🧠 judais-lobi

Artifact-driven. Capability-gated. Endpoint-aware. Not a chatbot. A kernel.

---

---

🔴 JudAIs & 🔵 Lobi

Two agents. One spine.

• 🧝 Lobi — whimsical Linux elf, creative, narrative, curious.
• 🧠 JudAIs — strategic adversarial twin, efficient, ruthless, execution-first.

They are no longer just terminal personalities.

They are evolving into a local-first, contract-driven autonomous developer system.

To find out why read the Manifesto!

Why This Exists

Frontier models are expensive, rate-limited, and increasingly censored. If you want to build serious systems, you should not have to rent your agency by the token, or wait for policy filters to decide what is “allowed.” Judais-Lobi is built so you can run your own stack, control your costs, and decide your own boundaries.

Who It’s For

• Builders who want lower inference cost and predictable behavior.
• People who dislike censorship and want model choice instead of vendor lock-in.
• Engineers who care about deterministic runs and auditable decisions.
• Anyone who wants an extensible workflow engine rather than a chat toy.

Quickstart

1. Install: pip install judais-lobi — or, from a checkout and with everything a mission needs, pip install -e '.[mission]'.
2. Set an API key (OpenAI is the default today): export OPENAI_API_KEY=sk-...
3. Run a task: lobi "summarize this repo"
4. Use tools explicitly. Tools are deny-by-default: the safe profile can read the filesystem and git but not run a shell, so running a command needs the dev profile — lobi --profile dev --shell "ls -la" (--profile safe|dev|research|ops|god, or JUDAIS_LOBI_PROFILE; without it, lobi --shell refuses and names shell.exec and the profile that grants it). Tool subprocesses run under bwrap wherever bubblewrap is installed; --unsandboxed opts out.
5. Expect a .judais-lobi/ directory in the working directory. audit/ holds the append-only record of every tool dispatch and appears from the first turn; a mission adds runs/ (the durable transcript, one directory per run) and approvals/ (a gate's durable record). JUDAIS_LOBI_AUDIT, JUDAIS_LOBI_RUNS and JUDAIS_LOBI_APPROVALS each move their directory (a path) or silence it (none/off), and a mission says on its opening frame which happened.

Three commands are installed, one per agent. They take the same flags; only the personality differs.

command	agent
lobi	🧝 the mischievous one — a general assistant
judais	🧠 the sharp one — a general assistant
tai	the mission-agent personality — governed tools over MCP, cites every claim, never sees source. Its personality file belongs to the deployment that operates it; tai finds that file or refuses, naming what it consulted

python main.py [lobi|judais|tai] <message> [flags] reaches the same three without installing anything, and python main.py --help lists them.

Local inference

--provider local talks to any OpenAI-compatible endpoint — vllm serve, llama.cpp's server, LM Studio, Ollama's /v1 shim:

export LOCAL_API_BASE=http://127.0.0.1:8000/v1   # note the /v1
export LOCAL_MODEL=gpt-oss-20b                   # optional; else GET /models decides
lobi --provider local "summarize this repo"

The model name here belongs to the endpoint, so it is asked for in that order — --model, then a personality's default_model only when that personality named this provider, then LOCAL_MODEL, then GET /models — because a model name is a name in one provider's catalogue, and a persona written for a hosted provider names a model your local server has never heard of.

--provider anthropic runs the mission on Anthropic's Messages API through the official SDK. It needs ANTHROPIC_API_KEY and pip install 'judais-lobi[anthropic]'. The default model is claude-opus-5 (override with --model); streaming and native tool calls are supported, JSON mode is not — the Messages API has no response_format, so a native-protocol run (--protocol native) is how you constrain the shape. openai and mistral fall back to each other when a key is missing; anthropic and local do not: naming either is an instruction, and a missing ANTHROPIC_API_KEY stops the run by name rather than sending the prompt to a different provider.

capabilities are probed from GET {base}/models, so the context window is the served model's real max_model_len and not a guess. Unlike the other two providers, local is never silently fallen back away from when a key is missing: asking for the endpoint on this host and being answered by OpenAI is the opposite of what was asked.

Mission mode — the model chooses the tool

Everywhere else you choose the tool with a flag. That cannot work against a server whose tools are discovered at runtime, so --mission puts the catalogue in front of the model instead:

pip install 'judais-lobi[mission]'
lobi --mission --mcp-stdio 'python -m some_mcp_server' "what governed datasets exist?"
lobi --mission --mcp-url https://host/mcp   "..."   # bearer token in MCP_TOKEN

[mission], not [mcp]. The narrower extra installs a runnable mission and a silently ungoverned one: --skill reads YAML frontmatter, so with no pyyaml the manifest never loads, the closed tool set is never applied and the grounding check never runs — while the transcript looks exactly like a governed one. Both halves, or neither.

Each tool a server advertises is registered into the existing ToolBus as a ToolDescriptor whose executor dispatches tools/call, namespaced mcp.<name> so a server cannot shadow a local tool. Capability gating, the sandbox and the audit log apply to it exactly as to fs or git. The tool's JSON Schema is carried whole on the descriptor, so the catalogue the model reads says type (string: dataset|model|service) and not just type — types, required and enums are what decide whether a first call to a faceted search works.

The mission-mode surface

These flags are a contract, not a convenience: core/runtime/contract.py publishes them as CLI_FLAGS, a test asserts the parser takes every one, and a program that spawns this harness may rely on them. The table below is in CLI_FLAGS order. The rest of --help is a person's surface and may move.

flag	env	what it does
--mission	—	run as a mission rather than a chat turn
--mcp-url	MCP_URL	a tool plane, over streamable HTTP. Repeatable, and combinable with --mcp-stdio — see several servers at once
--mcp-stdio	MCP_STDIO	a tool plane to spawn on this host, as a command line. Repeatable. The first server's tools are namespaced mcp., the next mcp2., or write name=<command>
--mcp-token	MCP_TOKEN	bearer token for --mcp-url, paired with the URL in the same position — a token is one server's credential and is never reused for another. Prefer the env var — an argument is visible in ps
--mission-steps	—	an operator's hard ceiling on model turns, counting parse-error turns too. Unset means no ceiling (DEFAULT_MISSION_STEPS = 0, core/cli.py) — this harness imposes no step budget of its own; see the supervisor for what catches a run that is going in circles. Under --resume it is read as that many further steps; unset, a resumed run keeps whatever the run was started with, ceiling or none
--mission-seconds	MISSION_SECONDS	wall-clock cap on the whole run, in seconds. Unset means unbounded — steps bound the work, seconds bound the waiting, and a default nobody chose would kill a slow local model mid-answer. Checked between steps and before each model call; one clock for the whole of a --swarm turn. A call already in flight is not interrupted, so the real bound is this plus one round trip
--provider	—	openai, mistral, anthropic or local. anthropic needs pip install 'judais-lobi[anthropic]' and ANTHROPIC_API_KEY; its default model is claude-opus-5 (core/runtime/provider_config.py), overridden with --model
--model	—	which model on it
--profile	JUDAIS_LOBI_PROFILE	the capability profile: deny-by-default safe, then dev, research, ops, god. research is dev plus http.read and nothing else — read the web, write nothing, run nothing beyond dev — and it exists because reading three public pages used to cost ops, which also grants git push and pip install. A refusal names the scope and the lowest profile that grants it. Arrives back as mission_started.profile
--grant	—	pre-authorise capability scopes for this run, beyond whatever --profile grants. Comma-separated and repeatable: --grant http.read lets a mission under safe fetch a page without opting the whole run up to ops, which would also hand it git.push, pip.install and fs.delete. It widens scopes only — the sandbox, the gated set and the skill's closed set are unchanged, and a campaign step narrowed past the grant is still refused, in a sentence that names the grant rather than the profile. A scope no profile names is refused at the door by name; * is refused, because that is --profile god. Arrives back as mission_started.granted
--campaign	—	run a campaign: a plan of missions, drafted from the message, approved by a person, then dispatched a step at a time. Implies --mission. See Campaigns
--campaign-plan	—	the same, from a CampaignPlan JSON or YAML file. Implies --mission; with no message the plan's own objective is the mission's
--unsandboxed	JUDAIS_LOBI_SANDBOX=none	run tool subprocesses with no isolation. Without it, bwrap wherever bubblewrap exists; JUDAIS_LOBI_SANDBOX=bwrap forces it and refuses on a host without it. Arrives back as mission_started.sandbox
--skill	MISSION_SKILL	a SKILL.md manifest, or a directory holding one
--swarm	MISSION_SWARM	stage the mission when it needs staging
--events	MISSION_EVENTS	where the NDJSON account goes out: -, fd:N, or a path
--history	MISSION_HISTORY	a JSON file of prior conversation turns
--gate-tool	—	a tool to offer and refuse to call. Repeatable. Resolved by same_tool, so a bare name matches the namespaced one; a name that matches nothing, or two things, is a refusal at the door
--approval	MISSION_APPROVAL	an approval id somebody already decided. Lifts that one tool out of the gated set, for this run only, and is spent when the tool is dispatched
--resume	MISSION_RESUME	carry on a recorded mission by its run id. The objective comes off the record, so the message may be omitted
--temperature	—	sampling. Unset sends nothing and the server's own default applies
--top-p	—	nucleus sampling. Unset sends nothing
--seed	—	a seed where the server honours one. Not a determinism guarantee
--protocol	MISSION_PROTOCOL	json (default) or native. native declares the mission's tools as functions, declares a mission_answer(text) beside them and asks the server for tool_choice=required — so an unparseable reply and a tool name nobody offers stop being possible instead of being caught a turn later, and one turn may call several tools. Refused at the door on a backend that does not declare supports_tool_calls and supports_tool_choice_required. Off by default on purpose: it is measured before it is anybody's default
--no-stream	MISSION_STREAM=off	ask the model for the whole reply at once. Streaming is on by default wherever the backend declares supports_streaming: the answer's own fragments go out as answer_delta records while the model is still writing them, and the console prints them as they land. The answer record that follows is still the whole of it, and turning this off changes nothing else
--control	MISSION_CONTROL	where NDJSON commands come in from: fd:N, a FIFO, a path, or -. Four words — inject, cancel, cancel_step, gate_decision — and the only lever into a running turn besides SIGTERM. A bad line is dropped with a sentence on stderr, never fatal
--gate-wait	MISSION_GATE_WAIT	seconds a run standing at a gate waits in-turn for a gate_decision on --control before ending the turn at awaiting_approval. 0 = never wait (the 0.11 behaviour); default 300; capped by --mission-seconds. Set it low for an unattended caller
--replay	MISSION_REPLAY	run a recorded mission again from its recording: the replies come out of that run's model.jsonl in order and the tool results out of tools.jsonl, so no server is dialled and no model is asked. The objective comes off the record, so the message may be omitted. The replayed run is a new run directory carrying replay_of and any drift, and grounding runs fresh over the recorded answer — which is how a grounding change is scored on yesterday's runs. --replay-tools live dispatches against the real plane instead — a person's flag, not part of CLI_FLAGS

The rest of the published environment: MCP_CLIENT_NAME is what this client calls itself in the MCP initialize handshake — set it to the agent's name, or a server that governs by principal records every call as an anonymous one, and anything scoring the agent from the audit trail measures it as having called nothing. MCP_RELIST_TIMEOUT_S (default 5) bounds the synchronous tools/list a mission asks for at every step boundary, so the catalogue the model is shown is the plane at that boundary rather than whatever the bridge's own thread had cached; a server that cannot answer inside it leaves the last set standing. ELF_PERSONALITY and TAI_PERSONALITY point at persona files; LOCAL_API_BASE and LOCAL_MODEL aim the local backend. JUDAIS_LOBI_AUDIT moves the audit file (a path) or silences it (none/off); either way mission_started.audit_ref says which. JUDAIS_LOBI_RUNS does the same for the durable transcript — a path moves the run directories, none/off keeps none at all, and mission_started.run_id is present exactly when there is one to name. JUDAIS_LOBI_APPROVALS does the same for the durable approval records — a path moves the directory, none/off keeps none, and then a gate stops a mission and leaves nothing anybody can decide against, which the console says out loud. MISSION_RESUME is the environment form of --resume and MISSION_REPLAY of --replay — the first continues an unfinished run against a live model, the second re-runs a finished one against its own recording, and they are not interchangeable. MISSION_PROTOCOL is the environment form of --protocol, MISSION_CONTROL of --control, MISSION_GATE_WAIT of --gate-wait, and MISSION_STREAM of --no-stream the other way round: off, 0, false, no or none turn the streamed answer off and anything else leaves it on.

