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Documentation Index

Fetch the complete documentation index at: https://mneno.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

When sending memories to an LLM, you often face a token budget. Mneno’s Context Builder helps you select the most relevant memories that fit within a specified budget, while explaining why each was included or excluded.

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Building Context

Use the build_context() method to generate a ContextPackage. 
# Build context for a query with a 500-token budget
context = client.build_context("What is the user's favorite language?", budget=500)

# The formatted string ready for your prompt
print(context.text)
# Output:
# Relevant memories:
# - User prefers Python for data science.
# - User recently switched to Rust for systems programming.

# Check the stats
print(f"Tokens used: {context.stats.used_tokens}/{context.stats.max_tokens}")

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Presets

Mneno comes with several built-in presets for common use cases:
  • cheap: Smallest useful context, optimized for cost. (max 400 tokens)
  • balanced: Default tradeoff between quality and cost. (max 1200 tokens)
  • high_recall: Includes more context to avoid missing useful memories. (max 2500 tokens)
  • agent_state: Prioritizes operational state, goals, constraints, and preferences.
# Use the high_recall preset
context = client.build_context("Analyze the project history", preset="high_recall")

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Custom Policies

For full control, you can define a ContextPolicy. 
from mneno.context import ContextPolicy

custom_policy = ContextPolicy(
    max_tokens=800,
    reserve_tokens=100,      # Tokens to keep free
    min_score=0.25,          # Don't include low-quality memories
    strategy="importance",   # Sort by importance instead of search score
    max_items=10             # Limit the number of items regardless of tokens
)

context = client.build_context("What matters now?", policy=custom_policy)

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Policy Parameters

ParameterDefaultDescription
max_tokens1200Total token budget for the context.
reserve_tokens200Tokens to reserve (subtracted from max_tokens).
min_score0.15Minimum score required for inclusion.
strategy"score"How to sort candidates (score, recency, or importance).
max_itemsNoneHard limit on the number of memories included.
dedupeTrueWhether to remove memories with duplicate content.

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Explainable Context

The ContextPackage contains detailed lists of included and excluded items, each with a reason. 
for item in context.excluded:
    print(f"Excluded {item.memory_id}: {item.reason}")
    # Reasons: "Excluded because budget exhausted", "Excluded because score 0.05 is below min_score 0.15"
This transparency is crucial for debugging why an LLM might have missed specific information.