mirror of
https://github.com/open-multi-agent/open-multi-agent.git
synced 2026-10-19 01:31:07 -05:00
Jack Chen
a30a03956f
The example catalog's controlled vocabulary had no term for runtime plan repair, so the only example that runs recovery in repairable mode was filed under generic run-tasks and structured-output capabilities and could not be reached by a capability filter. Adds adaptive-recovery to the runtime validator and the public catalog schema, and tags commission-reconciliation-recovery with it.
731 lines
27 KiB
JSON
731 lines
27 KiB
JSON
{
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"$schema": "./catalog.schema.json",
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"schemaVersion": 1,
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"examples": [
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{
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"id": "single-agent",
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"path": "basics/single-agent.ts",
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"title": "Single Agent",
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"description": "One agent with bash and file tools, followed by streaming and multi-turn conversation examples.",
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"section": "goal",
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"goal": "start-here",
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"capabilities": ["run-agent", "tool-use", "streaming", "multi-turn"],
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"format": "script",
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"level": "beginner",
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"featuredOrder": 1
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},
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{
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"id": "structured-input",
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"path": "basics/structured-input.ts",
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"title": "Structured Multimodal Input",
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"description": "Send caller-owned message history and an image through runAgent() as structured input.",
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"section": "goal",
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"goal": "start-here",
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"capabilities": ["run-agent", "multi-turn"],
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"format": "script",
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"level": "beginner"
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},
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{
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"id": "team-collaboration",
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"path": "basics/team-collaboration.ts",
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"title": "Team Collaboration",
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"description": "Use runTeam() to turn a goal into a coordinated multi-agent task graph and final result.",
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"section": "goal",
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"goal": "start-here",
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"capabilities": ["run-team", "task-dependencies", "parallel-execution"],
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"format": "script",
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"level": "beginner",
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"featuredOrder": 2
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},
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{
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"id": "task-pipeline",
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"path": "basics/task-pipeline.ts",
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"title": "Task Pipeline",
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"description": "Use runTasks() with an explicit task DAG, assignments, and dependency-scoped context.",
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"section": "goal",
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"goal": "start-here",
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"capabilities": ["run-tasks", "task-dependencies"],
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"format": "script",
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"level": "beginner",
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"featuredOrder": 3
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},
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{
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"id": "multi-model-team",
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"path": "basics/multi-model-team.ts",
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"title": "Multi-Model Team",
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"description": "Combine models from different providers with custom tools in one agent pool.",
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"section": "goal",
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"goal": "start-here",
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"capabilities": ["agent-pool", "multi-model", "custom-tools"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 4
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},
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{
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"id": "adaptive-customer-support",
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"path": "cookbook/adaptive-customer-support.ts",
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"title": "Adaptive Customer Support",
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"description": "Let runTeam() select the specialists needed for a shipping or billing escalation and synthesize a grounded response.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-team"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 1
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},
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{
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"id": "commission-reconciliation-recovery",
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"path": "cookbook/commission-reconciliation-recovery.ts",
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"title": "Commission Reconciliation Recovery",
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"description": "Repair a typed evidence gap with targeted archive lookups, or stop at a real recovery limit and fail closed to manual review, while retaining truthful task history.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-tasks", "adaptive-recovery", "task-dependencies", "structured-output"],
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"format": "script",
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"level": "intermediate"
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},
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{
