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  1. Home
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  3. Korg Ledger
Korg Ledger logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 7:47:16 PM

Korg Ledger

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe havenโ€™t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time โ€” check back soon.
View Repository5 GitHub StarsTotal stargazers on GitHub for the source repository (5 stars).Visit Website

Verifiable cognition: prove a session ledger is tamper-evident; audit & import any agent's logs.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "korg-ledger": {
      "url": "https://api.openai.com/v1"
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (3) Directory Badge Claim listing Alternatives๐Ÿ”’ More in Security

Capabilities & Tool Schemas (3) ~56 tokensApproximate context cost of this serverโ€™s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server โ€” may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Korg Ledger.

AskUserQuestion

Ask a clarifying question with optional multiple-choice.

Recall

Pull relevant facts from cross-session memory (drift-checked).

Retrieve

Pull the exact bytes of a large tool result that was sealed to a content-ref.

Documentation Overview

korgex โ€” the verifiable coding agent

PyPI version PyPI downloads Python versions CI tests MCP registry License: MIT

English ยท ็ฎ€ไฝ“ไธญๆ–‡ ยท ็น้ซ”ไธญๆ–‡

korgex

An AI coding teammate for your terminal โ€” that keeps the receipts.

Tell korgex what you want in plain English โ€” "fix the failing test," "add a healthcheck endpoint" โ€” and it reads your code, makes the change, runs the tests, and shows you exactly what it did. It's free and open-source, and it works with whatever AI you prefer (Claude, ChatGPT, Gemini, Grok, or a private model running on your own computer), so you're never locked to one company.

Why it's different: everything korgex does is saved to a tamper-proof record you can check later. If anyone alters that record โ€” even by a single character โ€” korgex can prove it. It's a coding assistant you can audit, not just hope to trust.

For developers: terminal-native, plan-first, speaks both the Anthropic and OpenAI tool-use protocols, runs on any OpenAI- or Anthropic-compatible model (incl. local via Ollama), connects to any MCP server, streams live, and records every run to a hash-chained causal ledger you can check with korgex verify. MIT-licensed.

bash
$ korgex "add a /healthz endpoint that returns 200 with uptime"
โžค Read(file_path=/app/routes.py)
โžค Edit(file_path=/app/routes.py, old_string=..., new_string=...)
โžค Bash(command=pytest tests/test_routes.py -q)
โœ“ Added GET /healthz returning {"status": "ok", "uptime_seconds": ...}

$ korgex verify
  โœ“ ledger intact โ€” 7 events, hash-chain + causal DAG verified

Table of Contents

  • Install
  • Quickstart
  • The REPL โ€” live in it
  • How it works
  • Verifiable cognition
  • Tools
  • Capabilities
  • Safety & sandboxing
  • CLI reference
  • Environment variables
  • Multi-model routing
  • MCP integration
  • Plugins
  • Streaming TUI
  • Architecture
  • Project structure
  • Development & testing
  • Building & releasing
  • Troubleshooting
  • Known limitations
  • License

Install

From PyPI (recommended)

Terminal
pip install -U korgex          # or, for an isolated global CLI:
uv tool install korgex@latest

Requires Python โ‰ฅ 3.10 (tested on 3.10, 3.11, 3.12, 3.13).

From source / latest main

bash
git clone https://github.com/New1Direction/korgex.git && cd korgex && pip install -e .
# or, without cloning:
pip install git+https://github.com/New1Direction/korgex.git

Quickstart

server.ts
# 1. Connect a provider (interactive โ€” saves to ~/.korgex/config.json)
korgex setup
# โ€ฆor just export a key; any of these works:
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-proj-..."
export KORGEX_API_KEY="sk-or-v1-..." KORGEX_API_URL="https://openrouter.ai/api/v1"   # OpenRouter
export KORGEX_API_URL="http://your-gpu-box:8000/v1"   # self-hosted vLLM/llama.cpp โ†’ korgex --model Qwen2.5-Coder-32B "โ€ฆ"

# 2. Run the agent on a naked prompt
korgex "fix the failing test in tests/test_auth.py"

# 3. Or pick a model / mode
korgex --model claude-sonnet-4-6 "refactor src/handler.py"
korgex --mode plan "design a rate limiter for the API"
korgex --quiet "list the python files in src/"     # no TUI โ€” pipe-friendly

# 4. Prove the run wasn't altered afterward
korgex verify

Run bare korgex with no prompt to drop into the interactive REPL.


The REPL โ€” live in it

Run bare korgex for a streaming, multi-turn session. It connects your MCP servers, reads your project rules, and keeps a per-session rewind log.

