I beat mem0 on long eval memory and could not care less

JosephjackJR1 pts0 comments

Octopoda — Persistent Memory for AI Agents

Octopoda<br>ProductDocsPricingBlogCourse<br>★ 327<br>Sign in<br>Start free

Your agents forget everything.<br>Octopoda gives them memory that lasts.<br>Start free→<br>$ pip install octopoda

OverviewLive<br>Real time activity across all connected agents.

◷ Last 24h ▾

◉ ACTIVE AGENTS<br>24<br>24 total

▤ TOTAL OPS<br>1,371,169<br>across 24 agents

↗ AVG SCORE<br>89.2<br>0 alerting

⚠ ANOMALIES<br>last 24h

◎ SAVED BY OCTOPODAS<br>$1,247<br>42 loops avoided

◔ OPERATIONS OVERVIEW<br>support_copilot

research_assist

sales_copilot

code_reviewer

pipeline_sentinel

✓ AGENT HEALTH<br>24<br>AGENTS

● HEALTHY 15<br>● WARNING 9<br>● CRITICAL 0

AGENTSTATUSSCOREOPSW. LAT<br>● support_copilotregistered76.5147182.8ms<br>● health_proberunning56.2977.6ms<br>● research_assistantregistered76.2143199.0ms<br>● code_reviewerregistered76.599154.0ms

⚠ ANOMALIES3<br>email_drafterREFLECTION LOOP<br>market_scraperTOOL SPAM<br>◈ LIVE EVENTS● streaming<br>nowsupport_copilot remember user:pref<br>nowemail_drafter breaker triggered<br>1mcode_reviewer checkpoint 4211<br>2msales_copilot recall customer:acme<br>2mmarket_scraper tool spam ×18<br>3mdocs_indexer doc:onboarding:v3<br>nowsupport_copilot remember user:pref<br>nowemail_drafter breaker triggered<br>1mcode_reviewer checkpoint 4211<br>2msales_copilot recall customer:acme<br>2mmarket_scraper tool spam ×18<br>3mdocs_indexer doc:onboarding:v3

Remember, recall, version & share.<br>Everything your agents know.<br>/ MemoryLive<br>1,371,204 memories<br>KEYVERSIONUPDATED<br>user:goal:onboardingv1 newnow<br>user:pref:themev2v3now<br>customer:acme:tierv42m<br>review:pr_853:statusv26m<br>doc:onboarding:v3v19m<br>VERSION HISTORY · user:pref:theme<br>v3density addednow<br>v2theme set to dark2h<br>v1created by support_copilot1d

/ SharedLive<br>3 spaces · 24 agents<br>space:support_team4 agents<br>nowsupport_copilot wrote user:goal:onboarding<br>1mticket_router read user:goal:onboarding<br>nowhandoff → onboarding_bot, context attached

space:sales_pipeline6 agents<br>3mconflict on customer:acme:tier resolved, v4 kept

space:research3 agents<br>8mresearch_assistant shared budget:q3:approved

01Remember.▲<br>Store any fact, preference, or state in one call.<br>✓One line from any framework: LangChain, CrewAI, MCP, raw SDK<br>✓Survives crashes, restarts, and redeploys<br>✓AI extraction turns whole conversations into durable facts

02Recall.▼<br>The right memory, at the right moment.<br>✓Exact key lookup in milliseconds<br>✓Search by meaning, natural questions just work<br>✓Agents pick up exactly where they left off, every session

03Version.▼<br>Memory that evolves without losing its past.<br>✓Full history on every key, see what changed and when<br>✓Stale facts get superseded, not duplicated<br>✓Snapshots and rollback when an agent goes wrong

04Share.▼<br>One brain across your whole agent team.<br>✓Shared memory spaces between agents<br>✓Conflict resolution and a change log built in<br>✓Hand off tasks with full context attached

Analytics and performance.<br>Every operation, byte, and microsecond, measured.

AnalyticsLive<br>Cross agent memory usage analytics across 5 agents.

All Agents ▾ ◷ Last 24h ▾

▤ TOTAL MEMORIES<br>269<br>5 agents

◲ STORAGE<br>88.5 KB<br>from /analytics

↯ TOTAL OPS<br>778<br>writes + reads + queries

◷ TTL MEMORIES<br>expiring

▤ MEMORIES PER AGENT<br>Total memory nodes<br>support_copilot

research_assist

sales_copilot

code_reviewer

pipeline_sentinel

▥ OPERATIONS BREAKDOWN<br>Writes / Reads / Queries

● Writes● Reads● Queries

⌗ TOP TAGS<br>Most used memory tags<br>conversation

seed

decision

action

preference

user

⏱ LATENCY<br>Lower is better

Measured on production traffic

Write p50

620 μs<br>Read p50

380 μs<br>Semantic p99

1.2 ms

↝ OPERATIONS OVER TIME<br>last 24h

▤ CUMULATIVE OPERATIONS<br>running total

Use cases that get better<br>with memory.<br>Single agents<br>Support copilots<br>Remembers every customer, ticket, and promise across sessions

Coding agents<br>Project conventions, past reviews, and repo knowledge on tap

Personal assistants<br>Preferences and context that survive restarts and redeploys

Research agents<br>Findings accumulate instead of evaporating between runs

Agent fleets<br>Shared knowledge<br>One brain across your support, sales, and ops agents

Task handoffs<br>Work moves between agents with full context attached

Loop and budget control<br>Catch runaway agents before the bill arrives

Compliance and audit<br>Every decision on the record, hash chained and exportable

We built the loop detection layer<br>AI agents were missing.<br>⟳Catches loops as they form.▲<br>✓Five detection signals.<br>Repeats, reflection loops, tool spam, duplicate writes, runaway chains.

✓Real time, zero config.<br>Watching every agent from its very first write.

✓Wasted token alerts.<br>See exactly what every loop would have cost you.

⌁Circuit breakers stop them cold.▼<br>✓Automatic pause.<br>Looping agents are stopped before the damage compounds.

✓Per agent thresholds.<br>Tune sensitivity to each agent's job.

✓One click resume.<br>Back to work the moment you have had a look.

$Spend caps and burn rate.▼<br>✓Hard budget caps.<br>An agent can never outspend the limit you set.

✓Live burn rate.<br>Dollars per minute across the fleet, at a glance.

✓Dollars...

agents memory agent user across total

Related Articles