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"id": "contract-review-dag",
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"path": "cookbook/contract-review-dag.ts",
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"title": "Contract Review DAG",
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"description": "Run a four-task contract review DAG with parallel checks and step-level retry.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-tasks", "task-dependencies", "retry"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 2
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},
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{
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"id": "incident-postmortem-dag",
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"path": "cookbook/incident-postmortem-dag.ts",
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"title": "Incident Postmortem DAG",
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"description": "Fan three independent incident investigations into root-cause analysis and a final postmortem.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-tasks", "task-dependencies", "parallel-execution"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 3
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},
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{
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"id": "meeting-summarizer",
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"path": "cookbook/meeting-summarizer.ts",
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"title": "Meeting Summarizer",
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"description": "Process a transcript in parallel into a summary, structured action items, and sentiment.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["agent-pool", "parallel-execution", "structured-output"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 4
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},
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{
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"id": "competitive-monitoring",
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"path": "cookbook/competitive-monitoring.ts",
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"title": "Competitive Monitoring",
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"description": "Monitor multiple sources in parallel, detect contradictions, and aggregate an intelligence report.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["agent-pool", "parallel-execution", "structured-output", "conflict-detection"],
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"format": "script",
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"level": "intermediate"
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},
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{
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"id": "paper-replication-triage",
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"path": "cookbook/paper-replication-triage.ts",
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"title": "Paper Replication Triage",
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"description": "Reconcile multiple evidence sources into a structured paper-replication go or no-go plan.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-tasks", "parallel-execution", "structured-output", "conflict-detection"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "rare-disease-information-triage",
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"path": "cookbook/rare-disease-information-triage.ts",
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"title": "Rare Disease Information Triage",
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"description": "Audit isolated mock evidence sources, detect conflicts, and apply an explicit medical safety boundary.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["agent-pool", "structured-output", "conflict-detection", "safety-arbitration"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "personalized-interview-simulator",
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"path": "cookbook/personalized-interview-simulator.ts",
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"title": "Personalized Interview Simulator",
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"description": "Run an interactive interviewer and observer loop with shared memory and a structured debrief.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["multi-turn", "shared-memory", "structured-output"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "narrative-puzzle-hint-arbitration",
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"path": "cookbook/narrative-puzzle-hint-arbitration.ts",
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"title": "Narrative Puzzle Hint Arbitration",
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"description": "Resolve conflicting hint sources through parallel analysis, synthesis, and an external safety veto.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["agent-pool", "parallel-execution", "structured-output", "safety-arbitration"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "market-data-integrity-verify-loop",
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"path": "cookbook/market-data-integrity-verify-loop.ts",
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"title": "Market Data Integrity Verify Loop",
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"description": "Challenge a provisional Binance L2 integrity report with source-specific protocol and backtest-risk judges before revising it to quarantine.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["run-tasks", "task-dependencies", "structured-output", "consensus", "conflict-detection"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "translation-backtranslation",
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"path": "cookbook/translation-backtranslation.ts",
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"title": "Translation and Backtranslation",
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"description": "Translate with one provider, backtranslate with another, and flag semantic drift with structured output.",
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"section": "goal",
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"goal": "use-case-recipes",
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"capabilities": ["agent-pool", "multi-model", "structured-output"],
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"format": "script",
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"level": "intermediate"
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},
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{
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"id": "fan-out-aggregate",
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"path": "patterns/fan-out-aggregate.ts",
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"title": "Fan-Out and Aggregate",