Slash commands

CommandWhat it does
/loop <task>Grind a task list unattended โ€” auto-continues turn after turn until done, with a hard cap (Ctrl-C stops).
/diff [n]Colored diffs of what changed in the last turn (or turn n).
/rewind [n]List undo points, or restore files to BEFORE prompt n.
/skills ยท /skills curateList skills korgex learned (โœฆ); curate merges near-duplicates.
/tasks ยท /jobsThe live task checklist; background shell jobs.
/plan [on|off]Plan mode โ€” read-only until you approve the agent's plan.
/model [id]Show a priced model menu, or switch the live model mid-session.
/verify ยท /costVerify the session ledger; show estimated $ spend from recorded tokens.
/resume [id]Reload a prior session's transcript into context and continue where you left off.
/<name> [args]Run a custom command โ€” a markdown prompt from .korgex/commands/ (or a built-in like /code-review, /build-fix, /checkpoint).
/clear ยท /help ยท /exitReset the conversation ยท help ยท quit.

Inline shortcuts

  • @path/to/file โ€” mention a file and its contents are pulled into the turn: refactor @src/auth.py to use @src/db.py.
  • !command โ€” run a shell command right there: !git status, !pytest -q.

Project rules. korgex init scaffolds an AGENTS.md; korgex auto-reads it โ€” plus any nested AGENTS.md up the tree and .korgex/rules/*.md โ€” every session, so it follows your house style.

Prompt caching keeps the system prompt + tools warm across turns (automatic on OpenAI/Gemini/Grok/DeepSeek; cache_control breakpoints on Claude/Qwen). Set KORGEX_CACHE_STATS=1 to see per-turn cache hits โ€” and every hit is recorded on the ledger, so korgex cost prices cached tokens at their real discounted rate (and shows what the cache saved you), provable with korgex verify.


How it works

mermaid
flowchart TD
    U["You โ€” prompt or REPL"] --> CLI["korgex CLI / REPL"]
    CLI --> AG["KorgexAgent loop<br/>plan โ†’ act โ†’ verify"]
    AG -->|model id| PB{Provider}
    PB -->|"claude / anthropic/*"| ANT["Anthropic SDK"]
    PB -->|everything else| OAI["OpenAI-compatible SDK<br/>OpenAI ยท OpenRouter ยท Ollama ยท Grok ยท DeepSeek"]
    ANT --> TR["Tool router<br/>(src/tool_abstraction.py)"]
    OAI --> TR
    TR --> BT["Built-in tools<br/>Read ยท Edit ยท Bash ยท Grep ยท โ€ฆ"]
    TR --> MCP["MCP servers<br/>(any in mcp.json)"]
    TR --> CA["CodeAct kernel<br/>code = the action (opt-in)"]
    BT --> LED[("korg-ledger<br/>hash-chained + causal DAG")]
    MCP --> LED
    CA --> LED
    LED -. "verify ยท trace ยท why ยท cost" .-> U

The agent is provider-agnostic by design: tool schemas are translated per provider ({name, description, input_schema} for Anthropic, {type:"function", function:{โ€ฆ}} for OpenAI), responses are normalized into a common shape, and tool results are formatted in whichever message structure the provider expects. Every tool call โ€” built-in, MCP, or CodeAct โ€” is recorded to the ledger as it happens.


Verifiable cognition

In plain terms: korgex keeps a logbook of everything it does โ€” every file it reads, every command it runs. Each entry is sealed to the one before it, like links in a chain, so if anyone later changes, adds, or removes even one entry, the chain visibly breaks. The result is honest, checkable proof of what the AI actually did โ€” for audits, compliance, debugging, or simple peace of mind. As far as we know, no other coding agent does this.

Under the hood: every run is recorded to a tamper-evident causal ledger, not an opaque log. Each event is hash-linked (prev_hash/entry_hash) to the previous one and causally linked (triggered_by) to what caused it โ€” so a whole session can be cryptographically proven intact, and any edit, deletion, reorder, or splice is detected and localized to the offending event.

mermaid
flowchart LR
    subgraph chain["korg-ledger โ€” each event hash-linked to the last"]
      direction LR
      E0["prompt<br/>a1f3"] --> E1["Read<br/>7c0e"] --> E2["Edit<br/>b42d"] --> E3["Bash: pytest<br/>9d11"]
    end
    E3 --> V{{"korgex verify"}}
    V -->|chain intact| OK["โœ“ proven unaltered"]
    V -->|"edit / splice / reorder"| BAD["โœ— localized to the bad event"]

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

GitHub stars
5
Stargazers on the source repository.
npm downloads
59k
Package downloads in the last 30 days.
Last commit
3mo ago
Most recent push to the default branch.
Tools exposed
3
Callable tools this server registers over MCP.

Reviews

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Frequently Asked Questions about Korg Ledger

korg-ledger is a hosted MCP server. Add it as a remote server in your client's config: "mcpServers": { "korg-ledger": { "url": "https://api.openai.com/v1" } }

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Technical Specs & Signals

Category๐Ÿ”’Security
More technical detailsExpand โ–พ
TransportSSE (Remote)
Last updatedJun 22, 2026
4/7 checks healthy over the last 46d
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars5
GitHub Star CountTotal stargazers on GitHub representing community popularity (5 stars).
Last commit3mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jun 22, 2026
npm downloads59,975/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
53Quality signal: Good ยท 53/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity6/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 20d ago via OSV.dev ยท https://api.openai.com/v1 (PyPI)

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