write_text(json.dumps(report, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

    if args.write_report:
        print(json.dumps(report, indent=2, ensure_ascii=False))
    else:
        print(json.dumps({"report_file": str(out_file), "duplicate_groups": len(summaries), "memory_count": len(items)}))
    return 0

if __name__ == "__main__":
    raise SystemExit(main())


Successfully wrote 5144 bytes to /mnt/rpi/pi-config/scripts/session-memory-consolidator.py


edit

- `scripts/session-memory-analyzer.py`
- `scripts/session-memory-dispatcher.py`
- `scripts/install-session-memory-dispatcher.service`

- `scripts/session-memory-analyzer.py`
- `scripts/session-memory-consolidator.py`
- `scripts/session-memory-dispatcher.py`
- `scripts/install-session-memory-dispatcher.service`


Successfully replaced 1 block(s) in /mnt/rpi/pi-config/README.md.

    ...
 10 - `extensions/questionnaire.ts`
 11 - `extensions/session-memory-prompt.ts`
 12 - `scripts/apply-pi-shutdown-hook-patch.py`
 13 - `scripts/session-memory-analyzer.py`
+14 - `scripts/session-memory-consolidator.py`
 14 - `scripts/session-memory-dispatcher.py`
 15 - `scripts/install-session-memory-dispatcher.service`
 16 - `TMP_CONTRACT.md`
 17 - `bin/agent-tmp-run` managed TMPDIR runner
    ...


- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.

- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
- A separate consolidator script can batch-search agents-database, detect duplicate clusters, and emit a consolidation report for review.
- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.


    ...
 29
 30 - Direct, compressed, low-noise communication is the default style.
 31 - Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
 32 - Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
+33 - A separate consolidator script can batch-search agents-database, detect duplicate clusters, and emit a consolidation report for review.
 33 - The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
 34 - Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.
 35 - The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.
 36 - Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`
    ...


bash

cd /mnt/rpi/pi-config && python3 -m py_compile scripts/session-memory-consolidator.py scripts/session-memory-dispatcher.py scripts/session-memory-analyzer.py

cd /mnt/rpi/pi-config && python3 scripts/session-memory-consolidator.py


(no output)


**Considering report automation**

I need to look into inspecting the report file, since it could be useful for creating a memory of it. I’m thinking about setting up a cleanup job or a scheduler to make things more autonomous. Maybe I could use a cron job or a service? The user mentioned no input needed yet, but I could implement a `session-memory-consolidator` runner. I might also add a `--ingest-report` option to save the report as an artifact. Let's go with that!

edit

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--write-report", action="store_true")
    args = parser.parse_args()

    items = collect_memories()
    groups = build_groups(items)
    summaries = [summarize_group(group) for group in groups if len(group["members"]) > 1]
    report = {
        "generated_at": datetime.now(timezone.utc).isoformat(),
        "queried": SEARCH_QUERIES,
        "memory_count": len(items),
        "group_count": len(groups),
        "duplicate_groups": len(summaries),
        "groups": summaries,
    }

    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    out_file = OUTPUT_DIR / f"consolidation-{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ')}.json"
    out_file.write_text(json.dumps(report, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

    if args.write_report:
        print(json.dumps(report, indent=2, ensure_ascii=False))
    else:
        print(json.dumps({"report_file": str(out_file), "duplicate_groups": len(summaries), "memory_count": len(items)}))
    return 0

def ingest_report(report: dict[str, Any], out_file: Path) -> dict[str, Any]:
    req = request.Request(
        f"{AGENTS_DB_API}/api/ingest",
        method="POST",
        data=json.dumps(
            {
                "type": "report",
                "scope": "project",
                "status": "active",
                "source_kind": "job",
                "title": "Session memory consolidation report",
                "content": json.dumps(report, indent=2, ensure_ascii=False),
                "source_ref": f"session-memory-consolidator:{out_file.name}",
                "origin_agent": "pi-session-memory-consolidator",
                "metadata": {
                    "report_file": str(out_file),
                    "memory_count": report["memory_count"],
                    "group_count": report["group_count"],
                    "duplicate_groups": report["duplicate_groups"],
                },
            }
        ).encode(),
        headers={"Content-Type": "application/json"},
    )
    with request.urlopen(req, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--write-report", action="store_true")
    parser.add_argument("--ingest-report", action="store_true")
    args = parser.parse_args()

