headlesscode MCP server

headlesscode

License: Apache-2.0

HeadlessCode is a coding-agent harness with a CLI, HTTP interfaces, and recursive self-improvement (RSI). Run one task directly, launch parallel workers in Git worktrees, or use the NVIDIA OpenShell provider for sandboxed sessions. The bounded RSI harness can inspect and improve a target project, including HeadlessCode itself, with independent evaluation of candidate changes.

Security controls

HeadlessCode's controls map to the seven principles in Satya Nadella's Models as Insider Risks in the Super Intelligence Era.

Principle	HeadlessCode implementation
Model diversity	Operators assign models by mode and RSI role, including review and adversarial roles. Programmatic gates determine candidate eligibility. See model assignment and RSI roles.
Observe everything	Evidence-required sessions wait for an external collector's authenticated acknowledgement before model calls and tool actions. The collector signs a hash-linked journal and session head; an offline verifier produces a readable history and distinguishes worker reports from supervisor observations. See evidence journal.
Verifiability	CI runs typechecking, the full automated suite, and mock workflows. Tests exercise missing evidence acknowledgements, tampering, replay, stop races, and cleanup failures. RSI applies regression and frozen proof gates; configured hidden verification checks bounded function cases against supervisor-held answers. See CI and hidden verification.
Independent controls	Operators configure command and file permissions, resource and inference budgets, and a contained profile that pins the OpenShell policy, image, gateway, evidence key, and emergency controller. Launch preflight checks those identities and the sandbox's effective policy. See permissions and security profiles.
Independent auditability	The external collector owns evidence signing keys and storage. The hidden verifier keeps expected answers and signing keys in the supervisor, binds reports to the candidate source, session, job, limits, and one-time nonce, and compares outputs outside the candidate guest. See evidence boundaries and hidden verification.
Containment	OpenShell runs sessions in disposable independent clones. The host emergency controller can pause or stop active work, abort model calls, terminate scoped command groups, and delete sandboxes. Result import requires confirmed sandbox deletion. See emergency controls and OpenShell lifecycle.
Incident disclosure	GitHub private vulnerability reporting provides confidential intake. The incident procedure covers containment, evidence preservation, fixes, affected-user notification, coordinated advisories, and review of failed controls and runtime configuration changes. See incident response, report a vulnerability, and advisories.

Use it for	What it does
One repository task	Reads project modes and instructions, edits files, runs commands, and reports a result.
Parallel issue work	Splits GitHub issues into Git worktrees, runs workers, monitors completion, and reviews changes.
Intake automation	Watches a GitHub label and starts bounded batches of work.
OpenShell sessions	Runs worker and review sessions in disposable clones, then validates and imports results after sandbox deletion.
Harness experiments	Runs a bounded recursive self-improvement loop against an external evaluator.
Project work with harness repair	A host work loop runs tasks, independently checks observed harness problems, and retries with verified repairs.

Security

Default-allow arbitrary command execution. By default, headlesscode runs shell commands with the invoking user's privileges. It can read and modify files, including credentials such as ~/.ssh and ~/.aws, without approval prompts. Use it only with trusted tasks and isolate it with a container, VM, or dedicated user when untrusted content is involved. The optional permissions layer is defense in depth, not a security boundary. See SECURITY.md.

OpenShell runs each session in a disposable Git clone mounted at /workspace; the host worktree and harness control files are not mounted. After the sandbox is deleted, HeadlessCode validates a Git bundle and imports the result into the host worktree. The gateway must support Docker bind mounts and must see the configured scratch root at the same absolute path as the host. See the OpenShell guide for setup and security details. This integration does not configure NVIDIA Sentry or hardware monitoring.

Quick start

Requires Node.js 22.19 or newer. Install globally or run with npx:

npm install -g headlesscode
# or
npx headlesscode --task "Fix the bug in src/index.ts" --workspace /path/to/repo

For a real model call, set the OpenRouter key. --dry-run does not need a key.

