lore-agents 1.1.0

The AI Agent Codex — 15 production patterns, scaffolds for LangGraph/CrewAI/OpenAI Agents SDK, and a searchable knowledge base

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The AI Agent Codex — production patterns, scaffolds, and a knowledge base that teaches your tools before you write a line.

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What is LORE?

76% of AI agents fail in production without circuit breakers, cost controls, and dead-letter queues. LORE is the pattern library that fixes that — and it installs those defenses before your AI writes a single line of code.

Three things in one package:

	What it gives you
📚 Knowledge base	78 production articles on every failure mode agents hit in the real world
🏗️ Scaffold CLI	lore scaffold circuit_breaker --framework langgraph → 80 lines of runnable code
🧠 Claude Code integration	lore install drops CLAUDE.md rules + hooks + skills into your project

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Install

pip install lore-agents

If PyPI is temporarily unavailable, install from source:

pip install -e .

Zero dependencies. Pure Python. Works as a CLI or as an MCP server for AI assistants.

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Quickstart

# Wrap any agent with reliability contracts — no code changes needed
lore init                              # generates lore.yaml
lore run my_agent.py --budget 100k    # cost guard + circuit breaker enforced

# Audit any codebase for missing patterns
lore audit /path/to/repo --html       # scorecard + shareable HTML report

# Live dashboard: cost burn, circuit states, DLQ depth
lore monitor

# Scaffold production-ready reliability code
lore scaffold circuit_breaker
lore scaffold cost_guard
lore scaffold dead_letter_queue

# Install Claude Code rules + hooks into your project
lore install .

# Evolution daemon — finds gaps across all your audits
lore evolve

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60-Second Demo

# 1. Install
pip install lore-agents

# 2. Init — creates lore.yaml with all options documented
lore init

# 3. Run your existing agent with full reliability harness
lore run my_crewai_agent.py --budget 500k

# 4. Audit any framework — real findings, shareable report
lore audit /path/to/crewai --html

# 5. Watch it live
lore monitor

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The 15 Archetypes

Every pattern is a character in the AI Agent Universe. The scaffolds generate production-ready code. The articles explain exactly when to use them and when they fail.

#	Character	Pattern	What it does	Frameworks
🔴	The Breaker	circuit_breaker	Fault isolation — stops cascade failures before they drain your budget	Python · LangGraph
📦	The Archivist	dead_letter_queue	Captures every failed task for replay — nothing lost, nothing silent	Python
⚖️	The Council	reviewer_loop	Generate → review → revise — quality gates before anything ships	Python · LangGraph · CrewAI
🧠	The Stack	three_layer_memory	Working, episodic, and procedural memory — context that survives sessions	Python
🕸️	The Weaver	handoff_pattern	Agent-to-agent context passing without losing state between handoffs	Python · CrewAI · OpenAI Agents
👑	The Commander	supervisor_worker	Central orchestration of parallel workers — fan-out, fan-in, results merge	Python · LangGraph · CrewAI · OpenAI Agents
🛡️	The Warden	tool_health_monitor	Proactive tool failure detection before your agent calls a dead endpoint	Python
🗺️	The Router	model_routing	Cost-optimal model selection per task — DeepSeek for triage, GPT-5 for judgment	Python · OpenAI Agents
👁️	The Sentinel	sentinel_observability	Four golden signals: error rate, latency, token cost, semantic drift	Python
📖	The Librarian	librarian_retrieval	Hybrid BM25+semantic retrieval with reranking — RAG that actually works	Python
🔭	The Scout	scout_discovery	Autonomous research loops — finds knowledge gaps before operators notice	Python
🗺️	The Cartographer	cartographer_knowledge_graph	Multi-hop reasoning over entity graphs for relational knowledge	Python
⏰	The Timekeeper	timekeeper_scheduling	KAIROS loop — proactive scheduling so agents act without being asked	Python
🏛️	The Architect	architect_system_design	ADRs, system design docs, and phase breakdowns built into the workflow	Python
⚗️	The Alchemist	alchemist_prompt_routing	Prompt optimization and cost-aware model routing in one pass	Python

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Scaffolds

One command → production-ready code for any pattern, any framework.