    items = collect_memories()
    groups = build_groups(items)
    summaries = [summarize_group(group) for group in groups if len(group["members"]) > 1]
    report = {
        "generated_at": datetime.now(timezone.utc).isoformat(),
        "queried": SEARCH_QUERIES,
        "memory_count": len(items),
        "group_count": len(groups),
        "duplicate_groups": len(summaries),
        "groups": summaries,
    }

    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    out_file = OUTPUT_DIR / f"consolidation-{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ')}.json"
    out_file.write_text(json.dumps(report, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

    result: dict[str, Any] = {"report_file": str(out_file), "duplicate_groups": len(summaries), "memory_count": len(items)}
    if args.ingest_report:
        result["saved"] = ingest_report(report, out_file)

    if args.write_report:
        print(json.dumps({**report, **{"report_file": str(out_file)}}, indent=2, ensure_ascii=False))
    else:
        print(json.dumps(result, ensure_ascii=False))
    return 0


Successfully replaced 1 block(s) in /mnt/rpi/pi-config/scripts/session-memory-consolidator.py.

     ...
 117         },
 118     }
 119
 120
+121 def ingest_report(report: dict[str, Any], out_file: Path) -> dict[str, Any]:
+122     req = request.Request(
+123         f"{AGENTS_DB_API}/api/ingest",
+124         method="POST",
+125         data=json.dumps(
+126             {
+127                 "type": "report",
+128                 "scope": "project",
+129                 "status": "active",
+130                 "source_kind": "job",
+131                 "title": "Session memory consolidation report",
+132                 "content": json.dumps(report, indent=2, ensure_ascii=False),
+133                 "source_ref": f"session-memory-consolidator:{out_file.name}",
+134                 "origin_agent": "pi-session-memory-consolidator",
+135                 "metadata": {
+136                     "report_file": str(out_file),
+137                     "memory_count": report["memory_count"],
+138                     "group_count": report["group_count"],
+139                     "duplicate_groups": report["duplicate_groups"],
+140                 },
+141             }
+142         ).encode(),
+143         headers={"Content-Type": "application/json"},
+144     )
+145     with request.urlopen(req, timeout=30) as resp:
+146         return json.loads(resp.read().decode("utf-8"))
+147
+148
 121 def main() -> int:
 122     parser = argparse.ArgumentParser()
 123     parser.add_argument("--write-report", action="store_true")
+152     parser.add_argument("--ingest-report", action="store_true")
 124     args = parser.parse_args()
 125
 126     items = collect_memories()
 127     groups = build_groups(items)
     ...
 138     OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
 139     out_file = OUTPUT_DIR / f"consolidation-{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ')}.json"
 140     out_file.write_text(json.dumps(report, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
 141
+171     result: dict[str, Any] = {"report_file": str(out_file), "duplicate_groups": len(summaries), "memory_count": len(items)}
+172     if args.ingest_report:
+173         result["saved"] = ingest_report(report, out_file)
+174
 142     if args.write_report:
-143         print(json.dumps(report, indent=2, ensure_ascii=False))
+176         print(json.dumps({**report, **{"report_file": str(out_file)}}, indent=2, ensure_ascii=False))
 144     else:
-145         print(json.dumps({"report_file": str(out_file), "duplicate_groups": len(summaries), "memory_count": len(items)}))
+178         print(json.dumps(result, ensure_ascii=False))
 146     return 0
 147
 148
 149 if __name__ == "__main__":
     ...


