OSS core · Enterprise-ready path
Build, run, and evolve multi-agent workflows with graph orchestration, multi-layer memory, and an extensible tool system. Start open-source, scale to enterprise.
Why GenXAI
GenXAI combines graph execution, memory, and tools into a framework you can grow from prototype to production.
Model workflows as graphs (not only linear chains). Route dynamically, run in parallel, loop, and compose subgraphs.
Multi-layer memory inspired by human cognition so agents can retain context, learn, and retrieve knowledge efficiently.
Built-in tools, custom tools, and Model Context Protocol integrations so agents can act in real systems.
Start with the MIT core and scale to enterprise features like observability, security, and the no-code Studio.
How it works
Use graph primitives for routing and coordination patterns. Start simple, then add memory, tools, and enterprise controls when needed.
Declare roles, goals, models, tools, and memory. Keep it explicit and versionable.
Nodes represent agents/tools/conditions. Edges define control flow, routing, and parallelism.
Execute with state, logs, and metrics. Add enterprise governance for production reliability.
Quick start
import os
from genxai import Agent, AgentConfig, AgentRegistry, Graph
os.environ["OPENAI_API_KEY"] = "sk-..."
classifier = Agent(
id="classifier",
config=AgentConfig(role="Classifier", goal="Categorize customer requests", llm_model="gpt-4"),
)
support = Agent(
id="support",
config=AgentConfig(role="Support Agent", goal="Resolve customer issues", llm_model="claude-3-opus", enable_memory=True),
)
AgentRegistry.register(classifier)
AgentRegistry.register(support)
graph = Graph()
# ... add nodes + edges ...
result = await graph.run(input_data="My app crashed")
Follow the getting-started guide, run an example, and evolve into production-grade orchestration patterns.