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Assis Peixoto"},"license":"MIT","homepage":"https://github.com/paulogustavopeixoto/agnostic-agents#readme","repository":{"type":"git","url":"git+https://github.com/paulogustavopeixoto/agnostic-agents.git"},"description":"A Node.js package for multi-LLM agent support","maintainers":[{"name":"pauloassispeixoto","email":"paulogustavopeixoto@gmail.com"}],"readme":"![NPM Version](https://img.shields.io/npm/v/agnostic-agents) ![NPM Downloads](https://img.shields.io/npm/dy/agnostic-agents)\n\n# agnostic-agents\n\n`agnostic-agents` is a Node.js runtime OS for building provider-agnostic agent systems.\nThe longer direction is broader than provider agnosticism alone: the package is\nmeant to stay agnostic to how intelligence is produced, whether that comes from\nan LLM, tool, workflow, simulator, verifier, or human checkpoint.\n\nIt is designed for projects that need more than a chat wrapper:\n\n- inspectable runs\n- checkpoints and replay\n- approvals and policy-gated tools\n- workflows and delegation\n- grounded retrieval and layered memory\n- governed memory with provenance, retention, conflict handling, and access controls\n- distributed handoff across processes or services\n- evals, benchmarks, and incident analysis\n- API and protocol tools imported from OpenAPI, curl, and MCP discovery\n- import, sandbox, record, and simulate external tools before production use\n\nThe package also includes a separate coordination layer above the runtime for:\n\n- structured critique\n- disagreement resolution\n- task decomposition\n- coordination benchmarks\n\nIt also includes governed learning, fleet rollout, and assurance surfaces for:\n\n- reviewed improvement proposals and bounded adaptation\n- staged rollout and canary control across many runtimes\n- invariant checks and rollout blocking before unsafe changes spread\n\nThe maintained architecture is:\n\n- runtime OS first\n- coordination intelligence above the runtime\n- governed learning above coordination\n- fleet and assurance layers above runtime, coordination, and learning\n- operator control above those layers\n- next-horizon capability routing, governed memory, and budgeted autonomy on top of the current core\n\n## Install\n\n```bash\nnpm install agnostic-agents\n```\n\nThe package ships a maintained `index.d.ts`, so TypeScript projects get typed access to the public surface without extra setup.\n\n## What This Package Is Good For\n\nUse `agnostic-agents` when you want:\n\n- a single-agent runtime with tools, approvals, memory, and replay\n- a workflow runner with explicit steps and child-run lineage\n- a provider-agnostic execution layer behind one adapter contract\n- a runtime substrate for higher-level worker or organization systems\n- a package that keeps control, observability, and governance in the open\n- a package that can turn evidence into governed improvement proposals instead of hidden self-modification\n- a package that can keep learned changes inside explicit safety envelopes and measure their effect after review\n- a package that can turn eval, incident, and branch evidence into concrete runtime or coordination change plans\n- a package that can benchmark learned changes and halt adaptation when those changes start regressing outcomes\n- a package that can stage, compare, and roll back changes across a fleet of runtimes\n- a package that can block unsafe rollout candidates with explicit invariants and assurance reports\n- a package that can evolve toward capability-aware routing instead of static model/provider defaults\n- a package that treats memory as governed operational knowledge instead of ad hoc retrieval glue\n- a package that can audit what memory was stored, recalled, blocked, expired, or superseded\n- a package that treats supervised autonomy as a core operating model rather than an exception path\n- a package that can turn OpenAPI files, curl commands, and MCP discovery into governed executable tools\n- a package that can bootstrap tools from Postman collections, record real tool I/O, mock them offline, and resolve secrets outside prompts and code\n\nDo not think of it as only a prompt helper or a chat abstraction. The maintained direction is a runtime control layer for serious agent systems.