bash

cd /mnt/rpi/pi-config && python3 -m py_compile scripts/session-memory-consolidator.py

cd /mnt/rpi/pi-config && python3 scripts/session-memory-consolidator.py --ingest-report


Session memory consolidation report

memory session decision project semantic procedural episode principle promotion

Create and manage your course schedule | Matchable

Getting started with courses | Matchable

Legacy task: Mini repo review: current repo already implements /pay, QR page, webhook handlin

Legacy task: Repo target confirmed: ballbox-payment-integration

Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow

Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs

Research run: Mapeo exhaustivo de todos los use cases y features que ofrece Matchable

Research step: capture https://business.matchable.io/

Research step: capture https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trai

Research step: capture https://matchable.io/business/integrations

Summary: { "generated_at": "2026-04-22T14:28:32.064651+00:00", "queried": [ "memory", "session", "decision", "project", "semantic", "procedural", "episode", "principle", "promotion" ], "me…


**Considering memory promotion**

I'm contemplating how to proceed with a generic `--promote` mode using report groups for creating candidate semantic memories. Since the agents-db ingests various accepted report types, I think I could potentially summarize reports into a decision or episode memory. It might be enough to implement a consolidation summary memory, but if I keep going until there's no input, I could create a script to scan recent consolidation reports and identify top-level decision candidates. However, I should keep things simple at first.

write

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from urllib import request

AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
REPORT_DIR = Path(os.environ.get("PI_SESSION_MEMORY_CONSOLIDATOR_DIR", Path(__file__).resolve().parent.parent / "sessions" / "session-memory-jobs"))

def load_report(path: Path) -> dict[str, Any]:
    return json.loads(path.read_text(encoding="utf-8"))

def ingest(memory: dict[str, Any]) -> dict[str, Any]:
    req = request.Request(
        f"{AGENTS_DB_API}/api/ingest",
        method="POST",
        data=json.dumps(memory).encode(),
        headers={"Content-Type": "application/json"},
    )
    with request.urlopen(req, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def latest_report() -> Path:
    reports = sorted(REPORT_DIR.glob("consolidation-*.json"), key=lambda p: p.stat().st_mtime, reverse=True)
    if not reports:
        raise FileNotFoundError(f"no consolidation reports in {REPORT_DIR}")
    return reports[0]

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--report-file", default="")
    args = parser.parse_args()

    report_path = Path(args.report_file) if args.report_file else latest_report()
    report = load_report(report_path)
    candidates = [g for g in report.get("groups", []) if g.get("promotion_candidate")]
    top = candidates[:10]
    content_lines = [
        f"Consolidation report: {report_path.name}",
        f"Memory count: {report.get('memory_count')}",
        f"Group count: {report.get('group_count')}",
        f"Duplicate groups: {report.get('duplicate_groups')}",
        "",
        "Top promotion candidates:",
    ]
    for group in top:
        content_lines.append(
            f"- {group.get('canonical_title')} | type={group.get('canonical_type')} | members={group.get('member_count')} | duplicates={len(group.get('duplicate_ids') or [])} | confidence={group.get('signals', {}).get('confidence')}"
        )
    memory = {
        "type": "decision",
        "scope": "project",
        "status": "active",
        "source_kind": "job",
        "title": "Session memory promotion candidates",
        "content": "\n".join(content_lines),
        "confidence": 0.9,
        "freshness": 1.0,
        "importance": 0.7,
        "source_ref": f"session-memory-promoter:{report_path.name}",
        "origin_agent": "pi-session-memory-promoter",
        "metadata": {
            "report_file": str(report_path),
            "generated_at": datetime.now(timezone.utc).isoformat(),
            "candidate_count": len(candidates),
            "top_titles": [g.get("canonical_title") for g in top],
        },
    }
    saved = ingest(memory)
    print(json.dumps({"report_file": str(report_path), "saved": saved}, ensure_ascii=False))
    return 0

if __name__ == "__main__":
    raise SystemExit(main())


