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Documentation Index

Fetch the complete documentation index at: https://docs.a1project.org/llms.txt

Use this file to discover all available pages before exploring further.

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Context Tracking

View all agent interactions across execution: 
from a1 import get_runtime

runtime = get_runtime()

# View all contexts
for name, ctx in runtime.CTX.items():
    print(f"{name}: {len(ctx.messages)} messages")

# Get chronological history
all_messages = runtime.get_full_context()

# Filter by context type
main_messages = runtime.get_full_context("main")          # User inputs/outputs
attempt_messages = runtime.get_full_context("attempt")    # Code generation retries
intermediate_messages = runtime.get_full_context("intermediate")  # LLM calls in code
Context types:
  • main - Successful user inputs and outputs
  • attempt_* - Code generation attempts and failures
  • intermediate_* - LLM calls from within generated code

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Debugging Failed Attempts

See what went wrong during code generation: 
runtime = get_runtime()

# View retry attempts
for name, ctx in runtime.CTX.items():
    if name.startswith("attempt_"):
        print(f"\n{name}:")
        for msg in ctx.messages:
            if "error" in msg.content.lower():
                print(f"  {msg.content}")

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Logging

Enable debug logs to see execution details: 
import logging

logging.basicConfig(level=logging.INFO)

# See code generation and execution
result = await agent.jit(problem="Calculate fibonacci")

# Logs show:
# - Code generation attempts
# - Verification results
# - Cost scores
# - Selected candidate

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OpenTelemetry

A1 integrates with OpenTelemetry for distributed tracing: 
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Setup tracing
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter())
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# Agent execution automatically traced
result = await agent.jit(problem="What is 2+2?")
Trace attributes:
  • agent.name - Agent identifier
  • generation.num_candidates - Parallel candidates generated
  • generation.best_cost - Selected candidate’s cost
  • execution.duration - Total execution time

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Custom Tracing

Add custom spans for your operations: 
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("custom_operation") as span:
    span.set_attribute("input.size", len(data))
    result = await process(data)
    span.set_attribute("output.size", len(result))

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Metrics

Track performance metrics: 
from opentelemetry.metrics import get_meter

meter = get_meter(__name__)

# Counters
agent_calls = meter.create_counter("agent.calls")
agent_calls.add(1, {"agent": "math_agent"})

# Histograms
latency = meter.create_histogram("agent.latency")
latency.record(execution_time, {"agent": "math_agent"})

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Integrations

Works with standard observability platforms:
  • Jaeger - Distributed tracing
  • Prometheus - Metrics collection
  • DataDog - APM
  • New Relic - Full-stack observability
  • Honeycomb - Observability platform