concursus 0.4.2

Compile a declarative DAG of subagents into an orchestrated team on AWS Bedrock AgentCore.

Concursus

Compile a declarative DAG of subagents into a deployed, orchestrated team on AWS Bedrock AgentCore.

Concursus (Latin "a running-together / convergence") is cursus's agent-orchestration sibling. Where cursus compiles a pipeline DAG + configs into a SageMaker pipeline, Concursus compiles an AgentDAG + per-agent .agent.yaml manifests into (1) an AgentCore provisioning plan — one CreateAgentRuntime per agent — and (2) a supervisor that dispatches the agents in topological order, wires each agent's declared output into its dependents' input, and routes shared state through AgentCore Memory.

It is the coordinator AgentCore deliberately doesn't ship: AgentCore gives you transport (A2A), tool discovery (Gateway), microVM isolation, identity, memory, and hosting — but no scheduler, dependency graph, or supervisor. You declare a DAG of agents; Concursus provisions them and runs them.

Status: alpha. This release ships the declarative core (AgentDAG + AgentManifest) and the offline compiler: the dependency resolver, the runtime builder, the OrchestrationAssembler (DAG + manifests → a ProvisioningPlan), and the topological Supervisor — plus the plan / deploy / run CLI verbs. The compiler is pure-Python; boto3 stays behind the [agentcore] extra and is imported lazily only when deploy --execute / run --execute actually calls AWS.

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Installation

pip install concursus                 # declarative core (pure Python)
pip install "concursus[agentcore]"    # + the AWS Bedrock AgentCore runtime binding (roadmap)

Requires Python 3.10+.

Quick start

Declare a team as an AgentDAG (nodes = agents, edges = data dependencies):

from concursus import AgentDAG

dag = AgentDAG()
for agent in ["ingest", "summarize", "critique", "format"]:
    dag.add_node(agent)
dag.add_edge("ingest", "summarize")
dag.add_edge("summarize", "critique")
dag.add_edge("critique", "format")

dag.topological_sort()   # ['ingest', 'summarize', 'critique', 'format']  <- dispatch order
dag.validate()           # raises if the topology has a cycle

Describe each agent with an .agent.yaml manifest — its AgentCore binding + typed interface:

# summarize.agent.yaml
registry:
  container_uri: 111122223333.dkr.ecr.us-east-1.amazonaws.com/summarize-agent:latest
  role_arn: arn:aws:iam::111122223333:role/ConcursusAgentRuntimeRole
  network_mode: PUBLIC       # or VPC
  protocol: HTTP             # HTTP (/invocations) | MCP (/mcp) | A2A (/)
  qualifier: DEFAULT
  # ...or reuse an already-deployed agent:
  # agent_runtime_arn: arn:aws:bedrock-agentcore:us-east-1:111122223333:runtime/summarize-xyz
contract:
  inputs:
    document: {type: string}
  outputs:                   # required — the dependency resolver's type gate
    summary: {type: string}
    key_points: {type: array, items: {type: string}}
spec:
  depends_on:
    - {from: ingest.document, to: document}

from concursus import AgentManifest

m = AgentManifest.from_yaml("summarize.agent.yaml").validate()
m.protocol        # 'HTTP'
m.output_schema   # {'summary': {...}, 'key_points': {...}}

Or from the CLI:

concursus info                        # overview
concursus validate *.agent.yaml       # validate manifests
concursus --version

Compile a plan (plan → deploy → run)