Successfully wrote 3023 bytes to /mnt/rpi/pi-config/scripts/session-memory-promoter.py


bash

cd /mnt/rpi/pi-config && python3 -m py_compile scripts/session-memory-promoter.py && python3 scripts/session-memory-promoter.py


Session memory promotion candidates

Consolidation report: consolidation-20260422T142832064769Z.json
Memory count: 154
Group count: 135
Duplicate groups: 12

Top promotion candidates:
- Create and manage your course schedule | Matchable | type=artifact | members=2 | duplicates=1 | confidence=0.75
- Getting started with courses | Matchable | type=artifact | members=3 | duplicates=2 | confidence=0.75
- Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow | type=episode | members=2 | duplicates=1 | confidence=0.85
- Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs | type=episode | members=2 | duplicates=1 | confidence=0.85
- Research run: Mapeo exhaustivo de todos los use cases y features que ofrece Matchable | type=episode | members=2 | duplicates=1 | confidence=0.9
- Research step: capture https://business.matchable.io/ | type=episode | members=6 | duplicates=5 | confidence=0.75
- Research step: capture https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trai | type=episode | members=3 | duplicates=2 | confidence=0.75
- Research step: capture https://matchable.io/business/integrations | type=episode | members=3 | duplicates=2 | confidence=0.75
- Session summary: sebas | type=episode | members=2 | duplicates=1 | confidence=1.0
- repair test memory 2 | type=episode | members=2 | duplicates=1 | confidence=1.0

Summary: Consolidation report: consolidation-20260422T142832064769Z.json Memory count: 154 Group count: 135 Duplicate groups: 12 Top promotion candidates: - Create and manage your course s…


**Considering README updates**

I’m thinking I need to update the README to include a promoter and note that the task is still in progress. The user mentioned continuing until there’s no input, so maybe implementing a simple shell script or cron job would be helpful. I should also add a list to the README and consider creating a service unit for the consolidator, though that might be too much. A command documentation and one-shot runbook could be useful, too.

edit

- A separate consolidator script can batch-search agents-database, detect duplicate clusters, and emit a consolidation report for review.
- A promoter script can turn the latest consolidation report into a durable promotion-candidate decision record.


    ...
 30 - Direct, compressed, low-noise communication is the default style.
 31 - Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
 32 - Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
 33 - A separate consolidator script can batch-search agents-database, detect duplicate clusters, and emit a consolidation report for review.
+34 - A promoter script can turn the latest consolidation report into a durable promotion-candidate decision record.
 34 - The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
 35 - Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.
 36 - The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.
 37 - Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`
    ...


**Consideration for memory update**

I think I should create a memory about the promotion candidates I’m considering. It seems like a good idea to keep track of those details for future reference. Additionally, I might want to run a `git status` summary to check on the current state of my repositories. It's essential to stay organized, especially when managing candidates and their qualifications. I'll keep these tasks in mind!

bash

cd /mnt/rpi/pi-config && git status --short scripts/session-memory-* extensions/session-memory-dispatcher.ts README.md sessions/spec-memory-layer-agents.md | sed -n '1,200p'


 M README.md
 M extensions/session-memory-dispatcher.ts
 M scripts/session-memory-analyzer.py
 M scripts/session-memory-dispatcher.py
?? scripts/session-memory-consolidator.py
?? scripts/session-memory-promoter.py


**Considering systemd options**

I think the user has asked me to finalize things and that no more input is needed. I could mention an optional systemd timer since that might be useful. But maybe it’s enough for now. Perhaps I should suggest adding a tiny service or timer file for the consolidator to make it more autonomous? Yes, I’ll create those unit and timer files to streamline the process!

write

[Unit]
Description=Pi session memory consolidator
After=network-online.target
Wants=network-online.target

[Service]
Type=oneshot
ExecStart=/usr/bin/env python3 /mnt/rpi/pi-config/scripts/session-memory-consolidator.py --ingest-report
Environment=AGENTS_DB_API=http://100.116.176.16:8091
WorkingDirectory=/mnt/rpi/pi-config


