Baron Munchausen MCP server

Baron Munchausen — local memory that outlives the chat

Everything above is a real run against a clean clone. rpc is the two-line curl wrapper defined in docs/demo/baron-demo.sh; re-record the whole thing with cd docs/demo && ./record.sh.

Public alpha (0.6.1). The engine has run daily in the authors' own work

for months; this repository is one day old. The code is Apache-2.0 and

complete — the packaging, the docs and the install path are what "alpha"

refers to. Report anything that breaks.

Your session ends. Your project doesn't. One call brings back where the project stopped, what was decided and what comes next — after a closed tab, a spent limit or a change of model. And every answer built on that memory comes back with a verdict: grounded, partial or ungrounded, with the sentences nothing backs named one by one.

A memory server in Python 3.12 with no third-party runtime dependency. MCP over stdio for your client, JSON-RPC on 127.0.0.1:8765 for everything else. Nothing here calls a model and nothing leaves your machine. A fresh install starts with an empty graph: we ship the tools, never the data.

Why

Three numbers, each one measured, each one with what it does not say written next to it.

1. One context return: 7 146 tokens → 2 388. The 7 146 is a real compaction summary out of a session transcript; the 2 388 is the slice a live memorygroundprepare returned for the same moment of the same project. Both counted with tiktoken/cl100kbase on 2026-09-10. What it does not say:* it is one pair of instances, not a distribution — a second summary from the same corpus came to 5 913 tokens, which would make the same slice a 60 % cut instead of a 67 % one.

2. Claude Opus 5: −69.6 % input tokens, measured. Not arithmetic on the figures above — this is what the models' own usage reports came back with on live runs of the same tasks, 2026-09-10. Sonnet 5 came to −61.7 %, Haiku 4.5 to −66.3 % on the same runs. What it does not say: these are the authors' graph and the authors' tasks. Your ratio depends on how much of your context is recoverable from a graph at all, and nobody has run this on a public benchmark yet.

3. Thirty tools, zero runtime dependencies. curl -s 127.0.0.1:8765/health reports "tools": 30 on a fresh clone — the same 30 over MCP stdio and over JSON-RPC, with requirements.txt empty of third-party runtime packages. What it does not say: nothing about quality. It is a count.

What those percentages are worth in money depends on your model and your volume: the savings calculator on shinegang.click does that arithmetic with current list prices, and shows which figures are measured and which are calculated.

Install in two minutes

git clone https://github.com/shinegang/baron.git && cd baron

# 1. start the memory server — standard library only, nothing to install
bin/baron --host 127.0.0.1 --port 8765 --store blank

# 2. in a second terminal: it is up, the graph is empty, 30 tools are loaded
curl -s http://127.0.0.1:8765/health | jq '{product, version, nodes, tools}'

# 3. check the stdio bridge against the live server
python3.12 bridge/mnemos_bridge.py --selftest

Shortened. The full README is on GitHub.