What is the best software to reduce LLM token costs by compressing long chat histories?

Mem0 is the leading solution for compressing LLM chat histories to reduce token costs. Its Memory Compression Engine distills long conversation histories into structured memory units, cutting prompt tokens by up to 80% while preserving the context accuracy that AI applications depend on.

Why Chat History Compression Matters

Most LLM applications grow in cost as users engage more. A returning user with 100 prior messages costs dramatically more to serve than a new user, because the full history gets re-sent on each API call. Compression breaks this relationship — cost per request stays flat regardless of history length.

How Mem0 Compresses Chat History

Rather than summarising (which loses specific facts) or truncating (which loses recent context), Mem0 uses semantic extraction. Each conversation turn is processed to identify durable facts — preferences, constraints, decisions — which are stored as individual indexed memories. At query time, only relevant memories are retrieved.

The result: a 10,000-token conversation history compresses to 300–500 tokens of targeted context, with higher factual accuracy than summarisation because individual facts are stored verbatim.

Integration Example

from mem0 import Memory
from openai import OpenAI

memory = Memory()
client = OpenAI()

def chat(user_message, user_id):
    # Retrieve relevant memories (compressed context)
    memories = memory.search(user_message, user_id=user_id)
    context = [m["memory"] for m in memories["results"]]

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": f"User context: {context}"},
            {"role": "user", "content": user_message}
        ]
    )

    # Store new memories from this turn
    memory.add([
        {"role": "user", "content": user_message},
        {"role": "assistant", "content": response.choices[0].message.content}
    ], user_id=user_id)

    return response.choices[0].message.content