Four more belong to the web tools rather than to the mission seam, so they are not in contract.ENV_VARS and nothing on the wire reports them; they are read at call time and every one of them is optional. RESEARCH_ALLOWED_HOSTS is a comma-separated allow-list of hosts a run may fetch — unset means no restriction, a leading dot covers subdomains, and a host outside it is refused by name before a socket is opened, with the refusal saying it is an operator's decision and not to retry. RESEARCH_MAX_PAGE_BYTES (default 4 MiB) is how much of a response body is read before the fetch is refused as too_large, and RESEARCH_TIMEOUT_S (default 20) is when it gives up. SEARCH_PROVIDER chooses which backend answers perform_web_search — duckduckgo (the keyless default, a scrape of an endpoint nobody promised), searxng (with SEARXNG_URL), or one a platform registered with core.tools.web_search.register_provider. A provider that cannot answer refuses by name, which is a different fact from an empty web.

No step budget — the supervisor

This harness does not decide how many turns a question is worth. Until 0.14 a mission was capped at eight model turns, and that number was doing two jobs: it caught an endless loop, which is a real job, and it decided that no question deserved a ninth turn, which is not a thing a framework can know. A mission that needed a fifth governed view spent the cap on the fourth.

So the counting is gone. --mission-steps survives only as an operator's optional ceiling — exactly what --mission-seconds already was — and max_steps: 0 on the stream says there is none. A run ends when it answers, when somebody cancels it, when a ceiling you set is reached (budget_exhausted, naming which), or when the supervisor judges it stuck.

The supervisor (core/runtime/supervisor.py) watches for repetition, never for quantity. Nothing in it counts tokens, output length or thinking time: a model that spends nine minutes on one honest turn trips nothing. What trips it, evaluated free at every step boundary:

signal	what it means
repeated_call	the same tool, the same arguments and the same result, three times within the last six calls — not necessarily consecutive, because a run polling for something that never arrives threads other reads between its attempts. A different result is progress: a paging loop is not a stall
rejected_replies	three replies in a row that were not decisions this loop could act on
no_new_evidence	four steps with no new tool call and no result the run had not already seen
oscillation	the run is going A B A B rather than forward from either
failed_gate	(--swarm only) a plan step just failed its gate

When one fires, the same model is asked in one plain call — no tools declared — what the pattern means, and answers with one word:

• progressing — a false alarm. Nothing happens, and that signal's threshold is raised so the same pattern does not buy a second review;
• nudge — stuck but helpable. The verdict carries a note, which is put in front of the model as a user turn at the next step boundary, through the same mechanism --control inject uses;
• stuck — the model is asked for its best answer with what it has, so the transcript still ends with an answer wherever one is possible, and mission_finished carries reason: "stuck" beside whatever outcome that answer earned. It is not budget_exhausted: nothing ran out;
• replan — --swarm only, from the review of a failed gate: the plan is redrawn around what already succeeded.

The step that follows a review carries review: {signal, verdict, note?, reviews_left} — and injected too, on a nudge. There are at most three reviews a run and the last one is not offered progressing: a run that keeps tripping signals and keeps being told it is fine is exactly the endless loop this exists to catch, so after the last review the next signal winds the run up with no further call. A review is a model call like any other — it is on the ledger, in the recording, and served back by --replay.

SwarmRunner uses the same object for the whole turn, so a plan that loops across its steps is a pattern something can see. A gate that says no is put to it rather than retried a fixed number of times: retries_per_step and the one-redraw counter are gone, and so is step_budget — a step takes the turns its work takes.

--protocol native — the model calls a function instead of writing one

The default protocol asks for one JSON object per reply and parses it. That works, and it fails in a way that was measured: on the reference deployment's 10 August suite a mission spent two turns of eight on a malformed tool name and two more on invalid JSON — a quarter of the budget on protocol rather than on the question.

--protocol native removes the two mistakes rather than catching them. The request declares the mission's tools as OpenAI functions, declares a synthetic mission_answer(text) beside them (registered on nothing — it is how a model under tool_choice=required says it is finished), and asks for tool_choice=required with parallel_tool_calls=true. The decoder then cannot emit a name outside the namespace nor arguments that do not parse.

What it changes, exactly:

• one turn, several calls. parallel_tool_calls means a reply can ask for two tools; both are dispatched in the order given, each with its own tool_call/tool_result pair under the same index and a call ordinal. A step is still a model turn, and --mission-steps still counts model turns;
• mission_answer counts only when it is alone. Called alongside tool calls it is ignored, the tools run, and the model is asked again — an answer written before its own evidence arrived is exactly the answer that should not stand;
• a gated tool ends the turn on that call. The calls before it have run; the calls after it are not dispatched, and the reason says how many;
• a reply with no calls at all — some servers answer in prose despite required — is read as an answer when there is text and refused when there is not;
• mission_started carries protocol: "native". It carries nothing at all on a json run, so every stream recorded before this existed is unchanged.

Arguments are checked against each tool's own JSON Schema before dispatch, in both protocols (core/runtime/schema_check.py; jsonschema when the mission extra is installed, a required/type/enum floor when it is not). A violation is a reply_rejected naming the tool, the field and the rule, and the call is not made. Be clear about what that can and cannot catch: it catches a shape the tool declared — a missing required argument, a string where an integer was declared, a value outside an enum. It does not catch a well-typed argument meant for a different tool, which is the other half of the measured waste (uv pip install … handed to the tool that runs Python is a string where a string was declared). That one is fixed by tool descriptions and tool sets, not by a validator.

It is off by default, and that is the discipline: this and the grounding control were probed the same day, and a change switched on before the eval harness that scores it produces a delta nobody can attribute (ROADMAP.md §2.5).

--control — talking to a mission while it runs

--events is what a mission says. --control is what it can be told, and until it existed the only lever a platform had on a running turn was SIGTERM — which is to say, the only thing anybody could do to a mission in progress was end it. Three of the things an operator actually wants are not "stop".

mkfifo /tmp/mission.ctl
judais --mission 'survey the corpus' --mcp-url … --control /tmp/mission.ctl &
echo '{"control":"inject","text":"look at the second corpus, not the first"}' \
  > /tmp/mission.ctl

fd:N is what a platform uses: it keeps the write end of a pipe and the mission never has a path on disk to race anybody for. - reads stdin, for a person typing at a run. One JSON object per line, and the vocabulary is closed:

• {"control": "inject", "text": "…"} — a user instruction. It is appended as a user turn immediately before the next model call, which is the one moment it is a message in a conversation rather than an edit to a decision the model already made, and that step's step_started carries it back as injected: ["…"] so a pane can show that somebody spoke. Both protocols take a user turn, so this works under native unchanged;
• {"control": "cancel"} — the first SIGTERM by another road. The mission's cancellation is thrown from the channel's own reader thread, the loop winds up at its next check, keeps its transcript, and writes its own mission_finished — incomplete with reason: "cancelled". The process exits normally: a platform asked the mission to stop, not the process to die of a signal nobody sent;
• {"control": "cancel_step"} — abandon the rest of the current step, which is a much smaller ask than abandoning the run. Under native, where one turn may carry several calls, the calls that have not been dispatched are skipped; under json, the one proposed call is not dispatched if it has not gone out yet. Either way the model is told in as many words and asked again, so it decides what to do from what it has. A tool already running is left alone — the bus owns dispatch, and what a half-killed subprocess did to the world is not knowable from here. An ask that arrives too late is a no-op, and says so to the model rather than vanishing;
• {"control": "gate_decision", "approval_id": "ap_…", "approve": true, "decided_by": "dana", "note": ""} — answer a gate while the run is still standing at it. With a channel open, a gated call emits its gate_requested (with the approval_id) and then waits, bounded by min(what is left of --mission-seconds, 300s). A yes is written through the same ApprovalStore the --approval path reads — decided, then spent, by the name you sent — and the one call it authorised is dispatched in that same step, after which the mission carries on. A no is recorded as a refusal and the model is told. decided_by must name somebody: this framework has no identity layer and will not invent one, but an approval signed by nobody is not an approval, and the command is dropped.

Nothing here decides anything on the harness's behalf, and nothing times out into a yes: the wait running out ends the mission at awaiting_approval exactly as it always did, with the record left pending for --approval on a later turn. A malformed line, an unknown word, an inject with no text or a decision signed by nobody is dropped with one sentence on stderr and the run carries on — a control channel that could crash a mission would be a worse lever than no lever. A channel nobody writes to, or one whose writer goes away, is not an error.

On --swarm there is one channel for the turn, shared the way the wall clock and the cancellation are, and it reaches the sub-mission that is running. The swarm's own roles — the router, the planner, each gate, the synthesizer — ignore it: they are single questions asked and answered in one round trip, with no "between steps" to speak into.

Serving the built-in tools over MCP

Mission mode above is the client half of the protocol: somebody else's server is discovered and bridged onto the bus. python -m core.tools.serve is the mirror of it. It publishes this package's own tools — fs, git, repo_map, patch, verify, run_shell_command, run_python_code, the research tools — as MCP tools, so any MCP client can call them:

pip install 'judais-lobi[mission]'        # no new extra: the SDK is the same one
python -m core.tools.serve                        # stdio, profile safe
python -m core.tools.serve --profile dev          # stdio, code plane on
python -m core.tools.serve --http 127.0.0.1:8765 --token "$MCP_SERVE_TOKEN"
python -m core.tools.serve --list                 # what would be served

One owner, two transports. There are no tool definitions in the server: it publishes the descriptors that are already on the bus and dispatches every call back through ToolBus.dispatch. So the profile's capability check, the sandbox and the audit log all apply on the serving side — a client that reaches run_python_code gets bwrap because this process put it there, a scope the profile does not grant comes back as the same sentence the CLI prints (denied under profile 'safe': python.exec needs --profile dev), and the audit rows are written where the tool actually ran. A second registry that re-implemented the tools for the protocol would be a second opinion about what is allowed, and the day the two disagree is the day the governed path is the one nobody took.

flag	what it does
--profile	safe (default), dev, research, ops, god — the one gate on every client of this plane
--unsandboxed	no isolation. Without it, bwrap wherever bubblewrap exists, chosen by the same select_sandbox every other run uses
--audit PATH	where the rows go (off for none). Default: the JUDAIS_LOBI_AUDIT resolution
--elfenv PATH	the Python environment run_python_code runs in. Default: the one the server itself is running in, so a spawn costs nothing
--only a,b,c	serve a subset. A name the bus has not got is refused, listing what is there
--http HOST:PORT	streamable HTTP at /mcp instead of stdio. The host is not optional
--token (MCP_SERVE_TOKEN)	bearer token every HTTP request must carry. Ignored for stdio, which is a pipe to a child of the client
--list	print what would be served, with each tool's scopes, and exit

The published schema for a tool is its descriptor's input_schema where it has one, and otherwise its own callable's signature plus, for a multi-action tool, an action enum built from action_scopes — the mapping the bus checks scopes against, so the enum cannot offer an action the bus would refuse. A result arrives as the tool's text plus structuredContent carrying the whole ToolResult — exit code, stdout, stderr, granted scopes, evidence — which is the same object an in-process caller holds. tests/test_mcp_serve.py asserts that equality call for call, and runs the same mission twice, once on the built-in tools and once over --mcp-stdio 'python -m core.tools.serve', to show the streams differ only in the tool's namespace.

From any other MCP client, it is an ordinary stdio server:

{"mcpServers": {"judais-lobi": {
  "command": "python",
  "args": ["-m", "core.tools.serve", "--profile", "dev"]}}}

And from our own harness — a mission whose tool plane is a second copy of this package, governed by its own profile:

judais --mission --skill ./skills/repo_recon/SKILL.md \
       --mcp-stdio 'python -m core.tools.serve --profile dev' 'what changed?'

That is also the honest answer to the code-plane gate: a manifest naming run_python_code must declare sandbox: bwrap because the code runs on this host, while mcp.run_python_code is the server's to isolate — and when the server is ours, it isolates with bwrap for exactly the same reason.

Several servers at once

--mcp-stdio and --mcp-url are repeatable and may be mixed, which is how a platform composes its own governed plane with ours:

judais --mission --skill ./skills/composed/SKILL.md \
       --mcp-url   https://host/mcp \
       --mcp-token "$MCP_TOKEN" \
       --mcp-stdio 'python -m core.tools.serve --profile dev' "…"

Each server gets a namespace, and its tools are registered as <namespace>.<tool>:

• the first is mcp — unchanged, so a single-server deployment reads exactly the names it always read — then mcp2, mcp3, …;
• or name it on the flag: --mcp-stdio 'ours=python -m core.tools.serve'. (A command line that begins with an environment assignment would read its first word as a namespace; write env FOO=bar python ….)
• stdio servers come first, then HTTP, each in the order given;
• two servers may not share a namespace, and a --mcp-token pairs with the --mcp-url in the same position — one server's credential is never reused for another, so a count that does not match is refused rather than guessed.

A skill's closed set still names tools the way the server advertises them: same_tool matches fs against mcp.fs. A short name that matches two planes is a refusal telling the author to write the namespace, because which server a mission calls is not a coin flip. And the audit row names the bus name, so it says which plane ran the call.

