Version 0.9 - Agent17

| Benchmark | v0.8 time | v0.9 time | Improvement | |------------------------------|-----------|-----------|-------------| | Single-step reasoning (100 runs) | 2.4 sec | 1.9 sec | 21% faster | | 10-step task pipeline | 34 sec | 22 sec | 35% faster | | Parallel tool use (5 tools) | 8.2 sec | 3.1 sec | 62% faster | | Memory retrieval across 10k records | 180 ms | 95 ms | 47% faster |

from agent17 import Agent, Tool @Tool(name="search_web", description="Search the internet") def search_web(query: str) -> str: # Implement search logic return f"Results for query..." Create agent with memory and tools agent = Agent( name="ResearchBot", model="gpt-4-turbo", memory_type="hybrid", # MemCore v2 tools=[search_web] ) Run a task result = agent.run("Find the latest AI research papers on multimodal learning") print(result) Performance Benchmarks: v0.9 vs v0.8 To evaluate the improvements, we ran standardized tests on a dual-GPU workstation (NVIDIA A6000). Here are the results: Agent17 Version 0.9

While there are still rough edges, the trajectory is clear: Agent17 is positioning itself as a serious alternative to proprietary frameworks like LangChain, AutoGen, or BabyAGI. For developers looking to explore the cutting edge of AI agents, version 0.9 is the perfect starting point. | Benchmark | v0