\n\nThe repo is intentionally split so that:\n\n- runtime primitives stay general-purpose and portable\n- coordination logic stays inspectable and separate from the runtime kernel\n- learning/adaptation remains governed instead of hidden in opaque agent glue\n- fleet rollout and assurance remain operator-visible instead of collapsing into opaque control-plane logic\n\n## Fastest Start\n\nIf you just want a working tool-using agent, start here:\n\n```js\nconst { Agent, Tool, OpenAIAdapter } = require('agnostic-agents');\n\nconst adapter = new OpenAIAdapter(process.env.OPENAI_API_KEY, {\n  model: 'gpt-4o-mini',\n});\n\nconst calculator = new Tool({\n  name: 'calculate',\n  description: 'Evaluate a basic arithmetic expression.',\n  parameters: {\n    type: 'object',\n    properties: {\n      expression: { type: 'string' },\n    },\n    required: ['expression'],\n  },\n  implementation: async ({ expression }) => ({\n    result: Function(`\"use strict\"; return (${expression})`)(),\n  }),\n});\n\nconst agent = new Agent(adapter, {\n  tools: [calculator],\n  description: 'Use tools when they help.',\n  defaultConfig: { temperature: 0.2, maxTokens: 300 },\n});\n\nconst answer = await agent.sendMessage('What is 12 * 7?');\nconsole.log(answer);\n```\n\nIf you want the maintained runtime path instead of the shortest chat path, use `Agent.run()`:\n\n```js\nconst { Agent, Tool, OpenAIAdapter, RunInspector } = require('agnostic-agents');\n\nconst adapter = new OpenAIAdapter(process.env.OPENAI_API_KEY, {\n  model: 'gpt-4o-mini',\n});\n\nconst sendUpdate = new Tool({\n  name: 'send_status_update',\n  parameters: {\n    type: 'object',\n    properties: {\n      recipient: { type: 'string' },\n      summary: { type: 'string' },\n    },\n    required: ['recipient', 'summary'],\n  },\n  metadata: {\n    executionPolicy: 'require_approval',\n    sideEffectLevel: 'external_write',\n  },\n  implementation: async ({ recipient, summary }) => ({\n    delivered: true,\n    recipient,\n    summary,\n  }),\n});\n\nconst agent = new Agent(adapter, {\n  tools: [sendUpdate],\n  verifier: adapter,\n  defaultConfig: { selfVerify: true },\n});\n\nlet run = await agent.run('Send Paulo a short update saying the runtime is ready.');\n\nif (run.status === 'waiting_for_approval') {\n  run = await agent.resumeRun(run.id, {\n    approved: true,\n    reason: 'approved in demo',\n  });\n}\n\nconsole.dir(RunInspector.summarize(run), { depth: null });\n```\n\n## Core Surfaces\n\n### Runtime\n\nThe maintained runtime surface centers on:\n\n- `Agent`\n- `Run`\n- `RunInspector`\n- `ToolPolicy`\n- `ApprovalInbox`\n- `GovernanceHooks`\n\nThat gives you:\n\n- pause/resume/cancel\n- tool approval gating\n- replay and branching\n- structured events\n- run inspection and trace export\n\n### API and MCP Tool Import\n\nThe maintained import surfaces are:\n\n- `OpenAPILoader`\n- `ApiLoader`\n- `CurlLoader`\n- `PostmanLoader`\n- `MCPDiscoveryLoader`\n\nUse them when you want to:\n\n- turn an OpenAPI file into executable runtime tools\n- normalize a custom API spec into runtime tools\n- bootstrap a tool from a working curl command\n- bootstrap tools from Postman collections and variables\n- discover remote MCP tools