PraisonAI vs litecrew: Choosing the Right Multi-Agent Framework

By Prahlad Menon Published 2026-03-31 2 min read

A new multi-agent framework called PraisonAI is getting a lot of attention right now — 5,600+ GitHub stars, benchmarks showing 3.77 microseconds average agent instantiation time, and a feature list that covers most of what production systems actually need.

It’s worth looking at seriously. But it also made me think about something we built here at The Menon Lab: litecrew — a multi-agent orchestration layer in ~150 lines with no abstractions.

They solve different problems. Here’s how to choose.

What PraisonAI Does

PraisonAI is a full-featured, production-ready multi-agent framework. The headline number is 3.77μs average agent instantiation — benchmarked against LangGraph, OpenAI Agents SDK, Agno, and PydanticAI. That’s roughly 1,209x faster than LangGraph on the same test.

Beyond speed, it ships with:

Built-in memory — short-term, long-term, entity, and episodic, all with a single parameter, no extra infrastructure
MCP Protocol support — stdio, WebSocket, SSE, Streamable HTTP; your agents can consume any MCP server as a tool and expose themselves as MCP servers for Claude, Cursor, or any other client
Deep Research Agent — connects to OpenAI and Gemini deep research APIs, streams results, returns structured citations
100+ model providers — OpenAI, Anthropic, Gemini, Groq, DeepSeek, Mistral, Ollama, xAI, Perplexity, AWS Bedrock, Azure, and more; change models by changing one line
24/7 scheduler — agents run on their own schedule without manual triggering
CLI that mirrors the SDK — auto mode, interactive terminal, deep research, session handling

pip install praisonaiagents

from praisonaiagents import Agent, Task, PraisonAIAgents

agent = Agent(
    name="Researcher",
    role="Research Analyst",
    goal="Find and summarize key information",
    backstory="Expert at gathering and synthesizing information",
    llm="gpt-4o"
)

task = Task(
    name="research_task",
    description="Research the latest developments in AI agent frameworks",
    expected_output="A concise summary with key findings",
    agent=agent
)

agents = PraisonAIAgents(agents=[agent], tasks=[task])
agents.start()

What litecrew Does

litecrew is the opposite philosophy: multi-agent orchestration for people who don’t want a framework.

The entire thing is ~150 lines. No config files, no YAML, no decorators, no 200-page docs. You define agents, wire them together, run them.

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. Sequential handoffs, parallel fan-out, tool calling, token tracking, and optional persistent memory via soul-agent — all without learning a new abstraction layer.

pip install litecrew

Head-to-Head Comparison

	litecrew	PraisonAI
Lines of code	~150	Large framework
Learning curve	Minutes	Hours
Setup time	< 5 minutes	15-30 minutes
Built-in memory	Via soul-agent plugin	Native, 4 types
Model providers	OpenAI + Anthropic + any OpenAI-compatible API	100+ natively
MCP support	No	Yes (all transports)
Scheduler	No	Yes (24/7)
Agent patterns	Sequential, parallel	Sequential, parallel, routing, loops, evaluator-optimizer
Deep research	No	Built-in
CLI	No	Full CLI
When to use	Prototypes, simple 2-5 agent workflows	Production systems, complex orchestration

How to Decide

Use litecrew if:

You have 2-5 agents passing data to each other
You’re prototyping and want something working in 5 minutes
You want to read and understand every line of your orchestration code
You’re learning how multi-agent systems actually work under the hood
You don’t need scheduling, MCP, or deep research built in

Use PraisonAI if:

You’re building for production and need built-in memory, scheduling, and observability
You need MCP support to integrate with Claude, Cursor, or other MCP clients
You want 100+ model providers with one-line switching
You need complex agent patterns — routing, loops, evaluator-optimizer
You want a full CLI alongside the SDK

litecrew’s honest take on itself (from the README):

We’re not better. We’re smaller. If you need complex orchestration, use the big frameworks. If you need something working in 5 minutes, we’re here.

The SQLite analogy applies: SQLite doesn’t try to be PostgreSQL. It does one thing well and is honest about when you’ve outgrown it. litecrew is the same. PraisonAI is closer to PostgreSQL — full-featured, production-hardened, more to learn.

Practical Migration Path

If you’re starting a new project:

Start with litecrew — get your logic working, understand your agent flow
Hit a wall — you need scheduling, MCP, memory management, or more complex patterns
Move to PraisonAI — the concepts transfer cleanly

The core mental model is the same: agents have roles, tasks have inputs and outputs, pipelines pass data between them. The difference is how much the framework does for you automatically.

FAQ

Q: Is PraisonAI actually 1,209x faster than LangGraph?
A: On agent instantiation specifically, yes — 3.77μs vs ~4.5ms measured in their benchmark. That matters most in high-throughput systems where agents are instantiated constantly for subtasks and retries. For most prototypes you won’t feel the difference.

Q: Does litecrew work with local models?
A: Yes. Point it at any OpenAI-compatible API — Ollama, LM Studio, vLLM. openai.base_url = "http://localhost:11434/v1" and you’re done. PraisonAI also supports Ollama natively.

Q: Can I mix models in the same pipeline?
A: Both frameworks support this. In litecrew: Agent("researcher", model="gpt-4o-mini") and Agent("writer", model="claude-3-5-sonnet-20241022") — they just work. In PraisonAI: same, just change llm= per agent.

Q: Does PraisonAI store my API keys?
A: No — like litecrew, it reads from environment variables. Neither framework proxies your keys.

Q: litecrew says it’s 20% of features — which 20%?
A: Sequential pipelines, parallel fan-out, tool calling, token tracking, basic memory. The 80% it skips: streaming, human-in-the-loop, hierarchical agent management, stateful branching, built-in scheduling, MCP, 100+ providers. If you need any of those, use PraisonAI or LangGraph.

Q: Why build litecrew when PraisonAI exists?
A: litecrew shipped in March 2026 alongside PraisonAI’s growth. The goal was different — a ~150-line reference implementation you can read in 20 minutes, fork, and own completely. No dependency surprises, no framework magic. PraisonAI is a product. litecrew is a starting point.

Q: Is PraisonAI production-ready?
A: 5,600+ stars, active maintenance, MIT licensed, and the benchmark numbers are solid. It’s ready for serious use. Run your own instantiation benchmarks against your current stack to verify the numbers hold in your environment.

Resources

PraisonAI: github.com/MervinPraison/PraisonAI · docs.praison.ai
litecrew: github.com/menonpg/litecrew · pip install litecrew
soul-agent (memory for litecrew): github.com/menonpg/soul.py