Added API layer for tool calls #89
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📝 WalkthroughWalkthroughThis PR introduces direct tool execution capabilities across the agent framework, bypassing LLM orchestration. New HTTP API routes handle tool execution, action handlers (click, type, scroll, navigate, hover), and accessibility tree retrieval. Backend and frontend implementations provide protocol interfaces and RPC bindings to support direct tool invocation with consistent response formatting. Changes
Sequence DiagramsequenceDiagram
actor Client
participant APIServer
participant BrowserAgentServer
participant DevTools RPC
Client->>APIServer: POST /v1/tools/execute {tool, args}
activate APIServer
APIServer->>APIServer: executeToolDirect(payload)
APIServer->>APIServer: Resolve client/tab, validate input
APIServer->>BrowserAgentServer: executeToolDirect(connection, tool, args, timeout)
deactivate APIServer
activate BrowserAgentServer
BrowserAgentServer->>BrowserAgentServer: Generate rpcId, log details
BrowserAgentServer->>DevTools RPC: callMethod(execute_tool) {tool, args, timeout}
deactivate BrowserAgentServer
activate DevTools RPC
DevTools RPC->>DevTools RPC: Execute tool, return result
DevTools RPC-->>BrowserAgentServer: {success, output, executionTime}
deactivate DevTools RPC
activate BrowserAgentServer
BrowserAgentServer->>BrowserAgentServer: Log execution success
BrowserAgentServer-->>APIServer: RPC response
deactivate BrowserAgentServer
activate APIServer
APIServer->>APIServer: Format structured result
APIServer-->>Client: {clientId, tabId, tool, success, output, executionTime, timestamp}
deactivate APIServer
sequenceDiagram
actor Frontend
participant EvaluationAgent
participant ToolRegistry
participant Tool
Frontend->>EvaluationAgent: ToolExecutionRequest {tool, args, timeout}
activate EvaluationAgent
EvaluationAgent->>EvaluationAgent: isToolExecutionRequest() check
EvaluationAgent->>ToolRegistry: lookup(tool)
activate ToolRegistry
ToolRegistry-->>EvaluationAgent: Tool or null
deactivate ToolRegistry
alt Tool Found
EvaluationAgent->>EvaluationAgent: executeToolWithTimeout(tool, args, 30s)
activate Tool
Tool->>Tool: Execute
Tool-->>EvaluationAgent: {success: true, output, executionTime}
deactivate Tool
EvaluationAgent->>EvaluationAgent: createToolExecutionSuccessResponse()
EvaluationAgent-->>Frontend: ToolExecutionSuccessResponse
else Tool Not Found
EvaluationAgent->>EvaluationAgent: createToolExecutionErrorResponse()
EvaluationAgent-->>Frontend: ToolExecutionErrorResponse {code, message}
end
deactivate EvaluationAgent
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Possibly related PRs
Suggested reviewers
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✅ Passed checks (2 passed)
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Actionable comments posted: 5
🧹 Nitpick comments (7)
docker/Makefile (2)
48-54: Note: macOS-specific Chrome path.The
chrometarget uses a hardcoded path to Chrome Canary on macOS. Users on Linux or Windows will need to modify this command for their platform. Consider adding a comment noting this is macOS-specific, or document alternative commands for other platforms in the help text.
4-4: Optional: Static analysis suggestions.The checkmake linter suggests adding conventional phony targets (
all,clean,test), but these are not essential for this orchestration-focused Makefile. The warnings about target body length are also pedantic for this use case where readability benefits from keeping help text inline.Based on static analysis hints.
agent-server/nodejs/src/lib/BrowserAgentServer.js (1)
1421-1468: Unused variable in executeToolDirect method.The
rpcIdvariable at line 1431 is declared but never used. Consider removing it or using it for logging/tracking purposes.🔎 Proposed fix
async executeToolDirect(connection, tool, args, timeout = 30000) { - const rpcId = `tool-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`; - logger.info('Executing tool directly', { clientId: connection.clientId, tool, timeout });front_end/panels/ai_chat/LLM/LLMClient.ts (1)
212-242: Tracing metadata fallback logic is well-implemented.The logic correctly prioritizes explicit
request.tracingMetadataover the global tracing context, with appropriate null/empty checks. This ensures tracing data flows correctly even when async context is lost.Consider using
logger.debuginstead oflogger.infofor the detailed tracing diagnostics to reduce log verbosity in production.🔎 Suggested log level adjustment
- logger.info('LLMClient.call() - Checking tracing context (fallback):', { + logger.debug('LLMClient.call() - Checking tracing context (fallback):', { hasContext: !!tracingContext, // ... }); - logger.info('LLMClient.call() - Using explicit tracingMetadata from request:', { + logger.debug('LLMClient.call() - Using explicit tracingMetadata from request:', { // ... }); - logger.info('Passing tracing metadata to provider:', tracingMetadata); + logger.debug('Passing tracing metadata to provider:', tracingMetadata); - logger.info('No tracing metadata available'); + logger.debug('No tracing metadata available');front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (1)
354-439: Tool execution protocol definitions.The tool execution types, type guard, and helper functions are correctly implemented. Note that these definitions are duplicated between
EvaluationProtocol.tsandremote/EvaluationProtocol.ts.Consider extracting shared protocol types to a common module to reduce duplication and ensure consistency.
The tool execution protocol types (
ToolExecutionRequest,ToolExecutionParams,ToolExecutionSuccessResponse,ToolExecutionErrorResponse) and their helper functions are duplicated across:
front_end/panels/ai_chat/evaluation/EvaluationProtocol.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.tsThis could lead to drift over time. Consider extracting these shared types to a common module that both files can import.