and expose them through the normal tool/runtime path\n\nThe maintained utility surfaces above the runtime also include:\n\n- `SecretResolver`\n- `SchemaNormalizer`\n- `ToolRecorder`\n- `ToolMockBuilder`\n- `ToolSandboxRunner`\n- `PromptArtifact`\n- `PromptRegistry`\n- `RunRecipe`\n- `WorkflowPreset`\n- `IncidentBundleExporter`\n- `CredentialDelegationKit`\n- `RoutePolicySimulator`\n\nThe first maintained memory-governance surfaces now include:\n\n- `MemoryProvenanceLedger`\n- `MemoryRetentionPolicy`\n- `MemoryAccessController`\n- `MemoryConflictResolver`\n- `MemoryAuditView`\n- `MemoryGovernanceBenchmarkSuite`\n- `MemoryGovernanceDiagnostics`\n- `MemoryGovernanceReviewWorkflow`\n\n### Workflow and Delegation\n\nFor explicit multi-step work, use:\n\n- `Workflow`\n- `WorkflowStep`\n- `AgentWorkflowStep`\n- `WorkflowRunner`\n- `DelegationRuntime`\n- `DelegationContract`\n\nThis is the maintained orchestration layer. Legacy `Task` and `Orchestrator` remain only for compatibility.\n\nExample:\n\n```js\nconst {\n  Agent,\n  Workflow,\n  AgentWorkflowStep,\n  WorkflowRunner,\n  DelegationRuntime,\n  DelegationContract,\n  OpenAIAdapter,\n} = require('agnostic-agents');\n\nconst adapter = new OpenAIAdapter(process.env.OPENAI_API_KEY);\nconst delegationRuntime = new DelegationRuntime();\n\nconst researcher = new Agent(adapter, {\n  description: 'Produce concise factual bullet points.',\n});\n\nconst writer = new Agent(adapter, {\n  description: 'Turn findings into short status updates.',\n});\n\nconst workflow = new Workflow({\n  id: 'daily-sync',\n  steps: [\n    new AgentWorkflowStep({\n      id: 'research',\n      agent: researcher,\n      delegationRuntime,\n      delegationContract: new DelegationContract({\n        id: 'research-contract',\n        assignee: 'researcher',\n        requiredInputs: ['prompt'],\n      }),\n      prompt: 'List three runtime capabilities in bullet form.',\n    }),\n    new AgentWorkflowStep({\n      id: 'draft_update',\n      agent: writer,\n      dependsOn: ['research'],\n      delegationRuntime,\n      delegationContract: new DelegationContract({\n        id: 'writer-contract',\n        assignee: 'writer',\n        requiredInputs: ['prompt'],\n      }),\n      prompt: ({ results }) => `Turn this research into a short update:\\n${results.research.output}`,\n    }),\n  ],\n});\n\nconst run = await new WorkflowRunner({ workflow }).run('Prepare a daily sync update');\nconsole.log(run.output);\n```\n\n### Memory and Retrieval\n\nUse:\n\n- `Memory` for conversation, working, profile, policy, and semantic layers\n- `RAG` for grounded retrieval with provenance\n\n```js\nconst { Agent, Memory, RAG, LocalVectorStore, OpenAIAdapter } = require('agnostic-agents');\n\nconst adapter = new OpenAIAdapter(process.env.OPENAI_API_KEY);\nconst memory = new Memory();\nconst rag = new RAG({\n  adapter,\n  vectorStore: new LocalVectorStore(),\n});\n\nawait rag.index(['Runtime control requires replay, branching, and inspection.']);\nawait memory.setWorkingMemory('current_project', 'Validate the runtime release');\n\nconst agent = new Agent(adapter, {\n  memory,\n  rag,\n  description: 'Use retrieved context when relevant.',\n});\n\nconsole.log(await agent.sendMessage('What does runtime control require?'));\n```\n\n### Distributed Execution\n\nUse shared stores plus distributed envelopes when one process creates a run and another continues it.