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"description": "Send the same question to multiple agents in parallel and aggregate their results.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["agent-pool", "parallel-execution"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 1
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},
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{
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"id": "agent-handoff",
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"path": "patterns/agent-handoff.ts",
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"title": "Agent Handoff",
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"description": "Delegate a synchronous sub-prompt from one specialist to another with delegate_to_agent.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["run-team", "delegation"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 2
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},
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{
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"id": "multi-perspective-code-review",
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"path": "patterns/multi-perspective-code-review.ts",
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"title": "Multi-Perspective Code Review",
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"description": "Generate code, run several specialist reviews in parallel, and synthesize structured findings.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["run-tasks", "task-dependencies", "parallel-execution", "structured-output"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 3
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},
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{
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"id": "consensus",
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"path": "patterns/consensus.ts",
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"title": "Consensus",
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"description": "Use a proposer and a panel of judges in a configurable refutation and quorum loop.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["consensus"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 4
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},
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{
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"id": "research-aggregation",
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"path": "patterns/research-aggregation.ts",
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"title": "Research Aggregation",
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"description": "Run independent research tasks in parallel and collate them through a synthesis task.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["run-tasks", "task-dependencies", "parallel-execution"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 5
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},
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{
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"id": "event-driven-dag",
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"path": "patterns/event-driven-dag.ts",
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"title": "Event-Driven DAG",
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"description": "Use deferred promises to show a downstream task starting when its dependency is satisfied without waiting for an unrelated task.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["run-tasks", "task-dependencies", "parallel-execution"],
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"format": "script",
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"level": "intermediate"
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},
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{
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"id": "cross-provider-reasoning",
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"path": "patterns/cross-provider-reasoning.ts",
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"title": "Cross-Provider Reasoning",
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"description": "Preserve a reasoning model's thought stream as text when a conversation moves between providers.",
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"section": "goal",
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"goal": "orchestration",
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"capabilities": ["multi-model", "reasoning-transfer", "multi-turn"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 6
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},
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{
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"id": "structured-output",
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"path": "patterns/structured-output.ts",
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"title": "Structured Output",
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"description": "Validate an agent's JSON response against a Zod output schema.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["run-agent", "structured-output"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 1
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},
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{
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"id": "rich-tool-results",
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"path": "patterns/rich-tool-results.ts",
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"title": "Rich Tool Results",
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"description": "Keep application-owned tool data separate while returning image content to the model.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["custom-tools", "tool-use"],
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"format": "script",
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"level": "intermediate"
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},
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{
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"id": "task-retry",
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"path": "patterns/task-retry.ts",
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"title": "Task Retry",
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"description": "Retry failed tasks with configurable delay and exponential backoff.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["run-tasks", "retry"],
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"format": "script",
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"level": "intermediate",
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"featuredOrder": 2
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},
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{
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"id": "risk-gated-bash",
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"path": "patterns/risk-gated-bash.ts",
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"title": "Risk-Gated Bash",