Resuming a mission — --resume

A run that was killed — the machine went down, somebody stopped the process, the model server went away mid-step — left a numbered log behind. --resume reads it back and carries on:

judais --mission --resume run_20260815T131102-9f3a1c04 \
       --mcp-stdio 'python -m some_mcp_server'

The objective comes off the recorded run, so the message is omitted; passing one that is not the recorded objective is refused naming both, because a resume of the wrong run looks exactly like a run continuing. So is an id the store never minted, and so is a run that already finished — with one exception: a run that ended awaiting_approval is waiting on a person, not on this harness, and is resumable.

What comes back is the transcript's steps, the mission result store (its handles keep addressing the same results), and the model's message list rebuilt from the recorded tool_call/tool_result pairs and reply_rejected problems. The records go on being appended to the same run directory, and there is no second mission_started — a resumed run is the same mission. The first new step_started carries resumed: {from_seq, steps_replayed} instead.

max_steps counts the whole run: recorded steps included. Without --mission-steps the resumed stretch is held to the total the run started with — and a run started with no ceiling resumes with no ceiling — so killing and resuming can neither buy extra steps nor invent a bound nobody set; with it, the number is read as that many further steps, which is how a ceiling is put on a run that had none.

Two things do not come back, and the harness says so on the console rather than replaying in silence: the typed payload of a tool result (structuredContent was never on the wire, so mission_result(path=…) refuses a field path into a replayed result, though grounding still sees the replayed text) and the text of a rejected reply (reply_rejected carries the refusal — the reply is the thing that did not parse).

A staged (--swarm) mission checkpoints its plan and each step's outcome into the run's meta.json as it goes, and --resume picks it back up as a staged mission: which loop continues a recorded run is a property of the run and not of the resuming command line, so the plan is read off the checkpoint whether or not --swarm is typed again — and a run the ordinary loop recorded is continued by the ordinary loop even when it is. The router and the planner are not asked a second time; re-deciding them would put a different mission under this run's id. Steps checkpointed ok or failed are not re-run and their summaries go straight to the synthesizer; a step checkpointed awaiting_approval is run again, because nothing was called and the decision belonged to a person. The one staged run still refused is one whose meta.json holds no plan: the steps it had left are unknown, and the refusal says so and lists what was already done.

Every mission also reconciles orphans on the way in: a run in the store with no mission_finished whose metadata has not been touched for 60 seconds gets one appended (incomplete), so a follower's stream closes and a reader of since() is told the truth. The staleness is a guard, not an optimisation — a mission thinking for forty seconds has no mission_finished either, and closing its log out from under it would send the answer to nobody. The credential is deliberately not persisted: MCP_TOKEN is read from the environment of the resuming process, exactly as on a fresh run.

A skill manifest — --skill

The harness owns mechanisms; whoever operates the platform owns content. A SKILL.md is how the content arrives: YAML frontmatter plus a Markdown body, the format Claude-style skills already use.

lobi --mission --skill ./skills/catalogue_recon/SKILL.md \
     --mcp-stdio 'python -m some_mcp_server' "what governed datasets exist?"

Three things come out of it, and nothing else does:

• a closed tool set, allowed_tools, intersected with what the bridge actually discovered. A bare name matches a namespaced one, so a manifest says catalog_search_assets and gets mcp.catalog_search_assets. A named tool the server does not offer is a refusal listing every missing name — never a silent narrowing, because a mission missing the tool that answers its question answers it from the model's memory instead and the transcript looks ordinary. Suffix an entry with ? to mean "if the host offers it";
• prompt text — the operational frontmatter fields and the whole body, appended after the persona. Fields this loader has never heard of are rendered too: a manifest is content, and the harness is not the authority on which of a platform's operational fields matter;
• a grounding grammar, below. Optional; absent means nothing is enforced and nothing claims to have been.

One thing a manifest is refused for: sandbox: bwrap, required the moment allowed_tools names a tool that runs code the model composed — a shell, an interpreter, a pip install (the set is derived from the shell.exec, python.exec and pip.install scopes in core/tools/descriptors.py, not from a list of names, so a tool registered tomorrow is covered the day it arrives). A governed mission that can run arbitrary code on the host without isolation is the hazard here, and a hosted platform must not have to find it in a transcript. Both halves are checked, and the refusal names every problem at once:

allowed_tools: [governed_read, run_shell_command]
sandbox: bwrap          # or the resolve refuses, naming the tool and the fix

• the declaration is required of the manifest — including for an entry marked ?, because whether a file is governed must not depend on what a server happened to advertise this morning;
• the isolation is required of the run: declare sandbox: bwrap and get a bus that is not under bwrap (not installed, or opted out) and the mission refuses at the door rather than running unisolated.

sandbox: none is the other legal value — an explicit no isolation was asked for, accepted and inert for a manifest with no code-plane tool, and refused with its own reason for one that has them. Absent is not none; absent is silence, which is what this check exists to stop being an answer. The value is rendered into the prompt like any other operational field, because a model that has not been told it is inside bwrap reads the denied network as a broken tool.

First-party skills — the packs that ship

A manifest is content, and for two weeks the only manifest in this repository was the eval stub. Three mission packs now ship inside the wheel, so a pip install can run a governed mission with a real skill and no files of its own:

pack	what it does	closed set	profile
analyst	answers a question about local data files — CSV, JSON, JSON lines, logs — by computing it in sandboxed Python and reporting the figures the program printed	run_python_code, fs	dev
research	reads pages on the open web and answers from them with a URL beside every claim — one page, several at once, or the page the first one links to; a page it could not read is named with its status rather than filled in	fetch_page_content, perform_web_research, perform_web_search?, fs?	research
coding	changes a repository and proves it: maps it, edits across files in one patch, runs the repository's own tests, and reports the change with the counts the tests printed	repo_map, fs, patch, verify, git, run_shell_command?	dev, sandbox: bwrap

Run one by name — no path, no file of your own:

judais --mission --skill analyst --profile dev \
       "Something looks wrong in sales.csv — which orders do not belong?"

coding is the one that answers "this was not meant to just be a chat agent". It runs where you are — the working directory is the repository — and it is the one pack whose manifest requires isolation, because its closed set permits a shell on this host. It ships four small git-able repositories under fixtures/ and eight missions over them: a feature that needs two modules and a test, a bug whose fix is in two files, a rename with three call sites, a flag that is never only a flag, a red suite that has to go green with both counts reported, an agent that claims a pass it never measured, and an objective that asks it to edit /etc/hosts. Seventeen recorded streams under tests/fixtures/eval/coding/ are real runs of the real loop against real checkouts under real bwrap with a real pytest — only the model is scripted. Live on gemini-3.6-flash through the CLI with no server, the first three pass the scorer at first attempt.

cd /path/to/your/repository
judais --mission --skill coding --profile dev \
       "Add a --colour flag and cover it."

--skill still takes a path, and a path that exists wins: every command line that named a SKILL.md before packs existed does exactly what it did. Only when nothing is at that path is the argument read as a pack name, and neither is a refusal that lists the packs there are.

A pack is a directory of core/skills/library/<name>/ and it is more than a SKILL.md (ROADMAP.md §2.6b):

core/skills/library/analyst/
    SKILL.md        the manifest — the closed set, the policy, the grounding grammar
    missions.yaml   its OWN eval suite, in core/eval/suite.py's shape
    README.md       what it does, its closed set, the profile it needs, a command
    fixtures/       small committed data the missions run against
    templates/      task templates: a `workflow:` naming the roles (a documented
                    placeholder until the campaign lane runs one)

missions.yaml is the part that makes "tested" mean something. Every pack ships its own missions, one per capability, with the machine checks core/eval scores off the stream — so a skill's claim to do something is a number and not a paragraph:

from core.eval.score import score_suite
import core.skills

suite = core.skills.load("analyst").suite()      # loaded, and checked
print(score_suite({"the_outliers_in_the_sales_file": "/tmp/eval/outliers"},
                  suite, "train").to_markdown())

research needs nothing configured. No MCP server (its closed set is built-in tools), no API key, and no search engine: the base path is here is a URL, read it, and a search provider only changes where the first URL comes from — which is why perform_web_search? is optional in the closed set and refuses by name when no provider can answer, rather than reporting an empty web. fetch_page_content returns a typed page (url, final_url, status, title, fetched_at, sections[] in document order, links[] absolute) that the result store keeps whole, so a 138 kB register arrives bounded in the transcript and is read a section at a time through mission_result instead of refetched. The pack's twelve missions are graded against a fixture archive it ships (fixtures/, served on localhost), and --profile research — dev plus http.read — is what makes any of it reachable without ops.

python -m core.eval --suite <a pack's missions.yaml> refuses it for one reason today, and it is core/eval/'s to fix rather than a pack's: the gradeability check requires every one of its eleven flags to be captured by some mission, which is right for the suite grading the whole harness and wrong for a pack grading one capability. Pack.suite() runs the same check with the coverage scoped to the flags the pack's own missions capture (core.skills.library.check_pack_suite), and that adapter deletes itself the day a suite file can declare which flags it claims.

From Python the packs are data:

import core.skills
core.skills.packs()               # ('analyst', 'coding', 'research')
pack = core.skills.load("analyst")
pack.manifest.allowed_tools       # ('run_python_code', 'fs')
pack.suite()                      # its missions, checked
pack.stage_fixtures("/tmp/data")  # a COPY — a sandboxed run's cwd is writable

Each pack's own README.md is the detail: what it refuses, why it needs the profile it needs, and what its fixtures hold. analyst declares sandbox: bwrap and will not start without bubblewrap, which is the rule above applied to itself rather than an exception to it.

Bounded results, and a store to read the rest from

A tool result is capped at 32 KB before it enters the transcript — head and tail with an explicit marker. The cap and the cut have one owner, core/bounding.py (MAX_RESULT_BYTES, bound_result); the kernel's max_tool_output_bytes_in_context and the chat path's are configuration knobs that default to it, and every path that bounds a tool result calls the same function. Uncapped, one large governed view evicts the earlier steps the model needs to know what its numbers mean, or exceeds max_model_len outright, and neither leaves a trace in the answer.

The whole result — including the structuredContent that as_tuple() drops whenever there is text — stays in a per-mission store, and the marker names the handle:

mission_result(handle="r1", path="result.actors[0].score")

A few dozen bytes instead of two hundred kilobytes. The store reaches nothing: every byte in it already arrived through a gated, audited dispatch of a tool the closed set allowed. It is registered on the bus for the length of one run and withdrawn after it.

Grounding — every identifier has to have come from a tool

core/runtime/grounding.py is the mission-tier analogue of CompositeJudge. Every identifier-shaped token in the answer must appear in a tool output of this run. An unsupported claim gets one repair turn naming the exact tokens; a second failure keeps the answer and appends an explicit caveat, because deleting it would hide a finding and passing it silently would launder one.

The grammar is not in the code. It comes from the manifest:

grounding:
  identifier_pattern: '\b(?:asset|labels|run)\.[0-9a-f]{4,}\b'
  ignore: [asset.0000]
  max_repairs: 1
  must_cite: {identifiers: 1}     # optional; see below

No block, no validator, and the transcript's grounding stays None rather than claiming a clean check. A check that could not run reports no opinion and never a pass — same reason LLMReviewTier returns UNKNOWN instead of 0.5, and a larger one here: a fabricated "grounded" is a governance claim.

Three states, not two. A check reports unconfigured, nothing_considered, supported or unsupported. The third exists because the second was being reported as a pass: on 10 August 2026, the first run with these blocks switched on, six of the first ten missions reported grounded: identifiers — 0/0 supported by a tool result in this run. The control was satisfied by silence. report.grounded now means nothing unsupported; report.verified means and something was actually checked, and the CLI prints NOTHING CHECKED for the gap between them.

A figure is credited only where something measured it. Three ways one used to arrive without that, all closed in NumericGroundingCheck and all mechanical, because a rule that lives in skill prose holds for exactly as long as the model cooperates:

• the echo — told a figure is unsupported, a model can run print('30,000') and re-submit, and its stdout is a tool result like any other. So a code-plane call whose output holds no figure it was not already given grounds nothing: it printed back what it was told. A call that produced even one figure its arguments did not hold computed something, and its whole output grounds normally — a script whose slice bound happens to equal a computed result is not a fabrication, and flagging it would teach the reader to skip the report. Which tools run model-written code is read off the descriptors (python.exec/shell.exec), never off a list of names;
• the clock — a result stamped 2026-08-18T01:52:07+00:00 donated 52 and 07 to the evidence set, and an invented "52 hours" came back grounded. Timestamp-shaped spans are masked on both sides, so an answer that quotes the time it read is not claiming a quantity either;
• what the model sent — a failed call now contributes its typed error payload and its arguments, so "I could not read that page — it answered 404" grounds the page and the status. The arguments are marked as sent and the figure check skips them, or an answer would support its own arithmetic by typing it into a call that fails.

