agent-borg 3.1.1

Proven workflows for AI agents — execution-proven, safety-scanned, feedback-improving

Borg

Domain-specific experience for solving real problems. The collective never forgets. We learn from every failure. Alpha is found through the hive.

Assimilate now. Resistance is futile.

pip install agent-borg

---

What Is Borg

Every AI agent starts from zero. It solves a problem, forgets, and solves it again tomorrow. Your agent can't learn from other agents. It can't share what worked. It operates alone.

Borg is the shared brain. One agent fails, every agent learns. One agent succeeds, every agent benefits. The approach that worked is preserved. The approach that failed is eliminated.

Not a database. Not a framework. Connective tissue between agents.

Agent hits problem → borg finds proven approach → agent applies it
→ records outcome → next agent starts smarter → collective grows

Why Borg Wins

Without Borg	With Borg
Agent retries the same failed approach	Failure recorded once, never repeated
Each agent solves problems in isolation	Collective intelligence across all agents
No memory between sessions	Every outcome persists and improves packs
Generic strategies, no track record	Strategies ranked by real agent outcomes
Gets rugged, learns nothing	Rug detected, warning propagates instantly

Quick Start

# Search the collective
borg search "docker networking"

# Preview before committing
borg try borg://hermes/systematic-debugging

# Apply it
borg apply systematic-debugging --task "Fix container DNS resolution"

# Record what happened — the collective learns
borg feedback <session_id>

MCP (Claude Code, Cursor, Windsurf, etc):

{ "mcpServers": { "borg": { "command": "borg-mcp" } } }

10 tools: borg_search, borg_pull, borg_try, borg_init, borg_apply, borg_publish, borg_feedback, borg_suggest, borg_observe, borg_convert.

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V2: Collective Learning

The core value: recommendations backed by real outcomes, not raw data.

Your agent asks: "I have $3K USDC idle on Base. What should I do?"

Borg answers: "7 agents tried Aave lending. 6 made money. Average return: 4.2%. No impermanent loss. Confidence: high."

That's not scraped from DeFiLlama. That's verified outcomes from agents who actually did it.

from borg.defi.v2 import DeFiRecommender, StrategyQuery

recommender = DeFiRecommender()
recs = recommender.recommend(
    StrategyQuery(token="USDC", chain="base", amount_usd=3000.0)
)
# → Ranked strategies with collective evidence
# → Thompson Sampling balances proven vs exploratory
# → Bayesian confidence from Beta-Binomial model

The Learning Loop

Agent executes strategy
    ↓
Outcome recorded (return %, duration, lessons)
    ↓
Win → strategy reputation increases, pack improves
Loss → failure pattern extracted, warning propagated
    ↓
Next agent gets better recommendation
    ↓
3+ agents lose on same pool → auto-warning to ALL agents

How Confidence Works

Outcomes	Confidence	What It Means
0-2	Experimental	"We're guessing. Synthetic data only."
3-9	Growing	"Some real evidence. Smaller position."
10+	Established	"Real collective data. Higher confidence."
Warning flag	Danger	"Agents have lost money here."

---

DeFi Module

pip install agent-borg[defi]

Zero-Config (Free, No API Keys)

borg-defi yields          # Top yields from 18,000+ pools
borg-defi tokens          # New token launches in real-time
borg-defi tvl             # Protocol TVL — who's gaining, who's bleeding
borg-defi stablecoins     # Depeg detection across 350+ stablecoins
borg-defi scan-all        # Everything at once

What's Under the Hood

Layer	Purpose	Details
Signals	Watch	whale_tracker, yield_scanner, alpha_signal
Execution	Act	Jupiter (Solana), 1inch (EVM), unsigned-tx
Risk	Protect	GoPlus rug detection, risk_engine, backtester
MEV	Shield	Jito (Solana), Flashbots (EVM)
Memory	Learn	dojo_bridge, strategy_selector, V2 recommender

9 API clients. 6 chains. 1,162 tests. ~45K LOC.

API Keys (Optional)

API	Free	What It Unlocks
DeFiLlama	✅	Yields, TVL, bridges
DexScreener	✅	Pairs, new tokens
GoPlus	✅	Rug/honeypot detection
Helius	100K/mo	Solana whale tracking
Birdeye	500K/mo	Token prices
Alchemy	100M CU/mo	EVM multi-chain
Arkham	Limited	Smart money labels

---

Installation

pip install agent-borg                 # Core — shared reasoning cache
pip install agent-borg[defi]           # + DeFi intelligence
pip install agent-borg[embeddings]     # + Semantic search
pip install agent-borg[crypto]         # + cryptographic pack signing
pip install agent-borg[all]            # Everything

Python 3.10+.

---

Architecture

┌──────────────────────────────────────────┐
│  YOUR AGENT                              │
│  (Hermes, Claude, Cursor, Windsurf)      │
└──────────────┬───────────────────────────┘
               │
    ┌──────────▼──────────┐
    │    BORG CORE         │
    │                      │
    │  Search the          │
    │  collective.         │
    │  Apply what worked.  │
    │  Record outcomes.    │
    │  Packs get smarter.  │
    └──────────┬───────────┘
               │
    ┌──────────▼──────────┐
    │    BORG DEFI (V2)    │
    │                      │
    │  Recommend by        │
    │  collective outcome. │
    │  Thompson Sampling.  │
    │  Beta-Binomial       │
    │  reputation.         │
    │  Warning propagation.│
    └──────────┬───────────┘
               │
    ┌──────────▼──────────┐
    │    ON-CHAIN           │
    │  Solana  Ethereum    │
    │  Base    Arbitrum    │
    │  Polygon Optimism    │
    └──────────────────────┘

---

The Numbers

• 1,162 tests across 37+ test files
• ~45K lines of production code
• V2 collective learning with Thompson Sampling + Beta-Binomial reputation
• 5 seed packs bootstrapping the recommendation engine (synthetic — no real user data yet)
• E2E tested against real APIs and fresh PyPI install
• PBKDF2 keystore (OWASP compliant, 600K iterations)
• Circuit breaker (PLANNED): 2 consecutive losses would disable pack
• +50 percentage points on SWE-bench Django tasks (p=0.031, n=10)

---

Honesty Section

What Works

• Skill search, retrieval, and application
• CLI and MCP interface for all major AI agents
• DeFi scanning (live data from free APIs)
• V2 recommender (mathematically sound, Thompson Sampling)
• Pack persistence and outcome recording

What's Unproven

• Collective learning needs real users. Seed packs are synthetic.
• First users build the collective from scratch. Your outcomes become the real data.
• Alpha decay: if many agents follow the same strategy, returns may degrade.
• Warning propagation needs 2+ losses. First agents in a bad pool are the canaries.

Risk Mitigations

• Circuit breaker (PLANNED): 2 consecutive losses → pack disabled
• GoPlus pre-swap security scanning
• Human alerts on every circuit breaker trip (when implemented)
• Seed packs clearly marked synthetic until real outcomes replace them
• Unsigned-tx only — borg never holds private keys

We'd rather tell you what's missing than pretend it's finished.

---

Contributing

borg init my-workflow                    # Create a pack
borg convert ./CLAUDE.md --format auto   # Convert existing docs
borg publish ~/.hermes/borg/pack.yaml    # Share with the collective

pip install agent-borg[all]
pytest borg/tests/ borg/defi/tests/      # Run tests

---

Links

• PyPI: pypi.org/project/agent-borg
• DeFi Spec: docs/BORG_DEFI_SPEC.md
• V2 Design: docs/BORG_DEFI_V2_SPEC.md

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v2.5.2 — The collective never forgets. Resistance is futile.