ai-agent-gateway

Deploy an AI agent as an HTTP/SSE service without rebuilding session, tool, approval, and execution infrastructure around the model loop. Requires Python 3.10 or newer.

Ownership

This package owns the generic FastAPI chat surface, sessions and JWTs, SSE event stream, provider/run loop, tool dispatch, approvals, code execution, skills, sub-agents, autonomous execution, and heartbeat support.

It does not own an embedding product's profiles, prompts, channel policy, domain tools, research data, or business schemas. Applications supply those through create_gateway_app() or the higher-level create_agent() inputs.

The wheel imports without an application checkout. Product integrations bind policy and identity callbacks plus optional batch/operator-schedule backends in GatewayServerConfig; schema-backed artifact tools and routes belong to the embedding application. server_policy is process-scoped: configure it before building runtimes and use one product policy per gateway process.

Main entrypoints

run_gateway_server() is the serving entry on both local supported launches, not only the privileged host one: the immutable runtime's serving child calls it directly, so claim-signing adoption, uvicorn.Config/Server construction and the launch-failure journal have one owner.

The package does not infer a new model or credential inside autonomous execution. The application resolves and passes the exact bound capability, session, billing mode, and skill limits before calling run_autonomous().

Model selection and model request facts come from model_authority (hank-model-authority; in the monorepo packages/model-authority/), which this package depends on and carries no copy of: the registry, the selection policy, each entry's per-family compat block and the rate tables. CapabilityExecutionResolver.select_bind() in capability_execution.py selects and authorizes a secret-free bind through that owner, without materializing credentials or constructing an adapter. resolve() selects and materializes once; materialize_bind() executes an already selected bind without reselection. Both selection methods accept already-lowered role/exact effort and the admission owner's effective role_map with its source. They use the shared model-authority resolver; this adapter does not capture principal preferences or own a map registry. agent_gateway/providers/ builds each request from the bound entry. Mechanism: docs/reference/model-authority.md.

admit(SpawnRequest(...), parent, owners=AdmissionOwners(...)) returns the selected context and exact ResolvedAuthority, without materializing credentials. admit(ContinueRequest(...), parent, owners=ContinuationOwners(...)) instead retains the original AdmittedTask: no model reselection, semantic recompile, or replacement tool grant. Its context preserves lineage and recorded spend; placement_lost refreshes only backend placement/environment facts. Both results carry the child's approval route from child_approval_route(): a child never holds DurableLocalApprovalRoute; an in-process child records into its parent's ledger through InProcessDelegatedApprovalRoute, a detached child adopts its inherited channel as ParentDelegatedApprovalRoute, and a sessionless root's children carry its ControlPlaneApprovalRoute. The existing task registry, transcript reconstruction, attempt creation and credential materialization remain outside this pure step. A valid older task without the context projection keeps its durable authority; missing durable routes/definitions retain the resume owner's existing handling. AdmissionOwners.frozen_bind retains a plan-frozen model bind without reselection; materialize_bind() is the single reauthorization owner. Fresh spawns select effort within the registered role; profile overlays remain deferred. Declared operation children determine nested spawning. Role narrowing reads the authenticated admitted tenant, not the presence or role of an optional runtime session. The product policy still reads real session/profile capability facts for its scoped exceptions; sessionless launch does not mint a synthetic session or gain those exceptions. Operation-private routes may extend the parent's visible surface, but cannot bypass its withheld routes. Admission matches withheld canonical route IDs and their registered policy names before freezing the executable grant. The admitted wire retains withheld_tools across descendants and durable continuation. Resume keeps its original executable grant while descendants retain recorded exclusions plus the current parent's known exclusions. MessageRequest and HandleRequest retain the caller's job context, using spawn-passed handles plus current own/descendant outputs supplied by the durable output owner. Message references are citations, never grants to the recipient. The authenticated turn edge calls root_context() after approval, verified research binding and withheld tools are ready; current_parent_context() reads that captured scope without minting authority. A child session installs context_for_child(..., job_id=<physical task id>) with capture_job_context() and cannot regain root scope. Sessionless control/schedule edges use root_context_from_identity() with their authenticated identity, live roster and approval owner. TaskRegistry recovery validates the canonical task and keeps its model bind as execution authority. Duplicate registration/message bind receipts are optional observations, not prerequisites or competing authority; canonical corruption and task/result/message identity checks still apply.

Quick start

Install a provider extra and set its credential:

pip install "ai-agent-gateway[anthropic]"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export USER_DATA_DIR="$PWD/.agent-data"
mkdir -p "$USER_DATA_DIR/gateway"
chmod 700 "$USER_DATA_DIR" "$USER_DATA_DIR/gateway"

Create and run a project:

agent init my-agent
cd my-agent
agent run

Or create a FastAPI app directly:

from agent_gateway import create_agent

app = create_agent(
    "You are a concise research assistant.",
    skills_dir="skills",
)

Serve the module with uvicorn agent:app. The default is the stable session.driver entry in the configured model registry; applications can pass another eligible stable model_key.

For OpenAI, install ai-agent-gateway[openai] and set OPENAI_API_KEY. Managed provider login flows are available through agent auth login anthropic, agent auth login codex, and agent auth login xai. Use agent auth status <provider> for all built-ins. agent auth login anthropic is this gateway's own Claude OAuth login: run it once per Claude account to enroll interchangeable siblings, then agent auth list anthropic (masked) and agent auth remove anthropic <identity> manage them. Gateway token-store logout applies to Anthropic and XAI; Codex credentials are managed by the Codex CLI, and OpenAI uses OPENAI_API_KEY.

The complete session-token, chat request, and SSE walkthrough is in the quickstart.

Request shape

HTTP client
  -> create_agent() or create_gateway_app()
  -> ChatRuntime
  -> AgentRunner.run() (provider stream and model/tool loop)
  -> ToolDispatcher.dispatch()
       |-- local Python handler
       |-- MCP stdio server
       |-- approval policy
       |-- code execution
       `-- run_agent sub-agent
  -> EventLog -> SSE response

Headless calls use the same runner, tools, skills, and provider abstractions, but return RunOutput instead of exposing an HTTP/SSE session.

Develop and verify

From the repository root, update a gateway dependency with the constrained target (the dev lock is compiled --constraint api/requirements.txt so the two locks cannot disagree; docs/reference/python-environments-and-packaging.md):

make gateway-deps-update PKG=<name>   # then: make gateway-deps-install

For coupled targets, use multiple space-separated --upgrade-package <name>==<ver> flags. In the monorepo checkout, the shortcut is make gateway-deps-update PKG='<name>==<ver>' (PKG accepts a space-separated list).

From the package root, run:

pytest tests

The suite and its gateway_test_support/ fixtures are package-local; no embedding application checkout or product configuration is required. Product integration scenarios belong in the embedding application's tests, not this package suite.

Use the focused test for the boundary you change. Runnable examples live under examples/; the production-style assembly example is examples/07-full-production/, and the headless fixture is examples/09-autonomous/.

Documentation

• Quickstart
• Architecture
• HTTP and SSE API
• Python API reference
• MCP server configuration
• Framework comparison
• Contributing

License

MIT