Point the compiler at your manifests. Edges are inferred from each manifest's depends_on (or pass --dag ingest->summarize to set them explicitly). plan prints a JSON ProvisioningPlan — a topological order, one create_agent_runtime entry per agent, and the resolved producer→consumer wiring — without touching AWS:

concursus plan *.agent.yaml

from concursus import AgentDAG, AgentManifest, OrchestrationAssembler, Supervisor

manifests = {m.name: m for m in map(AgentManifest.from_yaml, paths)}
dag = AgentDAG()
for name in manifests:
    dag.add_node(name)
dag.add_edge("ingest", "summarize").add_edge("summarize", "critique")

plan = OrchestrationAssembler(account="111122223333", region="us-east-1").assemble(dag, manifests)
plan.order           # ['ingest', 'summarize', 'critique']  <- dispatch order
plan.to_dict()       # JSON-serializable preview (what `concursus plan` prints)

deploy dry-runs what would be created (nothing imported); --execute provisions each agent end-to-end — ensure its IAM execution role, build + push its image to ECR (when the plan carries a placeholder URI), then CreateAgentRuntime — reusing an existing image or runtime ARN as-is. run dry-runs the topological dispatch; --execute invokes the live runtimes, threading each output into its dependents:

concursus deploy *.agent.yaml                          # dry-run: the role/image/create steps
concursus deploy *.agent.yaml --execute --source-dir . # + boto3 + docker: role → ECR image → create
concursus run    *.agent.yaml --inputs '{"uri": "s3://doc"}'            # dry-run the dispatch
concursus run    *.agent.yaml --inputs @inputs.json --execute          # live InvokeAgentRuntime

outputs = Supervisor(plan, manifests).run({"uri": "s3://doc"})   # {node_id: output_dict}

How it works (the compile target)

Concursus compiles AgentDAG + manifests through validate → resolve → provision → assemble, mapping cursus concepts onto AgentCore primitives:

cursus	Concursus	AgentCore primitive
PipelineDAG	AgentDAG	dispatch order (topological)
.step.yaml	.agent.yaml manifest	container image + roleArn + protocol
DependencyType enum	output JSON Schema (mandatory)	the resolver's type gate
PropertyReference (deferred)	AgentRef (eager JSONPath)	InvokeAgentRuntime response
step registration	agent registration	CreateAgentRuntime → ARN + V1 + DEFAULT endpoint
PipelineAssembler → Pipeline	OrchestrationAssembler → supervisor + plan	BedrockAgentCoreApp supervisor
S3 artifact channels	shared run state	AgentCore Memory

The supervisor dispatches agents in topological order, invokes each with InvokeAgentRuntime under one runtimeSessionId (session affinity → warm microVMs), extracts each producer's output by JSONPath and injects it into its consumers, and persists outputs to Memory so state survives the ephemeral microVMs.

Durable run state (the StateStore seam)

The supervisor threads every output through a StateStore — an append-only log of validated outputs plus a derived {node: output} projection (the slipbox's single-source-of-truth / derived-DB discipline). Three backends share one Protocol:

• InProcessStateStore — the zero-dependency, offline default. Nothing new to install.
• MemoryStateStore — opt-in, AgentCore Memory-backed. Each validated output is one Blob event; a run resumes by replaying its event log, so it survives micro-VM teardown / mid-run crashes — the supervisor skips any node already completed(). boto3 is imported lazily (the [agentcore] extra); pass run --memory-id <id> [--actor-id <id>] --execute. For long-lived / standing loops, an optional checkpoint() compacts the log so a warm resume reads only O(events-since-the-last-checkpoint) (a CHECKPOINT snapshot event + an epoch tag, resumed via bounded EQUALS_TO filters) instead of the whole session; the log stays the source of truth and resume falls back to the full rebuild when no checkpoint is present.
• FileVaultStateStore — opt-in, persistent on-disk (no AWS). Each record is written as a round-trip-exact markdown note under <vault>/runs/<session>/ (two authoritative base64 JSON blobs — meta + payload — are the source of truth; everything else is a greppable display copy), so a run is durable and inspectable offline and resumes by reloading the notes. Notes are Abuse-SlipBox-conformant by default (P.A.R.A. tags, a derived building_block, folgezettel/lineage forming a per-run trail, a typed H1, a ## Related Notes section) — they validate under check_note_format.py and read as a genuine slipbox trail; slipbox_form=False gives a lean machine schema. concursus.build_run_db materializes a derived, gitignored SQLite graph/index over the notes (metadata postings, consumes edges, the execution-address tree, a latest-validated projection view) — the notes stay canonical, the DB is disposable. Pure stdlib; pass run --vault <dir> --execute.

