Mangaba AI

pip install mangaba

# Or with RAG and embeddings support
pip install mangaba[all]

export GOOGLE_API_KEY=your-gemini-key
# or
export OPENAI_API_KEY=your-openai-key
# or
export ANTHROPIC_API_KEY=your-anthropic-key

from mangaba.core import Agent, Task, Crew, Process, tool
from mangaba.core.types import LLMConfig

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

researcher = Agent(
    role="Research Analyst",
    goal="Find accurate information",
    backstory="Expert researcher",
    tools=[search],
    llm_config=LLMConfig(provider="google", model="gemini-2.5-flash", api_key="YOUR_KEY"),
)

task = Task(
    description="Research the latest AI trends",
    expected_output="A list of top 5 trends with explanations",
    agent=researcher,
)

crew = Crew(agents=[researcher], tasks=[task], process=Process.SEQUENTIAL)
result = crew.kickoff()
print(result.final_output)

from mangaba.core.types import OpenRouterConfig

agent = Agent(
    role="Analyst",
    goal="Analyze data",
    llm_config=OpenRouterConfig(
        provider="openrouter",
        model=["google/gemini-2.5-flash", "anthropic/claude-3.5-sonnet"],
        api_key="YOUR_OPENROUTER_KEY"
    )
)

from mangaba.protocols import MCPContext

context = MCPContext()
context.set("shared_data", {"user_id": 123, "session": "abc"})
# Agents can now read this shared context