agi-memory

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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