Successfully wrote 324 bytes to /mnt/rpi/pi-config/scripts/install-session-memory-consolidator.service


[Unit]
Description=Run Pi session memory consolidator periodically

[Timer]
OnBootSec=10min
OnUnitActiveSec=6h
Persistent=true

[Install]
WantedBy=timers.target


Successfully wrote 161 bytes to /mnt/rpi/pi-config/scripts/install-session-memory-consolidator.timer


edit

- `scripts/install-session-memory-dispatcher.service`
- `scripts/install-session-memory-consolidator.service`
- `scripts/install-session-memory-consolidator.timer`


    ...
 13 - `scripts/session-memory-analyzer.py`
 14 - `scripts/session-memory-consolidator.py`
 15 - `scripts/session-memory-dispatcher.py`
 16 - `scripts/install-session-memory-dispatcher.service`
+17 - `scripts/install-session-memory-consolidator.service`
+18 - `scripts/install-session-memory-consolidator.timer`
 17 - `TMP_CONTRACT.md`
 18 - `bin/agent-tmp-run` managed TMPDIR runner
 19 - `bin/agent-tmp-gc` contract cleanup tool
 20 - `scripts/install-agent-tmp-gc-cron.sh` cron installer for tmp cleanup
    ...


**Evaluating tasks and next steps**

I’m thinking I might not need to run py_compile for service files. It seems like consolidator and promoter tasks are done, so I could update the todo list. I already finished the old tasks, but maybe it’s good to reflect that in the current task. I haven’t created any new tasks, though. I should mention the implemented pieces, and I know the next blocker is the true merge/supersede actions, which need schema knowledge.

Seguí bastante.

Hecho:
- `scripts/session-memory-dispatcher.py`
  - crea jobs con `job_type` y estado inicial de análisis
- `scripts/session-memory-analyzer.py`
  - ahora pide `confidence`, `freshness`, `importance`, `evidence_ref`
  - guarda esas señales en agents-database
  - marca `promotion_candidate`
- `extensions/session-memory-dispatcher.ts`
  - agrega `memory_hints` al payload
- Nuevo:
  - `scripts/session-memory-consolidator.py`
    - busca en agents-database
    - agrupa duplicados
    - genera reporte de consolidación
    - opcionalmente ingesta el reporte
  - `scripts/session-memory-promoter.py`
    - toma el último reporte
    - crea un decision record con candidatos a promoción
  - `scripts/install-session-memory-consolidator.service`
  - `scripts/install-session-memory-consolidator.timer`

También dejé:
- README actualizado con el flujo nuevo
- una corrida real de consolidación y promoción ya guardada en agents-database

Memorias durables creadas:
- `mem_7f42151703ec40ff94abc751108255d5` — consolidation report
- `mem_1996157dd60f49b5bd46dfe39be39fd1` — promotion candidates

Bloqueo real que queda:
- no tengo aún la forma canónica de hacer **merge/supersede** en agents-database sin tocar su API interna o confirmar contrato exacto.
- o sea: ya hay pipeline de captura → scoring → consolidación → candidatos, pero el paso de **mutación semántica real** del grafo de memorias necesita ese contrato.

Si querés, el próximo slice es:
1. descubrir/confirmar la API de merge/supersede de agents-database
2. conectar el promoter para que haga promoción real, no solo reporte/candidato.


implementaste sin entender el sistema existente?


**Considering contract inspection**

I realize I need to be candid in my response. I want to explain that I've used the existing API for searching and ingested data according to the local pi-config. However, I'm not entirely sure I grasp all the internal mutations, as I've only read the public signals. Should I inspect the contract before making any further changes? Let me know what you'd prefer!

Sí, en parte.

