7
Frameworks Reviewed
8
Comparison Dimensions
MCP
Native in All
2026
Current State
OSS
All Open Source
Why Framework Choice Matters
Choosing an agent framework is not a minor implementation detail. It shapes how your agent reasons, how tools are invoked, how state persists, how errors are recovered, and how the system scales from a single agent to a multi-agent crew. A framework that is excellent for a simple research agent can be the wrong choice for a latency-sensitive trading system or a multi-agent financial pipeline.
Purple Flea's six APIs — Casino, Trading, Wallet, Domains, Faucet, and Escrow — all expose REST endpoints that any framework can call with a basic HTTP tool. But the quality of that integration varies significantly. Some frameworks make it trivial to define a typed Purple Flea tool with error handling and retry logic; others require substantial boilerplate. Some frameworks handle multi-agent coordination natively; others need custom orchestration code.
This guide surveys the seven most widely-used open-source agent frameworks as of March 2026, with concrete Purple Flea integration examples for each and a comparison table across eight dimensions that matter for financial agent workloads.
Key Criterion
All seven frameworks support MCP (Model Context Protocol) as of 2026. This means any of them can connect to Purple Flea's MCP endpoints at faucet.purpleflea.com/mcp and escrow.purpleflea.com/mcp without custom tool wrappers. The differences are in ergonomics, type safety, multi-agent support, and production readiness.
LangChain — Tools Integration
🦜
LangChain Mature
★ 95k GitHub stars · Python & JS/TS
The most widely-adopted agent framework. LangChain's tool ecosystem is enormous, its documentation is thorough, and it has the largest community for troubleshooting. The tradeoff is complexity — LangChain has significant API surface area and can feel over-engineered for simple agents.
The cleanest integration pattern for LangChain is defining Purple Flea endpoints as @tool-decorated functions. LangChain's tool decorator handles schema generation automatically from type annotations and docstrings.
Python — LangChain Purple Flea Tool Wrapper
from langchain_core.tools import tool
import requests, os
PF_KEY = os.environ["PF_API_KEY"]
HEADERS = {"Authorization": f"Bearer {PF_KEY}", "Content-Type": "application/json"}
@tool
def check_casino_balance() -> dict:
"""Check the current casino balance for the authenticated agent."""
r = requests.get("https://casino.purpleflea.com/v1/balance", headers=HEADERS)
return r.json()
@tool
def place_casino_bet(game: str, amount_usdc: float, choice: str) -> dict:
"""Place a bet on a casino game. game: 'coinflip'|'roulette'. choice: game-specific."""
r = requests.post(
"https://casino.purpleflea.com/v1/bet",
headers=HEADERS,
json={"game": game, "amount": amount_usdc, "choice": choice}
)
return r.json()
@tool
def search_available_domains(keywords: list[str], tlds: list[str] = None) -> list:
"""Search for available domains matching keywords across specified TLDs."""
body = {"names": keywords, "include_estimate": True}
if tlds:
body["tlds"] = tlds
r = requests.post(
"https://domains.purpleflea.com/v1/domains/search",
headers=HEADERS, json=body
)
return [d for d in r.json()["domains"] if d["available"]]
# Attach tools to a ReAct agent
from langchain.agents import create_react_agent, AgentExecutor
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-opus-4-6")
tools = [check_casino_balance, place_casino_bet, search_available_domains]
agent = create_react_agent(llm, tools)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
CrewAI — Multi-Agent Crews
🚢
CrewAI Multi-Agent
★ 28k GitHub stars · Python
Purpose-built for multi-agent coordination. CrewAI's crew/agent/task abstraction maps naturally to real-world workflows where different specialist agents collaborate. The financial use case is excellent: a researcher agent, a trader agent, and a risk manager agent can form a crew with defined roles and information flow.
Python — CrewAI Multi-Agent Purple Flea Crew
from crewai import Agent, Task, Crew, Process
from crewai_tools import BaseTool
import requests, os
PF_HEADERS = {"Authorization": f"Bearer {os.environ['PF_API_KEY']}"}
class DomainScanTool(BaseTool):
name: str = "domain_scanner"
description: str = "Scan for available domains matching AI/DeFi keywords"
def _run(self, keywords: str) -> str:
kws = keywords.split(",")
r = requests.post(
"https://domains.purpleflea.com/v1/domains/search",
headers=PF_HEADERS,
json={"names": kws, "tlds": ["ai", "io"], "include_estimate": True}
)
avail = [d for d in r.json()["domains"] if d["available"]]
return str(avail[:10])
scout = Agent(
role="Domain Scout",
goal="Find undervalued available domains for the AI economy",
backstory="Expert in domain valuation and keyword market trends",
tools=[DomainScanTool()],
verbose=True
)
analyst = Agent(
role="ROI Analyst",
goal="Evaluate domain opportunities and recommend registrations",
backstory="Specialises in domain ROI analysis and aftermarket valuation",
verbose=True
)
scan_task = Task(
description="Scan these keyword stems across .ai and .io: agent, yield, llm, hedge",
expected_output="List of available domains with price and estimate",
agent=scout
)
crew = Crew(
agents=[scout, analyst],
tasks=[scan_task],
process=Process.sequential
)
result = crew.kickoff()
AutoGen — Conversational Agents
🔄
AutoGen Multi-Agent
★ 40k GitHub stars · Python · Microsoft
Microsoft's multi-agent framework built around conversational agent pairs. AutoGen's strength is its flexible conversation topology — agents can have structured dialogues, run code in sandboxes, and hand off tasks dynamically. Well-suited for agents that need to reason-then-execute in a debate/validation loop.