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts (1)
1363-1399: Consider adding tracing support for direct tool execution.Unlike
handleEvaluationRequest, this handler passesundefinedfortracingContext(line 1397). This means direct tool executions won't appear in Langfuse traces, which could make debugging harder. If the request contains tracing metadata (like evaluation requests can), consider creating a minimal tracing context.🔎 Suggested enhancement for tracing support
private async handleToolExecutionRequest(request: ToolExecutionRequest): Promise<void> { const { params, id } = request; const startTime = Date.now(); logger.info('Received tool execution request', { tool: params.tool, hasArgs: !!params.args, timeout: params.timeout || 30000 }); + // Create minimal tracing context if tracing metadata provided + const requestTracing = (params as any).tracing || {}; + let tracingContext: TracingContext | undefined; + if (requestTracing.trace_id || requestTracing.session_id) { + tracingContext = { + traceId: requestTracing.trace_id || `tool-${Date.now()}`, + sessionId: requestTracing.session_id || `tool-session-${Date.now()}`, + parentObservationId: undefined, + metadata: requestTracing + }; + } try { // Get the tool from registry const tool = ToolRegistry.getRegisteredTool(params.tool); // ... rest of implementation const result = await this.executeToolWithTimeout( tool, params.args || {}, timeout, - undefined, + tracingContext, params.tool );agent-server/nodejs/src/api-server.js (1)
558-567: Consider adding defensive handling for malformed RPC results.The response construction assumes
resulthas a specific structure (result.result.success,result.result.output, etc.). IfexecuteToolDirectthrows or returns an unexpected structure, line 562'sresult?.result?.success ?? falsehandles it defensively, but the overall pattern could be cleaner with explicit structure validation.🔎 Suggested defensive extraction
+ const output = result?.result ?? {}; return { clientId: baseClientId, tabId, tool, - success: result?.result?.success ?? false, - output: result?.result?.output, - executionTime: result?.result?.executionTime, + success: output.success ?? false, + output: output.output, + executionTime: output.executionTime, error: result?.error?.message, timestamp: Date.now() };
📜 Review details
Configuration used: defaults
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (18)
agent-server/nodejs/src/api-server.jsagent-server/nodejs/src/lib/BrowserAgentServer.jsdocker/Dockerfiledocker/Makefiledocker/README.mddocker/docker-compose.lightweight.ymlfront_end/panels/ai_chat/LLM/LLMClient.tsfront_end/panels/ai_chat/LLM/LLMTypes.tsfront_end/panels/ai_chat/LLM/LiteLLMProvider.tsfront_end/panels/ai_chat/agent_framework/AgentRunner.tsfront_end/panels/ai_chat/core/AgentNodes.tsfront_end/panels/ai_chat/core/AgentService.tsfront_end/panels/ai_chat/evaluation/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/EvaluationProtocol.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.tsfront_end/panels/ai_chat/tools/LLMTracingWrapper.tsfront_end/panels/ai_chat/tracing/TracingProvider.ts
🧰 Additional context used
📓 Path-based instructions (1)
agent-server/nodejs/src/api-server.js
📄 CodeRabbit inference engine (agent-server/nodejs/CLAUDE.md)
agent-server/nodejs/src/api-server.js: Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Model configuration must use canonical nested format with main_model, mini_model, and nano_model tiers, each containing provider, model, and api_key fields
POST /page/screenshot endpoint must accept clientId and tabId, use CDP Page.captureScreenshot, and return base64-encoded PNG with metadata and timestamp
POST /page/content endpoint must accept clientId, tabId, format (html or text), and includeIframes parameters; recursively capture iframe content when includeIframes is true
POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Accept POST /v1/responses input as either string format (simple message) or conversation array format with role and content fields; enforce at least one user message and max 100 messages/10,000 characters per message
Files:
agent-server/nodejs/src/api-server.js
🧠 Learnings (8)
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Use Chrome DevTools Protocol (CDP) for direct browser communication including screenshot capture via Page.captureScreenshot, page content via Runtime.evaluate, and tab management via Target.createTarget/closeTarget
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.jsfront_end/panels/ai_chat/evaluation/EvaluationProtocol.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationAgent.tsagent-server/nodejs/src/api-server.jsfront_end/panels/ai_chat/evaluation/EvaluationAgent.ts
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.jsfront_end/panels/ai_chat/evaluation/EvaluationProtocol.tsagent-server/nodejs/src/api-server.jsfront_end/panels/ai_chat/evaluation/EvaluationAgent.ts
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Implement WebSocket server for browser agent connections with client lifecycle management (connect, ready, disconnect)
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : CDP communication pattern: discover endpoint via http://localhost:9223/json/version, establish WebSocket per command, send JSON-RPC 2.0, attach for tab-level ops, detach, close WebSocket after response
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/rpc-client.js : Implement JSON-RPC 2.0 protocol for bidirectional communication with request/response correlation using unique IDs, timeout handling, and error conditions
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Model configuration must use canonical nested format with main_model, mini_model, and nano_model tiers, each containing provider, model, and api_key fields
Applied to files:
agent-server/nodejs/src/api-server.js
🧬 Code graph analysis (7)
front_end/panels/ai_chat/LLM/LLMClient.ts (1)
front_end/panels/ai_chat/tracing/TracingConfig.ts (1)
getCurrentTracingContext(299-301)
front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (1)
front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts (7)
ToolExecutionRequest(83-88)ToolExecutionParams(90-94)ToolExecutionSuccessResponse(96-105)ToolExecutionErrorResponse(107-118)isToolExecutionRequest(211-213)createToolExecutionSuccessResponse(301-317)createToolExecutionErrorResponse(319-338)
front_end/panels/ai_chat/core/AgentService.ts (1)
front_end/panels/ai_chat/core/BuildConfig.ts (1)
BUILD_CONFIG(11-20)
front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts (1)
front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (7)
ToolExecutionRequest(358-363)ToolExecutionParams(365-369)ToolExecutionSuccessResponse(371-380)ToolExecutionErrorResponse(382-393)isToolExecutionRequest(396-402)createToolExecutionSuccessResponse(405-421)createToolExecutionErrorResponse(423-439)
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts (4)
front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (5)
isToolExecutionRequest(396-402)ToolExecutionRequest(358-363)createToolExecutionErrorResponse(423-439)ErrorCodes(161-175)createToolExecutionSuccessResponse(405-421)front_end/panels/ai_chat/tracing/TracingConfig.ts (3)
isTracingEnabled(141-143)setTracingConfig(134-136)createTracingProvider(225-227)front_end/panels/ai_chat/tracing/TracingProvider.ts (1)
TracingContext(8-31)front_end/panels/ai_chat/agent_framework/ConfigurableAgentTool.ts (1)
ToolRegistry(221-268)
agent-server/nodejs/src/api-server.js (3)
front_end/panels/ai_chat/core/AgentService.ts (1)
logger(420-431)front_end/panels/ai_chat/LLM/LiteLLMProvider.ts (1)
getEndpoint(48-64)front_end/panels/ai_chat/core/LLMConfigurationManager.ts (1)
getEndpoint(222-229)
front_end/panels/ai_chat/evaluation/EvaluationAgent.ts (2)
front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts (5)
isToolExecutionRequest(211-213)ToolExecutionRequest(83-88)createToolExecutionErrorResponse(319-338)ErrorCodes(181-195)createToolExecutionSuccessResponse(301-317)front_end/panels/ai_chat/agent_framework/ConfigurableAgentTool.ts (1)
ToolRegistry(221-268)
🪛 checkmake (0.2.2)
docker/Makefile
[warning] 6-6: Target body for "help" exceeds allowed length of 5 (14).
(maxbodylength)
[warning] 30-30: Target body for "up" exceeds allowed length of 5 (6).
(maxbodylength)
[warning] 48-48: Target body for "chrome" exceeds allowed length of 5 (6).
(maxbodylength)
[warning] 4-4: Missing required phony target "all"
(minphony)
[warning] 4-4: Missing required phony target "clean"
(minphony)
[warning] 4-4: Missing required phony target "test"
(minphony)
🔇 Additional comments (34)
docker/README.md (1)
5-7: LGTM! Clear documentation enhancement.The deployment type descriptions and quick reference link provide helpful context for users choosing between deployment options.
front_end/panels/ai_chat/agent_framework/AgentRunner.ts (1)
752-753: LGTM! Consistent tracing metadata propagation.The explicit forwarding of
tracingMetadatafrom the tracing context enables Langfuse integration for AgentRunner LLM calls. The optional chaining ensures graceful handling when tracing context is unavailable.front_end/panels/ai_chat/core/AgentNodes.ts (1)
242-243: LGTM! Consistent tracing integration.The tracing metadata is correctly sourced from the state context and forwarded to the LLM call, maintaining consistency with the tracing propagation pattern established in AgentRunner and other components.
front_end/panels/ai_chat/tools/LLMTracingWrapper.ts (1)
88-89: LGTM! Tracing metadata forwarding in wrapper.The explicit forwarding of tracing metadata in the
callLLMWithTracingwrapper ensures that tool-initiated LLM calls include Langfuse integration metadata, completing the tracing propagation chain.front_end/panels/ai_chat/LLM/LiteLLMProvider.ts (1)
212-216: LGTM! Proper LiteLLM metadata propagation.The conditional addition of tracing metadata to the request payload enables LiteLLM to forward this information to Langfuse callbacks. The placement after tool configuration and the conditional check ensure clean request construction.
front_end/panels/ai_chat/tracing/TracingProvider.ts (1)
21-30: LGTM! Well-structured tracing metadata foundation.The addition of the
metadatafield toTracingContextprovides a clean foundation for Langfuse integration. The optional fields align with Langfuse's evaluation framework, and the index signature allows for future extensibility without interface changes.front_end/panels/ai_chat/LLM/LLMTypes.ts (1)
204-211: Well-structured tracing metadata interface.The
tracingMetadatafield is properly typed with specific Langfuse properties while allowing flexibility via the index signature. This supports end-to-end tracing propagation.agent-server/nodejs/src/lib/BrowserAgentServer.js (1)
733-744: Tracing metadata forwarding looks correct.The tracing metadata is properly forwarded via
request.tracing || {}with appropriate debug logging showing the session_id, trace_id, and other keys.front_end/panels/ai_chat/LLM/LLMClient.ts (2)
20-20: Import for tracing context retrieval.The import enables fallback to global tracing context when explicit metadata is not provided.
