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Agent Memory MCP server

by theprodsde·io.github.theprodsde/agent-memory·v0.1.5
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Persistent agent memory with a decision layer: replay, restore, verify, or none — explainable.

A92/100grade A
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Safety scan
A92/100

full report

Adoption
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If you have run it, two minutes of your experience saves the next person an afternoon.

Agent Memory tools (7, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • archive_memory

    Archive a memory so it is excluded from retrieval.

  • consolidate_memories

    Merge near-duplicate memories into summary entries.

  • forget_memorywrite action

    Delete a memory by ID.

  • get_memory_by_id

    Fetch a single memory by ID.

  • list_memories

    List stored memories with pagination and optional scope filter.

  • remember_memory

    Store a query/response pair in persistent agent memory.

  • resolve_memory

    Retrieve memory and decide how the agent should respond.

Public scan report

scanner v0.1.10 · 2026-11-03 · same rubric, same numbers if you re-run it

no findings
  • Code scan42 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 9 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 92/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Agent Memory compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
92/100
higher than 90% of 4,460 AI and agents servers
Tools
7
the typical AI and agents server has 6
Tools that change things
14%
typical: 0%. Fewer write tools means less an agent can break

What its tools let an agent do: 5 read · 1 change · 1 delete

Install Agent Memory in Claude Code, Cursor or VS Code

claude mcp add agent-memory -- uvx agent-memory-sdk
Add to Cursor

What the publisher says

From the Agent Memory repository's README, as published. We do not edit it. Read it on GitHub

Agent Memory

Persistent semantic memory for AI agents with intelligent decision-making.

🚀 Created by: TheProdSDE

The problem

Most AI memory systems retrieve and inject past context into every prompt. This leads to wasted tokens, inconsistent responses, and agents that blindly replay stale or wrong answers.

Agent Memory adds a decision layer:

flowchart TD
    A[User Query] --> B[Resolve Memory]
    B --> C[Decision Engine]
    C -->|High confidence match| D[🔄 Replay — return stored answer]
    C -->|Moderate match| E[📋 Restore — inject as context]
    C -->|Needs validation| F[✅ Verify — validate before reuse]
    C -->|No match| G[❌ None — answer from scratch]

    style D fill:#0d47a1,color:#fff
    style E fill:#e65100,color:#fff
    style F fill:#1b5e20,color:#fff
    style G fill:#b71c1c,color:#fff

Every resolve() returns an explicit action with a scored, explainable rationale — not just a retrieved chunk. Adversarial eval: 25/25 (100%) on trap queries — see benchmarks.

When to use it — real use cases

Where Agent Memory saves real money

A GPT-4o call costs ~$0.005. A support agent handling 50,000 queries/day with 70% repeat rate:

  • Without memory: 50,000 × $0.005 = $250/day
  • With Agent Memory: 15,000 LLM calls + cache misses = $75/day
  • Saving: ~$175/day (~$64k/year)

A REPLAY costs ~0.05ms of in-process computation. An LLM call takes 300–2,000ms and costs tokens.

Is it right for your use case?

Good fit:

  • Agent answers the same or similar questions across sessions
  • You have fact-sensitive answers that can go stale (prices, limits, policies)
  • Multiple agents or services share a knowledge base
  • You need audit trails — knowing which memory answered and why

Not the right tool:

  • Document RAG over a corpus of files → use a vector database for that
  • Replacing your application's source-of-truth database
  • Agents that never repeat similar queries

Features at a glance

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Agent Memory: common questions

Is Agent Memory MCP server safe?
Yes, by our scan: it is graded A (92/100). Read the Agent Memory safety report
How do I install Agent Memory?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Agent Memory need an API key?
No secret keys are declared. It reads 2 settings from the environment.
Is Agent Memory maintained?
The last commit was 11 days ago (2026-10-24). The latest release is v0.1.5.
What can I use instead of Agent Memory?
Servers from other publishers that do the same job: Memanto MCP server, Mockserver MCP server and Agent Recall MCP server.
How does Agent Memory compare with other AI and agents MCP servers?
safety score 92/100 (higher than 90% of 4,460 AI and agents servers); tools 7 (the typical AI and agents server has 6); tools that change things 14% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Agent Memory

Same job from other publishers: the closest match first, then the best rated.

  • Memanto
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  • Mockserver
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  • Agent Recall
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  • Total Agent Memory
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  • agent-device
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