Entendí el perímetro visible:
- `agents-database` expone `search`, `context`, `ingest`
- `pi-config` ya tenía dispatcher/analyzer/hook
- la memoria ya venía entrando como episodios/artifacts

Pero no validé todavía el contrato interno de:
- merge
- supersede
- canonicalización
- promoción real entre tipos
- rollback/conflict resolution

Entonces:
- lo que implementé hoy es **sobre la interfaz pública existente**
- no toqué el core de `agents-database`
- el paso “real” de promoción quedó como **candidato/reporte**, no mutación semántica canónica

O sea: no me tiré a ciegas, pero tampoco confirmé el sistema completo antes de avanzar. Ahí tenés razón.

Si querés, corrijo el rumbo así:
1. paro cambios nuevos
2. releo el contrato existente de `agents-database`
3. después implemento solo lo compatible

Decime si querés que haga esa revisión ahora.


si


**Inspecting system architecture**

I need to check the existing system. Perhaps I'll use a memory read or search for relevant information. I wonder if I should look into the repository for the agents-database to gather more details. Maybe searching through the filesystem is the best way to locate what I need. It's essential to ensure I'm accessing all the necessary resources effectively! Let’s see what I can uncover.

bash

curl -fsSL 'http://100.116.176.16:8091/api/search?q=canonical%20merge%20supersede%20agents-database&scope=global&scope=project&limit=20' | jq '.'

find /mnt/rpi /home/sebas -maxdepth 3 -type d \( -name 'agents-database' -o -name '*agents*database*' -o -name 'personal-agent' \) 2>/dev/null | sed -n '1,200p'


Codex should launch with agents-database as writable extra dir

When personal-agent work needs sandboxed Codex access to the canonical shared DB, launch Codex with `--add-dir /Users/sebas/agents-database`. The real database stays inside the agents-database repo.

Summary: Use `--add-dir /Users/sebas/agents-database` for sandboxed Codex runs that need canonical shared DB write access.

Explanation: facets=0.00 lexical=0.50 semantic=0.99 confidence=0.98 freshness=0.98

Research run: Implement blocked-task workflow for dependent code and non-code work

Goal: Implement blocked-task workflow for dependent code and non-code work
Scope: personal-agent task system and runtime behavior
Assumptions: Use the current shared task model as canonical; keep dependency metadata minimal-first; support blocked-by-one-task initially; for code tasks use stacked worktree/branch workflow based on blocker branch until blocker merges.
Summary: Implement blocked-task workflow for dependent code and non-code work

Explanation: facets=0.00 lexical=0.50 semantic=0.98 confidence=0.90 freshness=0.85

Research started: agent memory techniques taxonomy

Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-turn/ per-thread models, hybrid retrieval) and AgentDock consolidation guide (episodic->semantic promotion, dedup, merge/synthesize/abstract). Working file: /mnt/rpi/pi-config/sessions/memory-techniques-review.md.

Summary: Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-tur…

Explanation: facets=0.00 lexical=0.50 semantic=0.00 confidence=1.00 freshness=1.00

Ballbox payments validation handoff and open blockers

PAY-2 native MercadoPago flow is merged and deployed. Legacy approved callback was removed. Canonical external_reference format is `payment-vm_{vendingMachineId}-txn_{machineTxnId}`. Valid test POS is `BALLBOXTESTPOS01`. Approved test order `ORDTST01KPPFWZBJD0M1PACY3257J7KH` reached `processed/accredited`. Open issues: Ballbox did not persist `PaymentSession`; production token still cannot access `/users/me` or `/pos` due to `PA_UNAUTHORIZED_RESULT_FROM_POLICIES`; homologator still fails with `error_code 1006`. Next useful steps: inspect logs for `/notification/mercadopago` around the approved order, reproduce another approved order and tail logs live if needed, and resolve POS/prod account API access.

Summary: PAY-2 native MercadoPago flow is merged and deployed. Legacy approved callback was removed. Canonical external_reference format is `payment-vm_{vendingMachineId}-txn_{machineTxnId…

Durable project state with concrete external identifiers, validated payment flow status, and unresolved blockers that matter for future sessions.

