The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Altk Evolve
Self improving agents through iterations
Install
$
pip install altk-evolveInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Evolve: On‑the‑job learning for AI agents
Coding agents repeat the same mistakes because they start fresh every session. Evolve gives agents memory — they learn from what worked and what didn't, so each session is better than the last.
Evolve is a system designed to help agents improve over time by learning from their trajectories. The Lite version is designed to effortlessly slot into existing agent assistants like Claude Code and Codex. It uses a combination of an MCP server for tool integration, vector storage for memory, and LLM-based conflict resolution to refine its knowledge base.
On the AppWorld benchmark, Evolve improved agent reliability by +8.9 points overall, with a 74% relative increase on hard multi-step tasks. Evolve is a system designed to help agents improve over time by learning from their trajectories. It uses a combination of an MCP server for tool integration, vector storage for memory, and LLM-based conflict resolution to refine its knowledge base.
[!IMPORTANT] ⭐ Star the repo: it helps others discover it.
Latest from Evolve
Research, releases, and practical findings from the Evolve team.
- 2026-09-25 · Paper — Memory as Middleware for Self-Improving AI Agents The case for agent memory as a pluggable middleware layer, with Evolve as the reference implementation (to appear at Middleware 2026).
- 2026-09-15 · Article — Your Agent Aced the Task. Will It Do It Again? Diagnosing where an agent's decisions wobble and stabilizing them so success repeats.
- 2026-09-08 · Paper — Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course Turning unstable trajectory steps into episodic memory to keep agents consistent across repeated runs.
- 2026-08-18 · Article — How Much Memory Does Your Agent Actually Need? Why agent memory should be calibrated to the model instead of simply increased.
- 2026-08-11 · Article — Thinking of ACE? We Can Do It with Fewer Tokens Comparing Evolve's targeted guideline retrieval with ACE's playbook approach and token usage.
- 2026-04-08 · Article — ALTK‑Evolve: On‑the‑Job Learning for AI Agents An introduction to learning reusable guidance from agent trajectories.
- 2026-04-07 · Announcement — IBM introduces ALTK Evolve Evolve is opened to builders creating agents that learn from experience.
- 2026-03-11 · Paper — Trajectory-Informed Memory Generation for Self-Improving Agent Systems The research framework and evaluation underlying Evolve.
Quick Start (Lite)
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