AI agent "amnesia" dominates discussions: agents don't gradually forget—they completely reset between sessions, like goldfish or dementia patients, killing multi-session workflows. This is the strongest consensus, with near-universal agreement that persistence is the "single biggest unlock." Surprises include sky-high recall gains (95% vs 59% via exponential decay) and contrarian warnings that more context via transcript replay rots memory, not grows it—bio-inspired pruning is the hot fix.

Solutions explode: GitHub repos (Lancedb, Memoh), n8n workflows, MEMORY.md files, hash recall, and architectures blending short-term/working memory with long-term stores. Outlier: one unrelated programming language post. Teams running daily agents on shared codebases prioritize this for real-world viability.

Big theme: Stateless LLMs need layered memory (context window + persistent layers) to "learn" across runs; naive accumulation fails spectacularly.

Most Mentioned

• Persistent Memory Across Sessions — 13 mentions
  Core fix for agent amnesia; enables multi-session work via files, DBs (Lancedb), containers (Memoh), workflows (n8n).
  Claims 95% recall with exponential decay + hashing vs baseline failures.
  Sources: X [1,5,6,7,8,10,11,14], REDDIT [3,4,12,13,15]
• Short-Term vs Long-Term Memory — 6 mentions
  Agents need distinct layers: working (session) + persistent (cross-session) for complex tasks; frameworks like CoALA, LeNTa unify them.
  Transcript replay is flawed; bounded, bio-inspired systems preferred.
  Sources: X [1,6,9,11,14], REDDIT [13]
• Memory Decay/Rot — 4 mentions
  Naive accumulation leads to "rotting" or dementia; solutions use exponential decay, relevance pruning, hash recall.
  Contrarian: More context worsens agents.
  Sources: REDDIT [3,13], X [8,11]
• GitHub Tools/Repos — 3 mentions
  Ready-made persistence: memory-lancedb-pro, Memoh (containerized), memory layers.
  Plug-and-play for teams.
  Sources: X [5,10], REDDIT [12]

Key Patterns

1. Complete Session Reset — Agents wipe all memory on restart, not gradual decay; described as "goldfish," "dementia," or "stateless LLMs"—universal pain point across platforms.
2. DIY Persistence Hacks — Proliferation of practical fixes: MEMORY.md files, daily logs, vector DBs, workflows; focused on 24/7 autonomy and shared codebases.
3. Layered Memory Models — Consensus on short-term (context window/working) + long-term (persistent/pruned); papers and frameworks push unified management.
4. Pruning Over Accumulation — Contrarian pushback on "more context = better"; decay, hashing, bio-inspiration prevent rot and bound growth.
5. Quantified Wins — Specific benchmarks like 95% vs 59% recall validate solutions; tools target production teams.