Variable	Required	Purpose
HEADLESSCODE_OPENROUTER_API_KEY	For OpenRouter calls	OpenRouter API key.
OPENROUTER_MODEL	No	Default model; meta/muse-spark-1.3-contributor. --model overrides it.
OPENROUTER_HTTP_REFERER	No	OpenRouter application referer header.
OPENROUTER_APP_TITLE	No	OpenRouter application title header.
HEADLESSCODE_WORKSPACE_ROOT	No	Default workspace; otherwise the current directory.

export HEADLESSCODE_OPENROUTER_API_KEY=sk-or-...
headlesscode --task "Fix the bug in src/index.ts" --workspace /path/to/repo

The default model is Muse Spark 1.3 Contributor through OpenRouter. OpenRouter lists it at $0.10/M input tokens, $0.20/M output tokens, and $0.002/M cached input tokens. OpenRouter notes that prompts and outputs sent to this Contributor model may be used to improve Meta's products. Use OPENROUTER_MODEL or --model to select another OpenRouter model.

Using Meta's Model API (Muse) instead

Set HEADLESSCODE_LLM_PROVIDER=muse to run sessions against Meta's Model API (https://api.meta.ai/v1, OpenAI-compatible) with the Muse Spark family instead of OpenRouter. Get a key from the Model API dashboard. The default model is muse-spark-1.3 (1M context); --model and mode-models.json entries work the same as on the OpenRouter path. orchestrate preflight, per-session cost budgets, and the local Ollama backend all apply unchanged.

Variable	Required	Purpose
HEADLESSCODE_LLM_PROVIDER	No	"openrouter" (default) or "muse".
HEADLESSCODE_MUSE_API_KEY	For Muse calls	Meta Model API key (MODEL_API_KEY also works).
HEADLESSCODE_MUSE_MODEL	No	Default model; muse-spark-1.3. Shadows OPENROUTER_MODEL.
HEADLESSCODE_MUSE_BASE_URL	No	API base URL; https://api.meta.ai/v1.

export HEADLESSCODE_LLM_PROVIDER=muse
export HEADLESSCODE_MUSE_API_KEY='LLM|...'
headlesscode --task "Fix the bug in src/index.ts" --workspace /path/to/repo

Notes: the -contributor model variants (muse-spark-1.3-contributor, muse-spark-1.2-contributor) use the Contributor pricing tier and permit Meta to train on the traffic. Select them explicitly when using the direct Meta API; its default is the Standard model. Vision captioning (describe_image, screenshots) and OpenShell sandboxed execution still use OpenRouter regardless of this setting.

For a source checkout:

git clone https://github.com/Capsize-Games/headlesscode.git
cd headlesscode
npm install
node bin/headlesscode.mjs --task "Fix the bug in src/index.ts" --workspace /path/to/repo

To install a headlesscode wrapper on your PATH, run scripts/install-cli.sh. It writes to ~/.local/bin by default (override with HEADLESSCODE_BIN_DIR) and runs this checkout's local tsx. Re-run it if you move the checkout.

Terminal chat, skills, and saved sessions

headlesscode chat starts an interactive conversation. Each turn uses the existing runner and saves its messages for resume or fork. /help lists the session, model, mode, skill, and import controls.

headlesscode chat --workspace /path/to/repo
headlesscode skills import /path/to/my-skill
headlesscode sessions import --from codex --file /path/to/rollout.jsonl --workspace /path/to/repo
headlesscode chat --resume <session-id> --workspace /path/to/repo

Skills use SKILL.md instructions and captured text references. Session imports support Claude Code, Codex, and Muse history, with provenance and warnings. Imported tool calls stay historical context. See terminal chat, skills, and sessions for formats, discovery, and security behavior.

Coding sessions can also create and use reusable skills, Node tools, and local stdio MCP servers through extension_manage and extension_call. Each call pins an immutable revision and uses the session's existing permissions. The fixed interfaces keep extension contents out of the system prompt and tool catalog. See workspace extensions for the bundle format and runtime limits.

Check project instructions without calling a model

--dry-run builds the system prompt and validates project configuration. It prints the prompt and a summary of loaded modes, tools, and prompt size. A successful exit means prompt building and mode/rules loading succeeded.

headlesscode --dry-run --mode code --workspace /path/to/repo

Project .roomodes, .roo/rules-<slug>/, and AGENTS.md instructions are loaded by the prompt builder when applicable. The selected mode controls which tools are exposed. See Architecture for the relevant code.

Local MCP services

HeadlessCode can connect to user-configured stdio and Streamable HTTP MCP servers. It reads ~/.config/capsize-agent/mcp.json by default; set HEADLESSCODE_MCP_CONFIG to use another trusted, user-owned manifest. It does not execute MCP commands from a workspace .mcp.json. Connected server tools are exposed to modes that allow the mcp tool group. Offline servers are reported in the session prompt/log while other servers continue to work.

