A critical design flaw currently affects the pipeline. Until Issue #8 is resolved, this program is considered unstable and is not recommended for use.
A kernel for concurrent multi-agent software development.
Multiple agents edit one shared working directory at the same time.
No worktrees, no merge step, no late-stage reconciliation.
The Multi Agent Kernel arbitrates concurrent access the way an OS arbitrates shared memory between threads.
Most multi-agent coding systems give each agent a Git branch and merge at the end. A message-passing model where conflicts surface late, after the dependency information needed to resolve them is gone.
The Multi Agent Kernel takes the shared-memory approach.
-
The codebase is decomposed into independently lockable
AST nodes(functions, methods, classes, headers). -
Files on disk are derived artifacts reconstructed from a
versioned node store. -
The kernel owns a
symbol-level lock tableand resolves conflicts at scheduling time, where the dependency graph is still explicit. -
Each agent receives only the nodes it holds write locks on, edits them in isolation, and returns the modified fragments. The kernel reassembles the file.
-
Before dispatch, the kernel cross-checks the planner's proposed plan against that same AST-derived dependency graph — grounding hallucinated node ids, adding missing
depends_onedges, and flagging spurious ones — so a bad LLM guess is corrected before it reaches the scheduler, not after a collision.
Check out the knowledge graph for this project. (created with graphify)
# Recommended
uv tool install git+https://github.com/chaseungjoon/multi-agent-kernel
# Or with pipx
pipx install git+https://github.com/chaseungjoon/multi-agent-kernelmak --versionFrom source (for contributors)
git clone https://github.com/chaseungjoon/multi-agent-kernel
cd multi-agent-kernel
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
./bin/makmak update
⚠️ Currently, MAK only supports Python codebases, there are plans to add other language support in the near future.
Launch the interactive app from any directory:
mak
Features
Type
/to browse all commands with one-line descriptions. (Tab autocomplete)
/helplists commands and shortcuts.
/status- Live session status (models, planner, agents, workdir, approval, tokens)/apikey- Set api keys of providers/work-dir <path>- Set working directory/models <provider-1>:<model> <provider-2>:<model> ...- Set agent models/planner <provider>:<model>- Set planner model/refresh-models- Re-fetch the model list from each provider right now/max-agents <int>- Set number of agents/config- Returns to auto-discovery (see Configuration & API Keys)/config /path/to/config.yaml- Point to a custom config/no-review true- Omit user review of planner (default false, not recommended to turn on)/clear- clears the screen,/exit(or/quit, Ctrl+C) quits, Ctrl+J inserts a newline for multi-line tasks.
For scripted / non-interactive runs, use mak run (equivalently python3 -m mak
in a source checkout). Set your API keys first — see
Configuration & API Keys. You only need keys for the
agents you actually run.
⚠️ Just to be safe, create a separate branch for MAK to work on
# Example with claude opus 5, gpt-5.6 sol and gemini 3.5 flash
mak run --task "your task" --work-dir /path/to/project \
--models anthropic:claude-opus-5 openai:gpt-5.6-sol gemini:gemini-3.5-flash
# Example with claude sonnet 5 X 5 (provider default model)
mak run --task "your task" --work-dir /path/to/project \
--models anthropic --max-agents 5Command line arguments
# Describe task
--task "Describe your task here"
# Set working directory
--work-dir /path/to/project
# Omit human review (Not recommended)
--no-review
# Resume a crashed run from .mak/task_graph.json (no --task needed)
--recover
# Default model
--models anthropic
--models openai
--models gemini
# Set model
--models anthropic:claude-opus-5
--models openai:gpt-5.6-terra
--models gemini:gemini-3.1-pro-preview
# Use multiple providers (tasks are distributed round-robin across them)
--models anthropic openai gemini
--models anthropic:claude-opus-5 openai:gpt-5.6-sol gemini:gemini-3.5-flash
# Use single provider with multiple agents
--models anthropic --max-agents 5
--models anthropic:claude-opus-5 --max-agents 3
# Choose a custom config file (default: auto-discovered, see below)
--config /path/to/config.yaml
Default models list for each provider — kept current
automatically: MAK re-fetches each provider's model list in the background twice a
month (1st and 15th), so new models show up in /models and /planner without an
update. Run /refresh-models to fetch immediately instead of waiting.
Note on
claude-fable-5: MAK supports Anthropic's most capable model, but it comes with caveats — it requires an org with 30-day data retention (zero-data-retention orgs get a 400 on every request), it can decline requests with arefusalstop reason (which MAK treats as a failed task), and it is priced above Opus tier ($10/$50 per MTok). MAK prints this warning whenever you select it as a planner or agent model.
API keys. MAK drives hosted models from three providers — Anthropic, OpenAI,
and Google Gemini. Keys are read from the environment
(ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY) or from
~/.config/mak/.env — the TUI's /apikey command (and its first-run setup)
writes them there for you. Exported environment variables always win.
In a source checkout, a legacy mak/.env is also read.
Config file. When --config (or /config) is not given, MAK auto-discovers
its configuration, first match wins:
./mak.yaml— a per-project config in the current directory~/.config/mak/config.yaml(respects$XDG_CONFIG_HOME) — your user default- The built-in default shipped with the package (view it)
To customize, copy the built-in default to either location and edit it.
benchmark/ pits MAK against a traditional git-worktree multi-agent workflow on
the same workload with the same agents (3× claude-sonnet-4-6). Every operation must
edit one shared registry function. The numbers below are the mean of 10 independent runs
-
benchmark/project_template_2/— 90 operations, 9 modulesMAK Git worktrees Avg. Tokens 18,339 23,760 Avg. Time 226.5s 99.5s Avg. Accuracy 94% (253.1/270) 93% (251.6/270) Avg. Merge conflicts 0 2
MAK spends 23% fewer tokens and hits zero merge conflicts by construction. It also has a slight edge in accuracy.
Both sides got a few of the harder algorithms wrong, but the worktree side additionally resulted in 2 merge conflicts.
MAK is slower than traditional worktree based operations because every task contends on that one symbol, so MAK serializes those writes while the worktrees edit in parallel and reconcile afterward: the trade is correctness by construction and token efficiency for execution time on a deliberately maximally-contended workload.
Run it yourself (all targets) with
python3 benchmark/run_benchmark.py --mode real \
--models anthropic:claude-sonnet-5 anthropic:claude-sonnet-5 anthropic:claude-sonnet-5CONTRIBUTING.md is the full guide — architecture, every subsystem in depth, setup, the quality gates, coding standards, and where to help.
MIT © 2026 Seungjoon Cha