braintrust-tracing▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
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Comprehensive guide to tracing Claude Code sessions in Braintrust, including sub-agent correlation.
Braintrust Tracing for Claude Code
Comprehensive guide to tracing Claude Code sessions in Braintrust, including sub-agent correlation.
Architecture Overview
PARENT SESSION
+---------------------+
| SessionStart |
| (creates root) |
+----------+----------+
|
+----------v----------+
| UserPromptSubmit |
| (creates Turn) |
+----------+----------+
|
+--------------------+--------------------+
| | |
+---------v--------+ +--------v--------+ +--------v--------+
| PostToolUse | | PostToolUse | | PreToolUse |
| (Read span) | | (Edit span) | | (Task - inject) |
+------------------+ +-----------------+ +--------+--------+
|
+----------v----------+
| SUB-AGENT |
| SessionStart |
| (NEW root_span_id)|
+----------+----------+
|
+----------v----------+
| SubagentStop |
| (has session_id) |
+---------------------+
Hook Event Flow
| Hook | Trigger | Creates | Key Fields |
|---|---|---|---|
| SessionStart | Session begins | Root span | session_id, root_span_id |
| UserPromptSubmit | User sends prompt | Turn span | prompt, turn_number |
| PreToolUse | Before tool runs | (modifies Task prompts) | tool_input.prompt |
| PostToolUse | After tool runs | Tool span | tool_name, input, output |
| Stop | Turn completes | LLM spans | model, tokens, tool_calls |
| SubagentStop | Sub-agent finishes | (no span) | session_id of sub-agent |
| SessionEnd | Session ends | (finalizes root) | turn_count, tool_count |
Trace Hierarchy
Session (task span) - root_span_id = session_id
|
+-- Turn 1 (task span)
| |
| +-- claude-sonnet (llm span) - model call with tool_use
| +-- Read (tool span)
| +-- Edit (tool span)
| +-- claude-sonnet (llm span) - response after tools
|
+-- Turn 2 (task span)
| |
| +-- claude-sonnet (llm span)
| +-- Task (tool span) -----> [Sub-agent session - SEPARATE trace]
| +-- claude-sonnet (llm span)
|
+-- Turn 3 ...
Sub-Agent Tracing: What Works and What Doesn't
What Doesn't Work
SessionStart doesn't receive the Task prompt.
We tried injecting trace context into Task prompts via PreToolUse:
# PreToolUse hook injects:
[BRAINTRUST_TRACE_CONTEXT]
{"root_span_id": "abc", "parent_span_id": "xyz", "project_id": "123"}
[/BRAINTRUST_TRACE_CONTEXT]
But SessionStart only receives session metadata, not the modified prompt. The injected context is lost.
What DOES Work
Task spans in parent session contain everything:
agentId- identifier for the sub-agent runtotalTokens,totalToolUseCount- metricscontent- full agent response/summarytool_input.prompt- original task prompttool_input.subagent_type- agent type (e.g., "oracle")
SubagentStop hook receives the sub-agent's session_id:
- This equals the sub-agent's orphaned trace
root_span_id - Allows correlation between parent Task span and child trace
The Correlation Pattern
Current state: Sub-agents create orphaned traces (new root_span_id).
Correlation method:
- Query parent session's Task spans for agent metadata
- Match
agentIdor timing with orphaned traces - Sub-agent's
session_id= its trace'sroot_span_id
Future solution (not yet implemented):
SubagentStop fires -> writes session_id to temp file
PostToolUse (Task) -> reads temp file -> adds child_session_id to Task span metadata
This would link: Task.agentId + Task.child_session_id -> orphaned trace root_span_id
State Management
Per-Session State Files
~/.claude/state/braintrust_sessions/
{session_id}.json # Per-session state
Each session file contains:
{
"root_span_id": "abc-123",
"project_id": "proj-456",
"turn_count": 5,
"tool_count": 23,
"current_turn_span_id": "turn-789",
"current_turn_start": 1703456789,
"started": "2025-12-24T10:00:00.000Z",
"is_subagent": false
}
Global State
~/.claude/state/braintrust_global.json # Cached project_id
~/.claude/state/braintrust_hook.log # Debug log
Debugging Commands
Check if Tracing is Active
# View hook logs in real-time
tail -f ~/.claude/state/braintrust_hook.log
# Check if session has state
cat ~/.claude/state/braintrust_sessions/*.json | jq -s '.'