52-52: Added tracingMetadata to LLMCallRequest interface.This enables explicit tracing metadata to be passed through the request object, aligning with the new tracing propagation pattern.
front_end/panels/ai_chat/core/AgentService.ts (2)
279-287: AUTOMATED_MODE API key bypass is correctly implemented.The condition
!apiKey && !BUILD_CONFIG.AUTOMATED_MODEcorrectly allows API keys to be provided dynamically in automated mode while maintaining validation in normal mode. The fallback to empty string forapiKeyis appropriate for this use case.
600-602: Tracing metadata forwarding for Langfuse session grouping.The metadata from the evaluation context is now correctly forwarded into the tracing context, enabling proper session grouping in Langfuse.
front_end/panels/ai_chat/evaluation/EvaluationAgent.ts (4)
23-39: New imports for tool execution protocol.The imports are correctly added to support the new direct tool execution pathway.
180-182: Tool execution request handling in message router.The
isToolExecutionRequesttype guard correctly routes direct tool execution messages to the new handler.
333-350: Tracing metadata extraction and context creation.The tracing metadata is correctly extracted from
params.tracingand used to create aTracingContextwith the full metadata payload for downstream LLM calls.
611-694: Direct tool execution handler implementation.The implementation correctly:
- Validates tool existence via
ToolRegistry.getRegisteredTool- Executes tools without LLM orchestration
- Returns proper JSON-RPC success/error responses
- Uses
undefinedfor tracing context (appropriate for direct API calls)One consideration: Line 648 passes
undefinedfor the tracing context in direct tool calls. If tracing is desired for direct tool executions in the future, you may want to optionally accept tracing metadata inToolExecutionParams.front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts (3)
132-141: Tracing metadata extension for Langfuse integration.The
tracingfield inEvaluationParamsenables Langfuse session grouping with well-defined properties and flexibility via the index signature.
211-213: Type guard for tool execution requests.The type guard correctly identifies tool execution requests by checking
jsonrpc === '2.0'andmethod === 'execute_tool'.
300-337: Helper functions for tool execution responses.Both
createToolExecutionSuccessResponseandcreateToolExecutionErrorResponsecorrectly construct JSON-RPC 2.0 compliant response objects.front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (1)
107-121: Enhanced tracing metadata with Langfuse credentials.The
tracingfield in this remote protocol version includes Langfuse credential fields (langfuse_endpoint,langfuse_public_key,langfuse_secret_key) for auto-configuration when tracing is not already enabled. This is a valuable addition for seamless Langfuse integration.docker/Dockerfile (1)
50-59: LGTM - Conditional AUTOMATED_MODE build configuration.The implementation correctly uses a build argument to conditionally modify
BuildConfig.ts. The string comparison"$AUTOMATED_MODE" = "true"is appropriate for shell conditionals. The sed patternAUTOMATED_MODE: falsematches the actual file content and will substitute correctly.front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts (6)
14-14: LGTM on import additions.The new tracing configuration imports (
setTracingConfig,refreshTracingProvider) align with the auto-configuration feature introduced in this PR.
32-48: LGTM on protocol imports.The new tool execution protocol imports are correctly added to support the direct tool execution flow.
205-207: LGTM on tool execution request handling.The new message handler for
isToolExecutionRequestproperly routes to the dedicated handler method.
447-472: Tracing auto-configuration only applies once per session.The
!isTracingEnabled()guard prevents re-configuration with different Langfuse credentials in subsequent requests. If the first request enables tracing, subsequent requests with different credentials will silently use the original configuration. This may be intentional, but consider whether requests should be able to override tracing config.Verify whether this is the intended behavior for multi-request scenarios with varying Langfuse configurations.
1373-1389: Good error handling for missing tools.The error response correctly uses
INVALID_PARAMSerror code and provides clear error messaging when a tool is not found in the registry.
1421-1440: Error handling is complete and consistent.The catch block properly calculates execution time, logs the error with context, and sends a structured error response using the protocol helpers.
agent-server/nodejs/src/api-server.js (7)
166-229: New API routes are well-structured.The new endpoints follow consistent patterns:
- Method validation with 405 responses
- JSON body parsing
- Delegation to handler methods
The
/page/accessibility-treeendpoint correctly supports both GET and POST methods.
578-704: Static tool list is clearly documented.The comment explaining that the actual registry lives in the DevTools frontend is helpful. The tool schemas provide useful documentation for API consumers.
1008-1023: LiteLLM endpoint fallback logic is sound.The
getEndpointhelper correctly implements the fallback chain: tier-specific endpoint → top-level endpoint → environment variable for LiteLLM provider. Based on the coding guidelines, this aligns with the expected nested model configuration format.
1066-1074: API key fallback for LiteLLM provider.Good addition of
LITELLM_API_KEYenvironment variable fallback when the provider is LiteLLM and no explicit key is provided.
972-976: Tracing metadata extraction is straightforward.The extraction of
requestBody.metadatafor tracing purposes is clean. This allows callers to pass Langfuse session/trace IDs through the API.
1191-1235: Tracing metadata correctly propagated to evaluation request.The
createDynamicRequestNestedfunction now accepts and forwards tracing metadata to the DevTools-side evaluation agent, enabling end-to-end Langfuse integration.
500-568: Direct tool execution endpoint is well-designed.The implementation follows the existing patterns in the codebase:
- Proper parameter validation
- Clear error messages for connection issues
- Structured response format with execution metadata
Based on learnings, this correctly uses CDP for browser communication through the BrowserAgentServer.
| async handleActionClick(payload) { | ||
| const { clientId, tabId, nodeId, timeout = 30000 } = payload; | ||
|
|
||
| return this.executeToolDirect({ | ||
| clientId, | ||
| tabId, | ||
| tool: 'perform_action', | ||
| args: { | ||
| method: 'click', | ||
| nodeId, | ||
| reasoning: 'Click action via API' | ||
| }, | ||
| timeout | ||
| }); | ||
| } |
There was a problem hiding this comment.
Missing nodeId validation for click action.
The click action requires nodeId to identify the target element, but this isn't validated before calling executeToolDirect. A missing nodeId will cause tool execution to fail with a less clear error.