The MCP client does not add an approval layer around server tools. Treat each configured server's capabilities as available to the agent, and use HeadlessCode only for trusted tasks. See MCP servers for configuration, limits, and local setup details.

OpenShell

Build the worker image and import the OpenRouter provider profile once. Configure a Docker-backed OpenShell gateway with driver-config and bind-mount support; keep that gateway restricted to trusted operators.

# Run these commands from a HeadlessCode source checkout.
docker build -f docker/OpenShell.Dockerfile -t headlesscode-openshell:local .
openshell profile import -f shared/openshell/headlesscode-openrouter.yaml

export HEADLESSCODE_OPENROUTER_API_KEY=sk-or-...
export HEADLESSCODE_OPENSHELL_POLICY="$PWD/shared/openshell/headlesscode-policy.yaml"
headlesscode orchestrate --repo /path/to/repo --issue 42 \
  --execution-provider openshell

Each worker gets an independent clone and a temporary result directory. Neither bind source is the host worktree. After the sandbox is deleted, HeadlessCode imports a verified Git bundle into the host branch. The default scratch root is ~/.local/share/headlesscode/openshell-sessions; create it before starting the gateway and bind-mount it into the gateway container at the same absolute path. If you choose another root, set HEADLESSCODE_OPENSHELL_SCRATCH_ROOT and mount that host directory into the gateway container at the same path. Project data and shared instructions are copied into the scratch tree and mounted separately. The writable scratch checkpoint store supports checkpoints during a session and is discarded at teardown. Opt-in memory uses a scratch snapshot and merges validated JSONL records after sandbox deletion. No OpenRouter key is passed as a normal sandbox environment argument; OpenShell injects the imported provider credential.

headlesscode watch accepts the same --execution-provider openshell option. OpenShell applies to worker, plan-first, continuation, reviewer, QA, and rework sessions. The one-off headlesscode openshell-session command runs a single task and keeps its host worktree for review. Set up a gateway callback address that the sandbox's Docker bridge can reach; keep the client endpoint loopback only when possible. Read the OpenShell guide before adapting the sample gateway or filesystem policy.

Setting	Default	Meaning
--execution-provider	local	orchestrate/watch runtime: local or openshell. Also settable with HEADLESSCODE_EXECUTION_PROVIDER.
HEADLESSCODE_OPENSHELL_POLICY	No default	Base OpenShell policy YAML; required for OpenShell sessions.
HEADLESSCODE_OPENSHELL_IMAGE	headlesscode-openshell:local	Sandbox image.
HEADLESSCODE_OPENSHELL_PROVIDERS	headlesscode-openrouter	Comma-separated credential profile names.
HEADLESSCODE_OPENSHELL_CPU / HEADLESSCODE_OPENSHELL_MEMORY	2 / 4Gi	Per-sandbox resource requests.
HEADLESSCODE_OPENSHELL_NAME_PREFIX	hcls	Prefix for deterministic sandbox names.
HEADLESSCODE_OPENSHELL_SCRATCH_ROOT	~/.local/share/headlesscode/openshell-sessions	Host scratch directory that the gateway container must see at the same path.

The headlesscode openshell-session command provides a single-session live entry point; run headlesscode openshell-session --help for options.

Parallel work and GitHub intake

orchestrate runs a parallel round from GitHub issue numbers or a JSON file. It splits work, creates a worktree per group, starts workers, watches for completion, and reviews results. QA and deployment approval are optional.

headlesscode orchestrate --repo /path/to/repo --issue 42 --issue 43 --qa
headlesscode orchestrate status --repo /path/to/repo --wait --timeout-ms 7200000

watch polls GitHub for issues with a chosen label and starts bounded batches. It requires GH_TOKEN or GITHUB_TOKEN; it performs intake only. Use a later orchestrate run to monitor, review, QA, or deploy a batch.

