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06 · Journal Companion

A daily-journal Agent that genuinely remembers across runs — not by replaying conversation history, but by summarising each session into a /memory/working.md file in a KnowledgeStore and re-injecting that file at the start of every later conversation. Two sessions are run with different Agent instances pointed at the same store; the second one (a brand-new object) recalls what the user said in the first.

What it covers#

  • Persistent agent memory backed by a DiskKnowledgeStore on disk.
  • WorkingMemoryAggregate — an LLM-driven rollup that runs at the end of every conversation and writes /memory/working.md.
  • WorkingMemoryPolicy — an assembly policy that reads /memory/working.md and injects it as context before every LLM call.
  • The full KnowledgeConfig(store=..., aggregate=..., aggregate_trigger=...) shape and how it pairs with assembly=.
  • The AggregateTrigger(on_end=True) cadence — fire once when the conversation ends.

Primitives covered#

  • KnowledgeConfig with store, aggregate, aggregate_trigger
  • DiskKnowledgeStore
  • WorkingMemoryAggregate (rollup strategy)
  • AggregateTrigger(on_end=True)
  • Assembly policies: WorkingMemoryPolicy, ConversationPolicy
  • Reading the store directly via store.read("/memory/working.md")

Source#

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"""06 · Journal companion — knowledge store with working memory

Persistent agent memory using the framework's three knowledge primitives:

- ``KnowledgeStore`` — virtual filesystem for agent state.
- ``WorkingMemoryAggregate`` — an LLM-driven summary rollup that runs at
  the end of every conversation and writes ``/memory/working.md``.
- ``WorkingMemoryPolicy`` — an assembly policy that reads
  ``/memory/working.md`` and injects it as context at the start of every
  subsequent conversation.

The agent therefore "remembers" what you told it even after a full restart,
because the state lives in the knowledge store — not in conversation
history.

Run::

    .venv-beta/bin/python 06_journal_companion.py
"""

import asyncio
import shutil
import tempfile
from pathlib import Path

from autogen.beta import Agent, KnowledgeConfig
from autogen.beta.aggregate import AggregateTrigger, WorkingMemoryAggregate
from autogen.beta.config import GeminiConfig
from autogen.beta.knowledge import DiskKnowledgeStore
from autogen.beta.policies import ConversationPolicy, WorkingMemoryPolicy

def section(title: str) -> None:
    print(f"\n── {title} ───")

async def main() -> None:
    config = GeminiConfig(model="gemini-3-flash-preview", temperature=0)

    # Use a fresh tempdir so the example is reproducible.
    workdir = Path(tempfile.mkdtemp(prefix="journal-companion-"))

    try:
        store = DiskKnowledgeStore(str(workdir))

        def build_agent() -> Agent:
            return Agent(
                "journal",
                prompt=(
                    "You are a supportive daily journal companion. Keep a "
                    "running understanding of what the user is working on. "
                    "Be brief and reference their past entries when relevant."
                ),
                config=config,
                knowledge=KnowledgeConfig(
                    store=store,
                    aggregate=WorkingMemoryAggregate(config=config),
                    # on_end=True: roll up working memory when each conversation finishes
                    aggregate_trigger=AggregateTrigger(on_end=True),
                ),
                assembly=[
                    WorkingMemoryPolicy(),  # inject /memory/working.md on every LLM call
                    ConversationPolicy(),  # then filter to conversation events
                ],
            )

        section("Session 1 — tell the journal what you're doing")

        agent1 = build_agent()
        r = await agent1.ask(
            "Today I started learning to build a home espresso setup. Still "
            "choosing between a Silvia Pro and a Linea Mini."
        )
        print(r.body)
        r = await r.ask(
            "Also started reading The Pragmatic Programmer. On chapter 2 about orthogonality. That's the whole update."
        )
        print(r.body)
        # When the `with await agent1.ask(...)` exits the `_execute`,
        # WorkingMemoryAggregate writes /memory/working.md.

        working = await store.read("/memory/working.md")
        print()
        print("## /memory/working.md after session 1")
        print(working)

        section("Session 2 — new Agent instance, same store: memory persists")

        agent2 = build_agent()
        r2 = await agent2.ask("Quick check-in: what was I working on? Answer in one line.")
        print(r2.body)
        # The answer should mention espresso and/or Pragmatic Programmer
        # even though agent2 is a brand-new object with no prior state,
        # because WorkingMemoryPolicy injected /memory/working.md as a prompt.
    finally:
        shutil.rmtree(workdir, ignore_errors=True)

if __name__ == "__main__":
    asyncio.run(main())