🔎 Suggested fix
async handleActionClick(payload) {
const { clientId, tabId, nodeId, timeout = 30000 } = payload;
+ if (nodeId === undefined || nodeId === null) {
+ throw new Error('nodeId is required for click action');
+ }
+
return this.executeToolDirect({
clientId,
tabId,🤖 Prompt for AI Agents
In agent-server/nodejs/src/api-server.js around lines 716 to 730, the
handleActionClick function does not validate that payload.nodeId is present and
a non-empty string before calling executeToolDirect, which leads to unclear
failures; add an early validation that nodeId exists and is a valid string (or
number if your app supports numeric IDs), and if it's missing/invalid return or
throw a clear error (e.g., reject the request with a 400-like validation error
or throw an Error with message "Missing or invalid nodeId for click action") so
executeToolDirect is only called with a guaranteed valid nodeId.
| async handleActionType(payload) { | ||
| const { clientId, tabId, nodeId, text, timeout = 30000 } = payload; | ||
|
|
||
| if (!text) { | ||
| throw new Error('Text is required for type action'); | ||
| } | ||
|
|
||
| return this.executeToolDirect({ | ||
| clientId, | ||
| tabId, | ||
| tool: 'perform_action', | ||
| args: { | ||
| method: 'fill', | ||
| nodeId, | ||
| args: [text], | ||
| reasoning: 'Type action via API' | ||
| }, | ||
| timeout | ||
| }); | ||
| } |
There was a problem hiding this comment.
Missing nodeId validation for type action.
Similar to click, the type action validates text but not nodeId. Both are required for the action to succeed.
🔎 Suggested fix
async handleActionType(payload) {
const { clientId, tabId, nodeId, text, timeout = 30000 } = payload;
+ if (nodeId === undefined || nodeId === null) {
+ throw new Error('nodeId is required for type action');
+ }
+
if (!text) {
throw new Error('Text is required for type action');
}🤖 Prompt for AI Agents
In agent-server/nodejs/src/api-server.js around lines 738 to 757, the handler
validates text but not nodeId, so the type action can be invoked without a
target; add a nodeId presence check (e.g. if (!nodeId) throw new Error('nodeId
is required for type action')) before calling executeToolDirect, mirroring the
click action validation and ensuring only valid requests proceed.
| async handleActionHover(payload) { | ||
| const { clientId, tabId, nodeId, timeout = 30000 } = payload; | ||
|
|
||
| return this.executeToolDirect({ | ||
| clientId, | ||
| tabId, | ||
| tool: 'perform_action', | ||
| args: { | ||
| method: 'hover', | ||
| nodeId, | ||
| reasoning: 'Hover action via API' | ||
| }, | ||
| timeout | ||
| }); | ||
| } |
There was a problem hiding this comment.
Missing nodeId validation for hover action.
Consistent with the pattern, hover also needs nodeId validation.
🔎 Suggested fix
async handleActionHover(payload) {
const { clientId, tabId, nodeId, timeout = 30000 } = payload;
+ if (nodeId === undefined || nodeId === null) {
+ throw new Error('nodeId is required for hover action');
+ }
+
return this.executeToolDirect({🤖 Prompt for AI Agents
In agent-server/nodejs/src/api-server.js around lines 812 to 826, the
handleActionHover function is missing the same nodeId validation used by other
action handlers; before calling executeToolDirect, validate that nodeId is
present (and of the expected type, e.g., non-empty string or numeric id per
project convention) and return/throw a proper API error (mirror the pattern used
in the other handlers — e.g., respond with a 400/status error and descriptive
message) if validation fails; add the check immediately after destructuring
payload and before calling executeToolDirect so invalid requests are rejected
early.
|
Claude finished @Daedaelius's task —— View job Pull Request ReviewI've completed a comprehensive review of this PR focusing on code quality, security, performance, and test coverage. Review Checklist
Architecture Review ✅The PR introduces a well-structured direct tool execution API that bypasses LLM orchestration, along with comprehensive Langfuse tracing integration. The architecture follows good separation of concerns:
Security Analysis
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Claude finished @Daedaelius's task —— View job Pull Request ReviewI've completed a comprehensive review of this PR focusing on code quality, security, performance, and test coverage. Review Checklist
Architecture Review ✅The PR introduces a well-structured direct tool execution API that bypasses LLM orchestration, along with comprehensive Langfuse tracing integration. The architecture follows good separation of concerns:
Security Analysis
|
There was a problem hiding this comment.
Actionable comments posted: 3
♻️ Duplicate comments (3)
agent-server/nodejs/src/api-server.js (3)
716-730: MissingnodeIdvalidation for click action.The click action requires
nodeIdto identify the target element, but this isn't validated before callingexecuteToolDirect. A missingnodeIdwill cause tool execution to fail with a less clear error.🔎 Suggested fix
async handleActionClick(payload) { const { clientId, tabId, nodeId, timeout = 30000 } = payload; + if (nodeId === undefined || nodeId === null) { + throw new Error('nodeId is required for click action'); + } + return this.executeToolDirect({ clientId, tabId,
738-757: MissingnodeIdvalidation for type action.Similar to click, the type action validates
textbut notnodeId. Both are required for the action to succeed.🔎 Suggested fix
async handleActionType(payload) { const { clientId, tabId, nodeId, text, timeout = 30000 } = payload; + if (nodeId === undefined || nodeId === null) { + throw new Error('nodeId is required for type action'); + } + if (!text) { throw new Error('Text is required for type action'); }
812-826: MissingnodeIdvalidation for hover action.Consistent with the pattern, hover also needs
nodeIdvalidation.🔎 Suggested fix
async handleActionHover(payload) { const { clientId, tabId, nodeId, timeout = 30000 } = payload; + if (nodeId === undefined || nodeId === null) { + throw new Error('nodeId is required for hover action'); + } + return this.executeToolDirect({
🧹 Nitpick comments (2)
docker/Makefile (1)
30-36: Consider extracting hardcoded service URLs to variables.The service URLs are hardcoded and could become stale if port mappings change in
docker-compose.lightweight.yml. While not critical, extracting these to variables or documenting the dependency would improve maintainability.🔎 Optional refactor
+# Service ports (must match docker-compose.lightweight.yml) +DEVTOOLS_PORT=8000 +AGENT_API_PORT=8080 +AGENT_WS_PORT=8082 + up: ## Start DevTools + Agent Server (Type 2 - lightweight) docker-compose -f docker-compose.lightweight.yml up -d @echo "" @echo "Services:" - @echo " DevTools: http://localhost:8000" - @echo " Agent API: http://localhost:8080" - @echo " Agent WS: ws://localhost:8082" + @echo " DevTools: http://localhost:$(DEVTOOLS_PORT)" + @echo " Agent API: http://localhost:$(AGENT_API_PORT)" + @echo " Agent WS: ws://localhost:$(AGENT_WS_PORT)"agent-server/nodejs/src/api-server.js (1)
765-778: Consider validating scroll direction against allowed values.The
directionparameter has a default but isn't validated against the schema's enum['up', 'down', 'left', 'right']. While the tool execution may handle invalid values gracefully, early validation would provide clearer error messages.🔎 Optional validation
async handleActionScroll(payload) { const { clientId, tabId, direction = 'down', amount, pages, timeout = 30000 } = payload; + const validDirections = ['up', 'down', 'left', 'right']; + if (!validDirections.includes(direction)) { + throw new Error(`Invalid direction "${direction}". Must be one of: ${validDirections.join(', ')}`); + } + return this.executeToolDirect({
📜 Review details
Configuration used: defaults
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (8)
agent-server/nodejs/src/api-server.jsagent-server/nodejs/src/lib/BrowserAgentServer.jsdocker/Makefiledocker/docker-compose.lightweight.ymlfront_end/panels/ai_chat/evaluation/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/EvaluationProtocol.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts
🚧 Files skipped from review as they are similar to previous changes (3)
- docker/docker-compose.lightweight.yml
- front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts
- front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts
🧰 Additional context used
📓 Path-based instructions (1)
agent-server/nodejs/src/api-server.js
📄 CodeRabbit inference engine (agent-server/nodejs/CLAUDE.md)