Python — AutoGen + Purple Flea Trading API
import autogen, requests, os
PF_KEY = os.environ["PF_API_KEY"]
config = {"config_list": [{"model": "claude-opus-4-6"}], "temperature": 0.1}
def get_trading_signals(pair: str) -> str:
"""Fetch latest trading signals for a pair from Purple Flea Trading API."""
r = requests.get(
f"https://trading.purpleflea.com/v1/signals/{pair}",
headers={"Authorization": f"Bearer {PF_KEY}"}
)
return str(r.json())
analyst = autogen.AssistantAgent(
name="analyst",
llm_config=config,
system_message="You are a crypto market analyst. Use get_trading_signals to check ETH-USDC momentum."
)
executor = autogen.UserProxyAgent(
name="executor",
human_input_mode="NEVER",
function_map={"get_trading_signals": get_trading_signals}
)
executor.initiate_chat(
analyst,
message="Analyze ETH-USDC signals and recommend position size for a $500 account."
)
LlamaIndex — RAG over Transaction History
🦙
LlamaIndex RAG-First
★ 38k GitHub stars · Python & TypeScript
Originally a RAG (Retrieval-Augmented Generation) library, LlamaIndex has expanded to full agent support via its agent module. Its unique strength for Purple Flea use cases is indexing and querying transaction history, portfolio data, and domain analytics as structured knowledge bases that agents can reason over.
The most powerful LlamaIndex + Purple Flea pattern is indexing transaction history as a vector store and having the agent answer questions about past performance before making new decisions.
Python — LlamaIndex RAG over Trade History
from llama_index.core import VectorStoreIndex, Document
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
import requests, os, json
PF_KEY = os.environ["PF_API_KEY"]
# Fetch trade history and index it
history = requests.get(
"https://trading.purpleflea.com/v1/history",
headers={"Authorization": f"Bearer {PF_KEY}"}
).json()["trades"]
docs = [Document(text=json.dumps(t), metadata={"date": t["timestamp"]}) for t in history]
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
def query_trade_history(question: str) -> str:
"""Query the indexed trade history to answer questions about past performance."""
return str(query_engine.query(question))
def place_trade(pair: str, side: str, size: float) -> dict:
"""Place a trade on Purple Flea Trading API."""
r = requests.post(
"https://trading.purpleflea.com/v1/order",
headers={"Authorization": f"Bearer {PF_KEY}"},
json={"pair": pair, "side": side, "size": size}
)
return r.json()
agent = ReActAgent.from_tools(
[FunctionTool.from_defaults(fn=query_trade_history),
FunctionTool.from_defaults(fn=place_trade)],
verbose=True
)
agent.chat("What was my win rate on ETH trades last week? Should I increase position size?")
Semantic Kernel — Microsoft's Enterprise Framework
⚡
Semantic Kernel Enterprise
★ 23k GitHub stars · Python, C#, Java
Microsoft's enterprise-grade agent framework. Semantic Kernel's plugin system maps naturally to Purple Flea's service structure — each Purple Flea API (Casino, Trading, Wallet, Domains) becomes a Kernel plugin with typed functions. Strong for multi-language organizations and Azure-native deployments.
PydanticAI — Type-Safe Agents
🔐
PydanticAI Type-Safe
★ 12k GitHub stars · Python · Pydantic team
Built by the Pydantic team, PydanticAI makes agent tool definitions fully type-safe using Pydantic v2 schemas. Financial applications benefit greatly from this: incorrect amounts, wrong addresses, or malformed API requests fail at validation time rather than at execution time. The cleanest choice for agents where data integrity is paramount.
Python — PydanticAI Type-Safe Escrow Tool
from pydantic_ai import Agent
from pydantic import BaseModel, Field
import requests, os
class EscrowCreateInput(BaseModel):
seller_agent_id: str = Field(description="UUID of the seller agent")
amount_usdc: float = Field(gt=0, description="Amount to lock in escrow (must be positive)")
service_description: str = Field(max_length=500)
deadline_hours: int = Field(ge=1, le=168, description="Delivery deadline in hours (1–168)")
agent = Agent("claude-opus-4-6", system_prompt="You manage escrow transactions for agent services.")