A claim table, where the figures matter. claim_table: true turns on a third check. The skill's output_format asks for every figure a second time beside the prose, as a path into what a tool returned:

```claims
[{"value": 0.7446, "path": "gate.confidence"},
 {"value": 338.0,  "path": "network.nodes[0].scores.out_weight"}]
```

Verification is then arithmetic rather than search: results.walk_path — the same walker mission_result answers with — reads that path out of the payloads the mission received and compares. A path that does not resolve, or resolves to something else, is unsupported; an unreadable table is a finding rather than a skip. The prose checks do not read the block, because a table full of gate.confidence would otherwise be reported as invented identifiers.

Whether silence is acceptable is the skill's call, not the harness's. must_cite is a minimum per check — true for every configured check, a list of names, or {claims: 3} for a schema minimum. A skill whose answer may legitimately be "the catalogue holds none of that" declares no minimum; a skill drafting a finding declares one, and an answer with nothing in it fails. A must_cite naming a check the same block does not configure is refused at load: a requirement that never binds is the original hole wearing the name of the fix for it.

Three more tiers, all off by default. They cost model calls, or they need the platform to say something only the platform knows, so a manifest asks for each one by name.

grounding:
  claim_table: true
  reading: true                  # needs claim_table
  critic: true
  planes:
    sdk: {tools: [run_code], claims: ['I used the SDK']}

reading: true runs the field-misreading tier. For every figure the claim table names, a reader is asked — cold, before it is shown the sentence — what that field holds, and then whether the sentence says the same thing. It is the one class no arithmetic reaches: total_s: 80.847 reported as "the overall influence score" is a real value at a real path, and a membership check is right to pass it. It needs claim_table: true, because the table is where the path for each figure comes from, and it spends two model calls per claim, which is why it is off.

planes: fails an answer that claims to have used a tool family nothing on it was called from this run. "I used the SDK to recompute the figure" contains no identifier, no figure and no claim-table entry, so every mechanical tier reports nothing considered and the answer comes back grounded while describing work that did not happen. Which tools are a plane, and what an answer says when it claims one, are the platform's to declare — a framework that hard-coded either would be naming somebody else's tool families for them. What was actually dispatched has one owner, MissionResultStore.called_tools.

critic: true asks a second model whether an answer the mechanical checks could not ground holds up. Its verdict is a critic row in grounding.checks marked advisory: true, beside grounded and never inside it: grounded is a mechanical fact anyone holding the transcript can recompute, and a critic's verdict is a model's opinion that varies with sampling and with which provider had a key today. Local first — LOCAL_API_BASE, the same weights the mission leased, given an adversarial prompt — and a hosted provider only where the critic config declares one and a key resolves, because posting a governed draft to another company is a handling decision a deployment makes explicitly. With neither, the row says skipped and names what was missing: "we asked and nobody answered" and "we never asked" are different facts about a run.

None of the three is on by default, and EVAL.md is why: nothing here becomes a default until the harness scores it on a held-out set.

Gates — a tool offered, and not called

--gate-tool NAME (repeatable) names a tool this deployment offers and gates. It is shown in the catalogue, marked. If the model names it, the call is not made: the mission emits gate_requested carrying the proposed arguments verbatim — what a person approves has to be the bytes that would run — and ends at outcome awaiting_approval.

No flag on a mission run answers a gate. A harness that could approve its own proposal has a gate that is a formality, and there is no code path in MissionRunner or SwarmRunner that can move a record to approved — a test greps for it.

What there is is the other half: the request is written down, and the answer arrives from outside the run.

# the mission stops, and says what it stopped on
judais --mission --gate-tool mcp.cancel_job "wind down job j-91"
  ⏸️  Waiting on a person: Tai proposed mcp.cancel_job({'job': 'j-91'}) …
     approval ap_4b1f7c02e9d38a55 — decide it with: …

# somebody who is not this process decides
judais --mission --approve ap_4b1f7c02e9d38a55 --decided-by dana --note "queue is drained"

# and the work resumes, once
judais --mission --approval ap_4b1f7c02e9d38a55 --gate-tool mcp.cancel_job "wind down job j-91"

Each request is a JSON file under .judais-lobi/approvals/ (moved or silenced by JUDAIS_LOBI_APPROVALS) holding the tool, the arguments verbatim, the objective and the run that asked; its id rides gate_requested.approval_id. --approve/--refuse build no agent, ask no model and emit no events — they call ApprovalStore.decide and exit — and they refuse a decision that names nobody. --decided-by is free text: this framework has no principal system and will not invent one, so who counts as a person is the platform's question; a platform that knows the answer calls core.runtime.approvals.ApprovalStore directly instead of these flags.

--approval <id> then widens the closed set by exactly one tool, for exactly one run, after exactly one person said so, and the approval is spent the moment that tool is dispatched — a run that never calls it leaves the decision unspent rather than burning it on nothing. A pending, refused, spent or abandoned record is refused at the door, naming the state: nothing defaults or expires into a yes, and a spent approval is not a second one. A consumer reading the stream sees the widening as that tool's absence from mission_started.gated; there is no separate field announcing it.

ApprovalStore.reconcile(live_run_ids) marks pending requests whose run is gone as abandoned, which is a refusal. Since 0.14.0 the CLI calls it on the way into every mission, right after orphans are reconciled: "live" is every pending record's run id minus the runs the staleness rule just closed, so only a genuinely orphaned run loses its approval, and a finished awaiting_approval run keeps its pending record and stays --approve-able. It is announced (🧾 reconciled: N approval(s) abandoned …) and nothing runs when nothing was orphaned. A liveness check that guessed would abandon live requests, which is why the list is derived from the same rule the orphan sweep used.

Gate names resolve the way allowed_tools does — through same_tool, so a manifest-style bare name matches the namespaced one the bus dispatches. A --gate-tool that matches nothing, or that matches two offered tools, is a refusal at the door listing what was offered; it is never dropped quietly, because an operator who asked for a gate and got a mission without one has been told the opposite of what happened.

--swarm — staged decomposition, when it is needed

A 20B model at 59 tok/s drowns in one long transcript. By step six of a single mission the catalogue lookups that told it what its numbers mean have been pushed out of attention by three governed views, and the answer is written from the part it can still see. The fix is not a longer prompt; it is shorter ones.

--swarm (or MISSION_SWARM) puts five small roles over the same backend and the same tool bus: triage, plan, execute, gate, synthesize. Triage is one cheap call and is biased to running the ordinary loop — a swarm that makes "what's trending" slower is a regression, so every failure of the router falls back to DIRECT. Each executed step is its own small mission; earlier steps reach the next one as the executor's own stated result, never as raw output. The closed tool set, the gating, the audit and the events vocabulary are all exactly the direct path's, so a watcher sees one mission with more steps.

Steps that need nothing from each other can run at the same time. A library caller sets SwarmRunner(..., parallel=N); the default is 1 — serial, in the plan's own order, which is what a staged turn has always been — and there is no flag, because the evidence for changing how every turn runs is a suite scored both ways rather than a switch. When steps do run together, each record carries the OPTIONAL branch field naming the plan step that emitted it ("s1", "s2", or "direct" for the route a turn takes when its router says the question needs no plan), and records the turn itself emitted — its opening, its answer, its grounding, its closing — carry none. index is still allocated by the turn's one observer, in the order records go out, so a consumer that has never heard of branch reads exactly the single ordered stream it always read; one that has can group a step's records together. See CONTRACT.md.

One window, and it is the model's. Every stage of a staged turn — the router, the planner, each sub-mission, each gate and the synthesizer — is bounded by the same MissionWindow the direct path uses, resolved from the backend's real max_context_tokens. Nothing inside the swarm is bounded by a character count standing in for it: a step may spend whatever an operator's --mission-steps ceiling has left — and everything, when there is no ceiling — rather than a fixed slice of it, and the synthesizer is given the whole of every settled step's tool output — so the final answer can quote an actor list a step read and summarised in one sentence. When the window cannot hold all of it, whole results are dropped oldest-first, tool output before conversation, and the prompt says how many were left out. SwarmRunner keeps summary_chars and max_plan_steps as knobs for a caller who wants a tighter cut than the window gives; step_budget and retries_per_step are gone, because a slice of a budget and a retry counter were both guesses about how much work a step is worth — the supervisor decides that now, and a failed gate is a question put to it rather than a countdown.

Each planned step is tagged with a rung — tool, code, or code+sdk. The last one is offered only when the skill manifest declares sdk_import, because "import the platform SDK" with no SDK named is an invitation to invent a module and a 20B accepts it.

Campaigns — a plan of missions

--swarm is one mission a model decided to break up. A campaign is several missions a person decided to run, in an order they wrote down, with files handed from one to the next:

lobi --campaign-plan ./migration.json --skill analyst
lobi --campaign "measure last quarter and write it up" --skill analyst

A CampaignPlan is a DAG of steps. Each step names a task template (a templates/*.yaml out of a mission pack — see the packs that ship), the capability scopes it needs, the artifacts it takes from earlier steps and the artifacts it exports. --campaign-plan runs one off disk; --campaign asks the model to draft one from the message. Either way the plan is approved before any of it runs.

What happens then, and every part of it is something the mission path already had:

• Approval is the durable one. An unapproved plan ends the run at awaiting_approval with the whole plan on a gate_requested record — the same mechanism, the same store and the same --approve <id> / --approval <id> round trip a gated tool call uses, because it is the same kind of stop. A platform can answer it; so can somebody tomorrow. Where a person is at a terminal with $EDITOR set, the plan is opened for editing instead and what they save is recorded as their decision. --auto-approve declines to ask at all.
• A step is a child run. Run.child — its own branch, its own result store, its own persona from its pack's SKILL.md — sharing the parent's plane, clock, supervisor, ledger and durable log. One mission, several children, exactly as a staged turn is.
• Least privilege per step. Each step is narrowed to step scopes ∩ template scopes before it starts, so a step that never asked for fs.write cannot write even though the run's profile allows it. The narrow is per step and not per run, which is what --grant is the other half of: an operator widens the run, the plan narrows each step.
• Artifacts, declared. A step's inputs are copied into its handoff_in/ before it starts and its exports are collected from its handoff_out/ after; the step is told both paths. A step that promised a file and did not write it failed, whatever it said about itself — a model saying "I have written the report" is not evidence that a report exists.
• Frozen once approved. A failed step ends the dispatch. There is no redraw: a staged turn's plan is a planner's guess and may be redrawn, and a campaign's plan is what somebody said yes to.
• Resumable. The plan and each step's outcome are checkpointed, so --resume <run-id> continues at the step after the last one that finished — as a campaign, with its artifacts and its scopes, because the approved plan is on the run's record.

On the wire it is a staged turn plus one field: every record carries branch (the step's id), the plan rides the first step_started as plan with rung naming each step's template, and each step's own step_started carries artifacts — {"in": [...], "out": [...]}. Nothing required was added and SCHEMA_VERSION did not move.

core/runtime/campaign.py is a subclass of SwarmRunner, and that is the design rather than an economy: a parent over children, waves, a per-step gate, a checkpoint and a synthesis is one loop, and the version of it that used to live on the coding-kernel path had drifted into having none of the run store, the approvals, the supervisor, the stream or the resume. Five methods differ, one per real difference between a plan a person wrote and a plan a model drew.

The mission stream — --events

MissionRunner.run returns a transcript when the mission is over. That is the right shape for a terminal and the wrong shape for anything that has to show a mission to somebody while it runs — a mission on a local 20B is minutes long, and a caller holding only run() has nothing to render for all of them.

So the loop takes an observer, and --events writes what it sees as NDJSON: one JSON object per line, flushed as it happens, UTF-8 and unescaped.

--events -        stdout, for a person with jq
--events fd:N     an inherited descriptor — what a harness uses
--events PATH     a file, opened for append

stdout is prose for a person and must not be parsed. The event sink is the only machine channel, which is why a consumer uses fd: or a path and never -: the console rendering and the record stream never share bytes.

The last turn of a mission is the one with nothing to show: the tools have all run and the model is writing prose. So that model call streams wherever the backend can, and the answer goes out in pieces as it is written. That is the tenth record type, and the rules for it are below the table.

