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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.
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.
# 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-memoryThen 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.
| 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 |
MIT