\n\n```js\nconst { Agent, FileRunStore } = require('agnostic-agents');\n\nconst runStore = new FileRunStore({ directory: './runtime-runs' });\nconst processA = new Agent(adapterA, { runStore });\nconst processB = new Agent(adapterB, { runStore });\n\nconst run = await processA.run('Prepare the handoff.');\nconst envelope = await processA.createDistributedEnvelope(run.id, {\n  action: 'replay',\n  checkpointId: run.checkpoints[run.checkpoints.length - 1].id,\n  metadata: { handoffTarget: 'worker-b', transport: 'service_call' },\n});\n\nconst remoteRun = await processB.continueDistributedRun(envelope);\nconsole.log(remoteRun.status);\n```\n\n### Coordination Layer\n\nAbove the runtime core, the package now includes:\n\n- `CritiqueProtocol`\n- `CritiqueSchemaRegistry`\n- risk-class and artifact-type-aware critique defaults through `CritiqueSchemaRegistry`\n- `TrustRegistry`\n- `DisagreementResolver`\n- `CoordinationLoop`\n- `DecompositionAdvisor`\n- `CoordinationBenchmarkSuite`\n- `CoordinationRoleContract`\n- `RoleAwareCoordinationPlanner`\n- `CoordinationTrace`\n- `DisagreementResolver` strategies for weighted, majority, trust-consensus, and severity-first coordination\n- `VerificationStrategySelector`\n- `MultiPassVerificationEngine`\n- `CoordinationQualityTracker`\n- `CoordinationDiagnostics`\n\nThis layer is for:\n\n- structured critique records\n- trust-weighted disagreement handling\n- role-aware decomposition and assignment\n- disagreement strategies shaped by trust, severity, and consensus mode\n- multi-pass and adversarial verification paths for higher-risk coordination\n- verifier quality tracked separately from executor quality\n- operator-facing coordination diagnostics for disagreement, missing roles, and verification escalation\n- coordination evals and benchmarks\n\nIt is intentionally separate from the runtime kernel.\n\n## Forward Direction\n\nThe current package already includes:\n\n- `v9` Policy OS core\n- `v10` State OS core\n- `v11` Interop OS core\n- `v12` Coordination OS core\n- `v13` Learning OS core\n- `v14` Fleet OS core\n- `v15` Assurance OS baseline\n\nThe maintained direction after this is refinement and extension of those layers:\n\n- interop depth and external ecosystem adoption\n- stronger coordination quality and diagnostics\n- governed learning refinement instead of hidden autonomy\n- stronger fleet and assurance hardening above the completed core\n\nThe next horizon after the current shipped core is:\n\n- `v16` Operator OS\n  - richer day-2 control, intervention, triage, and governance continuity\n\nThat sequence is intentional.\nThe package should become smarter without collapsing policy, state, interop, coordination, and learning into one opaque layer.\n\nThe current maintained `v14` baseline starts with:\n\n- `FleetRolloutPlan`\n- `FleetHealthMonitor`\n- `FleetCanaryEvaluator`\n- `FleetSafetyController`\n- `FleetImpactComparator`\n- `FleetRollbackAdvisor`\n- `RouteFleetDiagnostics`\n\nThe current maintained `v15` baseline starts with:\n\n- `InvariantRegistry`\n- `AssuranceSuite`\n- `AssuranceReport`\n- `AssuranceGuardrail`\n- `AssuranceRecoveryPlanner`\n- `FleetSafetyController`\n- `FleetImpactComparator`\n- `FleetRollbackAdvisor`\n\nThe current `v16` baseline starts with:\n\n- `OperatorSummary`\n- `OperatorInterventionPlanner`\n- `OperatorTriageWorkflow`\n- `GovernanceRecordLedger`\n- `AuditStitcher`\n- `GovernanceTimeline`\n- `OperatorDashboardSnapshot`\n- `OperatorControlLoop`\n\nUseful docs for these layers:\n\n- [Governed improvement](docs/governed-improvement.md)\n- [Fleet OS](docs/fleet-os.md)\n- [Assurance OS](docs/assurance-os.md)\n- [Operator day-2 guidance](docs/operator-day2.md)\n- [Operator checklists](docs/operator-checklists.md)\n- [Operator OS](docs/operator-os.md)\n\n### Policy and Governance\n\nUse:\n\n- `ToolPolicy` for raw policy logic\n- `PolicyPack` for portable policy artifacts\n- `PolicySimulator` for policy simulation over requests, runs, and trace bundles\n- `PolicyEvaluationRecord` for portable policy evaluation artifacts\n- `PolicyDecisionReport.explain()` for