Teach your agent once. Let it replay forever.

Aionis turns agent execution into reusable workflows.

Most memory plugins focus on conversations.
Aionis also records what the agent actually did and allows the workflow to be replayed later.

Install PluginGitHubDocs
Replay Execution Session
$ agent.run("install clawbot")
trace_id: trc_0192
playbook_id: pbk_0f21
replay.mode: guided
policy.guardrails: enabled
repair.shadow_validation: true
status: replay_success
overall_status: passreplay_status: passdemo_session_latency: 1.2s

Latency note: this is guided end-to-end demo session latency. Benchmark latency below reports replay-step averages (replay2_avg: 136.9ms).

⟩ Watch an agent learn a workflow

  1. 01Agent installs Clawbot
  2. 02Execution trace recorded
  3. 03Playbook compiled
  4. 04Next run replays automatically
Agent Workflow Demo
$ openclaw plugins install @aionis/openclaw$ agent.run "install clawbot"[trace] execution trace recorded[compile] playbook created: pbk_0f21[replay] strict mode passed[replay] guided mode availableoverall_status: pass

Instead of re-reasoning every step, the agent simply reuses the workflow.

⟩ What Aionis actually does

Most agent memory systems store conversation history. Aionis stores execution history — turning one successful run into a reusable workflow.

run→execution trace→compile playbook→replay→repair→promote

Today's agents: reason → act → forget. Every task is solved from scratch.
With Aionis: reason → act → remember → reuse. Agents become more stable over time.

⟩ Core capabilities

Replayable Execution

Record agent runs and replay them as deterministic workflows.

Policy Loop

Memory influences tool routing. Rules and feedback shape behaviour over time.

Layered Context

Context assembled in layers: facts, episodes, rules, tools, citations.

Repair & Promotion

When a replay fails: repair, shadow validation, promotion.

⟩ Benchmark: replay performance in real workflows

In a 100-case install/config benchmark, Aionis keeps replay reliability high while cutting runtime sharply after compile.

Replay Benchmark Terminal (simulated)
compilereplay1replay2
▋
case: 17mode: strictdataset: workflow replay

Workflow Cases

100

Compile Success

98%

Replay Stability (R1→R2)

98%

Replay Speedup (R2 vs baseline)

16.51x
Benchmark Summary · run_id 20260305-162059-22132
baseline_avg: 2260.85ms
replay1_avg: 260.20ms  (8.69x faster than baseline)
replay2_avg: 136.90ms  (16.51x faster than baseline)
replay2_vs_replay1: -123.31ms (-47.4%)

95% confidence interval (Wilson)

Compile success98% · CI 93% - 99.4%
Replay stability98% · CI 93% - 99.4%

Method: each case runs baseline once, compiles once, then executes replay twice in strict workflow checks. Per-case replay_reason is included when failures occur (for example: run_not_found, playbook_not_found). View harness View cases.jsonl View summary.json

⟩ Memory plugins vs Aionis

Capabilitymem0supermemoryAionis
Chat memory✔✔✔
Vector recall✔✔✔
Execution trace✘✘✔
Replay workflows✘✘✔
Policy loop✘✘✔
Governed repair✘✘✔

Most memory plugins stop at retrieval. Aionis turns memory into automation.

⟩ Install in 30 seconds

Install the OpenClaw plugin and give your agent replayable execution memory.

Terminal
openclaw plugins install @aionis/openclaw
openclaw aionis-memory bootstrap
openclaw aionis-memory selfcheck

⟩ What people use this for

install development environments
deploy docker stacks
configure coding agents
automate research workflows
setup local AI tooling

If an agent can do it once, it can replay it.

⟩ FAQ

Is this just RAG memory?

No. RAG stores knowledge. Aionis stores execution. It records how the agent completed tasks and allows replay.

Does this require cloud services?

No. Aionis can run locally in standalone mode.

What agents are supported?

The OpenClaw plugin is available today. Other agent frameworks can integrate through the API.

Is replay deterministic?

Replay supports simulate, strict, and guided modes. Guided replay can repair failed steps and promote improved workflows.