Each record also persists its resolved AgentRef edges (consumes), turning the log into a queryable run graph (RunGraph: upstream/downstream, a structural validate, bounded context_order). Supervisor.context(node) returns a node's transitive upstream outputs — shared context as a query, not point-to-point wiring:

from concursus import Supervisor, InProcessStateStore

sup = Supervisor(plan, manifests, state_store=InProcessStateStore())
outputs = sup.run({"uri": "s3://doc"})   # {node_id: output_dict}
sup.context("critique")                  # {producer: output} for its transitive upstream

The governor (opt-in dynamic outer loop)

The compiler and supervisor are static: assemble freezes one ProvisioningPlan and Supervisor.run executes it in a single forward pass. When you want a dynamic control loop — standing "keep-the-lights-on" monitoring, replan-on-signal, program/portfolio rollups — the governor subpackage wraps a bounded cycle around the compiler. The design is a dynamic outer loop hosting freeze inner episodes: each round the governor forms a fresh frozen plan at the compiler front and dispatches one new bounded Supervisor.run episode. It never reaches inside a running supervisor, never mutates a frozen plan, and never turns the compiler into a runtime governor — the compiler-not-runtime-governor identity holds.

It is entirely opt-in. The zero-config path stays static assemble → Supervisor.run; you reach for the governor only when you want the cyclic driver. Like the reasoning tier, LangGraph stays optional — the loop mirrors the DKSEngine template (lazy import, pure-Python fallback), so everything imports and runs with no langgraph installed.

from concursus import GovernorLoop

# A bounded cycle: planner (assemble/recompile) -> router -> run_episode (one Supervisor.run)
# -> collect -> {replan | synthesize}. Terminates on frontier-exhaustion / stall / max_rounds
# / a hard step_cap. backend="auto" uses LangGraph if present, else the pure-Python driver.
loop = GovernorLoop(goal="triage-abuse-signal", manifests=manifests, max_rounds=8)
result = loop.run({"uri": "s3://signal"})   # GovernorResult: plan sequence + folded episode log

The subpackage is layered strictly outside the compiler (identity invariants INV-1..INV-5):

• GovernorState — persistent outer-loop state: the sequence of frozen plan VALUEs by plan_version + a pointer to the append-only StateStore log (the sole executed-prefix anchor), never a mutable compiler plan.
• GovernorLoop — the fixed cyclic driver. planner forms a fresh frozen plan each round (first via plan_from_goal + assemble, later via monotonic recompile); run_episode calls Supervisor.run once; collect folds outputs into the log and re-derives the executed prefix from store.completed(). Durable dual resume (outer plan_version checkpoint + inner log replay) when backed by a MemoryStateStore.
• TrustLadderScheduler — the router's per-decision matcher: matches each ready step to a standing agent (read-only AgentRegistry), reads its earned trust, and proposes a frontier (DISPATCH / ESCALATE L1→L3 / UNMATCHED) that feeds the next recompile — never mutating a frozen plan.
• AgentRegistry — the governor's process table: a read-only versioned view over the shipped DeployLedger answering "which standing agent, at which version, can do task X?" (the ledger answers content-identity only). Spawn/fork delegate to the shipped provision_agent.
• DirectorCockpit — a read-only director view composing a briefing, an exception queue, and a runs monitor purely out of render_precedent_hub + Supervisor.summary()/.index().
• KTLODaemon — a standing monitor above the loop (monitor → triage → escalate → replan | close) that wakes on a live EventSource + drift and dispatches one fresh bounded episode per investigation. LAUNCH (one-shot drain) vs KTLO (standing, max_ticks-bounded) is a config, not two code paths.
• scope — the org → portfolio → program → task layer above the single run: a ScopeAddress stack, a cross-program programs index (the program-grain analogue of the runs-grain precedent hub), and a 1:N director_leverage_view — all read-only projections over the per-run precedent notes.

