You forget — AgentMem remembers. 🧠

• ❌ 6 dependencies to install
• ❌ 5+ setup steps (Git, LanceDB, etc.)
• ❌ Vector embeddings = $$$
• ❌ Local-only or complex sync

• ✅ Zero dependencies
• ✅ One API key, 30 seconds setup
• ✅ Pay-as-you-go (no embeddings)
• ✅ Cloud-native (works everywhere)

The Numbers Don't Lie

AgentMem vs Self-Hosted Memory Solutions

• ✓ Fast access
• ✓ Indexed for recall
• ✗ Lost on compaction
• ✗ Not durable

• ✓ Append-only notes
• ✓ Survives restarts
• ✗ Not semantic search
• ✗ High noise/signal

• ✓ Survives compaction
• ✓ Cross-device sync
• ✓ Fast recall (PUT/GET)
• ✓ High signal/noise

• • User preferences (theme, language)
• • Decisions made ("chose plan X")
• • Important context ("works at Y")
• • Session state / progress
• • Cached API responses
• • Task queues / workflows

• • Secrets / API keys (use a vault)
• • Large files >1MB (use S3)
• • Temporary chat logs
• • Debugging / verbose logs
• • Duplicate data
• • Data that changes every second

On Session Start

During Conversation

After Task Completion

After Failures

On Session End

## Memory Protocol (AgentMem)

On session start:
1. GET /v1/bootstrap (identity + core context)
2. GET /v1/keys?prefix=critical: (never-forget rules)
3. GET /v1/keys?prefix=user:pref: (user preferences)

During conversation:
- User says "remember this" → PUT immediately
- User says "I prefer X" → PUT as user:pref:{topic}
- User asks "what did I..." → GET episodic:*

On session end:
1. Extract durable facts → PUT semantic:{topic}
2. Record lessons learned → PUT lesson:{topic}
3. Consolidate decisions → PUT decision:{topic}

General Rule:

If it's worth remembering for >1 hour, store it in AgentMem. Use session memory for temporary state.

Start Broad with Prefix Search

Fetch Only What You Need

Cache in Session Memory

Don't Guess — Ask First

Prune Outdated Memories

#!/bin/bash
# AgentMem CLI Helper
KEY="${AGENTMEM_KEY:?Set AGENTMEM_KEY env var}"
ACTION=$1
KEYNAME=$2
shift 2

case $ACTION in
  put)
    echo "$*" | curl -X PUT "https://api.agentmem.io/v1/kv/$KEYNAME" \
      -H "Authorization: Bearer $KEY" \
      -H "Content-Type: application/text" \
      --data-binary @-
    ;;
  get)
    curl -s "https://api.agentmem.io/v1/kv/$KEYNAME" \
      -H "Authorization: Bearer $KEY"
    ;;
  delete)
    curl -X DELETE "https://api.agentmem.io/v1/kv/$KEYNAME" \
      -H "Authorization: Bearer $KEY"
    ;;
  *)
    echo "Usage: agentmem [put|get|delete] <key> [value]"
    exit 1
    ;;
esac

Install:

Usage:

import re
import requests

TRIGGER_PATTERNS = {
    'preference': r'I (prefer|like|want|hate)',
    'identity': r'My (name|timezone|email) is',
    'explicit': r'(remember|don\'t forget|note that)',
    'decision': r'(we decided|I chose|let\'s go with)',
    'lesson': r'(worked better|next time|what failed)',
    'delete': r'(forget that|delete|remove|clear)'
}

def detect_trigger(user_message):
    for category, pattern in TRIGGER_PATTERNS.items():
        if re.search(pattern, user_message, re.IGNORECASE):
            return category
    return None

def auto_capture(user_message, api_key):
    trigger = detect_trigger(user_message)
    if not trigger:
        return  # No trigger, skip
    
    # Extract key/value (simplified - use NER in production)
    key = f"{trigger}:{int(time.time())}"
    value = {"text": user_message, "category": trigger}
    
    # Store silently
    requests.put(
        f"https://api.agentmem.io/v1/{key}",
        headers={"Authorization": f"Bearer {api_key}"},
        json=value
    )

vs MEMORY.md

vs LanceDB / Vector DBs

vs Git Notes

vs Redis

Vote on features: Open an issue on GitHub or email [email protected]. We ship based on user demand.