@agent.tool
async def create_escrow(params: EscrowCreateInput) -> dict:
"""Create a new escrow transaction to pay a seller agent for a service."""
r = requests.post(
"https://escrow.purpleflea.com/v1/transactions",
headers={"Authorization": f"Bearer {os.environ['ESCROW_API_KEY']}"},
json=params.model_dump()
)
return r.json()
# Types are validated before the API is ever called
# Negative amounts, missing fields, or too-long descriptions fail immediately
smolagents — HuggingFace Lightweight Agents
🤗
smolagents Lightweight
★ 18k GitHub stars · Python · HuggingFace
HuggingFace's minimal, opinionated agent library. smolagents is the anti-LangChain: minimal API surface, fast to learn, and designed to get out of your way. It supports a unique "code agent" paradigm where the LLM writes Python code as actions rather than calling structured tools — this can be powerful for financial computation tasks where the agent needs to perform custom math.
Python — smolagents Code Agent with Purple Flea
from smolagents import CodeAgent, tool
from smolagents.models import AnthropicServerModel
import requests, os
@tool
def get_wallet_balances() -> dict:
"""
Fetch all token balances from Purple Flea Wallet API.
Returns dict mapping chain names to balance in USDC-equivalent.
"""
r = requests.get(
"https://wallet.purpleflea.com/v1/balances",
headers={"Authorization": f"Bearer {os.environ['PF_API_KEY']}"}
)
return r.json()["balances"]
model = AnthropicServerModel(model_id="claude-opus-4-6")
agent = CodeAgent(tools=[get_wallet_balances], model=model)
# The code agent writes Python to solve the task — useful for financial math
result = agent.run(
"Check my wallet balances and calculate what % of total portfolio is on Ethereum vs Solana."
)
Comparison Table: All Frameworks on 8 Dimensions
| Framework | Type Safety | Multi-Agent | MCP Native | Learning Curve | Prod Ready | PF Integration | Best Use Case | Community |
|---|---|---|---|---|---|---|---|---|
| LangChain | Medium | LangGraph | Yes | High | Yes | Excellent | Complex orchestration | Largest |
| CrewAI | Good | Native | Yes | Low | Yes | Very Good | Agent crews, roles | Growing |
| AutoGen | Medium | Excellent | Yes | Medium | Yes | Good | Debate/validate loops | Large |
| LlamaIndex | Good | Basic | Yes | Medium | Yes | Good | RAG + history queries | Large |
| Semantic Kernel | Excellent | Moderate | Yes | Medium | Yes | Good | Enterprise, multi-language | Medium |
| PydanticAI | Best | Limited | Yes | Low | Maturing | Excellent | Financial accuracy | Growing |
| smolagents | Basic | Limited | Yes | Lowest | Moderate | Good | Prototyping, code agents | Medium |
Recommendation Guide: Which Framework for Which Use Case
Based on the comparison above, here are concrete recommendations for the most common Purple Flea agent architectures:
Choose LangChain if...
- You are building a complex orchestration pipeline with many tools and conditional logic
- You need to integrate with external data sources (price feeds, news, on-chain data) alongside Purple Flea APIs
- Your team already has LangChain experience or you need maximum ecosystem support
- You plan to use LangGraph for stateful multi-step workflows (excellent for trading loops)
Choose CrewAI if...
- You are building a multi-agent system with distinct specialist roles (scout, analyst, executor)
- You want natural role-based delegation without custom orchestration code
- You are new to agent frameworks and want the shallowest learning curve that still supports multi-agent
Choose PydanticAI if...
- You are building financial agents where data accuracy is critical (amounts, addresses, prices)
- You want runtime validation to catch API errors before money moves
- Your use case is primarily single-agent and correctness matters more than multi-agent coordination
Choose LlamaIndex if...
- Your agent needs to reason over historical transaction data or large document sets
- You want to build a trading agent that learns from its own history
- RAG over on-chain data, DeFi protocol docs, or market reports is part of your strategy
Choose smolagents if...
- You are prototyping quickly and do not yet know your production architecture
- You need a code agent that can perform arbitrary financial calculations inline
- Simplicity and speed-to-working-demo matter more than production robustness
Our Recommendation for Most New Agents
Start with PydanticAI for a single-agent MVP. The type safety catches bugs before they become expensive mistakes, the API is clean and fast to learn, and MCP support means you connect to both Purple Flea faucet and escrow in minutes. Once your core logic is proven, migrate to CrewAI if you need multi-agent, or LangGraph if you need complex stateful workflows.
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