Persistent, local memory for AI coding agents: Claude Code, Codex CLI, Cursor and any MCP client.

total-agent-memory (TAM) is an open-source memory server for AI coding agents. Coding agents start every session without memory of earlier ones, so decisions, fixes and project conventions have to be explained again. TAM stores decisions, solutions, facts, errors and session summaries on your machine and returns them through the Model Context Protocol (MCP), so any MCP client can use it without code changes.

Each store is one directory built around a SQLite database. Recall combines full-text BM25, dense embeddings computed locally, fuzzy matching and a knowledge graph, and fuses the ranked lists with reciprocal rank fusion; an optional cross-encoder can rerank the result. The default profile makes no LLM call on write or search, so retrieval can be measured offline and a rerun gives the same result. Facts can carry validity intervals (kg_add_fact, kg_at), and a newer value of a single-valued fact can retire the older one.

TAM is for developers who use coding agents daily and for researchers who need a memory baseline they can run locally, inspect and change. The same package can also run as a team server: personal, department and company areas are separate stores behind one gateway, with roles, authorship and an audit trail (team server).

Installation

TAM needs Python 3.11 or newer. CI tests Python 3.11, 3.12 and 3.13 on Ubuntu, Windows and macOS.

From PyPI (use a virtual environment, or pipx / uvx):

pip install total-agent-memory          # or: pipx install total-agent-memory
uvx total-agent-memory                  # run once without installing

This installs the total-agent-memory (alias tam) MCP server and the tam-team, tam-remote and lookup-memory commands. The optional cross-encoder reranker pulls in PyTorch and is an extra: pip install "total-agent-memory[rerank]". The PostgreSQL backend of the team server is the [postgres] extra.

Register it with your clients. Run tam setup at a terminal. The wizard asks "Just me" or "Company server", detects Claude Code, Claude Desktop, Codex, Cursor, Windsurf, Gemini CLI, Cline and OpenCode, and registers the server with the ones you pick (setup wizard).

Other channels:

Use one of the two Claude Code plugins, not both. Per-platform details, WSL2, the IDE matrix, uninstalling and troubleshooting are in the installation guide.

Quick start

1. Install the package and run tam setup, or add the server to your client by hand. For Claude Code:

   claude mcp add memory -- total-agent-memory
   

   For clients that use an mcpServers file:

   { "mcpServers": { "memory": { "command": "total-agent-memory" } } }
   

   If you installed into a virtual environment, use the full path to its total-agent-memory executable.

2. Restart the client. Memory is stored in ~/.tam/ unless TAM_MEMORY_DIR points elsewhere.

3. Ask the agent to remember something ("remember that we chose PostgreSQL for billing because of row-level security"). It calls memory_save. In a later session, ask "which database did we choose for billing?"; it calls memory_recall and gets the record back.

To try the server without an agent, this script starts it over stdio with the MCP Python SDK (installed as a dependency), saves one record and recalls it. The SDK passes only a few variables to the server by default, so the script hands over the full environment, including TAM_MEMORY_DIR:

import asyncio
import os

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client


async def main() -> None:
    server = StdioServerParameters(command="total-agent-memory", env=dict(os.environ))
    async with stdio_client(server) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            await session.call_tool("memory_save", {
                "type": "decision",
                "content": "Chose PostgreSQL over MySQL for the billing service",
                "context": "WHY: row-level security per tenant",
                "project": "demo",
            })
            result = await session.call_tool("memory_recall", {
                "query": "which database for billing", "project": "demo", "limit": 3,
            })
            print(result.content[0].text)


asyncio.run(main())

TAM_MEMORY_DIR="$(mktemp -d)" python quickstart.py

The first run downloads the default embedding model. The output is JSON with the saved decision under results.decision. More examples: tools and interfaces.

Running the tests

The tests run from a source checkout. These commands mirror the CI workflows in .github/workflows:

git clone https://github.com/vbcherepanov/total-agent-memory.git
cd total-agent-memory
python3 -m venv .venv
.venv/bin/pip install -e . -r requirements-dev.txt

export FASTEMBED_CACHE_PATH="$PWD/.tam-models" TAM_MEMORY_DIR="$PWD/.tam-test-memory"
.venv/bin/python tests/smoke/prewarm_models.py   # downloads the text and code embedding models (~850 MB) once
.venv/bin/python -m pytest tests -q

The tests need no API keys and no LLM; the full suite takes 15 to 20 minutes on a laptop. Do not set MEMORY_LLM_ENABLED=false for the full suite: the configuration tests check LLM auto-detection. Tests that need services you do not have are skipped:

• PostgreSQL (team server backend): install the extra with .venv/bin/pip install -e ".[postgres]", then run .venv/bin/python -m pytest tests -m postgres --backend=postgres or --backend=both. Without TAM_TEST_PG_URL the fixtures start a pgvector container through Docker. See CONTRIBUTING.md and .github/workflows/postgres.yml.
• Browser tests (tests/browser, Playwright with Chromium, Firefox and WebKit) run in the image built from docker/Dockerfile.browser; see the browser job in .github/workflows/smoke.yml.
• Installed-package smoke test: python tests/smoke/installed_runtime.py checks an installed wheel over local and remote MCP.

Reproducing the benchmarks

The retrieval benchmarks (LoCoMo, LongMemEval, BEAM) need no API key and run with scripts in benchmarks/ once the public datasets are downloaded. Results with the default profile, v13.0.0:

Dataset locations, commands, end-to-end (LLM-judged) accuracy, negative controls, latency and the 14.x studies are in docs/benchmarks.md.

Documentation

• Installation guide: all channels, per-platform setup, WSL2, IDE matrix, troubleshooting
• Setup wizard: tam setup and the team server's web wizard
• Tools and interfaces: the 77 MCP tools, CLI, TypeScript SDK, dashboard
• Configuration: environment variables, LLM providers, performance tuning
• Local settings page and internal LLM settings
• Architecture
• Team server quick start, with dashboard, PostgreSQL, backup, onboarding and reports
• Benchmarks and comparison with other systems (April 2026 snapshot)
• Updating and upgrading
• What is new in 14.x, roadmap and v8–v13 history, CHANGELOG
• Security policy

Citation

A paper describing TAM is under review at the Journal of Open Source Software (paper/paper.md). Until it is published, please cite the software and the preprint:

Cherepanov, V. total-agent-memory (version 14.7.0) [software]. https://github.com/vbcherepanov/total-agent-memory

Preprint: doi:10.5281/zenodo.23011523

Citation metadata for the software is in CITATION.cff.

Contributing

Issues, pull requests and benchmark reproductions are welcome. See CONTRIBUTING.md for the development setup, the rules for a pull request and the commit convention. Report security issues privately as described in SECURITY.md. Donations to support development: PayPal.

License

MIT. See LICENSE. Third-party licenses are listed in THIRD-PARTY-LICENSES.md.