Been using it for 2 weeks, and I definitely see improvements.
One thing I wish AgentMemory had -- team collab in some way. AgentMemory should have some way to export relevant memories to filesystem which can be committed to git. Also, some way to import them. This is something we want to try out internally in a few weeks once our devs all have used it some time and memories have evolved.
@pranavprakash I think skillkit.sh can do this in agentmemory
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Took a quick look , seems straightforward and usable. Curious how you're keeping memory relevant without it becoming noisy over time.
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Haven't tried yet... currently have supermemory and obsidian hybrid system for Hermes. I'm tempted to use Agent memory without complicating my current system. any suggestions?
@starchet agentmemory is simple to use and you can simply ask agent to transfer obsidian data into agentmemory state. it should work.
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I started using it to help with the memories of how my product has pivoted over time. I'm pre-revenue and it's CONSTANT pivots and cursor by default was impossible to keep tidy. Slight variations in direction looked identical in the landing pages, app, backend, and I needed something that tracked the conversations more deeply.
I backfilled agent memory on my past month's cursor Agent transcripts and asked it some questions to see how well it did, and it was surprisingly accurate. Picked up on things that I move away from, decided we're badly done, etc, which aren't immediately obvious if you look at my codebase despite my diligence with updating documentation and labeling commits.
HELLO @rohit_ghumare Really promising so far — I like that it tackles one of the biggest pain points with coding agents: losing useful project context across sessions without bloating the context window. The searchable memory + visibility into what the agent remembers feels especially valuable. 👏
Agent memory is the layer everyone needs but nobody wants to build themselves. The teams getting the most out of AI pipelines today are the ones who've solved persistent context. Curious how you're thinking about structured vs. unstructured memory for content-heavy workflows.
Agent memory feels most useful when it preserves why a prior attempt stopped, not just what files or observations existed. For coding agents, the missing piece is usually a small receipt: what context was retrieved, which verifier moved, what budget remains, and why the next attempt is still admissible. If memory can filter stale context instead of replaying everything, it becomes a safety layer instead of just a bigger notebook.
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Fere AI
Been using it for 2 weeks, and I definitely see improvements.
One thing I wish AgentMemory had -- team collab in some way. AgentMemory should have some way to export relevant memories to filesystem which can be committed to git. Also, some way to import them. This is something we want to try out internally in a few weeks once our devs all have used it some time and memories have evolved.
Agentmemory
Took a quick look , seems straightforward and usable. Curious how you're keeping memory relevant without it becoming noisy over time.
Agentmemory
I started using it to help with the memories of how my product has pivoted over time. I'm pre-revenue and it's CONSTANT pivots and cursor by default was impossible to keep tidy. Slight variations in direction looked identical in the landing pages, app, backend, and I needed something that tracked the conversations more deeply.
I backfilled agent memory on my past month's cursor Agent transcripts and asked it some questions to see how well it did, and it was surprisingly accurate. Picked up on things that I move away from, decided we're badly done, etc, which aren't immediately obvious if you look at my codebase despite my diligence with updating documentation and labeling commits.
So far, so good.
Agentmemory
Agentmemory
Agentmemory
HELLO @rohit_ghumare Really promising so far — I like that it tackles one of the biggest pain points with coding agents: losing useful project context across sessions without bloating the context window. The searchable memory + visibility into what the agent remembers feels especially valuable. 👏
Agentmemory
Cosmic
Agent memory is the layer everyone needs but nobody wants to build themselves. The teams getting the most out of AI pipelines today are the ones who've solved persistent context. Curious how you're thinking about structured vs. unstructured memory for content-heavy workflows.
Agentmemory
Agent memory feels most useful when it preserves why a prior attempt stopped, not just what files or observations existed. For coding agents, the missing piece is usually a small receipt: what context was retrieved, which verifier moved, what budget remains, and why the next attempt is still admissible. If memory can filter stale context instead of replaying everything, it becomes a safety layer instead of just a bigger notebook.