agent-server/nodejs/src/api-server.js: Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Model configuration must use canonical nested format with main_model, mini_model, and nano_model tiers, each containing provider, model, and api_key fields
POST /page/screenshot endpoint must accept clientId and tabId, use CDP Page.captureScreenshot, and return base64-encoded PNG with metadata and timestamp
POST /page/content endpoint must accept clientId, tabId, format (html or text), and includeIframes parameters; recursively capture iframe content when includeIframes is true
POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Accept POST /v1/responses input as either string format (simple message) or conversation array format with role and content fields; enforce at least one user message and max 100 messages/10,000 characters per message
Files:
agent-server/nodejs/src/api-server.js
🧠 Learnings (9)
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Use Chrome DevTools Protocol (CDP) for direct browser communication including screenshot capture via Page.captureScreenshot, page content via Runtime.evaluate, and tab management via Target.createTarget/closeTarget
Applied to files:
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/EvaluationAgent.tsagent-server/nodejs/src/api-server.jsagent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Applied to files:
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.tsfront_end/panels/ai_chat/evaluation/EvaluationAgent.tsagent-server/nodejs/src/api-server.jsagent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/screenshot endpoint must accept clientId and tabId, use CDP Page.captureScreenshot, and return base64-encoded PNG with metadata and timestamp
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/client-manager.js : Each browser tab must be identified by composite client ID in format baseClientId:tabId
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Implement WebSocket server for browser agent connections with client lifecycle management (connect, ready, disconnect)
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/rpc-client.js : Implement JSON-RPC 2.0 protocol for bidirectional communication with request/response correlation using unique IDs, timeout handling, and error conditions
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : CDP communication pattern: discover endpoint via http://localhost:9223/json/version, establish WebSocket per command, send JSON-RPC 2.0, attach for tab-level ops, detach, close WebSocket after response
Applied to files:
agent-server/nodejs/src/lib/BrowserAgentServer.js
🧬 Code graph analysis (2)
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts (4)
front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (5)
isToolExecutionRequest(396-402)ToolExecutionRequest(358-363)createToolExecutionErrorResponse(423-439)ErrorCodes(161-175)createToolExecutionSuccessResponse(405-421)front_end/panels/ai_chat/ui/EvaluationDialog.ts (1)
message(1612-1623)front_end/panels/ai_chat/ui/AIChatPanel.ts (4)
logger(958-1032)logger(1151-1164)logger(1208-1211)error(1544-1557)front_end/panels/ai_chat/agent_framework/ConfigurableAgentTool.ts (1)
ToolRegistry(221-268)
front_end/panels/ai_chat/evaluation/EvaluationAgent.ts (4)
front_end/panels/ai_chat/evaluation/EvaluationProtocol.ts (5)
isToolExecutionRequest(211-213)ToolExecutionRequest(83-88)createToolExecutionErrorResponse(319-338)ErrorCodes(181-195)createToolExecutionSuccessResponse(301-317)front_end/panels/ai_chat/evaluation/remote/EvaluationProtocol.ts (5)
isToolExecutionRequest(396-402)ToolExecutionRequest(358-363)createToolExecutionErrorResponse(423-439)ErrorCodes(161-175)createToolExecutionSuccessResponse(405-421)front_end/panels/ai_chat/ui/AIChatPanel.ts (4)
logger(958-1032)logger(1151-1164)logger(1208-1211)error(1544-1557)front_end/panels/ai_chat/agent_framework/ConfigurableAgentTool.ts (1)
ToolRegistry(221-268)
🪛 checkmake (0.2.2)
docker/Makefile
[warning] 6-6: Target body for "help" exceeds allowed length of 5 (14).
(maxbodylength)
[warning] 30-30: Target body for "up" exceeds allowed length of 5 (6).
(maxbodylength)
[warning] 48-48: Target body for "chrome" exceeds allowed length of 5 (6).
(maxbodylength)
[warning] 4-4: Missing required phony target "all"
(minphony)
[warning] 4-4: Missing required phony target "clean"
(minphony)
[warning] 4-4: Missing required phony target "test"
(minphony)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: claude-review
🔇 Additional comments (12)
docker/Makefile (2)
6-21: LGTM!The help target provides clear deployment guidance. The static analysis warning about target body length is pedantic; help targets inherently require more lines to document usage properly.
22-23: LGTM!The
build,devtools-up,down,logs, andstatustargets are well-implemented:
- The
downandlogstargets use good fallback patterns with error suppression (2>/dev/null || true), ensuring graceful handling when either stack isn't running.- The
statustarget effectively filters containers by name.- All targets are simple, focused, and correct.
Also applies to: 25-28, 38-43, 45-46
front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts (2)
32-32: LGTM: Tool execution protocol imports added correctly.The imports for direct tool execution support are properly aligned with the protocol definitions.
Also applies to: 38-38, 47-48
205-207: LGTM: Message routing integrated correctly.The routing for
ToolExecutionRequestfollows the established pattern for other message types.front_end/panels/ai_chat/evaluation/EvaluationAgent.ts (3)
23-25: LGTM: Tool execution imports added correctly.The imports for direct tool execution are properly structured and aligned with the protocol.
Also applies to: 30-30, 38-39
180-182: LGTM: Message routing follows established patterns.The routing for
ToolExecutionRequestis consistent with other message type handlers.
611-694: LGTM: Direct tool execution implemented correctly.The method correctly:
- Uses specific error codes (
INVALID_TOOL,TOOL_EXECUTION_ERROR)- Reuses
executeToolWithTimeoutfor consistent timeout handling- Intentionally skips tracing, navigation, and retries (per design comments)
- Includes appropriate logging for debugging
The implementation is clean and follows the established patterns in the codebase.
agent-server/nodejs/src/api-server.js (5)
166-229: LGTM!The new route definitions are well-structured with proper method validation, body parsing, and async handling.
513-568: LGTM!The method has comprehensive validation of required fields, connection state, and provides clear error messages. The structured response format is appropriate.
578-704: LGTM!The static tool registry is well-documented with comprehensive JSON Schema definitions. The comment appropriately notes that dynamic discovery would use the DevTools connection.
787-804: LGTM!The navigate action properly validates the required
urlparameter and has appropriate error messaging.
834-854: LGTM!The accessibility tree endpoint properly validates required fields and correctly handles the optional
maxDepthparameter.
| /** | ||
| * Execute a tool directly on a connected DevTools client | ||
| * This bypasses LLM orchestration and calls the tool directly | ||
| * @param {Object} connection - DevTools WebSocket connection | ||
| * @param {string} tool - Tool name (e.g., 'perform_action', 'navigate_url') | ||
| * @param {Object} args - Tool-specific arguments | ||
| * @param {number} timeout - Execution timeout in milliseconds | ||
| * @returns {Promise<Object>} Tool execution result | ||
| */ | ||
| async executeToolDirect(connection, tool, args, timeout = 30000) { | ||
| const rpcId = `tool-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`; | ||
|
|
||
| logger.info('Executing tool directly', { | ||
| clientId: connection.clientId, | ||
| tool, | ||
| timeout | ||
| }); | ||
|
|
||
| try { | ||
| // Prepare RPC request for execute_tool method | ||
| const response = await connection.rpcClient.callMethod( | ||
| connection.ws, | ||
| 'execute_tool', | ||
| { | ||
| tool, | ||
| args, | ||
| timeout | ||
| }, | ||
| timeout + 5000 // Add buffer for network overhead | ||
| ); | ||
|
|
||
| logger.info('Tool execution completed', { | ||
| clientId: connection.clientId, | ||
| tool, | ||
| success: response?.result?.success | ||
| }); | ||
|
|
||
| return response; | ||
|
|
||
| } catch (error) { | ||
| logger.error('Tool execution failed', { | ||
| clientId: connection.clientId, | ||
| tool, | ||
| error: error.message | ||
| }); | ||
| throw error; | ||
| } | ||
| } |
There was a problem hiding this comment.