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"description": "Combine tool grants with a per-call gate that allows, reviews, or blocks bash commands.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["tool-use", "tool-gating"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 3
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},
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{
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"id": "durable-approval",
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"path": "patterns/durable-approval.ts",
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"title": "Durable Approval",
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"description": "Suspend a task dispatch, record one atomic reviewer decision, and restore the exact approved task with a fresh orchestrator.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["run-tasks"],
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"format": "script",
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"level": "advanced"
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},
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{
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"id": "plan-replay",
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"path": "patterns/plan-replay.ts",
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"title": "Plan Replay",
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"description": "Freeze a coordinator plan into a diffable artifact and replay it without another planning call.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["run-team", "task-dependencies", "plan-replay"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 4
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},
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{
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"id": "eval-offline-regression",
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"path": "patterns/eval-offline-regression.ts",
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"title": "Offline Evaluation Regression",
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"description": "Run a no-key EvalSet regression across two targets with rule and judge scorers plus a gate.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["evaluation", "structured-output"],
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"format": "script",
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"level": "advanced",
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"featuredOrder": 5
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},
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{
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"id": "observability-v2",
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"path": "integrations/observability-v2",
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"title": "Observability v2",
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"description": "No-key examples for batching, trace stores, OpenTelemetry, a real OTLP/HTTP export, lifecycle handling, and the offline Run Viewer.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["observability", "trace-store", "open-telemetry", "run-viewer", "server-lifecycle"],
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"format": "multi-file",
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"level": "advanced",
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"featuredOrder": 6,
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"entrypoints": [
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"batching-exporter.ts",
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"in-memory-store.ts",
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"file-trace-store.ts",
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"otel-provider.ts",
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"otlp-backend.ts",
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"cli-lifecycle.ts",
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"server-lifecycle.ts",
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"serverless-lifecycle.ts",
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"run-viewer.ts"
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]
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},
|
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{
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|
"id": "cost-tiered-pipeline",
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"path": "patterns/cost-tiered-pipeline.ts",
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"title": "Cost-Tiered Pipeline",
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"description": "Compare the token and cost profile of an all-flagship pipeline with a tiered model mix.",
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"section": "goal",
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"goal": "production-controls",
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"capabilities": ["run-tasks", "cost-control", "multi-model"],
|
|
"format": "script",
|
|
"level": "intermediate"
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|
},
|
|
{
|
|
"id": "eval-online-sampling",
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|
"path": "patterns/eval-online-sampling.ts",
|
|
"title": "Online Evaluation Sampling",
|
|
"description": "Sample live runs for asynchronous scoring in a durable FileEvalStore with explicit shutdown.",
|
|
"section": "goal",
|
|
"goal": "production-controls",
|
|
"capabilities": ["evaluation"],
|
|
"format": "script",
|
|
"level": "advanced"
|
|
},
|
|
{
|
|
"id": "trace-observability",
|
|
"path": "integrations/trace-observability.ts",
|
|
"title": "Trace Observability",
|
|
"description": "Collect structured onTrace events for LLM calls, tools, tasks, and agent runs.",
|
|
"section": "goal",
|
|
"goal": "production-controls",
|
|
"capabilities": ["observability"],
|
|
"format": "script",
|
|
"level": "intermediate"
|
|
},
|
|
{
|
|
"id": "express-customer-support",
|
|
"path": "integrations/express-customer-support",
|
|
"title": "Express Customer Support",
|
|
"description": "Embed a structured runTasks() support pipeline behind an Express POST /tickets endpoint.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["http-server", "run-tasks", "structured-output"],
|
|
"format": "app",
|
|
"level": "intermediate",
|
|
"featuredOrder": 1,
|
|
"entrypoints": ["index.ts"]
|
|
},
|
|
{
|
|
"id": "mcp-github",
|
|
"path": "integrations/mcp-github.ts",
|
|
"title": "MCP GitHub",
|
|
"description": "Connect an MCP server over stdio and expose its tools through the OMA tool registry.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["mcp", "custom-tools"],
|
|
"format": "script",
|
|
"level": "intermediate",
|
|
"featuredOrder": 2
|
|
},
|
|
{
|
|
"id": "with-vercel-ai-sdk",
|
|
"path": "integrations/with-vercel-ai-sdk",
|
|
"title": "Vercel AI SDK",
|
|
"description": "Combine OMA runTeam() orchestration with an AI SDK streaming chat interface in Next.js.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["ai-sdk", "run-team", "streaming", "http-server"],
|
|
"format": "app",
|
|
"level": "intermediate",
|
|
"featuredOrder": 3,
|
|
"entrypoints": ["app/api/chat/route.ts"]
|
|
},
|
|
{
|
|
