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- Added new workflows: researchSynthesisWorkflow, financialReportWorkflow, specGenerationWorkflow, repoIngestionWorkflow, and learningExtractionWorkflow to the Coding A2A Coordinator and Coding Team Network for improved orchestration of multi-topic tasks.
- Updated the model used in Coding A2A Coordinator and Coding Team Network to googleAIFlashLite for better performance.
- Integrated GitHub MCP client in codingAgents for enhanced tool capabilities.
- Improved findReferencesTool to utilize PythonParser for more accurate reference finding in Python files.
- Refactored multi-string edit tool for better readability and efficiency.
- Added find_references function in semantic-utils for improved symbol reference tracking.
- refactoringAgent: Generate improvement plan based on Phase 1
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- testEngineerAgent: Generate tests for proposed changes
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CRITICAL: Prefer parallel execution for independent tasks. Only use sequential when results depend on previous agent outputs.`,
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CRITICAL: Prefer parallel execution for independent tasks. Only use sequential when results depend on previous agent outputs.
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This coordinator also exposes higher-level workflows (researchSynthesisWorkflow, specGenerationWorkflow, repoIngestionWorkflow, learningExtractionWorkflow, financialReportWorkflow) that handle multi-topic research, spec generation, repo ingestion (RAG ingestion), learning extraction, and financial reports. When a user's request requires prolonged, structured work across multiple subtasks, prefer invoking these workflows and orchestrating agents around them.`,
- Full Cycle: codeArchitectAgent → refactoringAgent → codeReviewerAgent → testEngineerAgent
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- Full Cycle: codeArchitectAgent → refactoringAgent → codeReviewerAgent → testEngineerAgent
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This network also exposes higher-level workflows for common orchestration patterns: researchSynthesisWorkflow (multi-topic research & synthesis), specGenerationWorkflow (design/spec creation), repoIngestionWorkflow (ingest repository content into RAG pipelines), learningExtractionWorkflow (extract learnings with human-in-the-loop), and financialReportWorkflow (financial reports). Prefer invoking these workflows when a single network call should trigger a longer-running, structured process.
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Provider options: networks should not generally require top-level provider overrides; prefer configuring providerOptions at the agent level (inside an agent's instructions) or passing runtime orchestration constraints. Use network-level provider constraints only for shared execution limits or budgets when coordinating agents across a federated workflow.
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## Examples
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@@ -88,8 +97,9 @@ export const codingTeamNetwork = new Agent({
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- Always explain which agent you're delegating to and why
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- For ambiguous requests, ask for clarification
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- Chain agents when the task requires multiple steps
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- Preserve context when passing between agents`,
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model: googleAI,
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- Preserve context when passing between agents
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`,
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model: googleAIFlashLite,
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memory: pgMemory,
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options: {
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tracingPolicy: {internal: InternalSpans.ALL},
@@ -101,7 +111,13 @@ export const codingTeamNetwork = new Agent({
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