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agi-memory

CI PyPI License: MIT Dependencies Latency Evals Python

Your AI coding assistant forgets everything between sessions. This remembers.

Decisions you already made, bugs you already fixed, what happened last session, how the codebase fits together — kept in a file on your machine and handed back to the assistant next time, so you stop re-explaining your own project.

Works across Claude Code, Cursor, Windsurf, OpenAI Codex, OpenCode, Antigravity CLI, Aider, Goose, Cline, Roo Code, Crush, Pi and Hermes Agent — one memory, whichever tool you open.

No dependencies, no vector database, no background daemon. ~32MB of RAM, sub-millisecond lookups, works offline. Comparable tools install ~500MB of machine-learning libraries and take 200–500ms per lookup.


Why agi-memory? The 4 Cognitive Memory Pillars

Most AI memory architectures solve only a fragment of developer memory while incurring heavy dependencies or requiring background Node.js daemons. agi-memory unifies all four cognitive memory pillars in pure Python stdlib + SQLite (<35MB RAM, <1ms speed, zero external pip dependencies):

Pillar Core Question Replaces Implementation in agi-memory Latency / Overhead
1. Epistemic "What have we learned?" Ad-hoc .cursorrules, forgotten bugfixes SessionLayer (SQLite FTS5 + BM25, Core Blocks) 0.23 ms (zero tokens)
2. Semantic "What does our information mean & how is it connected?" Heavy GraphRAG, Cognee, ChromaDB GraphLayer (Native SQLite Recursive CTEs) 0.28 ms (zero tokens)
3. Episodic "What happened during previous agent sessions?" claude-mem (heavy Node/Bun daemons) EpisodicLayer (SQLite Session History & Lifecycle) 0.23 ms (zero daemons)
4. Structural "How is this codebase structurally connected?" Graphify, Tree-sitter binaries, LSP daemons CodeLayer (stdlib AST + Streaming Regex Graph) 0.45 ms (zero daemons)

Every pillar is scored by its own eval suite — see Benchmarks for measured comparisons against Mem0, Zep, Cognee, LangChain and claude-mem, including a real 13,988-observation production dataset.


Install

# One-line installer (recommended)
curl -fsSL https://raw.githubusercontent.com/kdbhalala/agi-memory/main/install.sh | bash

# Or: Homebrew / PyPI
brew tap kdbhalala/agi-memory https://github.com/kdbhalala/agi-memory && brew install agi-memory
pipx install agi-memory

Then wire up your assistants and initialize a project:

agi-integrate install all     # configure every detected assistant + lifecycle hooks
agi-integrate status          # confirm what was detected and configured
cd your-project && agi-integrate init .

init wires the project and installs an /agi-init slash command in each assistant's own format. Run /agi-init inside your assistant and it reads the codebase and writes the project's rules/ and context/ files.

Full options, including uvx and from-source: Installation.


Documentation

Guide What's in it
Installation Installer script, Homebrew, PyPI/uvx, from-source, hooks setup
The Four Pillars Deep dive into L1 Epistemic, L2 Semantic, L3 Episodic, L4 Code Graph
Architecture Layer boundaries, storage model, multi-assistant production layout
Supported Assistants Per-tool config paths and rules files for all 13 assistants
CLI Usage Every agi-memory and agi-integrate subcommand
Python API Using the layers directly from Python
Vault & Git Sync Append-only JSONL vault, cross-device sync, compaction
Benchmarks Latency, memory and cost comparisons; real-dataset results
Testing & Evals The L1-L4 eval suites, chaos and stress tests
Integrations Manual per-tool configuration snippets

License

MIT

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