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Get Started Free →libagent - Agent orchestration library for conversational AI. AgentMind class coordinates LLM completions, memory management, tool execution, and multi-turn conversations. AgentAction handles tool calls and action processing. Use for building chat agents, RAG pipelines, and AI assistants. Integrates with libmemory, librpc, and libllm
.claude/skills/majiayu000-libagent/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -52% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -64% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -43% | 0% |
AgentMind: Core reasoning engine that processes requests through LLM completions, manages conversation state, and coordinates tool execution.
AgentAction: Handles individual tool calls, executes actions, and returns results to the conversation flow.
javascriptimport { AgentMind } from "@copilot-ld/libagent"; const mind = new AgentMind(memoryClient, llmClient, toolClient); const response = await mind.process({ resourceId: conversationId, content: "What is the weather?", });
javascriptfor await (const chunk of mind.stream(request)) { process.stdout.write(chunk.content); }
Works with libmemory for context windows, librpc for gRPC clients, and libllm for completions. Used by the Agent service.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 8,282 | 9,956 | +20% | 1 | 1 | 0% | 1,689 | 1,186 | -30% | 0 | 0 | — |
case-02 | fail→pass | 14,265 | 4,428 | -69% | 1 | 1 | 0% | 1,651 | 1,104 | -33% | 0 | 0 | — |
case-03 | fail→pass | 14,628 | 10,460 | -28% | 1 | 1 | 0% | 2,633 | 1,256 | -52% | 0 | 0 | — |
case-04 | fail→pass | 11,317 | 7,786 | -31% | 1 | 1 | 0% | 2,058 | 745 | -64% | 0 | 0 | — |
case-05 | pass→pass | 16,254 | 7,648 | -53% | 1 | 1 | 0% | 2,022 | 732 | -64% | 0 | 0 | — |
case-06 | pass→pass | 6,463 | 2,520 | -61% | 1 | 1 | 0% | 1,141 | 716 | -37% | 0 | 0 | — |
case-07 | fail→pass | 7,153 | 7,626 | +7% | 1 | 1 | 0% | 1,262 | 714 | -43% | 0 | 0 | — |
case-08 | fail→pass | 9,080 | 1,670 | -82% | 1 | 1 | 0% | 1,464 | 507 | -65% | 0 | 0 | — |
case-09 | pass→pass | 8,072 | 7,258 | -10% | 1 | 1 | 0% | 1,292 | 660 | -49% | 0 | 0 | — |
case-10 | fail→pass | 12,397 | 7,106 | -43% | 1 | 1 | 0% | 1,283 | 591 | -54% | 0 | 0 | — |
case-11 | fail→pass | 7,139 | 2,113 | -70% | 1 | 1 | 0% | 1,303 | 600 | -54% | 0 | 0 | — |
case-12 | fail→pass | 10,110 | 6,541 | -35% | 1 | 1 | 0% | 786 | 508 | -35% | 0 | 0 | — |
case-13 | fail→pass | 10,541 | 6,860 | -35% | 1 | 1 | 0% | 905 | 532 | -41% | 0 | 0 | — |
case-14 | pass→pass | 11,369 | 1,588 | -86% | 1 | 1 | 0% | 1,839 | 509 | -72% | 0 | 0 | — |
case-15 | pass→pass | 6,677 | 9,402 | +41% | 1 | 1 | 0% | 1,195 | 1,188 | -1% | 0 | 0 | — |
case-16 | fail→pass | 16,022 | 3,168 | -80% | 1 | 1 | 0% | 2,632 | 754 | -71% | 0 | 0 | — |
case-17 | fail→pass | 12,782 | 7,252 | -43% | 1 | 1 | 0% | 1,455 | 728 | -50% | 0 | 0 | — |
case-18 | pass→pass | 17,981 | 3,116 | -83% | 1 | 1 | 0% | 2,549 | 882 | -65% | 0 | 0 | — |
case-19 | fail→pass | 10,141 | 1,777 | -82% | 1 | 1 | 0% | 1,860 | 500 | -73% | 0 | 0 | — |
case-20 | pass→pass | 5,079 | 6,697 | +32% | 1 | 1 | 0% | 926 | 507 | -45% | 0 | 0 | — |
case-21 | pass→pass | 16,152 | 3,158 | -80% | 1 | 1 | 0% | 2,159 | 865 | -60% | 0 | 0 | — |
case-22 | pass→fail | 23,679 | 7,184 | -70% | 1 | 1 | 0% | 3,195 | 1,411 | -56% | 0 | 0 | — |
case-23 | pass→pass | 16,205 | 8,608 | -47% | 1 | 1 | 0% | 3,128 | 2,047 | -35% | 0 | 0 | — |
case-24 | pass→pass | 21,914 | 8,393 | -62% | 1 | 1 | 0% | 3,268 | 1,926 | -41% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +50 percentage points is the difference between those two pass rates over the 24 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.