# Enjambre – a durable kernel for swarms of AI agents (Python, MCP) > Source: > Published: 2026-09-20 08:55:30+00:00 **A small, honest operating system for swarms of AI agents.** *Enjambre* is Spanish for *swarm*. It takes the agents you already have (Claude Code, Codex, a local model behind Ollama, a hosted API, your own scripts) and gives them what a team of processes needs to work together without lying to each other: a kernel, a queue, leases, a permission gate, a router and a shared memory you can see. ``` pip install . # from a clone; one dependency (PyYAML) enjambre demo # open http://127.0.0.1:8765 ``` Runs on Linux, macOS and Windows with Python 3.10 or newer; every change is tested on all three. The demo needs no model and no API key. Four scripted agents run a small editorial pipeline: research, draft, review, publish. The critic stumbles once so you can watch a retry. The writer tries to publish without permission and the gate stops it before it runs. The router explains every choice it makes. Single agents are easy now. Swarms fail in boring, expensive ways: - A worker dies holding the GPU, and nobody notices for ten hours. - A task is reported as done, and the file it promised does not exist. - A nightly job looks offline two thirds of the time because it only runs every 30 minutes. - A rule written in the prompt is ignored the one time it matters. - A failed step leaves everything downstream waiting forever. enjambre was extracted from a real swarm that runs day and night on a solar-powered workstation and a small cloud server. Every mechanism below exists because one of those failures happened. | **Kernel** | One SQLite file. Processes report heartbeats and their state ( *alive* ,*stale* ,*offline* ) is derived from the age of the last one, never declared. | | **Leases** | GPUs, API quotas, browsers: every lock expires on its own. A crashed holder cannot block anyone for long. | | **Durable queue** | Priorities, atomic claims, idempotency keys, task leases that long jobs renew, automatic retries, a dead-letter state that always records a reason. | | **DAG** | Tasks wait for their dependencies, receive upstream results as *data* (never as instructions), and die visibly when something upstream fails. Retrying the upstream task brings them back. | | **Proof of delivery** | A task can declare what must exist when it is done, and agents declare artifacts. The kernel checks files, directories and URLs itself, and a file older than the task does not count. Record the verdict, or enforce it. | | **Policy gate** | Rules travel with every prompt, and permissions are enforced in code before any agent runs. A broken edit of the policy never replaces the last good one. | | **Router** | Picks an agent from measured success and latency, declared cost, and energy. Agents on solar power win while the sun is up. Every decision comes with a readable reason. | | **Adapters** | `cli` for any command-line agent (no shell, isolated environment),`openai` for any OpenAI-compatible endpoint,`scripted` for demos and tests. | | **MCP server** | Any MCP client can heartbeat, lease, claim, renew and complete tasks, check permissions and query memory. | | **Memory graph** | A folder of markdown notes becomes a 3D graph you can explore, search and teach with. Links to notes nobody wrote yet show up in red. | | **Dashboard** | Live board, agents, leases, trace and task timelines. No build step, no framework, strict Content Security Policy. | ``` my-swarm/ ├── swarm.yaml agents, router, scheduler, kernel, memory ├── policy.yaml rules and permissions (hot-reloaded) ├── prompts/.md the role of each agent └── memory/ markdown notes, shown as the memory graph enjambre init my-swarm # start from the demo template enjambre up my-swarm # API + dashboard + scheduler ``` Put the folder in git and every change to your swarm has an author, a date and a revert. ``` agents: coder: adapter: cli energy: solar # this machine runs on panels: prefer it while the sun is up options: command: ["claude", "-p"] env: [ANTHROPIC_API_KEY] # the only variables the child process can see reviewer: adapter: cli options: command: ["codex", "exec", "{prompt}"] stdin: false # the prompt goes in as an argument (never through a shell) env: [OPENAI_API_KEY] local: adapter: openai