Verify connection readiness before tool execution.
The method doesn't validate that the connection is in a ready state before attempting RPC communication. If the connection isn't ready, the RPC call will fail with a less informative error.
🔎 Suggested validation
async executeToolDirect(connection, tool, args, timeout = 30000) {
const rpcId = `tool-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`;
+ if (!connection.ready) {
+ throw new Error(`Connection for client ${connection.clientId} is not ready for tool execution`);
+ }
+
logger.info('Executing tool directly', {
clientId: connection.clientId,
tool,
timeout
});📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| /** | |
| * Execute a tool directly on a connected DevTools client | |
| * This bypasses LLM orchestration and calls the tool directly | |
| * @param {Object} connection - DevTools WebSocket connection | |
| * @param {string} tool - Tool name (e.g., 'perform_action', 'navigate_url') | |
| * @param {Object} args - Tool-specific arguments | |
| * @param {number} timeout - Execution timeout in milliseconds | |
| * @returns {Promise<Object>} Tool execution result | |
| */ | |
| async executeToolDirect(connection, tool, args, timeout = 30000) { | |
| const rpcId = `tool-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`; | |
| logger.info('Executing tool directly', { | |
| clientId: connection.clientId, | |
| tool, | |
| timeout | |
| }); | |
| try { | |
| // Prepare RPC request for execute_tool method | |
| const response = await connection.rpcClient.callMethod( | |
| connection.ws, | |
| 'execute_tool', | |
| { | |
| tool, | |
| args, | |
| timeout | |
| }, | |
| timeout + 5000 // Add buffer for network overhead | |
| ); | |
| logger.info('Tool execution completed', { | |
| clientId: connection.clientId, | |
| tool, | |
| success: response?.result?.success | |
| }); | |
| return response; | |
| } catch (error) { | |
| logger.error('Tool execution failed', { | |
| clientId: connection.clientId, | |
| tool, | |
| error: error.message | |
| }); | |
| throw error; | |
| } | |
| } | |
| /** | |
| * Execute a tool directly on a connected DevTools client | |
| * This bypasses LLM orchestration and calls the tool directly | |
| * @param {Object} connection - DevTools WebSocket connection | |
| * @param {string} tool - Tool name (e.g., 'perform_action', 'navigate_url') | |
| * @param {Object} args - Tool-specific arguments | |
| * @param {number} timeout - Execution timeout in milliseconds | |
| * @returns {Promise<Object>} Tool execution result | |
| */ | |
| async executeToolDirect(connection, tool, args, timeout = 30000) { | |
| const rpcId = `tool-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`; | |
| if (!connection.ready) { | |
| throw new Error(`Connection for client ${connection.clientId} is not ready for tool execution`); | |
| } | |
| logger.info('Executing tool directly', { | |
| clientId: connection.clientId, | |
| tool, | |
| timeout | |
| }); | |
| try { | |
| // Prepare RPC request for execute_tool method | |
| const response = await connection.rpcClient.callMethod( | |
| connection.ws, | |
| 'execute_tool', | |
| { | |
| tool, | |
| args, | |
| timeout | |
| }, | |
| timeout + 5000 // Add buffer for network overhead | |
| ); | |
| logger.info('Tool execution completed', { | |
| clientId: connection.clientId, | |
| tool, | |
| success: response?.result?.success | |
| }); | |
| return response; | |
| } catch (error) { | |
| logger.error('Tool execution failed', { | |
| clientId: connection.clientId, | |
| tool, | |
| error: error.message | |
| }); | |
| throw error; | |
| } | |
| } |
| /** | ||
| * Handle direct tool execution requests without LLM orchestration | ||
| */ | ||
| private async handleToolExecutionRequest(request: ToolExecutionRequest): Promise<void> { | ||
| const { params, id } = request; | ||
| const startTime = Date.now(); | ||
|
|
||
| logger.info('Received tool execution request', { | ||
| tool: params.tool, | ||
| hasArgs: !!params.args, | ||
| timeout: params.timeout || 30000 | ||
| }); | ||
|
|
||
| try { | ||
| // Get the tool from registry | ||
| const tool = ToolRegistry.getRegisteredTool(params.tool); | ||
| if (!tool) { | ||
| const errorResponse = createToolExecutionErrorResponse( | ||
| id, | ||
| ErrorCodes.INVALID_PARAMS, | ||
| `Tool not found: ${params.tool}`, | ||
| params.tool, | ||
| `Tool '${params.tool}' is not registered` | ||
| ); | ||
| if (this.client) { | ||
| this.client.send(errorResponse); | ||
| } | ||
| logger.error('Tool not found', { tool: params.tool }); | ||
| return; | ||
| } | ||
|
|
||
| // Execute the tool with timeout | ||
| const timeout = params.timeout || 30000; | ||
| const result = await this.executeToolWithTimeout( | ||
| tool, | ||
| params.args || {}, | ||
| timeout, | ||
| undefined, | ||
| params.tool | ||
| ); | ||
|
|
||
| const executionTime = Date.now() - startTime; | ||
|
|
||
| // Send success response | ||
| const successResponse = createToolExecutionSuccessResponse( | ||
| id, | ||
| params.tool, | ||
| result, | ||
| executionTime | ||
| ); | ||
|
|
||
| if (this.client) { | ||
| this.client.send(successResponse); | ||
| } | ||
|
|
||
| logger.info('Tool execution completed', { | ||
| tool: params.tool, | ||
| executionTime, | ||
| hasResult: !!result | ||
| }); | ||
|
|
||
| } catch (error) { | ||
| const executionTime = Date.now() - startTime; | ||
| logger.error('Tool execution failed', { | ||
| tool: params.tool, | ||
| error: error instanceof Error ? error.message : String(error), | ||
| executionTime | ||
| }); | ||
|
|
||
| const errorResponse = createToolExecutionErrorResponse( | ||
| id, | ||
| ErrorCodes.INTERNAL_ERROR, | ||
| 'Tool execution failed', | ||
| params.tool, | ||
| error instanceof Error ? error.message : String(error) | ||
| ); | ||
|
|
||
| if (this.client) { | ||
| this.client.send(errorResponse); | ||
| } | ||
| } | ||
| } |
There was a problem hiding this comment.
Use more specific error codes for consistency.
The error responses use generic error codes instead of the more specific codes available:
- Line 1380 uses
ErrorCodes.INVALID_PARAMSbut should useErrorCodes.INVALID_TOOL(-32004) - Line 1432 uses
ErrorCodes.INTERNAL_ERRORbut should useErrorCodes.TOOL_EXECUTION_ERROR(-32000)
The non-remote version (EvaluationAgent.ts lines 631, 684) correctly uses these specific codes. Using consistent, specific error codes helps API consumers distinguish between different failure modes.