operator-facing policy explanations\n- `PolicyScopeResolver` for scoped policy inheritance across runtime, workflow, agent, and distributed handoff layers\n- `PolicyLifecycleManager` for draft promotion and rollback of policy packs\n- `ApprovalEscalationPolicySuite` for simulating approval and escalation policy scenarios before rollout\n- `RecoveryPolicyGate` for applying policy constraints to replay, branch, and resume recovery paths\n- `CompensationPolicyPlanner` for applying policy to compensation recommendations on side-effecting work\n- `CoordinationPolicyGate` for applying policy to coordination outcomes like retry, reject, or escalate\n- `ExtensionHost` for contributed policy and governance behavior\n- `ProductionPolicyPack` for a maintained production-oriented preset\n- `ApprovalInbox` and `GovernanceHooks` for operator-facing control\n\n### State and Replay\n\nUse:\n\n- `Run` for inspectable runtime state\n- `TraceSerializer` for portable run/trace export\n- `StateBundle` for portable run-plus-memory state snapshots\n- `StateDiff` for high-level state comparison\n- `StateBundleSerializer` for import/export and validation of state bundles\n- `StateContractRegistry` for authoritative-versus-derived state contracts\n- `StateIntegrityChecker` for pre-restore integrity checks\n- `StateConsistencyChecker` for coherence checks across run state, memory, and portable job metadata\n- `MemoryAccessContractRegistry` for explicit memory access contracts across runtime, workflow, coordination, learning, and operator surfaces\n- `StateRestorePlanner` for cross-environment restore planning from portable state bundles\n- `StateDurableRestoreSuite` for process, queue, and service restore scenarios plus workflow/scheduler durability steps\n- `StateIncidentReconstructor` for offline incident reconstruction directly from portable state bundles\n\nThis means users can add or mutate policy dynamically without patching core runtime code.\n\nUseful docs for these surfaces:\n\n- [Policy OS](docs/policy-os.md)\n- [State OS](docs/state-os.md)\n- [State restore](docs/state-restore.md)\n- [State durable restore](docs/state-durable-restore.md)\n- [State incident reconstruction](docs/state-incident-reconstruction.md)\n\n## Provider Surface\n\nThe package supports multiple providers behind one adapter contract.\n\nShared maintained contract:\n\n- `generateText()`\n- `getCapabilities()`\n- `supports(capability)`\n\nCapability support and certification vary by provider. Use these docs instead of assuming parity:\n\n- [Provider compatibility](docs/provider-compatibility.md)\n- [Provider certification](docs/provider-certification.md)\n- [Support matrix](docs/support-matrix.md)\n- [Provider quickstarts](docs/provider-quickstarts.md)\n\nIf you want the strongest maintained end-to-end path today, start with OpenAI.\n\n## Maintained Examples\n\nLocal/no-key examples:\n\n- `npm run example:local-tool`\n- `npm run example:local-rag`\n- `npm run example:local-rag-tool`\n- `npm run example:reference-worker`\n- `npm run example:reference-incident`\n- `npm run example:reference-operator`\n- `npm run example:reference-operator-dashboard`\n- `npm run example:reference-evals`\n- `npm run example:reference-replay-benchmarks`\n- `npm run example:reference-adaptive-benchmarks`\n- `npm run example:reference-v7-audit`\n- `npm run example:reference-coordination-review`\n- `npm run example:reference-decomposition-advisor`\n- `npm run example:reference-coordination-benchmarks`\n- `npm run example:reference-coordination-policy-gate`\n- `npm run example:reference-role-aware-coordination`\n- `npm run example:reference-production-policy-pack`\n- `npm run example:reference-policy-simulation`\n- `npm run example:reference-policy-inheritance`\n- `npm