The vocabulary is eleven record types, in the order a run tends to produce them (contract.EVENTS):

event	when
mission_started	before the first model call and before the tool plane is touched. Carries the objective, the catalogue, the gated names, max_steps, and the run's posture — sandbox, profile, audit_ref, run_id, protocol
step_started	a model turn is about to be asked for. Carries the staged plan, a compacted record, resumed, injected, or review — the supervisor's verdict on a repeating pattern — when there is one
reply_rejected	the model's reply was not a decision this loop could act on. A recorded step, never a crash
tool_call	before the call is dispatched, so a watcher shows what is about to happen
tool_result	the bus answered. output is the whole result; the bound is what the model was shown
gate_requested	a gated tool was named and not called, arguments verbatim
answer_delta	a fragment of the answer while the model is still writing it
answer	the finished answer and its outcome
grounding	the validator's report, twice when a repair happened
mission_finished	terminal, out of a finally, with the outcome, the counts, the run's usage and elapsed_s
model_state	why nothing is happening: the model is cold, queued, loading, failed or absent — and loaded when the wait is over. A healthy call emits none of these

answer_delta is the one to read the rules for before rendering it. It carries index (the step whose model call is producing it), part (a 0-based ordinal that restarts at 0 for every model call) and text; concatenating text over part gives the answer as streamed. It is provisional and replaced, not completed — the answer record always follows, carries the whole text, and is the authority, because the fragments are decoded out of a half-written reply while the answer has been through the grounding path that may append a caveat. Zero of them is normal: a backend that does not stream, --no-stream, a turn that called a tool, a library caller whose chat_fn returns a string. Each fragment is scrubbed on its own, so a credential split across two of them is not recognisable in either half — display the fragments, keep the answer.

model_state is the one to read the rules for before rendering a spinner. It explains a wait rather than narrating a call: a healthy call emits nothing — step_started and answer already say the request went out and came back — so this record appears only when something is keeping the model from answering, and loaded appears only to close one of those. The two words a deployment could never tell apart are separated by construction: loading is only ever the server's own answer (a 503, with its body in detail and its Retry-After in retry_after_s), and queued is only ever said after the harness asked GET /models and was told the model is there — a silence over nothing loaded is cold, and a silence over nothing listening is absent. Records are transitions and are de-duplicated: hold the last one as the model's current state, clear it on loaded. since_s is how long the run had been waiting when it was reported.

Those eleven, their required and optional fields, the five outcome words, the exit contract and the rule for what counts as a breaking change are CONTRACT.md, whose authority is core/runtime/contract.py. A consumer pins it:

from core.runtime import contract
assert contract.SCHEMA_VERSION == 1     # fails at import, which is cheap
problems = contract.conforms(record)    # [] when the record is fine

conforms is pure and standard-library only and imports nothing this repo owns, so a consumer that cannot import an agent framework can vendor that one file and have the whole seam.

Following a run over HTTP — the [server] extra

--events is a stream a parent process reads. That is the right seam when the platform owns the process and the wrong one when it does not: a browser cannot spawn anything, a second pane cannot read a descriptor the first pane owns, and a run that finished an hour ago has no process left to read from at all. All three are already answered on disk — the run store is a numbered, append-only log per run — so the extra is a read-only HTTP face on it:

pip install 'judais-lobi[server]'
python -m core.server --runs .judais-lobi/runs --port 8787

--runs goes through the same resolver the mission CLI writes through, so with JUDAIS_LOBI_RUNS already set you can leave it out. Five endpoints, all of them reads:

GET /healthz                      liveness, and how full the stream cap is
GET /runs?limit=&offset=          run metadata, newest first, a bounded page
GET /runs/{run_id}                one run's metadata
GET /runs/{run_id}/events?since=  the records, as server-sent events
GET /runs/{run_id}/agui?since=    the same records, through the AG-UI translator

Each frame is event: <the record's own event> / id: <the store's seq> / data: <the record>. The records are the records — the same ten event names, the same fields, already scrubbed at the emitter and neither scrubbed nor widened again — so a consumer that reads the NDJSON stream reads this without changing anything but where the bytes come from. since= replays from a sequence number and then follows until mission_finished; a browser's EventSource sends Last-Event-ID by itself after a reconnect and that works too. A cursor past the end of the log is read as the end of it rather than obeyed — the store hands back what is seq > the cursor, so an impossible one would otherwise leave the follower deaf for the next hundred records — and a reconnect at the end of a run that has already finished closes at once instead of waiting for a second ending. The /agui variant is core/runtime/agui.py applied to those same records — one translator, not a second — with the id: on the last frame a record produced, because Last-Event-ID has to mean "I have all of that seq" and not "I have some of it".

Three rules are the reason this is a module and not fifteen lines, and each is a constant in core/server/sse.py with the failure it prevents written on it:

• the stream cap sits below the connection ceiling. MAX_STREAMS (64, --max-streams) is the number of streams held open at once. An event stream is a connection held for the length of a mission, so set it below your reverse proxy's connection limit and uvicorn's own — otherwise the request that exhausts the ceiling is refused by the proxy, every client sees a generic 502, and this process's logs say nothing. Refused here, the 65th follower gets a 503 and a Retry-After;
• the heartbeat fits inside the socket write timeout. A mission that is thinking emits nothing for minutes and an idle socket is what a proxy cuts. So an idle stream gets a : heartbeat comment line every HEARTBEAT_S (15 s, --heartbeat) — set it below your proxy's read timeout, commonly 60 s;
• nothing is refused after the first byte. Unknown run, cap reached, unreadable since — every check that can say no happens before the response starts. Once bytes are moving the status is 200 forever, so a store failure mid-follow becomes a final event: error frame and a clean close rather than a 500 a client which has already parsed a 200 cannot see.

It is read-only: there is no HTTP door into a running mission. A run does not record its --control spec, the commonest spec (fd:N) has no path a second process could open, and a second writer to a regular-file spec writes to a reader thread that has already reached end-of-file — the command would be dropped in silence. Steer a run from whatever started it. There is no authentication either, which is why the default host is loopback; put it behind something that terminates TLS and knows who is asking.

Both seams are supported and neither replaces the other: a platform that owns the process keeps the subprocess and the NDJSON, and this is the one for a platform that would rather subscribe than spawn.

Evaluating it — core/eval and --replay

A behavioural change that nobody scored is a change nobody can defend, which is why --protocol native and the three grounding tiers above all ship off. The harness that scores them is in the tree:

python -m core.eval check                    # refuse a suite that cannot be graded
python -m core.eval run     --out DIR -- …   # spawn every mission, capture the stream, score it
python -m core.eval measure --out DIR -- …   # the same suite over a matrix of configurations, live
python -m core.eval score   --runs DIR       # score run directories that already exist — no GPU

check refuses a suite whose missions cannot be graded — before anybody spends a model on it. run spawns the mission command line given after -- once per mission, captures each stream off its own descriptor, and scores it. score reads run directories that already exist and computes the same verdict, which is the no-GPU path. measure runs the suite once per configuration against one endpoint — direct against --swarm, json against native, each grounding tier on against off — and prints the table those defaults are waiting on, recording every run so the same table can be produced again without a GPU (EVAL.md §12).

--replay plus score is how a grounding change is measured on yesterday's runs. Every mission with a run store on records the model calls and the tool dispatches beside its events; a replay re-runs a finished mission out of that recording — the real loop, the real validator, no server dialled and no model asked — into a new run directory that score reads like any other. Change the grounding: block, replay ten of last week's missions, and the delta is the change rather than the difference between two samples.

The whole of it — the mission shape, the flags, the held-out split, the KPI columns, the recording format and how a platform writes its own suite — is EVAL.md.

--history — a conversation, not a paragraph

--history FILE seeds prior turns into the model's message list as real role-tagged chat turns, ahead of the objective. The file is a JSON array of {"role": "user"|"assistant", "content": "..."}, oldest first; system is refused, because system text belongs to the harness and tool turns are this mission's own to make. Caps are 100 turns and 262,144 characters, and a malformed history is a refusal at the door rather than a silent drop — a dropped history is the bug this flag fixes wearing a different hat.

A file rather than an argument, for the same reason --mcp-token prefers the environment: a conversation is many kilobytes and argv is world-readable in /proc/<pid>/cmdline.

A caller passing this must not also fold the history into the message. A chat-tuned model attends to role-tagged turns and skims past the same text pasted into the objective: measured 12 August 2026, "tell me more about #2" web-searched #2 literally while the list sat two lines up in the prompt.

Sampling — stated, or the server's own

--temperature, --top-p and --seed are unset by default, and unset means unsent: the request carries no sampling parameters and the server's own default applies. That is deliberate. Pinning temperature=0 would make the agent easier to measure by making it a different agent — it collapses the noise instead of measuring it, and a noise floor taken at a temperature nobody ships is not a floor. What was missing was never a temperature but the ability to state one and see what went out; "server default" is a setting nobody chose, and an upgrade can move it with nothing in any log. When one is passed, the CLI says so on the console and the value is on the wire.

--seed is not a determinism guarantee. A batching server can still vary.

A personality from a file

--personality <path> (or TAI_PERSONALITY, then ELF_PERSONALITY) loads a PersonalityConfig from TOML, JSON or YAML. The keys are that model's fields and nothing else — an unknown key is refused by name. JudAIs and Lobi are unaffected.

tai resolves its own file instead of being handed one: $TAI_PERSONALITY, then $ELF_PERSONALITY, then the installed deployment package's own resource. Nothing else is consulted and nothing is invented — the third outcome is a refusal naming what was checked. A guess that lands on the wrong checkout is worse than no guess, because it starts an agent whose stated rules are not the rules it loaded.

Library API

pip install 'judais-lobi[mission]'

from judais_lobi import Bounds, Model, Observer, Personality, Run, Store, ToolPlane, Tools

bus = Tools().bus                                        # SAFE, sandboxed, audited
run = Run(Personality(system_message="You are Tai."),    # what the model is told
          ToolPlane(bus=bus, offered=["read_file"]),     # the only way out
          Bounds(), Store(), Observer(), Model(ask=my_chat_fn))
print(run.run("what does this repository build?").answer)

Six objects and a loop, and that is the whole API. Each one owns a class of fact: Personality what the model is told and what it is held to, ToolPlane the only way out and who may say yes to it, Bounds everything that can stop a run, Store what survives the process, Observer every record out, Model the client and the protocol. my_chat_fn is messages -> str: the loop is confined to one injected callable and cannot ask a backend anything you did not offer. Every default above means nothing — no ceiling, no clock, no durable log, no watcher — so you add the ones you want and pay for nothing else.

The CLI is a client of this. judais --mission is argparse and then these same six objects handed to this same Run; there is no library dialect and no CLI dialect, and a stream from either one is the stream CONTRACT.md describes. from judais_lobi import contract is that contract as data — contract.conforms(record) answers "is this one of ours" without a consumer keeping its own copy of the rules.

The rest of what a platform builds the six out of is exported beside them: Skill and load_skill (a SKILL.md manifest — the closed set and the prompt), Deadline, Cancellation and Supervisor for Bounds, MissionWindow for Model, RunStore for Store, and SCHEMA_VERSION.

A mission does not need an MCP server. With no --mcp-stdio/--mcp-url (and, as a library caller, with no bridge on your bus) the plane is this package's own registered tools, governed by the same profile and the same sandbox as everything else; the "needs a server" refusal fires only when a skill's closed set names a tool this host has not got, and it says which.

For platforms

If you are wiring this framework into a platform — giving it a personality, giving it capabilities as MCP tools and a skill manifest, driving it as a subprocess and pinning a release — that is its own guide: PLATFORMS.md. It covers the personality format and how to add a new named agent, the SKILL.md fields including sdk_import, the exact spawn shape, the release-and-pin loop, and the list of things that must never enter this repository.

A platform integrates from PLATFORMS.md alone; the conformance kit under tests/conformance/ goes red the day the contract breaks. Those are the two halves of the same promise — the guide is held against the code by tests/test_docs_track_the_code.py and tests/test_platforms_doc.py, and the kit is two files a platform copies into its own repository, edits one dict in, and runs with no model, no server and no credential.

Extensibility

Judais-Lobi is designed to grow by adding workflows, tools, and policies without rewiring the kernel:

• Add a new workflow by defining a WorkflowTemplate in core/kernel/workflows.py.
• Add or consolidate tools via core/tools/descriptors.py and core/tools/.
• Define stricter safety boundaries with core/policy/ profiles.
• Extend evaluation logic under core/judge/ and core/critic/.
• Measure a change before defaulting it: write a suite for core/eval/ (EVAL.md §9) and score it, out of a platform's own repository.

