AgentLab/docs/en/configuration.md

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Configuration

This page covers where configuration lives, how to connect models, common environment variables and UI settings, and how to choose the sandbox backend and enable microVM. It is for admins and operators. For database and multi-process settings, see PostgreSQL and Scaling. The user interface is currently available in Simplified Chinese only.

Where configuration lives

• Environment variables. During development they can also go in a .env file at the repository root; environment variables take precedence. .env.example lists the groups for the app database, connection pool, PgBouncer, multiple processes, and the development PostgreSQL container.
• Docker deployments keep them in agentlab.env (see deploy/README.md, written in Chinese).
• Settings in the UI. For two items the environment variable takes precedence: AGENTLAB_AUTH_MODE over Settings → Identity, and AGENTLAB_PUBLIC_URL over the public URL under Settings → General.

Model access

Configure models under Settings → Model Access. Supported: Anthropic, OpenAI, any OpenAI-compatible service (DeepSeek, Kimi, Tongyi, Zhipu, SiliconFlow, Ollama, and others), and the built-in Mock.

• At startup, ANTHROPIC_API_KEY / ANTHROPIC_AUTH_TOKEN / OPENAI_API_KEY found in the environment are imported as providers automatically. Providers without a configured key also fall back to keys in the environment.
• API keys are encrypted with Fernet before being stored, and only masked values are returned to the frontend.
• Mock needs no API key: it produces streaming replies, tool calls, and schema-conforming structured data, so the whole orchestration path (canvas highlighting, approval interrupts, sandbox execution) runs end to end. But Mock writes no citation markers, so every number in its reports is marked "No evidence".
• Cost is computed at list prices.
• Some relay gateways accept only Authorization: Bearer and reject requests that also carry x-api-key: tick Authenticate with Authorization: Bearer in the provider settings.
• Models after Claude 4.6 enable thinking by default, and with too small a max_tokens you get "Thinking used up the budget; the body is empty"; see the FAQ.

Other places that use models: the drafting model is chosen under Settings → Preferences (see Data Catalog); the knowledge base's embedding model can use a local endpoint (see Workflows).

Public URL

Set the public URL when AgentLab sits behind a reverse proxy, or when external systems need to call back into it.

• Settings → General has a single public URL setting. AGENTLAB_PUBLIC_URL takes precedence, and a malformed value fails at startup.
• The OIDC callback is built from this address, <public-url>/api/auth/oidc/callback, and must match what is registered with the identity provider.
• HSTS is left to the reverse proxy.

Common environment variables

Variable	Default	Purpose
AGENTLAB_DATA_DIR	data/ under the repository	Data directory
AGENTLAB_PORT	8000	Backend port
AGENTLAB_WEB_PORT	5273	Frontend port during development
AGENTLAB_CONDA_ENV	agentlab	Conda environment name used by dev.sh
AGENTLAB_AUTH_MODE	off	Login mode: off / local / oidc
AGENTLAB_PUBLIC_URL	—	Public URL
AGENTLAB_CORS_ORIGINS	—	Other addresses allowed to send requests with an Origin
AGENTLAB_AUTH_SESSION_IDLE_HOURS	12	Session idle timeout (hours)
AGENTLAB_AUTH_SESSION_MAX_DAYS	7	Maximum session lifetime (days)
AGENTLAB_AUTH_COOKIE_SECURE	Depends on whether the request is https	Force the cookie's Secure flag
AGENTLAB_FRAME_ANCESTORS	— (only the same site may embed)	Same-site portals allowed to embed the whole site
AGENTLAB_EMBED_ORIGINS	—	Sites allowed to embed share pages
AGENTLAB_SCHEDULER	On	Scheduler switch; set to 0 to turn it off
AGENTLAB_SCHEDULER_TICK_S	15	Interval between scheduler loop ticks (seconds)
AGENTLAB_MAX_CONCURRENT_RUNS	20	Site-wide limit on concurrently executing runs
AGENTLAB_MAX_RUN_SECONDS	600	Time limit for a single execution (seconds)
AGENTLAB_MODEL_TIMEOUT_SECONDS	300	How long a model call may go without output before it times out (seconds)
AGENTLAB_MAX_GRAPH_STEPS	200	Step limit for a graph
AGENTLAB_MAX_AGENT_STEPS	—	Cap on agent steps
AGENTLAB_SANDBOX_BACKEND	Chosen automatically	Force a sandbox backend
AGENTLAB_MICROVM_IMAGE	python:3.12-slim	Image used by microVM
AGENTLAB_MICROVM_IDLE_SECONDS	—	How long an idle VM is kept before it is reclaimed
AGENTLAB_HTTP_TOOL_FAKE_IP_RANGES	—	The local proxy's fake-ip range, such as 198.18.0.0/15
AGENTLAB_HTTP_TOOL_DOH_URLS	—	DoH URLs used to verify fake-ip results, comma-separated