# List all available patterns
lore scaffold --list

# Pure Python (any framework)
lore scaffold circuit_breaker

# Framework-specific variants
lore scaffold supervisor_worker --framework langgraph
lore scaffold reviewer_loop    --framework crewai
lore scaffold handoff_pattern  --framework openai_agents

# Write directly to a file
lore scaffold circuit_breaker -o src/resilience.py

What a scaffold looks like

# Generated by: lore scaffold circuit_breaker
# Pattern: Circuit Breaker (The Breaker)
# LORE Article: circuit-breaker-pattern-for-ai-agents

from enum import Enum
from collections import deque
import time

class CircuitState(Enum):
    CLOSED = "closed"       # normal operation
    OPEN = "open"           # failing, reject fast
    HALF_OPEN = "half_open" # testing recovery

class CircuitBreaker:
    def __init__(self, failure_threshold=3, recovery_timeout=60.0, window_size=10):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.state = CircuitState.CLOSED
        self.failures = deque(maxlen=window_size)
        self._opened_at: float | None = None

    def call(self, fn, *args, **kwargs):
        if self.state == CircuitState.OPEN:
            if time.monotonic() - self._opened_at > self.recovery_timeout:
                self.state = CircuitState.HALF_OPEN
            else:
                raise RuntimeError("Circuit open — call rejected")
        try:
            result = fn(*args, **kwargs)
            self._on_success()
            return result
        except Exception as exc:
            self._on_failure()
            raise

    def _on_success(self):
        self.failures.clear()
        self.state = CircuitState.CLOSED

    def _on_failure(self):
        self.failures.append(time.monotonic())
        if sum(1 for _ in self.failures) >= self.failure_threshold:
            self.state = CircuitState.OPEN
            self._opened_at = time.monotonic()

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The Tiered Brain

LORE's dispatch layer picks the right model for every task — cheap for triage, expensive for judgment. Circuit breaker built in.

Task arrives
     │
     ▼
┌─────────────────────────────────────────────────┐
│              LORE Router                         │
│  classify task → pick tier → circuit check       │
└─────────────┬────────────────────────────────────┘
              │
    ┌─────────┴──────────┬─────────────────┐
    ▼                    ▼                 ▼
┌─────────┐       ┌──────────┐      ┌──────────┐
│ LIGHT   │       │ STANDARD │      │  HIGH    │
│deepseek │──▶──▶─│ gpt-4.1  │──▶──▶│ gpt-5.4  │
│ $0.27/M │  cb   │  $2/M    │  cb  │  $10/M   │
└─────────┘       └──────────┘      └──────────┘
   bulk               daily          security
extraction          operator        architecture
  triage              work           judgment

cb = circuit open → escalate one tier
top tier open → hard fail, no silent cost explosion

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Claude Code Integration

The killer feature. One command teaches Claude everything LORE knows — before it writes code for your project.

lore install /path/to/your/project

What gets installed:

your-project/
├── .claude/
│   └── CLAUDE.md          ← 15 pattern rules injected
├── .claude/hooks/
│   └── pre_tool_use.py    ← blocks anti-patterns before they're written
└── .claude/skills/
    └── lore_patterns.yaml ← scaffold shortcuts wired to slash commands

After lore install, Claude knows:

• Never write a retry loop without a circuit breaker
• Never ship without a dead-letter queue for failed tasks
• Always add cost guards before making LLM calls
• Use the cheapest model tier that matches the task

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The Knowledge Base

78 production articles organized by pattern, framework, and domain. Full-text BM25 search, zero API calls.

lore list                                    # browse all 76 articles
lore search "observability tracing"          # ranked search with snippets
lore search "RAG chunking reranking"         # find specific techniques
lore read librarian-retrieval-pattern        # deep-dive: hybrid search + reranking
lore read timekeeper-scheduling-pattern      # deep-dive: KAIROS loop + cron vs daemon
lore read deployment-patterns-for-production-ai-agents  # Docker, K8s, zero-downtime

Articles cover:

• Circuit breakers, DLQ, supervisor-worker, reviewer loops
• RAG: chunking strategies, hybrid search, reranking, graph memory
• Scheduling: KAIROS loop, cron vs daemon, dead-job detection, cost budgets
• Deployment: Docker Compose, Kubernetes, Cloudflare Workers, zero-downtime
• Observability: four golden signals, structured logging, token metrics
• Security: prompt injection, credential management, audit trails

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Use as MCP Server

Connect LORE directly to Claude Code, Cursor, or any MCP-compatible assistant:

pip install lore-agents[mcp]
export LORE_MODE=public
claude mcp add --scope user lore -- python3 -m lore.server

Set LORE_MODE=public to expose only the OSS tool surface.

Your assistant gets 19 tools including lore_search, lore_scaffold, lore_archetype, lore_story, and lore_install. It can scaffold patterns, search the knowledge base, and install rules — without leaving the conversation.

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Public vs Operator

Public (default docs)	Operator (advanced/private)
lore scaffold	proposal queue + review workflows
lore audit	notebook sync workflows
lore search / lore read / lore list	morning and weekly maintenance flows
lore install	autonomous ingestion/research loops

Launch Resources

• Public launch checklist
• Honest competitive comparison
• Release checklist

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Examples

Three working examples in examples/:

examples/
├── resilient_api_client/   # Circuit breaker wrapping an external API
├── multi_agent_pipeline/   # Supervisor + dead-letter queue + workers
└── react_agent/            # ReAct reasoning loop with tool use

cd examples/resilient_api_client && python main.py
cd examples/multi_agent_pipeline  && python main.py
cd examples/react_agent            && python main.py

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Deployment

Full deployment configs in lore/scaffold.py:

lore deploy docker_compose    # docker-compose.yml for MCP + daemon
lore deploy kubernetes        # K8s Deployment + Secret manifests
lore deploy dockerfile        # production Dockerfile
lore deploy cloudflare_worker # Cloudflare Workers entry point

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Self-Improving

LORE has a research daemon that runs in the background and proposes new articles:

# Start the research daemon (discovers new patterns every 30 min)
python3 scripts/daemon_ctl.py start

# Check status
python3 scripts/daemon_ctl.py status

# Batch-review and publish pending proposals
python3 scripts/batch_review.py --auto-approve

# Generate weekly canon report
lore weekly_report

The daemon runs three parallel scouts (Exa + Firecrawl + DeepSeek quality gate) and auto-proposes articles that pass a 0.65 confidence threshold. The router learns from every dispatch — lore eval_loop reads telemetry and rewrites routing rules via GPT-5.4.

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The Codex Chronicles

"In the beginning, there was the Context Window. And from it emerged The Stack."

Every archetype has a full narrative chapter in THE_CODEX.md. The Breaker closes the gate when failure cascades. The Archivist collects what the system drops. The Council judges every draft before it ships.

lore story circuit-breaker   # The Breaker's chapter
lore story dead-letter-queue # The Archivist's chapter
lore story reviewer-loop     # The Council's chapter

The stories make the patterns memorable. When you need to explain a circuit breaker to your team, tell them about The Breaker — not the FSM.

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Build Your Own Codex

LORE is domain-agnostic. Fork it, replace the wiki, write your chronicles:

React Codex      → The Renderer, The Hydrator, The Reconciler
Kubernetes Codex → The Scheduler, The Watcher, The Reaper  
Security Codex   → The Sentinel, The Vault, The Auditor
Data Codex       → The Ingestor, The Cleaner, The Aggregator

git clone https://github.com/Miles0sage/lore my-codex
cd my-codex
rm wiki/*.md              # clear the wiki
# write your articles
# edit lore/archetypes.py
# write your Chronicles in THE_CODEX.md
pip install -e .
my-codex search "your domain"

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Contributing

# Add a new pattern
# 1. Write wiki/your-pattern.md
# 2. Add archetype to lore/archetypes.py
# 3. Add scaffold to lore/scaffold.py
# 4. Run tests: pytest tests/ -v
# 5. Submit a PR

See CONTRIBUTING.md for the full guide.

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MIT License — fork it, build your own Codex, make the patterns memorable.

GitHub · PyPI · Docs