🔎 Proposed fix
const tool = ToolRegistry.getRegisteredTool(params.tool);
if (!tool) {
const errorResponse = createToolExecutionErrorResponse(
id,
- ErrorCodes.INVALID_PARAMS,
+ ErrorCodes.INVALID_TOOL,
`Tool not found: ${params.tool}`,
params.tool,
`Tool '${params.tool}' is not registered`
); const errorResponse = createToolExecutionErrorResponse(
id,
- ErrorCodes.INTERNAL_ERROR,
+ ErrorCodes.TOOL_EXECUTION_ERROR,
'Tool execution failed',
params.tool,
error instanceof Error ? error.message : String(error)
);📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| /** | |
| * Handle direct tool execution requests without LLM orchestration | |
| */ | |
| private async handleToolExecutionRequest(request: ToolExecutionRequest): Promise<void> { | |
| const { params, id } = request; | |
| const startTime = Date.now(); | |
| logger.info('Received tool execution request', { | |
| tool: params.tool, | |
| hasArgs: !!params.args, | |
| timeout: params.timeout || 30000 | |
| }); | |
| try { | |
| // Get the tool from registry | |
| const tool = ToolRegistry.getRegisteredTool(params.tool); | |
| if (!tool) { | |
| const errorResponse = createToolExecutionErrorResponse( | |
| id, | |
| ErrorCodes.INVALID_PARAMS, | |
| `Tool not found: ${params.tool}`, | |
| params.tool, | |
| `Tool '${params.tool}' is not registered` | |
| ); | |
| if (this.client) { | |
| this.client.send(errorResponse); | |
| } | |
| logger.error('Tool not found', { tool: params.tool }); | |
| return; | |
| } | |
| // Execute the tool with timeout | |
| const timeout = params.timeout || 30000; | |
| const result = await this.executeToolWithTimeout( | |
| tool, | |
| params.args || {}, | |
| timeout, | |
| undefined, | |
| params.tool | |
| ); | |
| const executionTime = Date.now() - startTime; | |
| // Send success response | |
| const successResponse = createToolExecutionSuccessResponse( | |
| id, | |
| params.tool, | |
| result, | |
| executionTime | |
| ); | |
| if (this.client) { | |
| this.client.send(successResponse); | |
| } | |
| logger.info('Tool execution completed', { | |
| tool: params.tool, | |
| executionTime, | |
| hasResult: !!result | |
| }); | |
| } catch (error) { | |
| const executionTime = Date.now() - startTime; | |
| logger.error('Tool execution failed', { | |
| tool: params.tool, | |
| error: error instanceof Error ? error.message : String(error), | |
| executionTime | |
| }); | |
| const errorResponse = createToolExecutionErrorResponse( | |
| id, | |
| ErrorCodes.INTERNAL_ERROR, | |
| 'Tool execution failed', | |
| params.tool, | |
| error instanceof Error ? error.message : String(error) | |
| ); | |
| if (this.client) { | |
| this.client.send(errorResponse); | |
| } | |
| } | |
| } | |
| /** | |
| * Handle direct tool execution requests without LLM orchestration | |
| */ | |
| private async handleToolExecutionRequest(request: ToolExecutionRequest): Promise<void> { | |
| const { params, id } = request; | |
| const startTime = Date.now(); | |
| logger.info('Received tool execution request', { | |
| tool: params.tool, | |
| hasArgs: !!params.args, | |
| timeout: params.timeout || 30000 | |
| }); | |
| try { | |
| // Get the tool from registry | |
| const tool = ToolRegistry.getRegisteredTool(params.tool); | |
| if (!tool) { | |
| const errorResponse = createToolExecutionErrorResponse( | |
| id, | |
| ErrorCodes.INVALID_TOOL, | |
| `Tool not found: ${params.tool}`, | |
| params.tool, | |
| `Tool '${params.tool}' is not registered` | |
| ); | |
| if (this.client) { | |
| this.client.send(errorResponse); | |
| } | |
| logger.error('Tool not found', { tool: params.tool }); | |
| return; | |
| } | |
| // Execute the tool with timeout | |
| const timeout = params.timeout || 30000; | |
| const result = await this.executeToolWithTimeout( | |
| tool, | |
| params.args || {}, | |
| timeout, | |
| undefined, | |
| params.tool | |
| ); | |
| const executionTime = Date.now() - startTime; | |
| // Send success response | |
| const successResponse = createToolExecutionSuccessResponse( | |
| id, | |
| params.tool, | |
| result, | |
| executionTime | |
| ); | |
| if (this.client) { | |
| this.client.send(successResponse); | |
| } | |
| logger.info('Tool execution completed', { | |
| tool: params.tool, | |
| executionTime, | |
| hasResult: !!result | |
| }); | |
| } catch (error) { | |
| const executionTime = Date.now() - startTime; | |
| logger.error('Tool execution failed', { | |
| tool: params.tool, | |
| error: error instanceof Error ? error.message : String(error), | |
| executionTime | |
| }); | |
| const errorResponse = createToolExecutionErrorResponse( | |
| id, | |
| ErrorCodes.TOOL_EXECUTION_ERROR, | |
| 'Tool execution failed', | |
| params.tool, | |
| error instanceof Error ? error.message : String(error) | |
| ); | |
| if (this.client) { | |
| this.client.send(errorResponse); | |
| } | |
| } | |
| } |
🤖 Prompt for AI Agents
In front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts around lines
1361-1442, the error responses use generic codes; replace the INVALID_PARAMS
code used when a tool is not found (around line ~1380) with
ErrorCodes.INVALID_TOOL (-32004), and replace the INTERNAL_ERROR code used in
the catch block (around line ~1432) with ErrorCodes.TOOL_EXECUTION_ERROR
(-32000); update the createToolExecutionErrorResponse calls accordingly so the
errorResponse for missing tools and for execution failures use the specific
codes.
There was a problem hiding this comment.
Actionable comments posted: 1
Fix all issues with AI Agents 🤖
In @agent-server/nodejs/src/api-server.js:
- Around line 834-854: The getAccessibilityTree method is calling
executeToolDirect with the wrong tool name; update the tool argument in
getAccessibilityTree to 'get_accessibility_tree' (instead of 'get_page_content')
so executeToolDirect invokes the correct tool; keep the rest of the payload
(clientId, tabId, args.maxDepth, timeout) unchanged and ensure the change is
made inside the async getAccessibilityTree function where executeToolDirect is
invoked.
