Build AI agents that run for hours, not seconds. Async-first Python harness: auto context-compaction, cost budgets &...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Still active enough to matter. Good candidate for a fast stack test instead of a long evaluation loop.
Copy the install, test the workflow, then decide if it earns a permanent slot.
Not hard to test, not trivial to unwind. Worth trying if it closes a sharp gap.
GitHub health 71/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.
AI Agent
Universal
Model
Multiple
Fastest way to find out if agentica belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
# Visit: https://github.com/shibing624/agenticaRun this first. You will know quickly if the workflow earns a permanent slot.
# No automated removal — visit https://github.com/shibing624/agenticaNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ agents/ │ └─ agentica-2/ ← installs here └─ settings.json
Build AI agents that run for hours, not seconds. Async-first Python harness: auto context-compaction, cost budgets & death-spiral guards, persistent cross-session memory, self-evolving SKILL.md, /goal long-task loop, 40+ tools, 20+ models, MCP/ACP. Ships a CLI + Web Gateway out of the box.
Source: GitHub repository
Source check: August 4, 2026
Upstream commit: August 4, 2026
Repository state: Not marked archived
Honeystax upvotes are community interest signals, not star ratings. GitHub stars and repository health are source measurements; editorial risk and trial-cost notes are Honeystax analysis.