run example:reference-policy-lifecycle`\n- `npm run example:reference-approval-escalation-policy-suite`\n- `npm run example:reference-recovery-policy-gate`\n- `npm run example:reference-compensation-policy-planner`\n- `npm run example:reference-state-bundle`\n- `npm run example:reference-state-restore-planner`\n- `npm run example:reference-state-incident-reconstructor`\n- `npm run example:reference-file-backed-stack`\n- `npm run example:reference-worker-coordination-benchmarks`\n- `npm run example:reference-openapi`\n- `npm run example:reference-durable-backends`\n- `npm run example:reference-distributed-handoff`\n- `npm run example:reference-distributed-incident`\n- `npm run example:reference-remote-control-plane`\n- `npm run example:reference-deployment-split`\n- `npm run example:reference-distributed-recovery`\n\nProvider-backed examples:\n\n- `npm run example:openai`\n- `npm run example:gemini`\n- `npm run example:openai-runtime`\n- `npm run example:openai-v3-runtime`\n- `npm run example:openai-v4-runtime`\n\nSee [examples/README.md](examples/README.md) for labels and intent.\n\n## Recommended Starting Paths\n\nIf you are new to the package:\n\n1. start with `local-tool` or `openai`\n2. move to `Agent.run()` and `RunInspector`\n3. add `ToolPolicy` and `ApprovalInbox` if side effects matter\n4. add `WorkflowRunner` and delegation if you need explicit orchestration\n5. move to file-backed or split deployment references when persistence and operations matter\n\nIf you are evaluating the package for serious runtime usage:\n\n1. read [docs/reference-integrations.md](docs/reference-integrations.md)\n2. read [docs/common-stack-integrations.md](docs/common-stack-integrations.md)\n3. run `reference-file-backed-stack`\n4. run `reference-deployment-split`\n5. run `reference-public-control-plane`\n6. review [docs/operator-workflows.md](docs/operator-workflows.md)\n\n## Documentation Map\n\nAPI and package surface:\n\n- [API reference](docs/api-reference.md)\n- [API stability policy](docs/api-stability-policy.md)\n- [Migration guides](docs/migration-guides.md)\n\nRuntime operations:\n\n- [Reference integrations](docs/reference-integrations.md)\n- [Common stack integrations](docs/common-stack-integrations.md)\n- [Operator workflows](docs/operator-workflows.md)\n- [Operator architecture](docs/operator-architecture.md)\n- [Operator day-2 guidance](docs/operator-day2.md)\n- [Operator checklists](docs/operator-checklists.md)\n- [Operator OS](docs/operator-os.md)\n- [Governed improvement](docs/governed-improvement.md)\n- [Fleet OS](docs/fleet-os.md)\n- [Assurance OS](docs/assurance-os.md)\n- [Multi-runtime operations](docs/multi-runtime-operations.md)\n- [Public control-plane references](docs/public-control-plane-references.md)\n- [Run and trace visualization](docs/run-trace-visualization.md)\n- [Distributed execution](docs/distributed-execution.md)\n- [Remote control planes](docs/remote-control-planes.md)\n- [Distributed identities](docs/distributed-identities.md)\n- [Storage backends](docs/storage-backends.md)\n\nPolicy, governance, and security:\n\n- [Policy and governance packs](docs/policy-governance-packs.md)\n- [Role-aware coordination](docs/role-aware-coordination.md)\n- [Secret handling](docs/secret-handling.md)\n- [Tool auth propagation](docs/tool-auth-propagation.md)\n\nEvals and ecosystem:\n\n- [Benchmarking](docs/benchmarking.md)\n- [Benchmark fixtures](docs/benchmark-fixtures.md)\n- [Capability fabric](docs/capability-fabric.md)\n- [Community summaries](docs/community-summaries.md)\n- [Community roadmap status](docs/community-roadmap-status.md)\n- [Plugin authoring](docs/plugin-authoring.md)\n- [Extension certification and compatibility](docs/extension-certification.md)\n- [Ecosystem