Local grounded memory for coding agents: MCP over stdio, stdlib only, a verdict per answer
io.github.shinegang/baron Model Context Protocol (MCP) Server
The io.github.shinegang/baron MCP server provides local grounded memory for coding agents. It runs via MCP over stdio and is intended to rely on stdlib only. The server description indicates it produces a verdict per answer, based on the provided context.
🛠️ Key Features
Local grounded memory for coding agents
MCP over stdio
stdlib only
Verdict per answer
🚀 Use Cases
Supplying grounded memory to coding agents during interactive runs
Generating a per-answer verdict for each response
⚡ Developer Benefits
Standard input/output integration via stdio
Reduced dependency surface via stdlib only
Per-answer verdict signal for downstream handling
⚠️ Limitations
Only described as using stdlib; no other runtime or integrations are specified
Tool count and additional capabilities are not provided in the available data
Baron Munchausen — local memory that outlives the chat
Baron Munchausen in 40 seconds: clone, start on an empty graph, write one fact,
get it back in a new session, and watch the verdict engine call a made-up
sentence ungrounded and the recorded one grounded
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
memory_ground_prepare returned for the same moment of the same project. Both
counted with tiktoken/cl100k_base 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
bash
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
# 4. register it with your MCP client (Claude Code shown; the rest are below)
bash integrations/baron_add.sh
Step 2 prints {"product": "Baron Munchausen", "version": "0.6.1", "nodes": 0, "tools": 30}. Without jq, drop the pipe and read the raw JSON.
Write a fact and get a verdict without any client at all — this is the same
JSON-RPC the demo above runs:
bash
curl -sX POST 127.0.0.1:8765/rpc -H content-type:application/json -d '{
"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"memory_add",
"arguments":{"items":[{"claim":"The release build is pinned to Python 3.12.",
"source":"team decision","kind":"rule"}],"session_id":"demo"}}}'
Installing the package (pip install .) puts the same server on PATH as
baron; python3.12 -m baron works from a checkout.
Clients
Any client that takes the standard mcpServers JSON block can use Baron. These
are the ones with a file in integrations/
already written:
python3.12 bridge/mnemos_bridge.py for stdio, http://127.0.0.1:8765 for JSON-RPC
—
Claude Code can go further than registration: the PreCompact and
SessionStart hooks in tools/hooks/claude/ re-inject a
slice of the graph when the context window is compacted, so what the window
drops the graph still holds.
What it does, with the number and where it is checked
Every number below was measured on 2026-09-10 on the authors' own graph and
their own machine, and every one of them can be re-measured from this
repository. Where a number does not exist yet, this page says so.
What it does
Measured
1. Sessions do not break
One memory_checkpoint returns the head of the thread, the last 3 sessions, every open loose end and the last 5 decisions.
The pulse walks the whole graph continuously: 3 300 nodes in 571.7 s at 0.72 % of one core; on a 3 455-node graph its first circuit filed 52 incidents.
mnemos/pulse.py
7. Memory can forget by rule
memory_retract closes a fact's validity window and drops it out of search, the slice and grounding; nothing is deleted from disk and undo=true restores it.
mnemos/store.py, tests/test_memory_retract.py
8. It survives context compaction
Claude Code hooks re-inject a slice of the graph on PreCompact and SessionStart, so what the window drops the graph still holds.
Numbers this project does not have. No LongMemEval or LoCoMo score: those
harnesses have not been run here, and until they are, the honest word is
"not measured". In fourteen days of live use the verdict distribution on the
authors' own journal was 81 ungrounded, 41 partial, 20 grounded over
142 passes — that is a measurement of how often agents answered without
consulting the graph first, not a quality score, and it is published because
hiding it would be the kind of thing this tool exists to catch.
How grounding actually works
Step
Tool
What it does
1 — before the answer
memory_ground_prepare(query, session_id)
Searches the graph, builds a prompt from the nodes it found, registers the pre-pass. Returns graph_first: if the answer is already in memory, take it and skip the model.
2 — the answer
(your model)
Generates from that excerpt — or does not generate at all.
3 — after the answer
memory_ground(answer_text, session_id)
Splits the answer into claims, checks each against the graph, returns the verdict plus unsupported_claims.
one call
memory_checkpoint(query, session_id, agent)
Steps 1 and search together, with the project thread.
write
memory_add(items=[{claim, source}, …])
Up to 50 facts per call, gated per item.
retract
memory_retract(node_id, reason)
The fact stopped being true. Reversible.
audit
memory_ground_log
Append-only journal of every pass.
No pre-pass, no credit. Call memory_ground without a matching
memory_ground_prepare and the verdict is ungrounded (notes: no_pre_pass),
however many claims the text happens to support.
blank never overwrites an existing file, and the graph you get really is
empty. When the two collide, the server refuses to start and tells you what to
do. Configuration: docs/CONFIGURATION.md.
Install from directories
Baron is published in the official MCP Registry as
io.github.shinegang/baron:
Registries that mirror the official index (Glama, and clients that read it
directly) pick the entry up from there. This repository carries the metadata
those directories read:
There is no package on PyPI or npm yet, so the registry entry points at the
source repository rather than at an installable artifact: install with the
git clone in Install in two minutes, or pip install . from the
checkout. When baron-munchausen lands on PyPI, a packages block goes into
server.json and the same directories will offer one-command installs.
Contributing
Issues and pull requests are welcome. Two house rules, and they are the rules
the software enforces on itself:
A claim comes with its source. A bug report with the command that
reproduces it is worth ten without one.
"I could not check" is a valid answer and a better one than a guess.
unknown is a status here, not a failure.
Run python3.12 -m pytest tests -q before opening a pull request.
