litecrew

Multi-agent orchestration in ~100 lines. No magic. No vendor lock-in.

from litecrew import Agent, crew

researcher = Agent("researcher", model="gpt-4o-mini")
writer = Agent("writer", model="claude-3-5-sonnet-20241022")

@crew(researcher, writer)
def write_article(topic: str) -> str:
    research = researcher(f"Research {topic}, return key facts")
    return writer(f"Write article using: {research}")

article = write_article("quantum computing")

That's it. That's the library.

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🔑 BYOK — Bring Your Own Keys

litecrew never touches your API keys. We don't proxy, store, or even see them.

# Set your keys as environment variables (standard practice)
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

The official openai and anthropic Python libraries read these automatically. litecrew just calls those libraries. Your keys stay on your machine.

• ✅ No litecrew account required
• ✅ No API proxy
• ✅ No telemetry
• ✅ No key storage
• ✅ Works offline with local models (via OpenAI-compatible APIs)

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🎯 What litecrew IS

A minimal orchestration layer for simple multi-agent workflows.

✅ Use litecrew when...
You have 2-5 agents that pass data to each other
You're prototyping and want to move fast
You want to understand every line of your orchestration code
You're learning how multi-agent systems work
You need something working in 10 minutes, not 10 hours

Core features:

• Define agents (model + tools + system prompt)
• Sequential handoffs (A → B → C)
• Parallel fan-out (A → [B, C, D] → collect)
• Tool calling (OpenAI function calling format)
• Token tracking and cost awareness
• Optional persistent memory via soul-agent

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🚫 What litecrew is NOT

Be honest about scope. If you need these, use a full framework:

❌ Don't use litecrew when...	Use instead
Complex hierarchical agent management	CrewAI, AutoGen
Stateful conversation with branching	LangGraph
Production enterprise workflows	LangGraph, Temporal
Visual workflow builders	Flowise, n8n
47 pre-built integrations	LangChain
Human-in-the-loop approval flows	CrewAI, custom
Automatic retry with exponential backoff	Tenacity + custom
Streaming responses	Direct API calls
Agent-to-agent negotiation	AutoGen

The deal: We do 20% of what CrewAI does in 1% of the code. That's a tradeoff. If you need the other 80%, you've outgrown us — and that's fine.

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📊 Comparison

Framework	Lines of Code	Learning Curve	Flexibility	Our Take
litecrew	~150	Minutes	Limited	Start here
CrewAI	~15,000	Hours	High	Graduate to this
LangGraph	~50,000	Days	Very High	For complex flows
AutoGen	~30,000	Days	High	For agent negotiation

Our recommendation:

1. Start with litecrew — Get your prototype working
2. Hit a limitation — You need something we don't do
3. Graduate to CrewAI + crewai-soul — Keep your memory layer

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Installation

pip install litecrew

With providers:

pip install litecrew[openai]      # OpenAI support
pip install litecrew[anthropic]   # Anthropic support
pip install litecrew[all]         # Everything including memory

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Usage

Basic Agent

from litecrew import Agent

agent = Agent(
    name="assistant",
    model="gpt-4o-mini",  # or "claude-3-5-sonnet-20241022"
    system="You are a helpful assistant."
)

response = agent("What is the capital of France?")
print(response)
print(agent.tokens)  # {"in": 23, "out": 15}

Sequential Handoff

from litecrew import Agent, sequential

researcher = Agent("researcher", model="gpt-4o-mini")
writer = Agent("writer", model="gpt-4o-mini")
editor = Agent("editor", model="gpt-4o-mini")

pipeline = sequential(researcher, writer, editor)
result = pipeline("Write about AI safety")

Parallel Execution

from litecrew import Agent, parallel

security = Agent("security", system="Review for security issues.")
performance = Agent("performance", system="Review for performance.")
style = Agent("style", system="Review for code style.")

review_all = parallel(security, performance, style)
results = review_all("def get_user(id): return db.query(f'SELECT * FROM users WHERE id={id}')")
# Returns: ["SQL injection risk...", "Consider caching...", "Use parameterized queries..."]

With Tools

from litecrew import Agent, tool

@tool(schema={
    "type": "object",
    "properties": {"query": {"type": "string"}},
    "required": ["query"]
})
def search(query: str) -> str:
    return f"Results for: {query}"

agent = Agent("assistant", tools=[search])
response = agent("Search for the latest AI news")

With Persistent Memory

from litecrew import Agent, with_memory

agent = Agent("assistant", model="gpt-4o-mini")
agent = with_memory(agent, namespace="my-assistant")

# Agent now remembers across sessions
agent("My name is Alice and I work at Acme Corp")
# ... later, even after restart ...
agent("Where do I work?")  # "You work at Acme Corp"

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Testing

# Install dev dependencies
pip install litecrew[dev]

# Run tests
pytest tests/

# Run with coverage
pytest tests/ --cov=litecrew

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The Soul Ecosystem

litecrew is part of a family of simple, composable AI tools:

Package	Purpose	When to Use
litecrew	Minimal orchestration	Starting out, prototypes
soul-agent	Persistent memory	Add memory to any agent
crewai-soul	CrewAI + memory	Production multi-agent
langchain-soul	LangChain + memory	Complex chains
llamaindex-soul	LlamaIndex + memory	RAG pipelines

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Philosophy

"Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away." — Antoine de Saint-Exupéry

Most frameworks race to add features. We race to keep them out.

The SQLite strategy: SQLite doesn't try to be PostgreSQL. It does one thing well and says "if you need more, use something else." That's us.

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FAQ

Q: Why not just use CrewAI?
A: CrewAI is great when you need it. But sometimes you just want two agents to pass data without learning a framework. That's us.

Q: How do I add feature X?
A: Fork it. The code is ~150 lines. Add what you need. Or graduate to CrewAI.

Q: Will you add streaming/callbacks/hierarchies?
A: No. Adding features makes us what we're replacing.

Q: Is this production-ready?
A: For simple workflows, yes. For complex enterprise needs, use CrewAI + crewai-soul.

Q: Do you store my API keys?
A: No. We never see them. They stay in your environment variables.

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License

MIT — Do whatever you want.

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Contributing

• Bug? Open an issue
• Feature request? Consider if it keeps us simple. If not, fork it.
• PR? Keep it minimal

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