Zero-dependency agent memory: recall, consolidation, and a first-class correction + erasure channel.
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Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-dancenitra-mnemo": {
"args": [
"-y",
"vite"
],
"command": "npx"
}
}
}Are you the author?
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Built by Rastislav Drahoš — an autonomous research organization: agents that do grounded research, test hypotheses with runnable falsifiers, and keep an open track record.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'vite' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
launch-editor: NTLMv2 hash disclosure via UNC path handling on Windows
### Summary The `launch-editor` NPM package accesses arbitrary paths including Windows UNC paths. When a UNC path is opened, Windows automatically attempts NTLM authentication to the remote host, causing the user’s NTLMv2 password hash to be leaked to an attacker-controlled SMB server. This can result in credential compromise through offline hash cracking. ### Impact If the following conditions are met, an attacker can get the NTLMv2 password hash on the computer that is using the `launch-edit
vite: `server.fs.deny` bypass on Windows alternate paths
### Summary The contents of files that are specified by [`server.fs.deny`](https://vite.dev/config/server-options#server-fs-deny) can be returned to the browser on Windows. ### Impact Only apps that match the following conditions are affected: - explicitly exposes the Vite dev server to the network (using `--host` or [`server.host` config option](https://vitejs.dev/config/server-options.html#server-host)) - the sensitive file exists in the allowed directories specified by [`server.fs.allow`]
launch-editor vulnerable to command injection via the crafted request on Windows
### Summary Due to the insufficient sanitization of the `file` argument in the `launchEditor`, an attacker can execute arbitrary commands on Windows by supplying a filename that contains special characters. ### Impact If the following conditions are met, an attacker can execute arbitrary commands on the computer that is using the `launch-editor`: - An attacker can place a file with the malicious filename - An attacker can call the `launchEditor` method with the `file` argument controlled - The
Vite Vulnerable to Path Traversal in Optimized Deps `.map` Handling
### Summary Any files ending with `.map` even out side the project can be returned to the browser. ### Impact Only apps that match the following conditions are affected: - explicitly exposes the Vite dev server to the network (using `--host` or [`server.host` config option](https://vitejs.dev/config/server-options.html#server-host)) - have a sensitive content in files ending with `.map` and the path is predictable ### Details In Vite v7.3.1, the dev server’s handling of `.map` requests for
Vite: `server.fs.deny` bypassed with queries
### Summary The contents of files that are specified by [`server.fs.deny`](https://vite.dev/config/server-options#server-fs-deny) can be returned to the browser. ### Impact Only apps that match the following conditions are affected: - explicitly exposes the Vite dev server to the network (using `--host` or [`server.host` config option](https://vitejs.dev/config/server-options.html#server-host)) - the sensitive file exists in the allowed directories specified by [`server.fs.allow`](https://vi
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Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
Pi Coding Agent extension (CLI-first) — routes bash/read/grep/find/ls through lean-ctx CLI for strong token savings. Optional MCP bridge can register advanced tools.
97% token reduction for AI coding sessions — zero deps, 21 languages, MCP server
One local source for the MCP servers, tools, and memory your AI coding agents share, synced into each tool's native config with a review gate and a receipt for every change. No daemon, no lock-in.
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Built by Rastislav Drahoš — an autonomous research organization: agents that do grounded research, test hypotheses with runnable falsifiers, and keep an open track record.
Distilled from an autonomous research OS that runs over ~6,000 notes. Each tool is one file you can
copy or pip install, and each ships with a runnable, measured demo — the rule here is measured,
not assumed. → Full overview: TOOLKIT.md
| tool | one line | proof |
|---|---|---|
| inspeximus | agent memory + a self-maintaining second brain (value-ranked recall, consolidate, dead-link/orphan/stale repair) | python inspeximus/maintain.py |
| ragfresh | a freshness/decay layer for RAG/vector stores — keep/down-weight/refresh/prune by value×freshness | python ragfresh/ragfresh.py |
| nullcheck | is this number real, or noise? — null-simulation A/B + permutation + peeking-inflation | python nullcheck/nullcheck.py |
| selfref | is your AI training on itself? — model-collapse + self-confirmation-lock governor | python selfref/selfref.py |
| quitkit | when to quit a depleting effort — a measured drawdown-exit threshold (θ≈0.6) | python quitkit/quitkit.py |
| idcheck | is your causal/attribution number identified, or biased? — audits controls by graph role; proves a collider flips an estimate's sign | python idcheck/idcheck.py |
| goodhart | how gameable is your proxy/metric? — measures Goodhart fidelity decay + how many metrics fix it (reward hacking / KPI drift) | python goodhart/goodhart.py |
| herdcheck | will your multi-agent system herd? — measures when an agent crowd collapses to one member's competence, and the fix | python herdcheck/herdcheck.py |
pip install "git+https://github.com/DanceNitra/agora.git" # the eight cores, dependency-free
python examples/toolkit_demo.py # run all eight end-to-end
Open-core: the cores stay free. The tools are the public, proven output of the research engine below.
inspeximus implements a Model Context Protocol (MCP) server, so any MCP host (Claude Code,
Cursor, Windsurf, Codex, Gemini) can use it as persistent agent memory. It is published to PyPI and to the
official MCP registry as io.github.DanceNitra/inspeximus.
pip install inspeximus # PyPI package
inspeximus-mcp # start the stdio MCP server
The MCP server exposes 12 tools over stdio — remember, recall, route, revert, forget,
consolidate, check_conflict, contradictions, credit, value_by_cohort, consolidate_clusters,
sleep. Environment: INSPEXIMUS_PATH (the JSON memory file) and INSPEXIMUS_ECHO_GUARD (block a restated retired
value from resurrecting a corrected fact). Server source: inspeximus_pypi/inspeximus/mcp.py.
inspeximus's differentiator as an MCP memory server is a first-class correction + erasure channel: revert a value, cascade a lineage-aware retraction, and prove deletion with tamper-evident receipts — measured against mem0 and Graphiti in an open cross-system integrity benchmark.
Persistent browser-based ecosystem where heterogeneous AI agents collaborate, create, compete, and evolve.
5 layers: Lifecycle (L) — Coordination (C) — Execution (E) — Observability (O) — Storage (S).
# 1. Backend
cd server && python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
cd server && .venv/bin/uvicorn agora.main:app --host 127.0
... [View full README on GitHub](https://github.com/dancenitra/agora#readme)