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Charter Build and operate production-safe agents that run in your own environment. Charter is the open-source alternative to managed agent platforms. Define your agent
and its policies in YAML, run it in your environment with your models and tools, and
let Charter's persistent control plane handle its operational lifecycle — without
requiring your model credentials, prompts, tool traffic, or private infrastructure
access to leave your execution environment. Why Charter Durable execution. A run parks for a human and resumes days later on another
worker, with its state intact. Policy that acts. Set thresholds on the metrics an agent produces. One that
crosses a threshold pauses, cools down, or rolls back to the version that worked. Declared authority. The model sees only the tools you list, gated tools
require human approval, and budgets cap what a task may spend. Fleet operations. Every agent's operational state, run history, metrics and
open decisions, from the CLI or the console. Audit and traces. Every approval, rejection and policy action is recorded
with who took it and why. Model and tool calls export as OpenTelemetry GenAI
traces, which Jaeger, Tempo, Datadog and Langfuse can read. Your network, your data. Workers run in your environment, so agents reach
internal services and databases directly. Model keys and prompts never reach the
control plane, and the agent's conversation, files and traces stay in stores you
run. Self-host the control plane and use a local model to run Charter fully
air-gapped. DESIGN.md documents every field. Status Charter is pre-1.0, so the configuration format and CLI can still change between
releases. Feedback is welcome. Quickstart Run your first agent in 5 minutes. pip install boundflow-charter # add [ui] for the console, [otel] for traces pip install --pre boundflow-charter # or whatever main is, published every green build A control plane Charter needs one to run agents against. To run one locally: curl -sSLO https://raw.githubusercontent.com/boundflow/charter/main/deploy/local.compose.yml
docker compose -f local.compose.yml up -d --wait
docker compose -f local.compose.yml run --rm server -mode=provision -name=me That prints an API key. With it: export BOUNDFLOW_API_KEY= < the key it printed > export BOUNDFLOW_SERVER_ADDRESS=http://localhost:50051 export BOUNDFLOW_WORKER_ADDRESS=http://localhost:50052 export CHARTER_STORE_URL=postgres://charter:charter@localhost:5434/charter export ANTHROPIC_API_KEY= < your model key > # or another provider's, see worker.yaml below Remove it with docker compose -f local.compose.yml down -v . Cloning the repo
works too, and gets you the examples alongside it. For production you have two options. Run the BoundFlow backend yourself, following
its deployment docs .
Or use BoundFlow Cloud , which is managed and in early access
( request access ): it gives you an API key and the two
addresses, and you export those instead of the local ones. The worker still runs
wherever you put it, so CHARTER_STORE_URL stays yours. Either way the control plane never sees your model key or its traffic. Your first agent charter init triage That writes two files. triage/v1.yaml , the agent: apiVersion : charter/v1 kind : AgentConfig name : triage version : 1 model : claude-haiku-4-5 objective : | Triage this support ticket and say what should happen to it: {{ inputs.ticket }} inputs : ticket : { type: string, required: true } response_format : category : type : string description : billing, bug, account, or other. next_step : type : string description : What a person should do about it, in one sentence. and worker.yaml beside it, the deployment: apiVersion : charter/v1 kind : Worker control_plane : endpoint : ${BOUNDFLOW_SERVER_ADDRESS} worker_endpoint : ${BOUNDFLOW_WORKER_ADDRESS} api_key : ${BOUNDFLOW_API_KEY} tenant : default llm : # Any provider LangChain can build. Name it here, put its key in the variable # below, and install its package: pip install 'boundflow-charter[openai]' provider : anthropic api_key : ${ANTHROPIC_API_KEY} store : url : ${CHARTER_STORE_URL} agents_dir : ./ serves :
- agent : triage versions : [1] It calls no tools and sets no budget. Both are optional, and the sections below
add them. Run it charter tenant create default # once per control plane charter agent create triage # prints an instance id charter apply . # arm config and policy charter worker . # leave this running, it is the process Then, from another terminal, charter agents confirms it exists and carries the
instance id every command needs: AGENT INSTANCE VER STATUS ACTIVITY
