Langextract - PraisonAI

Langextract transforms PraisonAI agent runs into self-contained interactive HTML visualizations, grounding extractions in the original input text for deeper analysis.

Quick Start

Install langextract

Install PraisonAI with langextract support:

pip install 'praisonai[langextract]'

Programmatic Usage

Set up langextract observability for any agent run:

from praisonaiagents import Agent
from praisonaiagents.trace.protocol import TraceEmitter, set_default_emitter
from praisonai.observability import LangextractSink, LangextractSinkConfig

# Configure langextract sink
sink = LangextractSink(LangextractSinkConfig(
    output_path="trace.html",
    auto_open=True
))

# Set up trace emitter
emitter = TraceEmitter(sink=sink, enabled=True)
set_default_emitter(emitter)

# Run your agent
agent = Agent(
    name="Writer",
    instructions="Write a haiku about code."
)
result = agent.start("Write a haiku about code.")

# Close sink to render the trace
sink.close()  # Creates trace.jsonl and trace.html

CLI Usage

Use langextract with any PraisonAI workflow:

# Render a YAML workflow with langextract
praisonai langextract render workflow.yaml -o trace.html

# View an existing JSONL trace
praisonai langextract view trace.jsonl -o trace.html

# Instrument any praisonai run
praisonai --observe langextract agents.yaml

How It Works

Event Type	Extraction Class	Grounded	Description
`AGENT_START`	`agent_run`	First 200 chars of input	Agent run initiation
`TOOL_START`	`tool_call`	No (ungrounded)	Tool execution start
`TOOL_END`	`tool_result`	No	Tool execution result
`OUTPUT`	`final_output`	First 1000 chars of output	Agent final output
`ERROR`	`error`	No	Error events

Configuration Options

The LangextractSinkConfig class provides comprehensive configuration:

Option	Type	Default	Description
`output_path`	`str`	`"praisonai-trace.html"`	HTML file written on close()
`jsonl_path`	`Optional[str]`	`None`	Annotated-documents JSONL path (derived from output_path if None)
`document_id`	`str`	`"praisonai-run"`	Document ID in the JSONL
`auto_open`	`bool`	`False`	Open the HTML in a browser after render
`include_llm_content`	`bool`	`True`	Include response text in attributes
`include_tool_args`	`bool`	`True`	Include tool args in attributes
`enabled`	`bool`	`True`	Master switch

CLI Reference

render command

Render a YAML workflow with langextract observability:

praisonai langextract render workflow.yaml [OPTIONS]

Options:

-o, --output FILE: Output HTML file path (default: workflow.html)
--no-open: Don’t open HTML in browser automatically
--api-url URL: API URL if using remote API

view command

Render an existing annotated-documents JSONL to HTML:

praisonai langextract view trace.jsonl [OPTIONS]

Options:

-o, --output FILE: Output HTML file path (default: trace.html)
--no-open: Don’t open HTML in browser automatically

—observe flag

Instrument any PraisonAI command with langextract:

praisonai --observe langextract <command>

Common Patterns

Single Agent with Custom Config
CLI Workflow
Post-hoc Analysis

from praisonaiagents import Agent
from praisonaiagents.trace.protocol import TraceEmitter, set_default_emitter
from praisonai.observability import LangextractSink, LangextractSinkConfig

sink = LangextractSink(LangextractSinkConfig(
    output_path="analysis.html",
    document_id="data-analysis",
    include_tool_args=False,  # Exclude tool args for cleaner view
    auto_open=False  # Don't auto-open in CI
))

set_default_emitter(TraceEmitter(sink=sink, enabled=True))

agent = Agent(name="Analyst", instructions="Analyze the data.")
result = agent.start("Analyze quarterly sales data")
sink.close()

# Create workflow YAML
echo 'agents:
  - name: "Researcher"
    instructions: "Research the topic"
    task: "Research climate change impacts"' > research.yaml

# Render with langextract
praisonai langextract render research.yaml -o research-trace.html

# If you have an existing JSONL trace
praisonai langextract view old-trace.jsonl -o updated-view.html

# Useful for re-rendering with different langextract versions

Troubleshooting

"Trace was not rendered" / empty HTML

Ensure you call sink.close() to trigger the rendering process. The CLI commands handle this automatically, but programmatic usage requires explicit closure.

# ✅ Correct
sink = LangextractSink(config)
set_default_emitter(TraceEmitter(sink=sink))
agent.start("task")
sink.close()  # Required!

ImportError: langextract not found

Install langextract with the PraisonAI extra:

pip install 'praisonai[langextract]'

This installs both PraisonAI and the required langextract dependency.

Empty trace output

Verify that your agent actually emits trace events. Single agents require proper trace emitter setup as shown in the examples above.

Browser doesn't open automatically

Check the auto_open configuration:

config = LangextractSinkConfig(auto_open=True)

For CLI commands, remove the --no-open flag.

Best Practices

Use auto_open=False in CI/CD

Disable automatic browser opening in automated environments:

sink = LangextractSink(LangextractSinkConfig(
    output_path="ci-trace.html",
    auto_open=False
))

Scope the emitter properly

Restore the previous emitter after your run to avoid affecting other code:

from praisonaiagents.trace.protocol import get_default_emitter

# Save current emitter
previous_emitter = get_default_emitter()

# Set up langextract
sink = LangextractSink(config)
set_default_emitter(TraceEmitter(sink=sink))

try:
    # Your agent work
    agent.start("task")
finally:
    sink.close()
    # Restore previous emitter
    set_default_emitter(previous_emitter)

Use descriptive document IDs

Set meaningful document IDs for easier trace identification:

config = LangextractSinkConfig(
    document_id=f"analysis-{datetime.now().isoformat()}"
)

Handle rendering failures gracefully

Langextract rendering failures don’t break agent execution - check logs for details:

import logging
logging.basicConfig(level=logging.DEBUG)
# Rendering errors will appear in logs without stopping your agent

Observability Overview

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AgentOps

Langfuse

​Quick Start

​How It Works

​Configuration Options

​CLI Reference

​render command

​view command

​—observe flag

​Common Patterns

​Troubleshooting

​Best Practices

​Related