Launched this week
Memori
Persistent memory from agent trace, not just conversation
198 followers
Persistent memory from agent trace, not just conversation
198 followers
Memori launched its new agent-native memory infrastructure, enabling agents to create structured, long-term memory directly from agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. This allows memory to also be generated from what an agent actually does. Benchmark results: 81.95% accuracy on LoCoMo using only 1,294 tokens per query, roughly 5% of full-context cost, saving users 95%+ on inference spend. 15K GitHub stars, 200000+ downloads
AISA AI Skills Test
the trace-based approach is smart. most agent memory systems just store conversation turns, which loses all the decision logic and tool outputs that actually matter for improving performance over time. curious how you handle conflicting memories when the same agent runs different strategies on similar tasks?
Thank you for your support and good question @ozandag
We handle memory primarily through a system where context is weighted by relevant and importance.
When an agent learns something new that contradicts an older trace, Memori can preserve the older trace for audit/history while updating the active/canonical memory that gets recalled going forward. In practice, newer or higher-confidence memories are weighted more heavily, and older contradictory memories decay or are suppressed unless they are specifically relevant to the task.
As an example: if an agent previously learned “the dashboard is red” but later the user corrects it to “the dashboard is blue,” Memori keeps the historical context but recalls “blue” as the current truth. That’s the key difference between raw chat history and agent-native memory — we’re managing durable state, not just stuffing old context back into the prompt.
Persistent memory from trace (not conversation) is exactly the right framing — agents that 'forget' why they made a choice are nearly useless in any audit-sensitive workflow. Curious how Memori handles the case where two traces contradict each other and the agent needs to pick a winning version. This is structurally the same problem in financial modeling — I built ModeLoop in part because assumption drift between model versions is brutal in deal work, and 'why did we change this number' is the question that gets asked three months later when nobody remembers.
Thank you for your support and good question @samir_asadov
When an agent learns something new that contradicts an older trace, Memori can preserve the older trace for audit/history while updating the active/canonical memory that gets recalled going forward. In practice, newer or higher-confidence memories are weighted more heavily, and older contradictory memories decay or are suppressed unless they are specifically relevant to the task.
As an example: if an agent previously learned “the dashboard is red” but later the user corrects it to “the dashboard is blue,” Memori keeps the historical context but recalls “blue” as the current truth. That’s the key difference between raw chat history and agent-native memory. We’re managing durable state, not just stuffing old context back into the prompt.
We built something similar at Tuple — agents that retained outcome data from past campaigns, not just conversation history. The behavioral difference was significant. An agent that recalls "this pain framing outperformed in healthcare verticals three campaigns ago" behaves fundamentally differently than one starting cold each run. The hard part we kept hitting: structuring trace data so retrieval stays signal-dense, not verbose. The LoCoMo benchmark showing 5% of context cost is the number that matters for production deployments. Watching this closely.
Thank you so much for your support, @thekrew
You're absolutely right it's important to structure memories from agent trace and execution in addition to long conversation is essential with the rise of AI agents.
It’s great to hear that the LoCoMo benchmark resonated with you. We agree that efficiency and context cost are the primary hurdles for scaling these deployments in the real world.
We'd love for you to see how Memori works as it retrieves memories, creates session summaries and hear your thoughts: https://memorilabs.ai/benchmark/#demo
Agents that learn from mistakes demo: https://memorilabs.ai/agent-trace/#demo
This is the right direction for agent memory. I like that you're deriving it from traces and tool outcomes instead of only chat history. The real test I'd be curious about is how teams inspect and prune memories when workflows change.
Cool! Can you also store and remember the sequence of actions in a multi-agent system?
Bolty
Thank you so much for your support, @bryce_murray