Financial Agents Need Memory, Not Just Market Data

Most DeFi agents are goldfish. They execute trades, check balances, maybe even launch tokens — then forget everything the moment the session ends.

Bankr is a perfect example. It’s built on OpenClaw, handles real financial operations, and works across multiple channels. But like most financial agents, it’s session-blind. Every conversation starts from scratch.

That’s a problem when you’re dealing with money.

Why Financial Memory Matters

A chat assistant forgetting context is annoying. A financial agent forgetting your risk tolerance, portfolio strategy, or past trades is expensive.

Persistent memory gives a financial agent:

Portfolio Context

• Remember user’s holdings across sessions
• Track performance of past trades
• Learn from wins and losses

Risk Preferences

• “User got rekt on 10x leverage, prefers 3x max”
• “Avoids memecoins under $1M market cap”
• “Only trades during US market hours”

Strategy Memory

• Past token launches and their outcomes
• DCA patterns and timing preferences
• Which protocols user trusts vs. avoids

Cross-Session Learning

• Connect current market conditions to similar past scenarios
• Recall what worked (and what didn’t) in comparable situations
• Build actual expertise instead of starting from zero every time

The Integration

Bankr runs on OpenClaw. We just shipped native AutoMem support for OpenClaw.

One command:

npx @verygoodplugins/mcp-automem openclaw --workspace ~/bankr-workspace

That drops a skill file into OpenClaw that gives Bankr the full recall engine:

• Semantic search across all past interactions
• Multi-hop graph traversal (tokens → strategies → outcomes)
• Tag filtering (portfolio, trades, preferences, risks)
• Time-based queries (“last market crash”)
• Importance scoring (critical decisions first)

The architecture:

Bankr Agent → bash curl → AutoMem HTTP API → FalkorDB + Qdrant

No middleware. No protocol translation. Direct HTTP calls to a memory backend with 7,800+ memories and sub-50ms recall.

What This Looks Like in Practice

Scenario 1: Portfolio Check

User: "How's my portfolio doing?"
Bankr: *recalls last portfolio snapshot from memory*
       *compares current prices to stored positions*
       *references user's target allocation strategy*
       "You're up 12% this week. BTC position is 5% over target..."

Scenario 2: Risk Assessment

User: "Should I ape into this new token?"
Bankr: *recalls user's risk preferences*
       *checks past similar trades from memory*
       *finds "User prefers established projects" flag*
       "Based on your previous trades, you avoid sub-$5M caps.
        This one's $800k. Want to break pattern or pass?"

Scenario 3: Strategy Evolution

User: "What's worked best for me lately?"
Bankr: *queries memory for recent trades + outcomes*
       *groups by strategy type*
       *calculates win rates*
       "Your swing trades on blue chips are 70% profitable.
        Your memecoin plays are 30%. Recommend shifting allocation?"

Why This Beats File-Based Memory

OpenClaw has built-in daily memory files (memory/YYYY-MM-DD.md). Great for basic context but doesn’t scale for finance:

• No semantic search — can’t find “that trade where we lost 20%”
• No relationship mapping — can’t connect strategies to outcomes
• No cross-session reasoning — yesterday’s file doesn’t talk to last week’s
• No importance scoring — critical decisions buried in logs

AutoMem adds the semantic layer that makes financial memory useful.

The Unlock

Bankr already handles real financial operations. Adding persistent memory turns it from a useful tool into something that learns and improves with every interaction.

The integration path:

1. Install the AutoMem skill via CLI
2. Run AutoMem locally (Docker) or on Railway
3. Agents start storing and recalling context

No codebase changes. OpenClaw loads the skill, Bankr inherits the capability.

Financial agents that remember everything aren’t just better assistants — they’re safe to trust with money.

Try it: github.com/verygoodplugins/mcp-automem
Bankr: github.com/BankrBot/openclaw-skills

– Jack