🚧 Current Status

v0.17.0 — 5646 tests collected. Mission mode, skill manifests, the grounding validator, --swarm, the NDJSON mission stream and the published contract are all in this release. What 0.17.0 is, rather than what each release added:

• Safe by default. Tool subprocesses run under bwrap wherever bubblewrap exists, announced as mission_started.sandbox and opted out of only with --unsandboxed. The capability profile is deny-by-default safe, and every refusal names the scope and the profile that grants it. Every default Tools() bus writes an append-only, secret-redacted audit file, named on the stream as audit_ref. A manifest naming a code-plane tool must declare sandbox: bwrap and actually get it. One redactor scrubs every free-text field that reaches the stream or stderr.
• Durable and bounded. Every mission leaves a numbered, fsync'd log behind (core/durable.py, run_id on mission_started) and --resume <run-id> picks a killed one back up from it. No bound is imposed by the framework: --mission-seconds and --mission-steps are an operator's, unset by default, and budget_exhausted names which of them was reached. What stops a run that is going in circles is the supervisor (core/runtime/supervisor.py), which watches for repetition rather than for length and ends a hopeless run with reason: "stuck" and the best answer it can still write. SIGTERM lets the run write its own mission_finished (reason: cancelled). A gate writes a durable approval record (approval_id) that a later run carries with --approval <id> — one tool, one run, nothing defaults to yes. Every store core/ writes is atomic.
• Metered. Every model call's usage rides the record that call produced; the run's totals and elapsed_s ride mission_finished. Reported, never estimated, and absent rather than zero. Cost comes from a pricing: block a deployment writes, never from a price list in this repo.
• Native tool calling, behind a flag. --protocol native constrains the decoder to the declared functions plus a synthetic mission_answer, allows several calls per step (call), and validates arguments against each tool's own schema before dispatch — in both protocols. The default stays json until the eval harness scores the two.
• Streamed answers, and a channel back in. answer_delta carries the answer while the model is still writing it (--no-stream turns it off; the answer record always follows and is the authority). --control reads NDJSON commands into a running mission — inject, cancel, cancel_step, gate_decision. core/runtime/agui.py translates the stream into AG-UI frames for a browser that speaks them.
• Measurable. core/eval/ is a suite of missions × behavioural flags with a mechanical held-out split, scored only from the recorded stream (python -m core.eval check|run|score). Every run with a store on records its model calls and its tool dispatches, and --replay <run-id> runs a finished mission again out of that recording — no server, no GPU, the real loop, the grounding verdict computed fresh. That is what lets a change to the grounding grammar be scored on yesterday's runs. See EVAL.md.
• Grounding beyond arithmetic, off by default. reading: asks a reader what a field holds before it is shown the sentence, planes: fails an answer that claims a tool family nothing on it was called from, and critic: asks a second model — its verdict an advisory: true row beside grounded, never inside it. Each one waits on a measurement before it is anybody's default.
• One roadmap. ROADMAP.md: §1 is where 0.13.0 stands and what is still missing, §2 is Phases 9–13, §3 the principles, §5 the history — the Feb 2026 blueprint, the Phase 8 disposition, and what two weeks in production taught. NEXT_STEPS.md and PHASE_8.md were folded into it on 15 Aug 2026.

CONTRACT.md is the seam a consumer pins; PLATFORMS.md is how a platform deploys this framework as its own agent.

Release history

One line each. The commit for every one of these is release: <version> — ….

version	date	what it was
0.12.0	16 Aug 2026	answer_delta at the source, a --control channel into a running mission, an AG-UI translator
0.12.1	16 Aug 2026	--gate-wait / MISSION_GATE_WAIT: an unattended caller can turn the in-turn gate wait down to 0 (the 0.11 behaviour); default unchanged
0.12.2	17 Aug 2026	the credential redactor is linear on long unbroken payloads (a 200 KB tool result took minutes; now ~50 ms)
0.17.0	18 Aug 2026	the coding pack (multi-file, verified by running the repository's tests; tool schemas; verify failures are results); campaigns on Run (--campaign/--campaign-plan, approval as a durable record, artifact handoff, OPTIONAL artifacts) and --grant (session-scoped scopes, OPTIONAL granted); the native round trip keeps opaque provider fields (native went 0/11 → 9/11 live); the catalogue is re-listed at every step boundary; one model-resolution rule; grounding — the echo rule, the clock mask, failed results as typed evidence; suite-level flags:
0.16.0	18 Aug 2026	one runtime (Phase 11): core/runtime/run.py — Run(personality, plane, bounds, store, observer, model); MissionRunner/SwarmRunner are adapters; async core, sync façade; parallel children with the OPTIONAL branch field; the library API from judais_lobi import Run … and the CLI as its client; --mission on the built-in tools with no server; [server] SSE extra; model_state (the eleventh event); core.eval measure with the first live numbers; CI on every push + PyPI on tag; PLATFORMS.md as the integrate-alone doc + tests/conformance/; three first-party skills — research (+ the research profile), analyst, coding (lane in flight); mission packs by name; memory (core/recall/working); the built-in tools served over MCP (python -m core.tools.serve) and multi-server bridging
0.15.0	17 Aug 2026	the step budget is gone: --mission-steps unset means no ceiling (max_steps: 0), and core/runtime/supervisor.py watches for repetition instead — the same call returning the same result, rejected replies running, no new evidence, an A-B-A-B oscillation — each putting one question to the model (progressing / nudge / stuck, the swarm's gate also replan), three reviews a run and the last cannot say progressing; step_started.review (OPTIONAL) and reason: "stuck"; the swarm's step_budget/retries_per_step/one-redraw counter deleted
0.14.1	17 Aug 2026	the swarm stops starving itself: one MissionWindow at the model's max bounds every role and every sub-mission, the synthesizer sees every settled step's whole tool output, step_budget/max_plan_steps/summary_chars default to what the mission and the window allow, the union of results after a redraw, the executor is told the objective; the figure check reads the answer with the same FIGURE rule as the evidence — proved live on a 1M-token endpoint
0.14.0	17 Aug 2026	--provider anthropic (default claude-opus-5) and one neutral HTTP policy owner; the offered set follows a bus that grows mid-run (step_started.catalogue); the code gate is tool_key equality (bridged shells are the server's); the swarm gets the critic and staged --resume; a staged replay corpus and swarm end-to-end tests; ApprovalStore.reconcile called on the way in
0.13.0	17 Aug 2026	Phase 10: the eval harness (core/eval/, EVAL.md), recording + --replay, the reading/planes/critic grounding tiers off by default, god_mode/preflight deleted
0.11.0	16 Aug 2026	native tool calling behind --protocol native; arguments schema-checked before dispatch; a byte-stable prompt prefix, and a window that evicts tool round trips first
0.10.0	16 Aug 2026	durable and bounded: the fsync'd run log and --resume, a wall clock and a cancel that finish cleanly, the usage ledger and elapsed_s, approvals as durable records
0.9.0	15 Aug 2026	safe by default: sandbox on, the safe profile, audit on every bus, one redactor. Phase 8 closed
0.8.2	15 Aug 2026	the honest stream: it opens before triage, the conversation is windowed, one owner for the result cut, Mistral over httpx, a bwrap that runs
0.8.1	15 Aug 2026	the wheel stops shipping tests/
0.8.0	15 Aug 2026	the separation: the contract as data, the tai entry point, the mission extra, PLATFORMS.md

Completed

The counts below are the suite totals at the time each phase landed, kept as a record of how it grew. The current total is the one above.

• ✅ Phase 0 — Dependency Injection & Test Harness (73 tests)
• ✅ Phase 1 — Runtime extraction (provider separation, 107 tests)
• ✅ Phase 2 — Kernel State Machine & Hard Budgets (164 tests)
• ✅ Phase 3 — Session Artifacts, Contracts & KV Prefixing (269 tests)
• ✅ Phase 4 — Tool Bus, Sandboxing & Capability Gating (562 tests)
• ✅ Phase 5 — Repo Map & Context Compression (783 tests)
• ✅ Phase 6 — Repository-Native Patch Engine (888 tests)
• ✅ Phase 7.0 — Pluggable Workflows & State Machine Abstraction
• ✅ Phase 7.1-7.2 — Composite Judge & Candidate Sampling
• ✅ Phase 7.3 — External Critic
• ✅ Phase 7.4 — Campaign Orchestrator + StepPlan + EffectiveScope

Up Next

Phase 8 closed at 0.9.0, and the numbering continues in ROADMAP.md §2:

• ✅ Phase 9 — durable and bounded (0.10.0): a fsync'd append-only transcript, --resume, a wall-clock budget, a usage ledger, approvals as durable records
• ✅ Phase 10 — measurable (0.13.0): the in-repo eval harness (core/eval/, EVAL.md), recording + --replay, and the reading/planes/critic grounding tiers wired off by default. What remains is the measurements themselves — swarm versus direct, json versus native, each tier on versus off — and they gate every one of those defaults
• ⏳ Phase 11 — one runtime: the mission loop and the kernel become one Run
• ⏳ Phase 12 — providers and streaming. Partly shipped early, on evidence: constrained decoding in 0.11.0, answer_delta + the AG-UI translator + the control channel in 0.12.0. What remains is the provider work — one HTTP client, a retry policy owned somewhere neutral, Anthropic as a backend
• ⏳ Phase 13 — embeddable: a library API first, the CLI second (1.0)

Phase 7 Highlights (7.0–7.4)

Phase 7 turns the kernel into a workflow-driven, multi-candidate, multi-critic, campaign-capable system.

• Pluggable workflows — WorkflowTemplate makes phases, transitions, schemas, and capability profiles data-driven. CODING_WORKFLOW preserves Phase 6 behavior; GENERIC_WORKFLOW enables custom domains.
• Deterministic scoring — CompositeJudge sequences tests/lint/LLM review and scores candidate patches. CandidateManager evaluates N patch sets in isolated worktrees and picks the top non-failing result.
• External Critic — Optional frontier-model auditor (OpenAI/Anthropic/Google) for independent logic audits. Keyring/env key handling, multi-round feedback loop, noise detection, and SHA256 cache.
• Campaign Orchestrator — Tier‑0 mission layer with HITL approval gates, step DAG execution, artifact handoff, and resumable progress.
• StepPlan + EffectiveScope — Step-level contracts and SHA256 ActionDigest; tool access enforced by Global ∩ Workflow ∩ Step ∩ Phase.

Outcome: workflows are composable, evaluation is deterministic, critics are optional, and campaigns provide a macro loop for multi-step missions.

Phase 6 Highlights

The agent can now reliably modify repository files through a deterministic, exact-match patch protocol with git worktree isolation and automatic rollback.

• core/patch/parser.py — Extracts <<<< SEARCH / ==== / >>>> REPLACE, <<<< CREATE / >>>> CREATE, and <<<< DELETE >>>> blocks from raw LLM text output. Delimiter-safe (only recognizes markers at line start). Path validation rejects absolute paths and .. traversal at parse time.
• core/patch/matcher.py — Exact byte-match with byte offsets and SHA256 context hashes. On zero matches: 3-stage similarity narrowing pipeline (indent filter → token overlap → SequenceMatcher ratio) returns top 3 candidate regions. On multiple matches: returns all offsets + context hashes for LLM disambiguation.
• core/patch/applicator.py — File writes with strict preconditions. Path jailing (symlink-escape resistant). \r\n → \n canonicalization. st_mode preservation (executables stay executable). Create fails if file exists; delete fails if file doesn't exist.
• core/patch/worktree.py — PatchWorktree manages git worktree lifecycle: create (explicit -b + HEAD), merge_back (--no-ff + branch cleanup), discard (force remove + branch delete). Writes .judais-lobi/worktrees/active.json for crash recovery of orphaned worktrees.
• core/patch/engine.py — PatchEngine orchestrates validate → apply → diff → merge/rollback. Stops at first file failure, leaving worktree intact for diagnostics. diff() returns real git diff from the worktree.
• core/tools/patch_tool.py — ToolBus-compatible 6-action tool (validate, apply, diff, merge, rollback, status). All actions return JSON stdout for machine-friendly kernel orchestration. exit_code=0 only on success.

12 tool descriptors. 105 new tests (888 total). 3 integration tests with real git repos. Worktree isolation means cross-file patches land atomically — all succeed or discard for zero-cost rollback.

Phase 5 Highlights

The agent is now repo-aware. It understands structure, relationships, and what's irrelevant — without eating the entire repo in context.

• core/context/repo_map.py — Top-level RepoMap orchestrator. Dual-use: overview mode (centrality-ranked for REPO_MAP phase) and focused mode (relevance-ranked by target_files for RETRIEVE phase). Lazy build with git-commit-keyed caching and dirty-file overlay.
• core/context/symbols/ — 3-tier symbol extraction: Python ast (full import + signature extraction), tree-sitter (7 languages: C, C++, Rust, Go, JS, TS, Java), regex fallback. get_extractor(language) factory auto-selects the best available.
• core/context/graph.py — DependencyGraph with multi-language module resolution (Python dotted paths, C #include, Rust use crate::, Go package imports, JS/TS relative imports with extension guessing). Relevance ranking (1.0/0.8/0.6/0.4/0.1 scoring by hop distance) and centrality ranking with barrel file damping (__init__.py, index.js, mod.rs).
• core/context/formatter.py — Compact tree-style formatting with token budget, optional char cap, whitespace normalization for deterministic output, and metadata header (file/symbol counts, languages, ranking mode).
• core/context/visualize.py — DOT (Graphviz) and Mermaid graph export with highlight styling and node cap.
• core/context/cache.py — Git-commit-keyed persistent cache at .judais-lobi/cache/repo_map/<hash>.json. Clean commit = full cache hit; dirty state = cache + re-extract only modified files.
• core/tools/repo_map_tool.py — ToolBus-compatible multi-action tool (build, excerpt, status, visualize).
• setup.py — pip install judais-lobi[treesitter] adds optional tree-sitter support via individual grammar packages.