options: base_url: http://localhost:11434/v1 model: qwen3:8b researcher: adapter: openai cost: 0.5 options: base_url: https://openrouter.ai/api/v1 model: deepseek/deepseek-chat api_key_env: OPENROUTER_API_KEY # the name of the variable, never the key router: weights: {success: 35, latency: 15, cost: 20, energy: 30} solar_window: {start: "09:30", end: "17:30", timezone: Europe/Madrid} ``` On Windows, CLIs installed with npm are `.cmd` launchers. enjambre finds them, but only passes them the prompt through stdin (`stdin: true`, the default): `cmd.exe` would re-parse a prompt given as an argument. Agents answer with a small JSON contract (`status`, `result`, `artifacts`...). The scheduler appends it to every prompt, together with the agent's role, the policy and the results of upstream tasks. ``` # policy.yaml rules: - id: verify rule: Never report work as done without a check that passed. operations: publish: [editor] # only the editor may run tasks with operation: publish agents: writer: forbidden: [Publishing anything yourself.] vetoed_operations: [publish] # Claude Code claude mcp add enjambre -- enjambre mcp --dir ./my-swarm # a remote swarm started with `ENJAMBRE_TOKEN=... enjambre up --host 0.0.0.0` claude mcp add enjambre -e ENJAMBRE_TOKEN=... -- enjambre mcp --url http://swarm-host:8765 # Codex (~/.codex/config.toml) [mcp_servers.enjambre] command = "enjambre" args = ["mcp", "--dir", "/path/to/my-swarm"] ``` Tools: `swarm_status`, `heartbeat`, `acquire_resource`, `release_resource`, `enqueue_task`, `claim_task`, `renew_task`, `complete_task`, `get_task`, `list_tasks`, `cancel_task`, `retry_task`, `policy_for`, `check_permission`, `memory_query`, `memory_node`. ``` python from enjambre import AgentResult, Kernel k = Kernel("swarm.db") research = k.enqueue("Collect benchmark numbers", agent="scout")["id"] chart = k.enqueue("Draw the chart", depends_on=[research], proof="/tmp/chart.png")["id"] task = k.claim("scout") # atomic k.complete(task["id"], "scout", AgentResult.completed("numbers collected")) k.claim("plotter")["id"] == chart # unblocked enjambre demo | init DIR | up [DIR] | run [DIR] | mcp enjambre enqueue "Title" --agent writer --after TASK_ID --proof /abs/path enjambre tasks | ps | memory "query" ``` `enjambre run` processes the queue until it is idle and exits, which makes a swarm usable from CI. enjambre is not an agent framework and not a prompt library. Frameworks such as LangGraph or CrewAI compose model calls inside one program. enjambre sits one level below: it coordinates separate agents and processes, on one machine or several, that already know how to do their job. You can run agents built with any framework inside it. - **Honest state.** Liveness is derived, fitness is measured, and a missing measurement is`None` , never a decorative zero. - **Leases everywhere.** Locks, running tasks and resources all expire. Recovery is the default, not a cleanup script. - **Verification is recorded before it is enforced.** A verification gate that blocked closing tasks once cut a swarm's throughput by 90%. Start with`proof_mode: record` , switch to`enforce` when your agents declare their artifacts. - **Data is not instructions.** Upstream results are labelled as data in every prompt. - **Small on purpose.** Standard library plus PyYAML. One SQLite file. No build step for the UI. Details in [docs/architecture.md](https://github.com/santibccc-sudo/enjambre-os/blob/main/docs/architecture.md). The API binds to `127.0.0.1` by default and refuses other addresses without `ENJAMBRE_TOKEN`. CLI agents run without a shell and see only the environment variables you list. API keys are read from environment variables and rejected if written into `swarm.yaml`. See [SECURITY.md](https://github.com/santibccc-sudo/enjambre-os/blob/main/SECURITY.md). - **Evolution loop** : the swarm proposes changes to its own genome from measured fitness, a human approves, and a regression triggers an automatic revert. - **Versioned memory segments** with atomic writes and rollback per agent. - Rate-limit buckets per provider, OpenTelemetry export, a Postgres backend for larger swarms. MIT. Made by GreenAI Network. Bundled third-party code is listed in [THIRD_PARTY_NOTICES.md](https://github.com/santibccc-sudo/enjambre-os/blob/main/THIRD_PARTY_NOTICES.md).