For the app database, checkpoint database, connection pool, PgBouncer, and multi-process variables, see PostgreSQL and Scaling.

Settings in the UI

Location	Contents
Settings → Model Access	Providers and keys
Settings → Preferences	Run defaults (default memory scope, default knowledge base, require approval for dangerous tools by default); the evidence judge (judge model and limits); verification (verification model, the switches and limits for double-blind recomputation, adversarial review, and approval inquiry, and the daily spending cap; see The review suite); evaluation concurrency, per-question time limit, and per-evaluation and daily spending; analysis (the thresholds used for verdicts, how many analyses an agent may run per execution, the drill-down depth, how many segments are listed; see Analysis settings); run concurrency (admins); the caliber ledger (automatic issuance by caliber and its daily cap, the caliber matching time limit, the recall limit ledger.recall_limit; see Caliber ledger); drafting model; apps (the daily cap on on-demand runs per app, letting users create personal apps, allowing share links); interface theme
Settings → General	Public URL
Settings → Runtime Environment	Sandbox candidates and their availability, the current choice and its reason; current values such as time limits
Settings → Identity	Login mode; enabling Ask Data for users
Settings → Notifications	Channel subscriptions; Mail Service (SMTP)
Settings → Access Keys	Service accounts, the personal access key switch, the API reference
Settings → Audit	Audit records

Other settings: notify.digest_at (the time of the daily task digest), and the agent token budget, spending budget, and step count.

Analysis settings

The Analysis section under Settings → Preferences governs how change decomposition, the fluctuation baseline, the trend test, and the difference test judge, and how many analyses an agent may run (see Analysis). It is snapshotted into a run when the run starts, and resuming or continuing uses the same snapshot; an analysis card can carry threshold overrides, and agents can't change any of it.

Setting	Default	Range	What it does
Main-source share	0.5	0.3–0.9	A segment (a factor, an effect) accounting for this share of the total change is the main source
Concentration share	0.8	0.5–0.99	The top few segments together accounting for this share count as concentrated; can't be below the main-source share
Offset ratio	0.25	0.05–1	When the segments moving the other way add up to more than this share of the total change, a caveat says they offset each other
Flat threshold	0.005	0–0.1	A total change below this share of the base counts as essentially flat
Outside-fluctuation multiple	2	1–5	Fluctuation baseline: this many robust standard deviations from the historical median counts as outside the usual fluctuation
Far-outside multiple	3	1.5–8	This many counts as far outside; must be greater than the outside-fluctuation multiple
Minimum history periods	8	4–104	With less history than this, the fluctuation baseline can't be done
Maximum history periods	52	8–260	The fluctuation baseline and the trend test use at most this many periods; can't be below the minimum
Minimum trend periods	6	4–104	The trend test needs at least this many periods
Trend significance level	0.05	0.001–0.2	Rising or falling is judged only when p is below this
Difference-test significance level	0.05	0.001–0.2	A corrected p below this means the difference holds up
Minimum sample size for means	30	2–10000	When a group has fewer samples in a comparison of means, a caveat says the sample is small
Minimum expected frequency for rates	5	1–50	When a cell's expected frequency is below this, Fisher's exact test is used
Minimum denominator for ratio breakdowns	30	1–100000	Segments with a smaller denominator are marked small and left out of the Simpson's paradox judgment
Maximum analyses per agent execution	12	1–50	Once used up, further analysis tool calls from the agent are told the analyses are used up
Maximum drill-down depth	3	1–6	How many levels a change decomposition can drill down
Segments listed	10	3–30	How many segments of a result table the writing catalog and the reply to the agent list