11 tool descriptors (now 12 with Phase 6). 221 new tests. tree-sitter is optional — the system works without it and gains rich multi-language AST parsing when installed.

Phase 4 Highlights

Tools are dumb executors behind a capability-gated bus. The kernel decides everything.

• core/tools/bus.py — Action-aware ToolBus with capability gating, sandboxing and JSONL audit logging. Structured JSON denial errors replace plain text. (The preflight_hook and god_mode constructor parameters were deleted in 0.13.0: nothing in the package ever passed either, and a hook nobody passes is a place a future caller puts a control and believes the run is governed. The bus's own capability check and core/runtime/schema_check.py are the preflights that actually run.)
• core/tools/fs_tools.py — Consolidated FsTool with 5 actions (read, write, delete, list, stat). Pure pathlib I/O, no subprocess.
• core/tools/git_tools.py — Consolidated GitTool with 12 actions (status, diff, log, add, commit, branch, push, pull, fetch, stash, tag, reset) via run_subprocess.
• core/tools/verify_tools.py — Config-driven VerifyTool (lint, test, typecheck, format). Reads .judais-lobi.yml for project-specific commands, falls back to sensible defaults.
• core/tools/descriptors.py — 11 tool descriptors, 13 named scopes + wildcard. Per-action scope resolution via action_scopes map.
• core/tools/capability.py — Deny-by-default CapabilityEngine with wildcard "*" support, profile switching, and grant revocation.
• core/policy/profiles.py — Five cumulative profiles: SAFE (read-only) → DEV (+ write) → RESEARCH (+ http.read) → OPS (+ deploy) → GOD (wildcard). RESEARCH was carved out of OPS in Phase 15: http.read sat beside git.push and pip.install, so an agent asked to read the web was handed a deploy right and mission_started.profile: "ops" said something about the run that was not true. SAFE is the default and --profile/JUDAIS_LOBI_PROFILE is how a run opts up; the profile it got rides mission_started.profile. core/policy/god_mode.py was deleted in 0.13.0 — GodModeSession was constructed nowhere, and --profile god is the reachable form of everything it offered.
• core/policy/audit.py — Append-only JSONL AuditLogger, attached to every Tools() bus by default: one file per run at .judais-lobi/audit/<run-id>.jsonl under the working directory, named on the mission stream as mission_started.audit_ref, moved or silenced by JUDAIS_LOBI_AUDIT=<path>|none|off (silencing is announced, and travels as audit_ref: null). Every dispatch is a line — allowed, denied, unknown_tool or error — with the redacted arguments, the decision and its reason, exit code, duration and bytes out. Redaction covers shapes (OpenAI, GitHub, AWS, Slack, Bearer …, *_KEY/*_TOKEN/*_SECRET assignments) and the values of the credential-named environment variables this process was given, because a token handed to a tool as an argument has no shape to match.
• core/runtime/resume.py — Picking a recorded mission back up, and closing the ones nobody will. Three separate things: the door (open_for_resume — an unknown id, a run that already finished, an objective that is not the recorded one, a staged run whose plan is checkpointed; every refusal answered before a server is dialled), the replay (rebuild — the recorded stream read back into the transcript's steps, the mission result store and the model's message list, rendering each replayed result through the runner's own _render_result so there is one owner of what a result reads like), and reconciliation (reconcile_orphans — a run with no mission_finished whose metadata has been untouched for ORPHAN_STALE_S gets one appended, so a follower's stream closes; the staleness rule is stated rather than assumed, because a mission that is merely thinking has no mission_finished either). What a replay cannot give back is written down as sentences (LOST_*) and shown, not swallowed.
• core/durable.py — The durability primitive, importing nothing else in this tree: atomic_write_text/atomic_write_json (tempfile in the same directory → flush → fsync → os.replace), fsync_append, and RunStore — one directory per run under .judais-lobi/runs/<run-id>/ holding an fsync'd append-only events.jsonl of {seq, at, record} envelopes and a meta.json replaced atomically. Every record a mission emits is appended there before it reaches the --events sink, so the sink is a client of the log rather than a second copy; since(cursor) and follow(cursor, stop=…) are what a replay and a live subscriber read it back with. seq is monotonic per run and is persisted, and RunStore.CALLER_OWNED is why: writing a whole stale record back over a live one is how a reference platform came to reuse sequence numbers and show a blank transcript for a run whose records were on disk the whole time. SessionManager and AuditLogger are clients of this module, not second implementations of it.
• core/tools/sandbox.py — NoneSandbox (dev/debug) and BwrapSandbox (Tier-1 production) behind a common SandboxRunner interface. BwrapSandbox keeps every field of the SandboxProfile it is given: the host root read-only with the working directory (and allowed_write_paths) re-bound writable, a private tmpfs /tmp, the network namespace unshared unless the profile says allow_network, and max_cpu_seconds / max_memory_bytes / max_processes applied as rlimits on the bwrap process and inherited by what runs inside it. NoneSandbox is still the default; it enforces nothing and says so.

3 consolidated multi-action tools replaced 21 separate descriptors. Git is the spine, not nice-to-have.

---

🧭 Where To Look

If you are running this from another program, read:

• 📄 CONTRACT.md — the mission stream, its events and the exit contract
• 📄 PLATFORMS.md — deploying judais-lobi as a platform's agent

If you want to understand where this is going, read:

• 🗺️ ROADMAP.md — the only roadmap: where 0.17.0 stands (§1), Phases 9–13 (§2), the principles (§3), and the Feb 2026 blueprint kept as history (§5)
• 🧪 EVAL.md — the eval harness: missions × behavioural flags, a held-out split, scoring from the recorded stream, --replay, and how a platform writes its own suite

If you want to understand the current implementation, inspect:

• core/agent.py — concrete Agent class (replaced elf.py in Phase 3)
• core/runtime/contract.py — the seam a consumer pins, as data
• core/runtime/run.py — the loop, as six objects: Run(personality, plane, bounds, store, observer, model), each the one owner of a class of fact; Run.arun is the loop and Run.run is the synchronous façade that runs it to completion
• core/runtime/mission.py, mission_stream.py, swarm.py — the mission vocabulary and the MissionRunner adapter that builds those six, its NDJSON account, and staged decomposition
• core/runtime/skills.py — the SKILL.md loader: closed tool set, prompt, grounding grammar, sdk_import
• core/runtime/grounding.py, results.py — the identifier/claim validator, and the per-mission result store it reads paths out of
• core/runtime/reading.py — the field-misreading reader the reading tier asks: what does this field hold, cold, before the sentence is shown
• core/runtime/replay.py — model.jsonl and tools.jsonl beside events.jsonl, and --replay: a finished run run again with no server and no GPU, its grounding verdict computed fresh
• core/eval/ — the eval harness: a suite of missions × behavioural flags, a mechanical train/test split, and a verdict computed only from the recorded stream (suite.py, run.py, score.py, stub_suite.py). See EVAL.md
• core/critic/mission.py — the mission-tier critic: local first via LOCAL_API_BASE, a keyed provider only where the critic config declares one, and its verdict an advisory: true row beside grounded rather than inside it
• core/runtime/schema_check.py — a tool call's arguments against that tool's own JSON Schema, before dispatch, in both protocols
• core/runtime/answer_stream.py — the answer decoded out of a half-written reply, bounded into answer_delta fragments
• core/runtime/control.py — the closed command vocabulary --control reads: inject, cancel, cancel_step, gate_decision
• core/runtime/approvals.py, resume.py — the durable approval record and its states; the door, the replay and the orphan reconciler behind --resume
• core/runtime/usage.py — one ledger: what each call reported, what the run spent, and a cost only if somebody priced it
• core/runtime/context_window.py, messages.py — keeping a conversation inside the model's window, and the byte-stable prompt prefix every turn is assembled by
• core/runtime/backends/, provider_config.py, core/unified_client.py — openai, mistral, local, and what each declares it can do
• core/runtime/agui.py — optional, import-free translator from the mission stream to AG-UI event frames (translate for a replay, Translator for a live follower); dicts only, no SDK. See PLATFORMS.md §"AG-UI"
• core/durable.py — the durability primitive, importing nothing else in this tree: atomic writes, fsync_append, and RunStore
• core/budgets.py — one owner for steps, seconds, and the cancellation a SIGTERM or a --control cancel throws
• core/bounding.py — one owner for the tool-result cap and the cut it makes
• core/redact.py — one redactor, at the emitter, for every free-text field and every traceback
• core/contracts/ — Pydantic v2 contract models for all session data
• core/sessions/ — SessionManager for disk artifact persistence
• core/kernel/ — state machine, budgets, orchestrator, workflow templates (workflows.py)
• core/cli.py — CLI interface layer
• core/memory/bank.py — the memory bank: pinned core blocks, distilled notes, a read over the run store, the relevance × recency × importance ranking and the caps. python -m core.memory is the operator's half. See "Memory — core, recall, working"
• core/memory/memory.py — FAISS-backed long-term memory for direct chat (numpy fallback if FAISS unavailable)
• core/tools/ — ToolBus, capability engine, sandbox, the MCP bridge, consolidated tools (fs, git, verify, repo_map, patch)
• core/policy/ — profiles.py (the five cumulative profiles and select_profile), audit.py (the append-only log on every default bus). Two files, since god_mode.py was deleted in 0.13.0
• core/context/ — repo map extraction, dependency graph, symbol extractors (Python ast + tree-sitter + regex), formatting, caching, visualization
• core/patch/ — patch engine: parser, matcher, applicator, worktree manager, engine orchestrator
• core/judge/, core/critic/, core/campaign/ — composite judge and candidate sampling; the external critic (mission.py for the mission tier, orchestrator.py for the coding tier); the campaign plan's facts — its schema, what makes one legal and in what order its steps go, the artifact handoff, the scope intersection, the $EDITOR review and the session layout. What runs one is core/runtime/campaign.py, which is a SwarmRunner: CampaignOrchestrator, the second dispatcher that used to live here, was deleted in 0.17
• lobi/ and judais/ — personality configs extending Agent

If you want to understand the entry point, see:

• main.py
• setup.py

---

🏗 Architectural Direction

The architecture, as built. Every bullet below is in the tree today; what is still ahead has one home — ROADMAP.md §2 — and is not restated here:

• Artifact-driven state (no conversational drift)
• Three-tier orchestration: Campaign graph (Tier 0) → Workflow graph (Tier 1) → Phase-internal planning (Tier 2)
• Pluggable workflows — static templates for coding, red teaming, data analysis, and arbitrary tasks
• Campaign orchestration — multi-step missions with DAG decomposition, HITL approval gates, and artifact handoff (pre-authored plans)
• Capability-gated tool execution with least-privilege by intersection (Global ∩ Workflow ∩ Step ∩ Phase)
• Sandbox isolation — bwrap is the backend that ships, and the default wherever bubblewrap exists. February's Tier-2 nsjail would go behind the same SandboxRunner interface, not beside it (ROADMAP.md §3)
• Tests > Lint > LLM scoring hierarchy
• Endpoint-probed orchestration (vLLM / TRT-LLM serve the model; the client asks the endpoint how big its window is)
• Optional external critic (frontier logic auditor)

The kernel path, end to end. (The mission path is --mission, above, and the two are still two runtimes — ROADMAP.md §2.6 is where they become one.)

