KodeAgent
The Minimal Agent Engine
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for KodeAgent, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
KodeAgent is a frameworkless, minimalistic approach to building AI agents. Written in ~2.2K statements (pure Python), KodeAgent is designed to be the robust reasoning core inside your larger system, not the entire platform. KodeAgent adheres to the Unix Philosophy: do one thing well and integrate seamlessly. By default, an agent is memoryless across tasks—each task begins with no prior context, a clean slate. To enable context from the previous task (only), use Recurrent Mode. In addition to the logs, KodeAgent enables agent observability via third-party solutions, such as Langfuse and LangSmith.
Key features and capabilities
- Supports both ReAct (tool-calling) and CodeAct (code-executing) agents out-of-the-box
- Includes a "Planner" and "Observer" to plan, monitor, and fix agent trajectory
- With only a few dependencies, it perfectly integrates with existing platforms
- KodeAgent easily swaps between models (e.g., Gemini, OpenAI, and Claude)
- "Glassbox," allowing to read the source and debug or modify your agents
- Native support for Langfuse and Langsmith observability
Use cases
- Web research using existing tools and APIs
- Literature review and summarization
- Build agents and understand how they work
- Solve (ephemeral or short-term memory) tasks described in natural language
- Use code-executing agents to solve complex workflows, e.g., data cleaning