Sandbox backend

Settings → Runtime Environment lists every candidate and its availability, and marks the current choice and its selection reason. Automatic selection works in this order:

1. When the microVM dependencies are installed and the image is cached (and, on Linux, /dev/kvm is accessible), choose microvm.
2. Otherwise macOS uses seatbelt, and Linux uses bubblewrap once bwrap is installed (apt install bubblewrap).
3. When none is available, fall back to local. This path has no access control at all, and the UI clearly marks it in red.

• An installed bwrap counts as available only if it can actually create namespaces: the first selection after startup launches bwrap once as a probe. Under Docker's default permissions or Ubuntu 24.04's AppArmor restrictions the probe fails, bubblewrap is skipped, and the selection reason says so.
• Force a backend with AGENTLAB_SANDBOX_BACKEND, or choose the isolation tier on an individual code node (strict = microVM, fast = OS sandbox).
• Once the backend has chosen a sandbox, the whole process keeps using it. After installing microVM, restart the service for it to choose again.

For what each sandbox tier guarantees and its limits, see Identity, Permissions, and Security.

Enable microVM

There are three preparations, each done only once; day-to-day startup doesn't change:

1. The Python package: already installed when the environment is created from environment.yml (including the [microvm] extra).
2. The runtime (about 50MB, placed in ~/.microsandbox): once per machine.
3. The OCI image (measured at about 55 seconds): once per image.

Items 2 and 3 are done by hand. After installing, check the machine's virtualization support with doctor, then restart dev.sh:

conda run -n agentlab python -c "exec('import asyncio, microsandbox as m\nasync def main():\n    await m.install()\nasyncio.run(main())')"
conda run -n agentlab microsandbox pull python:3.12-slim   # the default image; if you changed AGENTLAB_MICROVM_IMAGE, pull that one
conda run -n agentlab microsandbox doctor

• Pull the image first. Auto mode only chooses microVM when its image is already cached (so the first code run doesn't hang for a minute), but the image is only pulled when code runs inside a microVM. If you don't pull it by hand, auto mode never chooses it. If you don't want to wait, set AGENTLAB_SANDBOX_BACKEND=microvm, and the image is pulled the first time code runs.
• If pulling fails with registry error: error sending request for url (https://index.docker.io/...), Docker Hub can't be reached. msb honors HTTPS_PROXY / ALL_PROXY, so you can point it at a proxy: HTTPS_PROXY=http://<proxy-host>:<port> microsandbox pull python:3.12-slim. Or pull from a mirror you can reach (write the full name, such as <mirror>/library/python:3.12-slim), then set AGENTLAB_MICROVM_IMAGE to the same name.
• install must be called inside a running event loop. Writing asyncio.run(m.install()) would call it outside the loop first and fail with RuntimeError: no running event loop, which is why the command above wraps it in an async def.
• Linux needs /dev/kvm, and the current user needs permission for it. When doctor reports "/dev/kvm is not accessible by your user":

sudo usermod -aG kvm $USER               # permanent; takes effect after you log in again
sudo setfacl -m u:$USER:rw /dev/kvm      # usable immediately in the current session

• You can also just run microsandbox doctor --fix. Run doctor again afterwards; everything should be ✓.
• All green in doctor doesn't mean item 2 is installed: doctor only checks the msb and libkrunfw bundled in the wheel, while AgentLab decides with microsandbox.is_installed(), which looks at ~/.microsandbox. So you still need to run the install command above.
• All three live on disk, so restarting the machine or reopening the terminal doesn't mean redoing them. When the microvm entry under Settings → Runtime Environment shows warm as true, all three are in place, and auto mode switches to microvm by itself.
• Change the image with AGENTLAB_MICROVM_IMAGE; how long idle VMs are kept before being reclaimed is set by AGENTLAB_MICROVM_IDLE_SECONDS.