Documentation Index Fetch the complete documentation index at: https://docs.hellofriday.ai/llms.txt
Use this file to discover all available pages before exploring further.
Basic progress emission from friday_agent_sdk import agent, ok @agent ( id = "long-task" , version = "1.0.0" , description = "Takes a while" ) def execute ( prompt , ctx ): ctx.stream.progress( "Starting analysis..." ) # Do work... result = ctx.llm.generate( ... ) ctx.stream.progress( "Processing results..." ) # More work... data = process(result.text) ctx.stream.progress( "Complete!" ) return ok({ "data" : data})
Intent emission Emit high-level intents for significant state changes:
ctx.stream.intent( "Analyzing repository structure" ) # Walk directory tree... ctx.stream.intent( "Identifying issues" ) # Run analysis... ctx.stream.intent( "Generating report" )
With Tool Context Associate progress with specific tools:
ctx.stream.progress( "Fetching repository data" , tool_name = "GitHub" ) # Call GitHub MCP tools... ctx.stream.progress( "Analyzing code patterns" , tool_name = "Analyzer" ) # LLM analysis... ctx.stream.progress( "Creating summary" , tool_name = "Reporter" )
Real Example: Multi-Phase Agent from friday_agent_sdk import agent, ok, AgentExtras @agent ( id = "analyzer" , version = "1.0.0" , description = "Multi-phase analysis" ) def execute ( prompt , ctx ): # Phase 1: Extract parameters ctx.stream.progress( "Parsing request" ) params = extract_params(prompt) # Phase 2: LLM preprocessing ctx.stream.progress( "Running initial analysis" , tool_name = "LLM" ) analysis = ctx.llm.generate( messages = [{ "role" : "user" , "content" : f "Analyze: { params } " }], model = "claude-haiku-4-5" , ) # Phase 3: Tool calls ctx.stream.progress( "Fetching related data" , tool_name = "GitHub" ) issues = ctx.tools.call( "search_issues" , { "query" : params[ "query" ]}) # Phase 4: Synthesis ctx.stream.progress( "Synthesizing results" , tool_name = "Synthesizer" ) result = synthesize(analysis.text, issues) ctx.stream.progress( "Analysis complete" ) return ok({ "summary" : result[ "summary" ], "recommendations" : result[ "recommendations" ], })
When to Emit Emit progress when:
Starting a distinct phase of work
Before expensive operations (LLM calls, HTTP requests)
After completing significant milestones
When handling fallback scenarios (“Retrying with different model…”)
Do not emit:
In tight loops (debounce or batch instead)
For trivial operations (< 100ms)
Excessively verbose detail (“Step 1 of 50”, “Step 2 of 50”…)
Emission during LLM calls Emit progress before expensive operations - ctx.stream.progress() is fire-and-forget over NATS and does not block the handler:
ctx.stream.progress( "Starting LLM call..." ) # progress event already sent to connected clients result = ctx.llm.generate(messages, model = "claude-sonnet-4-6" ) ctx.stream.progress( "LLM complete, processing..." )
Raw Event Emission For custom event types, use emit():
ctx.stream.emit( "custom-event" , { "phase" : "validation" , "count" : 42 })
The data parameter accepts either a dict (JSON-serialized) or string.
API Reference: ctx.stream Full stream capability API reference
How Friday Agents Work The subprocess model and host capabilities