GH_TOKEN=... headlesscode watch \
  --owner my-org --repo /path/to/repo --label needs-agent --run-once

Command	Important options	Behavior and limits
headlesscode orchestrate --repo <path>	Repeat --issue <n> or use --issues-json <file>; `--execution-provider local	openshell, --qa, --deploy, --no-review, --dry-run, --max-iterations , --max-rework-cycles , --max-continuations `
headlesscode orchestrate status --repo <path>	--wait, --timeout-ms <n>, --json	Reads durable round state; --wait blocks until terminal state or timeout.
headlesscode orchestrate stop --repo <path>	Repeat --group <name>	Stops selected groups' worker process trees.
headlesscode watch --owner <o> --repo <path> --label <name>	`--execution-provider local	openshell, --run-once, --dry-run, --max-per-sweep , --max-concurrent-sessions , --retry-failed`

Both orchestration and watcher enforce a shared concurrency cap, defaulting to 3 (HEADLESSCODE_MAX_CONCURRENT_SESSIONS). Orchestration aborts when the cap is full; the watcher leaves excess issues pending. The watcher records idempotency state under <repo>/.worktrees/.watcher-state.json by default. Use --help on a command for its complete options. Detailed guides: orchestration, issue watcher, QA implementation, and deployment gate.

Project registration and search

Registering a project detects its stack, ensures .gitignore excludes .headlesscode/, and builds a code-search index and codemap. Indexing calls an embedding API and can incur charges unless skipped or configured for Ollama.

headlesscode init --workspace ~/Projects/your-project
headlesscode init --workspace ~/Projects/your-project --skip-index

See headlesscode init --help for --skip-codemap and embedding-backend options. The CLI also provides index, codemap, project-store, GitHub App, and decision-proxy commands; run headlesscode --help for the command list and each subcommand's --help for complete usage.

Recursive self-improvement

headlesscode improve --dry-run resolves the base commit and prints planned candidate worktrees. A real run requires PostgreSQL, a shared S3-compatible artifact store, and at least one registered headlesscode rsi-worker. The coordinator queues sanitized mutation snapshots and visible evaluation jobs; workers run each in a fresh OpenShell guest. Fitness, paired baseline comparison, archives, and selection stay in the coordinator. Hidden evaluation remains disabled. A 2026-09-29 bounded run used two workers on the same local OpenShell gateway; both candidate evaluation jobs were leased concurrently. The run used a temporary clamp.js fixture and does not establish general headlesscode improvement or multi-gateway operation. An earlier bounded run passed visible checks but made no source change and was rejected.

npx tsx src/cli.ts improve --repo . --dry-run
npx tsx src/cli.ts improve --repo . --population 2 --generations 1

Before starting workers, build the standard and RSI guest images on each OpenShell gateway host that will run RSI jobs. Build the larger training image only on gateways whose workers will accept model-training or paired model-evaluation jobs:

docker build -f docker/OpenShell.Dockerfile -t headlesscode-openshell:local .
docker build -f docker/OpenShell-RSI.Dockerfile -t headlesscode-openshell-rsi:local .
docker build -f docker/OpenShell-RSI-Training.Dockerfile -t headlesscode-openshell-rsi-training:local .

The RSI Dockerfile extends headlesscode-openshell:local and removes the RSI source, test files, and hidden evaluation suite from the guest image. See the RSI design for worker and queue configuration.

The queue stores job state, worker registrations, leases, retries, and admission policy in PostgreSQL; artifact bytes remain in object storage. Local integration tests used 12 simulated workers and 100 queued jobs, including a 20 MiB artifact. Each worker process currently runs one guest at a time and must be configured with a unique worker ID and OpenShell gateway ID. The two-worker run observed 4.008 seconds of concurrent evaluation leases on one gateway. A local PostgreSQL/ RustFS test drained the remaining 96 jobs with 12 simulated workers in 446 ms (215.2 jobs/s), after the initial four jobs were claimed and completed to verify admission limits; claims still serialize on a queue-policy row, and neither result qualifies a multi-host deployment. Remaining work is:

Issue	Work
#3	OpenShell candidate isolation is implemented; live multi-gateway qualification remains.
#4	Content-addressed artifacts are implemented; signed evaluator provenance and hidden evaluation remain.
#5	PostgreSQL queue, leases, retries, admission, and configured workers are implemented; live fleet qualification remains.
#6	Bounded adaptive independent search supports an initial population plus one evidence-driven follow-up; repeated stages remain out of scope.
#7	Fixture-backed curriculum replay and explicit promotion are implemented; wording-to-capability measurement remains unproven.
#8	Adversarial review and bounded OpenShell break tests are implemented; live provider-backed review is unvalidated.
#9	An OpenShell QLoRA prototype is covered by mocked job tests; the supplied GGUF is rejected before enqueue, and no live training is validated.

See the RSI design and run progress.