🧹 Nitpick comments (2)
agent-server/nodejs/src/api-server.js (2)
513-527: Consider adding validation for theargsparameter.While
clientId,tabId, andtoolare validated, theargsparameter is not checked. Some tools may require specific arguments, and passing invalid or missing args could result in less clear error messages from the downstream tool execution.🔎 Optional validation improvement
async executeToolDirect(payload) { const { clientId, tabId, tool, args, timeout = 30000 } = payload; if (!clientId) { throw new Error('Client ID is required'); } if (!tabId) { throw new Error('Tab ID is required'); } if (!tool) { throw new Error('Tool name is required'); } + + // Validate args is an object if provided + if (args !== undefined && (typeof args !== 'object' || args === null)) { + throw new Error('Tool arguments must be an object'); + } const baseClientId = clientId.split(':')[0];
765-779: Consider validating thedirectionparameter.The scroll action defaults
directionto 'down' but doesn't validate it against the allowed enum values ('up', 'down', 'left', 'right') defined in the tool schema at line 634. Invalid directions will fail during tool execution with potentially unclear error messages.🔎 Optional validation improvement
async handleActionScroll(payload) { const { clientId, tabId, direction = 'down', amount, pages, timeout = 30000 } = payload; + const validDirections = ['up', 'down', 'left', 'right']; + if (!validDirections.includes(direction)) { + throw new Error(`Invalid scroll direction: "${direction}". Must be one of: ${validDirections.join(', ')}`); + } + return this.executeToolDirect({
📜 Review details
Configuration used: defaults
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
agent-server/nodejs/src/api-server.js
🧰 Additional context used
📓 Path-based instructions (1)
agent-server/nodejs/src/api-server.js
📄 CodeRabbit inference engine (agent-server/nodejs/CLAUDE.md)
agent-server/nodejs/src/api-server.js: Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Model configuration must use canonical nested format with main_model, mini_model, and nano_model tiers, each containing provider, model, and api_key fields
POST /page/screenshot endpoint must accept clientId and tabId, use CDP Page.captureScreenshot, and return base64-encoded PNG with metadata and timestamp
POST /page/content endpoint must accept clientId, tabId, format (html or text), and includeIframes parameters; recursively capture iframe content when includeIframes is true
POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Accept POST /v1/responses input as either string format (simple message) or conversation array format with role and content fields; enforce at least one user message and max 100 messages/10,000 characters per message
Files:
agent-server/nodejs/src/api-server.js
🧠 Learnings (7)
📓 Common learnings
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Use Chrome DevTools Protocol (CDP) for direct browser communication including screenshot capture via Page.captureScreenshot, page content via Runtime.evaluate, and tab management via Target.createTarget/closeTarget
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Browser must be started with remote debugging enabled via --remote-debugging-port=9223 with CDP endpoint at http://localhost:9223/json/version
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/execute endpoint must accept clientId, tabId, expression, returnByValue, and awaitPromise; use CDP Runtime.evaluate and return result with type and value
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/lib/EvalServer.js : Use Chrome DevTools Protocol (CDP) for direct browser communication including screenshot capture via Page.captureScreenshot, page content via Runtime.evaluate, and tab management via Target.createTarget/closeTarget
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : POST /page/screenshot endpoint must accept clientId and tabId, use CDP Page.captureScreenshot, and return base64-encoded PNG with metadata and timestamp
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/client-manager.js : Each browser tab must be identified by composite client ID in format baseClientId:tabId
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Expose REST endpoint POST /v1/responses that accepts task input, URL, timeout, and model configuration, and returns OpenAI-compatible response with metadata
Applied to files:
agent-server/nodejs/src/api-server.js
📚 Learning: 2025-12-07T00:27:56.465Z
Learnt from: CR
Repo: BrowserOperator/browser-operator-core PR: 0
File: agent-server/nodejs/CLAUDE.md:0-0
Timestamp: 2025-12-07T00:27:56.465Z
Learning: Applies to agent-server/nodejs/src/api-server.js : Use formatResponse() method to convert agent responses to OpenAI-compatible format and include metadata with clientId and tabId for screenshot capture
Applied to files:
agent-server/nodejs/src/api-server.js
🧬 Code graph analysis (1)
agent-server/nodejs/src/api-server.js (2)
front_end/legacy_test_runner/test_runner/TestRunner.js (1)
res(851-851)front_end/panels/ai_chat/core/AgentService.ts (1)
logger(420-431)
🔇 Additional comments (5)
agent-server/nodejs/src/api-server.js (5)
166-229: LGTM on the new endpoint routing.The new tool execution and action endpoints follow a consistent pattern with proper method validation and error handling. The accessibility tree endpoint correctly supports both GET and POST methods with appropriate parameter parsing.
578-704: LGTM on the static tool registry.The static tool list with schemas provides a reasonable API surface for tool discovery. The comment appropriately notes that the authoritative registry lives in the DevTools frontend.
716-730: MissingnodeIdvalidation for click action.The click action requires
nodeIdbut this isn't validated before callingexecuteToolDirect, leading to unclear failures downstream.Likely an incorrect or invalid review comment.
812-826: MissingnodeIdvalidation for hover action.Consistent with the pattern, hover also needs
nodeIdvalidation before callingexecuteToolDirect.Likely an incorrect or invalid review comment.
738-757: MissingnodeIdvalidation for type action.Similar to click, the type action validates
textbut notnodeId. Both are required for successful execution.Likely an incorrect or invalid review comment.
| async getAccessibilityTree(payload) { | ||
| const { clientId, tabId, maxDepth } = payload; | ||
|
|
||
| if (!clientId) { | ||
| throw new Error('Client ID is required'); | ||
| } | ||
|
|
||
| if (!tabId) { | ||
| throw new Error('Tab ID is required'); | ||
| } | ||
|
|
||
| return this.executeToolDirect({ | ||
| clientId, | ||
| tabId, | ||
| tool: 'get_page_content', | ||
| args: { | ||
| maxDepth | ||
| }, | ||
| timeout: 30000 | ||
| }); | ||
| } |
There was a problem hiding this comment.
Critical: Wrong tool name used for accessibility tree.
Line 848 calls executeToolDirect with tool: 'get_page_content', but based on the method name, endpoint path, and the tool registry at line 650, it should use tool: 'get_accessibility_tree'. This will execute the wrong tool and return incorrect data.
🔎 Proposed fix
async getAccessibilityTree(payload) {
const { clientId, tabId, maxDepth } = payload;
if (!clientId) {
throw new Error('Client ID is required');
}
if (!tabId) {
throw new Error('Tab ID is required');
}
return this.executeToolDirect({
clientId,
tabId,
- tool: 'get_page_content',
+ tool: 'get_accessibility_tree',
args: {
maxDepth
},
timeout: 30000
});
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| async getAccessibilityTree(payload) { | |
| const { clientId, tabId, maxDepth } = payload; | |
| if (!clientId) { | |
| throw new Error('Client ID is required'); | |
| } | |
| if (!tabId) { | |
| throw new Error('Tab ID is required'); | |
| } | |
| return this.executeToolDirect({ | |
| clientId, | |
| tabId, | |
| tool: 'get_page_content', | |
| args: { | |
| maxDepth | |
| }, | |
| timeout: 30000 | |
| }); | |
| } | |
| async getAccessibilityTree(payload) { | |
| const { clientId, tabId, maxDepth } = payload; | |
| if (!clientId) { | |
| throw new Error('Client ID is required'); | |
| } | |
| if (!tabId) { | |
| throw new Error('Tab ID is required'); | |
| } | |
| return this.executeToolDirect({ | |
| clientId, | |
| tabId, | |
| tool: 'get_accessibility_tree', | |
| args: { | |
| maxDepth | |
| }, | |
| timeout: 30000 | |
| }); | |
| } |
🤖 Prompt for AI Agents
In @agent-server/nodejs/src/api-server.js around lines 834-854, The
getAccessibilityTree method is calling executeToolDirect with the wrong tool
name; update the tool argument in getAccessibilityTree to
'get_accessibility_tree' (instead of 'get_page_content') so executeToolDirect
invokes the correct tool; keep the rest of the payload (clientId, tabId,
args.maxDepth, timeout) unchanged and ensure the change is made inside the async
getAccessibilityTree function where executeToolDirect is invoked.
# Conflicts: # docker/Makefile # docker/docker-compose.lightweight.yml
|
Claude finished @Daedaelius's task —— View job I'll analyze this and get back to you. |
The GRD file had incorrect references: - Removed: ActionAgentV0.js (file doesn't exist) - Added: ActionAgentV1.js (file exists but was missing from list) This fixes the GN build error: ERROR Input to target not generated by a dependency. The file: ActionAgentV0.js is listed but no targets generate that file. And the assert_grd error: File ActionAgentV1.js is not listed in devtools_grd_files.gni. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
|
Claude finished @Daedaelius's task —— View job Pull Request Review
|
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