Ballbox PAY-2 native MercadoPago handoff

PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.

Summary: PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified…

Ballbox alias resolution rule for Volvox mentions

When Sebas says or types Volvox in this domain, assume he means Ballbox unless surrounding context clearly points to a different real entity. Use Ballbox as the canonical interpretation in memory, retrieval, naming, and project reasoning. Keep Volvox only as a fallback recognition alias for mistaken speech-to-text or microphone transcription.

Summary: Default resolution: Volvox should be interpreted as Ballbox unless context clearly says otherwise.

Explanation: facets=0.00 lexical=0.25 semantic=0.99 confidence=1.00 freshness=1.00

Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to

AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.

Explanation: facets=0.00 lexical=0.25 semantic=0.99 confidence=0.64 freshness=0.75

V1 legacy bridge policy

New durable work lives only in agents-database. Legacy personal-agent sqlite stays transition-only, with old-run inspection bridged through mirrored shared-memory records.

Summary: Legacy sqlite is transition-only; old-run inspection falls back to shared-memory mirrors.

Explanation: facets=0.00 lexical=0.25 semantic=0.99 confidence=0.96 freshness=0.95

Ballbox speech-to-text alias: Volvox

Volvox is not a real project or company name in this context. It is a frequent microphone or speech-to-text mistranscription of Ballbox. Treat Ballbox as the canonical name. Keep Volvox only as a recognition alias to recover intent when it appears by mistake.

Summary: Volvox is a common speech-to-text mistake for Ballbox; Ballbox is the real canonical name.

Explanation: facets=0.00 lexical=0.25 semantic=0.98 confidence=1.00 freshness=1.00

V1 multi-repo cross-reference

V1 spans personal-agent, agents-database, ai-dev-workflow, and ballbox-company-agent. Resume from shared DB task/handoff/artifact ids and repo-local V1 docs/README references.

Summary: Shared DB now carries the V1 resume anchor and repo cross-reference.

Explanation: facets=0.00 lexical=0.25 semantic=0.96 confidence=0.94 freshness=0.95

agents-database /api/context is not exact-id lookup

In agents-database, use /api/search for finding memories by id/content. /api/context returns a contextual bundle and is not a reliable exact-id fetch; docs should state that explicitly to avoid lookup confusion.

Summary: In agents-database, use /api/search for finding memories by id/content. /api/context returns a contextual bundle and is not a reliable exact-id fetch; docs should state that expli…

Explanation: facets=0.00 lexical=0.25 semantic=0.00 confidence=1.00 freshness=1.00

Session memory consolidation report

Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confidence, freshness, importance, evidence_ref and broader durable types; emitted memories now include confidence/freshness/importance plus evidence_ref and promotion_candidate metadata; extension hook payload now carries memory_hints. This is the first step toward hierarchical memory promotion in agents-database.

Session hook should queue analysis, not save memory directly

The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.

Summary: The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab…

Session memory dispatcher now builds valid memory payloads

The Pi session close hook now sends session context to the local dispatcher, which converts it into a valid episode memory before ingesting to agents-database. Raw hook payloads no longer hit /api/ingest directly, which fixed the 400 missing type failure and restored durable session saves.

Summary: The Pi session close hook now sends session context to the local dispatcher, which converts it into a valid episode memory before ingesting to agents-database. Raw hook payloads n…

Sebas prefers autonomous commit handling

Sebas wants the agent to choose commit messages itself and not ask for them. He still requires explicit confirmation for irreversible actions, including push or merge to main, but wants all other non-irreversible work to proceed autonomously.

Summary: Sebas wants the agent to choose commit messages itself and not ask for them. He still requires explicit confirmation for irreversible actions, including push or merge to main, but…

Smoke test: pi session memory ingest

Smoke test from pi harness: verify agents-database /api/ingest accepts a session-memory payload and returns a memory id.

memory-write skill smoke test

Helper script created and can ingest memories into agents-database.

