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vbcherepanov/total-agent-memory

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Persistent local memory for AI coding agents — Claude Code, Codex CLI, Cursor, any MCP client. Temporal knowledge graph, procedural memory, AST codebase ingest, cross-project analo

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vbcherepanov/total-agent-memory

Added 8 Sept 2026

#agent-memory#ai-memory#claude-code#claude-code-mcp#claude-code-plugin#claude-memory#codex-cli#developer-tools
Catalogue record, not a reviewed recommendation. This record can include automatically collected descriptions and setup details. Confirm important claims with the original source. Explore source-reviewed tools.

Overview

Persistent local memory system for AI coding agents, providing a temporal knowledge graph, procedural memory, AST-based codebase ingestion, and cross-project analogy. It integrates with multiple AI coding tools via 74 MCP tools and supports 9 IDEs, with all data stored locally.

Best for

Best for
Developers and teams using AI coding agents who need persistent, private, cross-project memory

Use cases

  • Give AI agents long-term memory across coding sessions and projects
  • Enable cross-project code reuse and pattern recognition via analogy
  • Index codebases using AST for structured, queryable project knowledge

How to use

Install

pip install "total-agent-memory[rerank]"      # pip / uvx / pipx

Tools exposed

  • single-hop
  • multi-hop
  • open-domain
  • contradiction_resolution
  • temporal_reasoning
  • knowledge_update
  • multi_session_reasoning
  • information_extraction
  • preference_following
  • event_ordering
  • instruction_following
  • knowledge-update
  • multi-session
  • single-session-user
  • single-session-assistant
  • temporal-reasoning
  • single-session-preference
  • MCP-native

Clients listed in this record (not verified here)

Claude Code, Cursor, Windsurf, Cline, Continue, VS Code, ChatGPT

Notes

67 stars on GitHub. Last updated 2026-09-07. Licensed MIT.

Pros

  • 100% local storage keeps code and metadata private
  • Broad IDE and agent compatibility through MCP and 9 IDE integrations
  • Benchmarked on LongMemEval, LoCoMo, and BEAM with strong retrieval scores

Cons

  • Requires setup and maintenance of a local memory service
  • Performance depends on the quality and frequency of AST ingestion
  • May add overhead for very large or rapidly changing codebases

Open-source & AI alternatives

Swap-in tools that solve the same job. Weigh the trade-offs before you commit.

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