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Documentation Index

Fetch the complete documentation index at: https://docs.memanto.ai/llms.txt

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

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Memory Operations

Store, retrieve, and maintain high-quality memories in Memanto.

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Memory Fundamentals

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What is a Memory?

A memory in Memanto includes:
  • Content: The core information.
  • Type: Semantic category (fact, preference, decision, etc.).
  • Title (optional): Short label for readability.
  • Confidence (optional): Reliability score from 0 to 1.
  • Metadata (optional): Extra structured context.

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Memory Lifecycle

Store -> Index (instant) -> Recall -> Conflict detection -> Resolve

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Core Operations

Use these commands for most workflows:
  • Store a memory: memanto remember "..." --type fact
  • Batch store: memanto remember --batch memories.json
  • Recall semantically: memanto recall "..."
  • Answer from context: memanto answer "..."
  • Detect contradictions: memanto conflicts
  • Export memory history: memanto memory export

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Uploading Files into Memory

When information already exists in documents, upload files instead of manually adding many individual memories.

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Supported Formats

.pdf, .docx, .xlsx, .json, .txt, .csv, .md (up to 5 GB per file).

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CLI

Activate an agent session, then upload: 
memanto agent activate customer-support
memanto upload ./customer-profile.pdf
Recall uploaded knowledge with normal search: 
memanto recall "What is the customer's annual revenue?"

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REST API

import httpx

with open("customer-profile.pdf", "rb") as f:
    response = httpx.post(
        "http://localhost:8000/api/v2/agents/customer-support/upload-file",
        files={"file": ("customer-profile.pdf", f, "application/pdf")},
        headers={"X-Session-Token": session_token},
    )

result = response.json()
print(f"Status: {result['status']}, File: {result['file_name']}")

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Upload vs Remember

Use upload when…Use remember when…
You have existing documentsYou are storing short atomic facts
Content spans many pagesYou want explicit memory typing per item
You ingest structured data filesYou want precise confidence per memory

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Recall Patterns

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Semantic Recall

memanto recall "What do I know about user preferences?"

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Filter by Type

memanto recall "How should I contact this user?" --type preference

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Limit Result Volume

memanto recall "customer details" --limit 10

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Temporal Recall

Use the temporal recall variants (--as-of, --changed-since, and --recent) to query memory across time. See Temporal Memory Details for complete patterns and examples.

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Answering and Conflict Management

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Grounded Answers

memanto answer "Based on memory, how should we communicate with this customer?"

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Conflict Detection

memanto conflicts
When contradictions are found, resolve them by keeping the new memory, the old one, both, removing both, or replacing them with a manual entry. See List Conflicts and Resolve Conflict for the API contract.

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Export and Sync

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Export to Markdown

memanto memory export

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Export to Custom Path

memanto memory export --output /path/to/memory.md

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Sync to MEMORY.md

memanto memory sync

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Performance Tips

  1. Use specific memory types instead of defaulting everything to fact.
  2. Batch ingest when importing many items.
  3. Keep recall limits tight for faster, cleaner responses.
  4. Use confidence scoring when information quality varies.

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Best Practices

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DO

  • Keep memories concise and atomic.
  • Record source context in metadata when useful.
  • Resolve conflicts explicitly when contradictions arise rather than deleting history.

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DON’T

  • Store the same fact repeatedly.
  • Mix multiple unrelated facts in one memory.
  • Over-fetch with very high recall limits by default.

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Next Steps

Memory operations are the core of Memanto. Keep this flow lean, typed, and conflict-aware for best results.