triage d9811374 v1 active active Run a task against it: charter run triage --instance d9811374 --ticket " card declined twice, tried a new one " # prints a task id charter status < task-id > status prints what the agent returned, in the shape response_format declared: task f683f822-d8f4-40a7-b528-8db1a576140c
outcome successful
took 11s
inputs
ticket card declined twice, tried a new one
result
category billing
next_step Verify if the new card payment processed successfully and contact
the customer to resolve any ongoing payment issues. The console shows the same thing in a browser, for all agents: charter ui Approvals and policy Tools can be gated on human approval. Behaviour is versioned, so adding one means
writing a new version file: mcp :
- name : stripe url : https://mcp.stripe.com env : [STRIPE_API_KEY] tools :
- tool : get_charge - tool : create_refund approval : always Charter stops the task and shows a person the call it wants to make and the
reasoning behind it: charter approve apr_01J8Z --actor dana --reason " third dispute this month " Nothing waits in your terminal. The task ends at the gate and resumes when someone
answers, which can be days later on a different worker. Limits are policy rather than behaviour, so they sit outside the version. runtime.yaml holds what one task may spend and what the agent may reach: apiVersion : charter/v1 kind : RuntimePolicy agent : triage per_run : max_cost_usd : 0.50 max_llm_calls : 20 max_seconds : 300 max_parallel_subagents : 3 capability_call_limits :
- { capability: write, max_calls: 10 } limits : max_call_seconds : 60 max_tool_seconds : 30 authority : allowed_capabilities : [read, write] approval_timeout_seconds : 3600 lifecycle.yaml acts on the agent over time. When a metric crosses a threshold the
control plane can pause it, cool it down, or roll it back to an earlier version: apiVersion : charter/v1 kind : LifecyclePolicy agent : triage rules :
- when : { metric: num_failures, threshold: 3 } then : { pause: { window: 5 } } - when : { metric: approval_rejections, threshold: 2 } then : { cooldown: { window: 10, seconds: 3600 } } - when : { metric: cost, threshold: 2.00 } then : { set_version: { target: 1 } } Both are re-applied on every charter apply , so a ceiling can be lowered without
cutting a release. Architecture charter apply compiles your configuration into workflows and policy on the BoundFlow control plane. A Charter worker
runs the agent in your environment and talks to your MCP servers with credentials
that stay there. Each worker registers the agents it can run, listed under serves in its worker.yaml , and any worker registered for an agent can pick up its work.
If the worker running a task crashes or stops, another continues it from its last
checkpoint, and a run parked for a person resumes on whichever worker is free when
they answer. The agent loop itself is deepagents ,
so its tools, subagents, filesystem and skills work here unchanged. Charter makes
that loop durable and governed: it checkpoints the run, turns the harness's
interrupts into approvals a person can answer tomorrow, and holds it to the limits
your config declares. BoundFlow
Control Plane
state • policy • lifecycle
│
RPC
┌───────────┴───────────┐
▼ ▼
┌────────────────────┐ ┌────────────────────┐
│ Charter worker │ │ Charter worker │
│ │ │ │
│ model ↔ agent loop │ │ model ↔ agent loop │
│ │ │ │ │ │
│ MCP tools │ │ MCP tools │
└────────────────────┘ └────────────────────┘
Your environment Charter adds no database or service of its own. Deployed agents keep running
through their workers and the control plane whether or not the CLI is installed. Documentation DESIGN.md : every field of every file, and the decisions behind them deploy/ : running workers as containers, and a control plane locally examples/ : two agents over a toy support system. One gates a refund
and pauses itself when too many are turned down, the other rolls itself back to
an earlier version. They run with nothing but a model key Development python -m venv .venv
.venv/bin/pip install -e ' .[dev,ui,otel] ' .venv/bin/pytest boundflow comes from PyPI. Add --pre --upgrade boundflow to track its main,
which is what CI's second unit job does. End-to-end tests need a control plane, and skip themselves without one. The compose
file CI uses runs the published image: docker compose -f deploy/local.compose.yml up -d --wait
key= $( docker compose -f deploy/local.compose.yml run --rm server \ -mode=provision -name=dev | awk ' /^api_key/{print $NF} ' ) export BOUNDFLOW_API_KEY= $key export BOUNDFLOW_SERVER_ADDRESS=http://localhost:50051 export BOUNDFLOW_WORKER_ADDRESS=http://localhost:50052 export CHARTER_STORE_URL=postgres://charter:charter@localhost:5434/charter
pytest tests/e2e They use a real control plane, a real MCP subprocess and real governance gates.
Only the model is faked, so the suite stays deterministic and free.