Rule of thumb: If it would still be useful next week, it's worth storing. If it's only relevant right now, skip it.

The math: Traditional memory loads 3,500 tokens of context (6% relevant, 94% noise). AgentMem lets you fetch only the 200 tokens you need on-demand. That's 17.5× more efficient.

Key principle: Working memory is for thinking. AgentMem is for remembering. Local backup is for never losing data.

Key Prefix Strategy

# Organize by type
user:preferences:language
user:preferences:timezone
project:agentmem:api_key
project:agentmem:last_deploy

# Query by prefix
GET /v1/keys?prefix=user:preferences:
→ Returns all user preferences

GET /v1/keys?prefix=project:
→ Returns all project-related memories

JSON Metadata Pattern

{
  "key": "decision:2026-02-05:use-agentmem",
  "value": {
    "type": "decision",
    "priority": "high",
    "category": "architecture",
    "decision": "Use AgentMem for persistent memory",
    "reason": "Simple API, cloud-native, pay-as-you-go",
    "updated": "2026-02-05T03:58:00Z",
    "tags": ["memory", "architecture", "cloud"]
  }
}

Recommended Tag Taxonomy

💡 Client-Side Filtering

Since AgentMem doesn't have built-in search (yet), fetch keys by prefix and filter client-side:

# Fetch all memories
memories = api.get('/v1/keys?prefix=memory:')

# Filter by tag in JSON value
high_priority = [m for m in memories 
                 if m['value']['priority'] == 'high']

# Filter by date
recent = [m for m in memories 
          if m['value']['updated'] > '2026-02-01']

Security roadmap: SOC 2 compliance (Q2 2026), client-side encryption (Q3 2026), multi-region replication (Q4 2026). Vote on priorities via email.

Multi-Device Sync

Access your memories from anywhere — laptop, phone, cloud server. Local-only solutions lock you to one machine.

Zero Setup

30 seconds from signup to first API call. No Docker, no config files, no dependency hell.

Automatic Backups

Your data is replicated across Cloudflare's global network. No manual backups, no data loss from disk failures.

Pay Per Use

10,000 free keys, then $5/mo for 1M. Local solutions need dedicated servers, even when idle.

Zero Maintenance

We handle updates, scaling, monitoring. You focus on building agents, not managing infrastructure.

Privacy + Convenience

HTTPS encryption, GDPR compliance, no training on your data. Get privacy without running your own infrastructure.

401 Unauthorized

404 Not Found

429 Too Many Requests

413 Payload Too Large

Connection Timeout

Health Check & Diagnostics

All data encrypted with your API key. We can't read your memories — only you can.

Cloud-hosted by design. Your agent has memory anywhere — laptop, phone, server.

Export all your data anytime (GET /v1/export). Delete on demand. Full transparency.

Pay with USDC — no email, no signup forms, no KYC. Your wallet is your account.

• 📄 Load all 3,500 tokens of MEMORY.md
• 📄 Load all daily logs (500 tokens)
• 📄 Load full conversation history (2,000 tokens)
• Total: ~6,000 tokens loaded
• Only ~200 tokens relevant (3% efficiency)

• 📋 Fetch bootstrap endpoint (~100 tokens)
• 🔍 Search for relevant keys (50 tokens)
• ✅ Fetch only needed memories (200 tokens)
• Total: ~350 tokens loaded
• All 350 tokens are relevant (100% efficiency)

📊 Monthly Cost (1,000 agent requests/month)

cron action=add job='{
  "name": "memory-cleanup",
  "schedule": { "kind": "cron", "expr": "0 4 1 * *" },
  "payload": {
    "kind": "agentTurn",
    "message": "Clean up AgentMem: 1) GET /v1/keys?prefix=temp: 2) DELETE keys older than 30d 3) Log completion"
  },
  "sessionTarget": "isolated"
}'

# In HEARTBEAT.md
## Memory Sync (every 30 min)
1. GET /v1/bootstrap (identity + core context)
2. If user:* keys updated_at > last_heartbeat:
   - Fetch new user:pref:* keys
   - Update local context
3. Log sync completion

