agi-memory

Zero-dependency, high-performance four-pillar cognitive memory framework (Epistemic, Semantic, Episodic, Structural Code Graph) for AI coding assistants.

Share synchronized context, recent bugfixes, durable architectural decisions, session timelines, and codebase structure seamlessly across Claude Code, Cursor, Windsurf, OpenAI Codex, OpenCode, Antigravity CLI, Aider, Goose, Cline, Roo Code, Crush, Pi, and Hermes Agent.

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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