certification guidance](docs/ecosystem-certification.md)\n- [Runtime extension contributor guidance](docs/runtime-extension-contributors.md)\n- [Interop OS](docs/interop-os.md)\n- [Interop artifact registry](docs/interop-artifact-registry.md)\n- [Interop schema evolution](docs/interop-schema-evolution.md)\n- [Interop contract validation](docs/interop-contract-validation.md)\n- [Interop certification kits](docs/interop-certification-kits.md)\n- [Third-party control plane interop](docs/interop-control-planes.md)\n- [MCP interoperability](docs/mcp-interoperability.md)\n- [OpenAPI examples](docs/openapi-examples.md)\n- [Cookbook](docs/cookbook.md)\n\nProvider guidance:\n\n- [Provider quickstarts](docs/provider-quickstarts.md)\n- [Provider compatibility](docs/provider-compatibility.md)\n- [Provider certification](docs/provider-certification.md)\n- [Support matrix](docs/support-matrix.md)\n\n## Development\n\nRun the maintained test suites with:\n\n```bash\nnpm test\n```\n\nOther useful commands:\n\n```bash\nnpm run test:unit\nnpm run test:integration\nnpm run test:live\nnpm run test:all\nnpm run test:coverage\n```\n\n## Current Scope\n\nThis package currently targets:\n\n- provider-agnostic runtime execution\n- inspectable runs with replay and lineage\n- approval-gated tool execution\n- layered memory and grounded retrieval\n- explicit workflows and delegation\n- distributed handoff and recovery\n- adaptive runtime tuning and operator-facing evals\n- a separate coordination layer above the runtime\n- governed learning and bounded adaptation\n- fleet rollout and rollback control\n- assurance suites and rollout guardrails\n- operator-centered triage and intervention workflows\n- operator dashboard snapshots and control-loop references\n\n## Forward Direction\n\nThe current package already covers the runtime, coordination, governed\nlearning, fleet, assurance, and operator core program through `v16`.\n\nThe next horizon is not \"add basic runtime features.\"\nThat substrate already exists.\nThe next versions are about turning that substrate into a stronger autonomy\noperating layer:\n\n- `v17` Capability Fabric OS\n  - capability-aware routing across models, tools, methods, simulators, verifiers, budgets, and trust zones\n- `v18` Memory Governance OS\n  - governed memory with provenance, retention, redaction, conflict handling, and operator-visible lifecycle rules\n- `v19` Budgeted Autonomy OS\n  - uncertainty thresholds, reusable human approvals, jurisdiction/tenant-aware policies, and explicit autonomy budgets\n- `v20` Enterprise Autonomy OS\n  - integrated execution graphs, transactional side-effect discipline, multi-agent safety controls, and a coherent AI operating-layer story\n\nThe target shape is six layers:\n\n- kernel\n- policy\n- workflow\n- intelligence\n- memory\n- operator\n\nThe governing rule stays the same:\n\n- reliability over demo complexity\n- observability over abstraction hype\n- supervised autonomy over blind automation\n- governed memory over ad hoc retrieval\n- policy and evaluation over hidden self-modification\n\nThe first maintained `v17` surface is `CapabilityRouter`, which adds\nexplainable capability-aware ranking above raw provider fallback.\nThat routing surface now also plugs into decomposition, role-aware coordination,\nand verification strategy selection.\n\nIt does not try to be:\n\n- a hosted control plane\n- a closed worker-management product\n- a provider-specific framework disguised as a generic runtime\n\nAnd it should not become:\n\n- a runtime that hides coordination and learning decisions behind unreadable internal heuristics\n- a self-modifying agent loop that bypasses policy, replay, or operator review\n","readmeFilename":"README.md"}