The Perfect Agent is an ambitious open-source project to create a powerful AI agent in pure Python without external libraries. Using frontier or open-source models, we're developing an agent that excels at both technical tasks like coding and practical actions like booking flights.
Most agent frameworks are black boxes built on proprietary stacks. We're taking a different approach - building transparently, sharing our research, and creating an agent that's:
- Powerful: Handling everything from code generation to everyday tasks
- Transparent: Every component built and explained in public
- Benchmark-driven: Evaluated against GAIA, BCFL, and AgentBench
- Adaptable: Integrating with MCP and other tool ecosystems
- Memory Systems: Short-term for context, long-term for knowledge retention
- Thinking Module: Internal reasoning for complex problem-solving
- Knowledge Source: Search capabilities including image data
- Self-correction: Built-in mechanisms to identify and fix errors
- Tools/Actions: Browser control and API integrations
- Does test-time thinking improve tool call accuracy?
- Is the traditional way of tool calling the best way for building AI Agents? Is there another format we could use?
- What belongs in long-term memory vs. what should be discarded?
- Can system prompts achieve 100% reliable tool calls?
- How do we optimize cost per task?
This project isn't just about building an agent - it's about advancing our understanding of what agents can become. We're documenting every step, challenge, and breakthrough.
Follow along as we benchmark against established standards and push the boundaries of what's possible with pure Python implementation.
- Core agent implementation
- Memory system integration
- Tool connection framework
- Benchmark testing
- Real-world task validation
- Performance optimization
Passionate about AI agents? Join us! Whether you're interested in architecture, tool use, memory systems, or practical applications, there's a place for you in this project.