These modules are now wired into the loop behind opt-in seams (identity-preserving; the default GovernorLoop(...) with no scheduler= and deliberate=False is byte-for-byte today's behavior):

• router gates the frontier by earned trust — pass scheduler=TrustLadderScheduler(...) and each round router holds below-bar (ESCALATE) and no-agent (UNMATCHED) nodes out of the episode via the Supervisor's opt-in held skip param — a pure non-dispatch that never mutates the frozen plan.order (the held node stays in the open frontier for a later round). Held nodes surface on GovernorResult.escalated / .unmatched.
• collect re-earns trust GOV-side — with a scheduler wired, each node re-earns its grade via update_trust the round it first completes (keyed by matched agent name); the only place earned trust moves across episodes, never in the compiler, never per-invocation.
• planner can deliberate before freezing — pass deliberate=True to author round-1's DAG via the bounded form_plan deliberation (adjust → converge → lower to a frozen AgentDAG) strictly before assemble; later rounds still use recompile. Defaults to deterministic stubs — no LLM.
• live read-only cockpit / scope — loop.cockpit(), loop.programs_index(vault), and loop.leverage_view(vault) render the DirectorCockpit / scope projections over the loop's own log and final frozen plan — pure reads that dispatch nothing.

The scheduler/deliberation seams also thread up through the standing daemon and out to the cockpit (opt-in; the default KTLODaemon(...) with no scheduler= and deliberate=False is byte-for-byte today's plain episode):

• the KTLODaemon can spawn governed episodes — pass scheduler=TrustLadderScheduler(...) (and/or deliberate=True) and the daemon forwards them into each fresh bounded GovernorLoop it enqueues per triggered investigation, so a keep-the-lights-on run is trust-gated and can deliberate before freezing. The daemon still only enqueues fresh loops over fresh stores and holds no mutable plan (INV-1/INV-4).
• the cockpit surfaces the governance holds — DirectorCockpit.exception_queue() now folds the last episode's below-bar ESCALATE and no-standing-agent UNMATCHED holds in alongside the failed-node rows (threaded through loop.cockpit()), so a held frontier is operator-visible, not just failures — still a pure read that dispatches nothing.
• an unmatched-node stall is named — when a governed loop can make no further progress solely because every remaining open-frontier node is UNMATCHED, GovernorResult.terminated_by reports the distinct unmatched_stall label instead of a generic stall, so the terminal cause is explicit.

Two shipped-but-idle core seams are now wired into the dispatch path (C-3, identity-preserving): the Supervisor constructor runs the shipped RunGraph.validate() once as a pre-dispatch structural gate (a dangling AgentRef or cycle is rejected before the first invoke; run() stays a single static pass), and MemoryStateStore.replay() is documented as a full cold rebuild — the INV-5-correct choice, since AgentCore's nextToken is an opaque pagination cursor, not an events-after filter.

Roadmap

• Declarative core: AgentDAG + AgentManifest (.agent.yaml) + validation + CLI
• Dependency resolver over declared output JSON Schemas (AgentRef wiring + type-gating)
• OrchestrationAssembler: emit an AgentCore provisioning plan (CreateAgentRuntime per agent + synthesized IAM roles + endpoints)
• The supervisor: topological dispatch over InvokeAgentRuntime with AgentRef wiring + one stable runtimeSessionId
• plan / deploy / run CLI verbs (deploy/run --execute bind boto3 lazily)
• Memory-backed shared run state (the StateStore seam: in-process default / AgentCore Memory opt-in, replay-resume, the AgentRef link graph + context(node))
• The governor: an opt-in dynamic outer loop (GovernorLoop / TrustLadderScheduler / AgentRegistry / DirectorCockpit / KTLODaemon / scope) that drives the freeze compiler as bounded episodes (LangGraph optional)
• Gateway/A2A node types; a data-driven catalog + recommender of team topologies

License

MIT © Tianpei Xie