"id": "external-agent-acp",
|
|
"path": "integrations/external-agent-acp.ts",
|
|
"title": "External Agent over ACP",
|
|
"description": "Use an external coding CLI over ACP as a team member in an OMA task DAG.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["external-agents", "acp", "run-tasks"],
|
|
"format": "script",
|
|
"level": "advanced",
|
|
"featuredOrder": 4
|
|
},
|
|
{
|
|
"id": "with-tencentdb-memory",
|
|
"path": "integrations/with-tencentdb-memory",
|
|
"title": "TencentDB Agent Memory",
|
|
"description": "Back an OMA team's shared memory with TencentDB-Agent-Memory through its Hermes Gateway.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["long-term-memory", "shared-memory", "custom-tools", "run-team"],
|
|
"format": "multi-file",
|
|
"level": "advanced",
|
|
"featuredOrder": 5,
|
|
"entrypoints": ["team-with-memory.ts"]
|
|
},
|
|
{
|
|
"id": "external-agent-process",
|
|
"path": "integrations/external-agent-process.ts",
|
|
"title": "External Agent Process",
|
|
"description": "Run deterministic local subprocesses as external agents without a model API key.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["external-agents", "process-backend", "run-tasks"],
|
|
"format": "script",
|
|
"level": "intermediate"
|
|
},
|
|
{
|
|
"id": "mcp-bilig-workpaper",
|
|
"path": "integrations/mcp-bilig-workpaper.ts",
|
|
"title": "MCP Bilig WorkPaper",
|
|
"description": "Use Bilig WorkPaper MCP tools for formula readback, recalculation, and persisted workbook data.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["mcp", "custom-tools"],
|
|
"format": "script",
|
|
"level": "advanced"
|
|
},
|
|
{
|
|
"id": "mcp-open-design",
|
|
"path": "integrations/mcp-open-design.ts",
|
|
"title": "MCP Open Design",
|
|
"description": "Drive multiple asynchronous Open Design MCP jobs in parallel through an explicit task pipeline.",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["mcp", "custom-tools", "run-tasks", "parallel-execution"],
|
|
"format": "script",
|
|
"level": "advanced"
|
|
},
|
|
{
|
|
"id": "with-engram",
|
|
"path": "integrations/with-engram",
|
|
"title": "Engram Memory",
|
|
"description": "Use Engram for manual agent memory tools or as the shared-memory store behind runTeam().",
|
|
"section": "goal",
|
|
"goal": "connect-your-stack",
|
|
"capabilities": ["long-term-memory", "shared-memory", "custom-tools", "run-team"],
|
|
"format": "multi-file",
|
|
"level": "advanced",
|
|
"entrypoints": ["research-team.ts", "team-research.ts"]
|
|
},
|
|
{
|
|
"id": "azure-openai",
|
|
"path": "providers/azure-openai.ts",
|
|
"title": "Azure OpenAI",
|
|
"description": "Run the standard three-agent team with Azure OpenAI deployments.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "bedrock",
|
|
"path": "providers/bedrock.ts",
|
|
"title": "AWS Bedrock",
|
|
"description": "Run the standard three-agent team through the AWS Bedrock Converse API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "intermediate"
|
|
},
|
|
{
|
|
"id": "copilot",
|
|
"path": "providers/copilot.ts",
|
|
"title": "GitHub Copilot",
|
|
"description": "Run the standard three-agent team through GitHub Copilot's OpenAI-compatible endpoint.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "deepseek",
|
|
"path": "providers/deepseek.ts",
|
|
"title": "DeepSeek",
|
|
"description": "Run the standard three-agent team with DeepSeek models.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "doubao",
|
|
"path": "providers/doubao.ts",
|
|
"title": "Doubao",
|
|
"description": "Run the standard three-agent team through ByteDance Volcengine Ark.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "gemini",
|
|
"path": "providers/gemini.ts",
|
|
"title": "Google Gemini",
|
|
"description": "Run the standard three-agent team with the native Google Gemini adapter.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "gemma4-local",
|
|
"path": "providers/gemma4-local.ts",
|
|
"title": "Gemma 4 Local",
|
|
"description": "Run both execution modes locally with Gemma 4 through Ollama and no cloud API key.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "local-models", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "intermediate"
|
|
},
|
|
{
|
|
"id": "grok",
|
|
"path": "providers/grok.ts",
|
|
"title": "Grok",
|
|
"description": "Run the standard three-agent team with xAI Grok models.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "groq",
|
|
"path": "providers/groq.ts",
|
|
"title": "Groq",
|
|
"description": "Run the standard three-agent team through Groq's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "hunyuan",
|
|
"path": "providers/hunyuan.ts",
|
|
"title": "Tencent Hunyuan",
|
|
"description": "Run the standard three-agent team with Tencent Hunyuan through MaaS.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "local-quantized",
|
|
"path": "providers/local-quantized.ts",
|
|
"title": "Local Quantized Model",
|
|
"description": "Tune sampling and tool-call behavior for a quantized local model on vLLM or llama-server.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "local-models", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "advanced"
|
|
},
|
|
{
|
|
"id": "mimo",
|
|
"path": "providers/mimo.ts",
|
|
"title": "MiMo",
|
|
"description": "Run the standard three-agent team with Xiaomi MiMo's API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "minimax",
|
|
"path": "providers/minimax.ts",
|
|
"title": "MiniMax",
|
|
"description": "Run the standard three-agent team with MiniMax models.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "mistral",
|
|
"path": "providers/mistral.ts",
|
|
"title": "Mistral",
|
|
"description": "Run the standard three-agent team through Mistral's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "moonshot",
|
|
"path": "providers/moonshot.ts",
|
|
"title": "Moonshot AI",
|
|
"description": "Run the standard three-agent team through Moonshot AI's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "ollama",
|
|
"path": "providers/ollama.ts",
|
|
"title": "Ollama",
|
|
"description": "Mix a local Ollama model with a cloud model in one task pipeline.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "local-models", "openai-compatible", "multi-model"],
|
|
"format": "script",
|
|
"level": "intermediate"
|
|
},
|
|
{
|
|
"id": "openrouter",
|
|
"path": "providers/openrouter.ts",
|
|
"title": "OpenRouter",
|
|
"description": "Run the standard three-agent team through OpenRouter's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "qiniu",
|
|
"path": "providers/qiniu.ts",
|
|
"title": "Qiniu",
|
|
"description": "Run the standard three-agent team through Qiniu's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "qwen",
|
|
"path": "providers/qwen.ts",
|
|
"title": "Qwen",
|
|
"description": "Run the standard three-agent team through DashScope's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
},
|
|
{
|
|
"id": "zhipu",
|
|
"path": "providers/zhipu.ts",
|
|
"title": "Zhipu GLM",
|
|
"description": "Run the standard three-agent team through Zhipu's OpenAI-compatible API.",
|
|
"section": "models-providers",
|
|
"capabilities": ["provider-adapter", "openai-compatible"],
|
|
"format": "script",
|
|
"level": "beginner"
|
|
}
|
|
]
|
|
}
|