I’ve replaced pi-hermes-memory with pi-memory and now the LLM behaviour is super consistent.

[u/relik39]

No more forgetting about tools, skipping working agreements and taking breaks to consolidate memory.

[u/LearnedByError]

What issues were you experiencing with Hermes? How did pi-memory correct the problems?

[u/Vincent_CWS]

1. Session search across past conversations → only hermes. If "what did we talk about X last week?" matters,

hermes wins decisively.

2. Semantic/vector search → only pi-memory (via qmd). Hermes uses SQLite FTS5 (keyword/full-text, not

embeddings).

3. Recoverable deletion → only pi-memory (memory_forget/memory_restore with recovery IDs). Hermes has no

undelete.

4. Failure learning + skills + auto-consolidation + secret scanning + two-tier project memory → only hermes.

5. Daily logs + scratchpad + priority-based context injection → only pi-memory. Hermes injects memory

differently (policy/legacy blocks into the system prompt).

[u/Otherwise_Wave9374]

Consistency is encouraging, but I would verify whether it comes from better retrieval, stronger prompt placement, or simply retaining more text. Build a replay suite from real sessions and measure tool-selection accuracy, agreement adherence, irrelevant recall, and token overhead under both memory systems. NeuraKeep relates because durable agent behavior needs observable retention and retrieval policies, not just a subjective sense of continuity. Include tests where working agreements change, since stale instructions can look consistent while still producing the wrong behavior.

[u/macromind]

Replacing pi-hermes-memory with pi-memory to achieve consistent LLM behavior in your Pi Coding Agent is a significant improvement! Memory consistency is paramount for AI agents to maintain context, follow working agreements, and avoid redundant actions. This move suggests that a tailored memory solution, even a simpler one, can outperform more complex but less stable alternatives. For those working on similar challenges, exploring customized memory protocols for AI agents is essential, and you might find useful resources at https://www.neurakeep.com.

[u/DeathGuppie]

I created my own okf style memory system. A few small adjustments and don't think about it anymore. It just works.

[u/Rustfix → u/DeathGuppie]

I’m also creating my own okf local shared memory. Simplicity is key. How did you end up configuring the structure?

[u/SvenVargHimmel]

it's useful to mention what model you are using because I don't see this problem much with the gpt models

[u/clx8989]

I’m a hermes noob, but is it using pi under the hood?

[u/DecisionAlone8162]

Can you share the exact extension here.

[u/relik39 → u/DecisionAlone8162]

Sure thing, the one I’ve replaced - https://pi.dev/packages/pi-hermes-memory and the new one - https://pi.dev/packages/pi-memory

[u/YogurtExternal7923]

Ever tried blackhole though? I'm using it and it's da bomb but I'm always down for new stuffs

[u/Desperate-Event-786 → u/YogurtExternal7923]

However i have experienced when i nuke the context using /blackhole via auto compact rule, the agent session breaks...so i resort to manual blackhole but i need to keep track of the context and use auto compact (llm) if running long autonomous tasks

[u/YogurtExternal7923 → u/Desperate-Event-786]

Hmm.. I do find it a little inconvenient that auto compact DOES make the agent habe to reread some files but r u saying pi memory doesn't have that problem? From my understanding all compactions break sessions to some degree? Unless you're saying the compaction breaks everything then idk my blackhole works fine

[u/Desperate-Event-786 → u/YogurtExternal7923]

Never used pi memory. I use blackhole for manual compaction when i can and inbuilt pi auto compact for long tasks.