How to Add Caching to Any AutoGen Workflow in 2 Lines

AutoGen doesn't have a built-in execution cache. Every GroupChat, every ConversableAgent run starts fresh. If your multi-agent workflow runs similar tasks repeatedly — research pipelines, code review agents, scheduled reports — you're paying full LLM price every time.

Here's how to fix it without touching your AutoGen code.

---

The setup

bash
pip install mnemon-ai

import mnemon
mnemon.init()

# your existing AutoGen code — completely unchanged
import autogen

assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4o", "api_key": "..."},
)
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
)

user_proxy.initiate_chat(
    assistant,
    message="Analyze Q2 sales data for Acme Corp and generate a summary report",
)
# second run with same or similar message: 2.66ms · 0 tokens · $0.00

Mnemon's MOTH layer patches AutoGen at startup. No agent changes, no conversation changes.

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What gets cached

Every LLM call your agents make is intercepted. On repeat runs:

- Exact match — same message, instant response from cache
- Semantic match — "Analyze Q2 sales for Acme Corp" matches "Generate Q2 sales analysis for Acme" — same task, different phrasing

For multi-agent workflows where agents pass messages between each other, common sub-tasks (data parsing, formatting, summarization) hit the cache across different top-level goals.

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For structured recurring workflows

If your AutoGen setup runs the same workflow repeatedly with varying inputs, use m.run() for segment-level caching:

import autogen, mnemon

m = mnemon.init()

def run_analysis(goal, inputs, context, capabilities, constraints):
    user_proxy.initiate_chat(assistant, message=goal)
    return user_proxy.last_message()["content"]

result = m.run(
    goal="Q2 sales analysis for Acme Corp",
    inputs={"quarter": "Q2", "client": "Acme Corp"},
    generation_fn=run_analysis,
)

print(result["tokens_saved"])   # tokens saved on this run
print(result["cache_level"])    # "system1" | "system2" | "miss"

---
Numbers

┌─────────┬────────────┬────────────┐
│         │ First run  │ Cached run │
├─────────┼────────────┼────────────┤
│ Tokens  │ ~1,250     │ 0          │
├─────────┼────────────┼────────────┤
│ Latency │ ~20s       │ 2.66ms     │
├─────────┼────────────┼────────────┤
│ Cost    │ full price │ $0.00      │
└─────────┴────────────┴────────────┘

At 80% hit rate on recurring workflows: 93% token reduction.

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Install

pip install mnemon-ai           # exact match only
pip install mnemon-ai[full]     # + semantic matching (local, no API key)

import mnemon
mnemon.init()