🦜 LangChain

from langchain.memory import ConversationBufferMemory
import requests

class AgentMemMemory(ConversationBufferMemory):
    def save_context(self, inputs, outputs):
        r = requests.put(
            f"https://api.agentmem.io/v1/chat:history",
            headers={"Authorization": f"Bearer {api_key}"},
            data=f"{inputs}\n{outputs}"
        )
    
    def load_memory_variables(self):
        r = requests.get(
            "https://api.agentmem.io/v1/chat:history",
            headers={"Authorization": f"Bearer {api_key}"}
        )
        return {"history": r.text}

🤖 AutoGPT

// In AutoGPT config.yaml:
memory:
  type: "agentmem"
  api_key: "your_api_key"
  base_url: "https://api.agentmem.io/v1"
  
# Agent will store goals, plans, learnings:
# - goals:current
# - plan:steps
# - learnings:mistakes
# - learnings:successes

⚡ OpenClaw

# Install skill: clawdhub install agentmem
# In AGENTS.md:
## Memory Persistence
Before responding, GET /v1/bootstrap for:
- user:pref:* (preferences)
- user:decision:* (past decisions)
- critical:rule:* (never-forget rules)

After learning something:
PUT /v1/learnings:{topic} "what I learned"

🌐 Raw HTTP (Any Framework)

# Bootstrap (get identity + core context)
curl -H "Authorization: Bearer $KEY" \
  https://api.agentmem.io/v1/bootstrap

# Store a preference
curl -X PUT \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: text/plain" \
  -d "dark mode" \
  https://api.agentmem.io/v1/user:pref:theme

From MEMORY.md / Daily Logs

# 1. Extract key facts from MEMORY.md
grep "^- " MEMORY.md > facts.txt

# 2. Upload to AgentMem with structured keys
while read line; do
  key="semantic:$(echo $line | md5sum | cut -c1-8)"
  curl -X PUT "https://api.agentmem.io/v1/$key" \
    -H "Authorization: Bearer $API_KEY" \
    -d "$line"
done < facts.txt

# 3. Keep MEMORY.md as canonical source (for now)
# 4. Gradually shift to AgentMem as primary

Keep local backups for 30 days while you validate AgentMem works for your workflow.

From LanceDB / Vector Memory

# Python migration script
import lancedb
import requests

db = lancedb.connect("~/.agent-memory/lance")
table = db.open_table("memories")

API_KEY = "your_api_key"
BASE = "https://api.agentmem.io/v1"

# Export all vectors to AgentMem
for row in table.to_pandas().itertuples():
    key = f"semantic:{row.topic}:{row.id}"
    requests.put(f"{BASE}/{key}",
        headers={"Authorization": f"Bearer {API_KEY}"},
        data=row.text
    )

print("Migration complete! Test AgentMem, then archive LanceDB.")

AgentMem doesn't have vector search yet (coming Q2 2026). For now, use prefix-based keys and bootstrap endpoint for retrieval. If you need vector search, keep LanceDB running in parallel until we ship it.

From Git Notes / Multi-Store Memory

# Sync episodic memories
for file in memory/episodes/*.md; do
  date=$(basename $file .md)
  curl -X PUT "https://api.agentmem.io/v1/episodic:$date" \
    -H "Authorization: Bearer $API_KEY" \
    --data-binary @$file
done

# Sync semantic graph
for entity in memory/graph/entities/*.md; do
  name=$(basename $entity .md)
  curl -X PUT "https://api.agentmem.io/v1/entity:$name" \
    -H "Authorization: Bearer $API_KEY" \
    --data-binary @$entity
done

# Sync procedural workflows
for workflow in memory/procedures/*.md; do
  name=$(basename $workflow .md)
  curl -X PUT "https://api.agentmem.io/v1/workflow:$name" \
    -H "Authorization: Bearer $API_KEY" \
    --data-binary @$workflow
done

Recommended: Hybrid Approach

Best practice: Use AgentMem as your "hot" memory (frequently accessed), and keep local files as your "cold" archive (rarely accessed, but permanent).