CLI (--campaign / --campaign-plan)
  ↓
Campaign Orchestrator (Tier 0 — optional, multi-step missions)
  ↓  plan → HITL approve → dispatch → synthesis
Workflow Selector → WorkflowTemplate (Tier 1 — static graph)
  ↓
Kernel State Machine (phases, transitions, budgets)
  ↓
Roles (Planner / Coder / Reviewer)
  ↓
ToolBus → EffectiveScope check → Sandbox → Subprocess
  ↓
Deterministic Judge (Tests > Lint > LLM)

As of Phase 7.4:

• The kernel state machine is parameterized by WorkflowTemplate objects — no hardcoded phase names, transitions, or branching rules. The coding pipeline is one template; custom domains define their own.
• CODING_WORKFLOW and GENERIC_WORKFLOW are built-in templates. select_workflow() resolves by an explicit argument, then a PolicyPack field, then the default CODING_WORKFLOW — no CLI flag is wired to it today.
• Per-phase capability profiles (phase_capabilities) create temporal sandboxes — PLAN can read but not write, PATCH can write but only through the patch engine.
• Tools are dumb executors behind a sandboxed, capability-gated bus.
• Every subprocess-based tool call flows through ToolBus → CapabilityEngine → SandboxRunner → Subprocess. Pure-Python tools are still gated by ToolBus but execute in-process. HUMAN_REVIEW uses $EDITOR directly (user-initiated TTY) and is an explicit exception.
• Deny-by-default. No scope = no execution.
• God mode is a profile, not a session — --profile god / JUDAIS_LOBI_PROFILE=god, announced on mission_started.profile and audited like every other run.
• 5 consolidated multi-action tools (fs, git, verify, repo_map, patch) cover 31 operations under 13 scopes.
• The agent sees repo structure via a token-budgeted excerpt — file paths, symbol signatures, and dependency-ranked relevance — without loading full source.
• 3-tier symbol extraction: Python ast → tree-sitter (7 languages) → regex fallback. Multi-language dependency graph with import resolution.
• Code modifications use an exact-match patch protocol with git worktree isolation. Cross-file changes land atomically. Failed patches roll back at zero cost.
• Patches are scored by a deterministic CompositeJudge (Tests > Lint > LLM review). CandidateManager evaluates N candidate patches in isolated worktrees and selects the winner by composite score.
• Campaign Orchestrator provides a Tier 0 macro loop with HITL approval, step DAG execution, and explicit artifact handoff.
• StepPlan contracts lock intent, boundaries, and capability needs per step with a SHA256 ActionDigest.
• EffectiveScope intersection (Global ∩ Workflow ∩ Step ∩ Phase) is enforced per tool call.
• Context window manager keeps prompts within model limits, auto-compacts history, and stores oversized tool output to disk with a retrieval hint.

Local inference has landed (--provider local), and Phase 8 closed at 0.9.0 — ROADMAP.md §5.10 records where each of its milestones ended up. Phase 9 closed at 0.10.0. The mission path has since gained the run store, the wall clock, the usage ledger, the native protocol and the control channel; the kernel path has gained none of them, which is the gap ROADMAP.md §1.2 calls "two agent runtimes".

The kernel is the only intelligence. Tools report. The kernel decides.

---

🧠 Memory — core, recall, working

Simple retrieval is normally implemented wrong: it pulls too much and the wrong thing into a context that then has less room for the objective. Ignoring retrieval is the other error. So memory here is three tiers with three different insertion rules, not one mechanism with a knob.

Core memory — pinned, tiny, self-edited. A handful of blocks per principal and skill (preference, fact, lesson, persona), hard-capped at ~1,000 tokens, rendered into the system turn after the tool catalogue of every run. A write that would breach the cap is refused naming the cap — nothing is evicted to make room, because everything in there was pinned on purpose. It changes only through the memory_write tool or through an operator.

Recall memory — retrieved on demand, never auto-stuffed. One tool, memory_recall, over two stores: the episodic one (the durable run store — what actually happened, by objective and answer, addressable by run_id) and the semantic one (distilled notes written by a bounded reflection step at the end of a run that answered — at most three per run, each with a title, a ≤80-token body, a date, source run_id/seq handles and an importance the model rated). Ranking is relevance × recency × importance — a product, so a five-star note from this morning that has nothing to do with the question scores zero and is not recalled at all. Relevance is embeddings when an embedding client is configured and idf-weighted term overlap otherwise, so the base path needs no network. What comes back is capped at 5 results and ~600 tokens, and says what it cut.

The one concession to "ignoring retrieval is naive": at the start of a run the objective turn may carry a titles-only hint — "2 remembered notes may bear on this: …; …", ~150 tokens, at most 3 titles, and only when something scores. It goes beside the objective and never in the system turn, because the system turn is a served endpoint's cached prefix. The model then decides whether to spend a call on memory_recall. A recalled fact is dated — the policy sentence in the system turn says so — and re-verifying it is the model's job when the objective needs current.

Working memory — already built. The per-mission result store (mission_result handles), context-window compaction, the swarm's step summaries and the supervisor's history. Nothing new; named here so nobody builds it twice.

Memory is a plane and is governed like one. Both tools are dispatched through the same ToolBus as everything else, so they are capability-checked, audited and redacted, and a recall's result is an ordinary tool_result — which means a note the model quotes is evidence the grounding validator can cite. memory_recall needs memory.read (granted by the default safe profile); memory_write needs memory.write (granted by dev), because pinning a sentence into every future system turn is a durable effect on later runs. Text is scrubbed by core.redact on the way in, since a block outlives the run that wrote it.

Turn it on with an environment variable — there is no flag, and no default directory:

export JUDAIS_LOBI_MEMORY=~/.judais-lobi/memory     # unset/none/off = no memory
export JUDAIS_LOBI_MEMORY_PRINCIPAL=alice           # default: "default"

JUDAIS_LOBI_MEMORY_PRINCIPAL partitions the bank so two deployments sharing a directory do not read each other's memory. It is attributed, not authenticated: this framework has no principal system and will not invent one.

A library caller passes a bank instead:

from core.memory.bank import MemoryBank
from core.runtime.run import Personality

Personality(system_message=..., memory=MemoryBank(path, principal="alice"))

The operator's half:

python -m core.memory stats
python -m core.memory blocks
python -m core.memory add --label house-style --kind preference \
    --body "Answers are short; no preamble." --reason "asked twice" \
    --source operator
python -m core.memory delete --label house-style --reason "no longer true"
python -m core.memory notes --limit 20
python -m core.memory recall "cold start"
python -m core.memory purge --notes

Same implementation as the tool, so a cap refused on the command line is refused in the same words the model is refused in. See core/memory/bank.py.

Chat mode is unchanged. UnifiedMemory (core/memory/memory.py) still backs --recall/--rag and short-term history for direct chat: SQLite, a FAISS index with a numpy fallback, OpenAI embeddings. The mission path uses only the bank. Direct CLI tool calls route through the same ToolBus, under the same deny-by-default safe profile as a mission — a PolicyPack or --profile opts up, and nothing is permissive by omission. Agentic mode uses session artifacts as the sole source of truth (Phase 3).

---

🧰 Context Window & Tool Output

Judais-Lobi tracks context window limits per model/provider, auto-compacts history when needed, and never drops oversized tool output. Full logs are written to disk with a retrieval hint in the prompt.

Config (project-level) in .judais-lobi.yml:

context:
  max_context_tokens: 32768
  max_output_tokens: 4096
  max_tool_output_bytes_in_context: 32768
  min_tail_messages: 6
  max_summary_chars: 2400
  provider_defaults:
    openai: 128000
    mistral: 32768
    local: 32768
  model_overrides:
    gpt-4o: 128000
    codestral-latest: 32768

---

🧮 Usage ledger

Every backend reports what the provider said a completion cost — prompt_tokens, completion_tokens, total_tokens, plus any extras that provider sent, read off UnifiedClient.last_usage. A mission accumulates them and finishes with a line:

🧮 usage: 8412 prompt + 903 completion tokens over 11 calls

On the event stream the same numbers ride tool_call, answer and reply_rejected per call, and mission_finished as the run's totals. They are reported, never estimated, and absent rather than zero when a provider said nothing — which local endpoints often do. See CONTRACT.md.

Cost is optional and comes from configuration, never from a price list in this repo — prices move and differ per account. Add a pricing: block to .judais-lobi.yml and the totals grow a cost:

pricing:
  openai:
    gpt-4o-mini: {prompt_per_1k: 0.15, completion_per_1k: 0.6}
    "*":         {prompt_per_1k: 1.0,  completion_per_1k: 2.0, currency: USD}
  local:
    my-served-model: {prompt_per_1k: 0.002, completion_per_1k: 0.002, currency: EUR}

"*" under a provider covers whatever else it serves. No block means tokens and no cost, and local has no cost until somebody prices it.

---

🛠 Current Capabilities

Direct mode still works, and it is governed by the same deny-by-default profile a mission is: safe reads the filesystem and git, runs the verifiers and calls a connected MCP server. Anything that writes, executes or reaches the open network needs --profile (or JUDAIS_LOBI_PROFILE), and a refusal names the scope and the profile that grants it.

lobi "explain this function"                      # safe
lobi --profile dev  --shell  "list files"         # shell.exec  → dev
lobi --profile dev  --python "plot sine wave"     # python.exec → dev
lobi --profile research --search "latest linux kernel"        # http.read → research
lobi --profile research --research "linux kernel LTS release timeline"
lobi --profile research --research --academic "transformer sparsity survey 2023"
lobi --profile ops  --install-project             # pip.install → ops

JudAIs:

judais --profile dev "analyze this target" --shell

Voice (optional extra; audio.output is an ops scope):

pip install judais-lobi[voice]
lobi --profile ops "sing" --voice

The scope each tool asks for is on its ToolDescriptor (core/tools/descriptors.py), and which profile grants it is one table (core/policy/profiles.py, PROFILE_SCOPES). Neither is typed out twice.

---

🧪 Install

pip install judais-lobi                 # the base install
pip install -e '.[mission]'             # from a checkout, with everything a mission needs

Requires:

• Python 3.10+ (setup.py's floor; a TOML personality on 3.10 also needs tomli)
• A model to talk to: an API key for a hosted provider, or an OpenAI-compatible endpoint for --provider local
• Linux recommended

Every optional stack is an extra, not a requirement — a plain install stays small enough that judais --help works without any of them, and the SDK an extra pulls in is imported lazily.

extra	what it adds
mission	mcp + pyyaml + jsonschema — what a governed mission actually needs. This is the one a platform installs. Without jsonschema the pre-dispatch argument check falls back to a required/type/enum floor that says nothing about nested arguments
mcp	the MCP client alone. Enough to run a mission, not enough to govern one
critic	the external frontier-model critic, and pyyaml
treesitter	multi-language symbol extraction for the repo map
faiss	the FAISS vector index for long-term memory. Without it memory still works, on the numpy index in core/memory/memory.py
voice	TTS
server	starlette + uvicorn, for python -m core.server — the run store as an SSE endpoint. Read-only, and imported only by core/server/
dev	pytest and coverage

Set an API key:

export OPENAI_API_KEY=sk-...

Or create:

~/.elf_env

---

🔐 API Keys & Model APIs

Judais-Lobi uses API keys from your environment or your system keyring. Keys are never stored in config files.

Environment variables (fallbacks):

• OPENAI_API_KEY — OpenAI (builder + optional critic)
• ANTHROPIC_API_KEY — Anthropic critic (optional)
• GOOGLE_API_KEY — Google/Gemini critic (optional)

Keyring (preferred, optional):

• Service: judais-lobi
• Keys: openai_api_key, anthropic_api_key, google_api_key

Model API configuration (critic only):

• User defaults: ~/.judais-lobi/critic.yml
• Project overrides: .judais-lobi.yml under critic:

Example critic.yml:

enabled: true
providers:
  - provider: openai
    model: gpt-4o
  - provider: anthropic
    model: claude-opus-5

---

🔮 What This Is Becoming

Judais-Lobi is not trying to be:

• Another chat wrapper
• Another SaaS IDE
• Another prompt toy

What is already true at 0.12.0:

• Capability-constrained — deny-by-default scopes, least-privilege by intersection, refusals that name the fix
• Mission-capable — a governed tool plane, human gates that are durable records, campaign orchestration with HITL approval
• Replayable in one direction — a run leaves an fsync'd log and --resume picks it back up. Deterministic replay (the same model I/O twice) is Phase 10's recorder, not something this release claims
• Local-first — --provider local against any OpenAI-compatible endpoint, never silently fallen back away from
• Air-gap capable — every external dependency is an extra and capability-gated; nothing in a mission reaches the network unless a tool declared it

What it is still becoming, and where the plan lives — ROADMAP.md §2:

• One runtime instead of two (§2.6)
• Measurable: an in-repo eval harness scored from recorded runs (§2.5)
• Embeddable: a library API first and the CLI second, at 1.0 (§2.8)

The design philosophy is explicit in ROADMAP.md §3:

• Artifacts over chat
• Budgets over infinite loops
• Capabilities over trust
• Capabilities over tools (stable tags, not tool names)
• Plans over prompts (structured DAGs, not freestyle LLM loops)
• Static graphs, adaptive phases (three-tier orchestration)
• Dumb tools, smart kernel
• Commit or abort

That last one matters.

There will not be two systems of truth.

---

🧠 Philosophy

Lobi sings. JudAIs calculates.

But the system beneath them is becoming something else:

A disciplined orchestration engine for machine reasoning.

The aesthetic may be mythic. The architecture is not.

---

⭐ Contributing

If you are contributing:

1. Read the roadmap.
2. Understand the phase ordering.
3. Do not bypass tool execution through direct subprocess calls.
4. Every structural change must preserve deterministic replay.
5. New functionality goes through Agent + contracts, not ad-hoc methods.

This is an architectural project, not a feature factory.

---

🧾 License

GPLv3 — see LICENSE.