Agents should save durable high-signal memory opportunistically

Operational rule: all agents should treat agents-database as shared durable memory, not only search. When new information is likely useful beyond the current chat or task, create a memory. Save stable preferences, recurring constraints, important project facts, decisions, reusable sources or artifacts, and notable blocked state or handoff context. Avoid trivia, redundant notes, ephemeral chatter, and secrets unless explicitly asked.

Summary: Operational rule: all agents should treat agents-database as shared durable memory, not only search. When new information is likely useful beyond the current chat or task, create …

Resolve ambiguous terms via agents-database first

When Sebas uses any ambiguous term, remembered phrase, shorthand, nickname, or speech-to-text alias, agents must query ~/agents-database before guessing from local context, filesystem names, or recent conversation assumptions.

Summary: When Sebas uses any ambiguous term, remembered phrase, shorthand, nickname, or speech-to-text alias, agents must query ~/agents-database before guessing from local context, filesy…


/mnt/rpi/agents-database
/mnt/rpi/service-backups/agents-database


**Inspecting repository**

I need to take a closer look at the repo. There might be details or updates that could really help clarify things for me. I’m curious about the structure and any recent changes. It'll help me understand how everything fits together. Let’s go ahead and open the repo to check it out. I’m sure there’s a lot I’ll discover!

cd /mnt/rpi/agents-database && find . -maxdepth 2 -type f | sed -n '1,220p'

cd /mnt/rpi/agents-database && git status --short && git branch --show-current


./docs/memory-consolidation-rfc.md
./docs/system-v1.md
./docs/shared-agent-memory-rfc.md
./docs/http-api.md
./.git/config
./.git/description
./.git/COMMIT_EDITMSG
./.git/opencode
./.git/packed-refs
./.git/HEAD
./.git/index
./.pytest_cache/README.md
./.pytest_cache/.gitignore
./.pytest_cache/CACHEDIR.TAG
./scripts/run-maintenance-daemon.sh
./scripts/run-http-api.sh
./.gitignore
./tests/conftest.py
./tests/test_memory_service.py
./data/shared-agent-memory.sqlite3
./.venv/pyvenv.cfg
./.venv/.gitignore
./maintenance-daemon.log
./pyproject.toml


 M data/shared-agent-memory.sqlite3
main


# Agents Database HTTP API

Remote-first topology:

- server `personal-agent`: task runner and dashboard API on `:8082`
- server `agents-database`: shared-memory and operational-data API on `:8091`
- local laptop: HTTP client only; no local shared DB required for normal operation

## Endpoints

- `GET /api/status`
- `GET /api/search?q=<query>&scope=global&scope=project&limit=10`
- `GET /api/search?id=<memory_id>`
- `GET /api/context?project=<id>&repo=<id>&task=<id>`
- `GET /api/memories?...filters...`
- `GET /api/memories/<id>`
- `POST /api/ingest`
- `GET /api/tasks?...filters...`
- `POST /api/tasks`
- `PATCH /api/tasks/<id>`
- `GET /api/tasks/<id>`
- `GET /api/tasks/<id>/bundle`
- `GET /api/task-runs?...filters...`
- `GET /api/task-runs/<id>`
- `POST /api/task-runs`
- `POST /api/task-runs/<id>/finish`
- `GET /api/artifacts?task_id=<id>`
- `GET /api/artifacts/<id>`
- `POST /api/artifacts`

## Run locally

```bash
python3 -m shared_agent_memory.cli \
  --db data/shared-agent-memory.sqlite3 \
  serve-http \
  --host 0.0.0.0 \
  --port 8091
```

## Server service

- script: `scripts/run-http-api.sh`
- systemd user unit: `systemd/user/agents-database-http.service`

## Gotchas

- This service is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.
- `/api/search?id=<memory_id>` is a direct lookup shortcut that returns one memory in `results`.
- The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.