# Verify environment
echo "TRACE_TO_BRAINTRUST=$TRACE_TO_BRAINTRUST"
echo "BRAINTRUST_API_KEY=${BRAINTRUST_API_KEY:+set}"
Query Braintrust Directly
# List recent sessions
uv run python -m runtime.harness scripts/braintrust_analyze.py --sessions 5
# Analyze last session
uv run python -m runtime.harness scripts/braintrust_analyze.py --last-session
# Replay specific session
uv run python -m runtime.harness scripts/braintrust_analyze.py --replay <session-id>
# Find sub-agent traces (orphaned roots)
uv run python -m runtime.harness scripts/braintrust_analyze.py --agent-stats
Debug Hook Execution
# Enable verbose logging
export BRAINTRUST_CC_DEBUG=true
# Test hooks manually
echo '{"session_id":"test-123","type":"resume"}' | \
bash "$CLAUDE_PROJECT_DIR/.claude/plugins/braintrust-tracing/hooks/session_start.sh"
# Test PreToolUse (Task injection)
echo '{"session_id":"test-123","tool_name":"Task","tool_input":{"prompt":"test"}}' | \
bash "$CLAUDE_PROJECT_DIR/.claude/plugins/braintrust-tracing/hooks/pre_tool_use.sh"
Troubleshooting Checklist
-
No traces appearing:
- Check
TRACE_TO_BRAINTRUST=truein.claude/settings.local.json - Verify API key:
echo $BRAINTRUST_API_KEY - Check logs:
tail -20 ~/.claude/state/braintrust_hook.log
- Check
-
Sub-agents not linking:
- This is expected - sub-agents create orphaned traces
- Use
--agent-statsto find agent activity - Correlate via timing or
agentIdin parent Task span
-
Missing spans:
- Check
current_turn_span_idin session state - Ensure Stop hook runs (turn finalization)
- Look for "Failed to create" errors in log
- Check
-
State corruption:
- Remove session state:
rm ~/.claude/state/braintrust_sessions/*.json - Clear global cache:
rm ~/.claude/state/braintrust_global.json
- Remove session state:
Key Files
| File | Purpose |
|---|---|
.claude/plugins/braintrust-tracing/hooks/common.sh |
Shared utilities, API, state management |
.claude/plugins/braintrust-tracing/hooks/session_start.sh |
Creates root span, handles sub-agent context |
.claude/plugins/braintrust-tracing/hooks/user_prompt_submit.sh |
Creates Turn spans per user message |
.claude/plugins/braintrust-tracing/hooks/pre_tool_use.sh |
Injects trace context into Task prompts |
.claude/plugins/braintrust-tracing/hooks/post_tool_use.sh |
Creates tool spans, captures agent/skill metadata |
.claude/plugins/braintrust-tracing/hooks/stop_hook.sh |
Creates LLM spans, finalizes Turns |
.claude/plugins/braintrust-tracing/hooks/session_end.sh |
Finalizes session, triggers learning extraction |
scripts/braintrust_analyze.py |
Query and analyze traced sessions |
~/.claude/state/braintrust_sessions/ |
Per-session state files |
~/.claude/state/braintrust_hook.log |
Debug log |
Environment Variables
| Variable | Required | Default | Description |
|---|---|---|---|
TRACE_TO_BRAINTRUST |
Yes | - | Set to "true" to enable |
BRAINTRUST_API_KEY |
Yes | - | API key for Braintrust |
BRAINTRUST_CC_PROJECT |
No | claude-code |
Project name |
BRAINTRUST_CC_DEBUG |
No | false |
Verbose logging |
BRAINTRUST_API_URL |
No | https://api.braintrust.dev |
API endpoint |
Session Learnings
What We Learned About Sub-Agent Tracing (Dec 2025)
Attempted: Inject trace context via PreToolUse into Task prompts.
Result: Failed - SessionStart only receives session metadata, not the prompt.
Discovery: Task spans already contain rich sub-agent data:
metadata.agent_type- agent type fromsubagent_typemetadata.skill_name- skill from Skill tooltool_input- full prompt sent to agenttool_output- agent response
Current correlation path:
- Parent session Task span has
agentIdand timing - Sub-agent creates orphaned trace with
root_span_id = session_id - SubagentStop provides the sub-agent's
session_id - Manual correlation: match timing or use
session_idlink
Future work: Write child_session_id to Task span metadata from PostToolUse after SubagentStop.
What We Learned About Sub-Agent Correlation
The Problem
- Sub-agents spawned via Task tool create orphaned Braintrust traces
- Parent session has Task spans with
agentId, sub-agent has separatesession_id - No built-in link between them
What DOESN'T Work
1. Prompt injection via PreToolUse
SessionStart hook only receives session metadata (session_id, type, cwd), NOT the prompt. Injected trace context is never seen.
The hook receives:
{
"session_id": "...",
"type": "start|resume|compact|clear",
"cwd": "...",
"env": {...}
}
No prompt field exists - context injection is impossible at SessionStart.
2. SubagentStop → PostToolUse file handoff
Race condition. These are independent async hooks with no timing guarantees:
- SubagentStop fires when sub-agent session ends
- PostToolUse (Task) fires when Task tool completes
- No ordering guarantee between them
- Writing to a correlation file creates a race
3. PreToolUse correlation files
SessionStart can't access the task_span_id because it has no context about which Task spawned it. PreToolUse modifies prompts but doesn't create a reliably accessible state file that SessionStart can find.
What DOES Work
Post-hoc matching for dataset building:
Parent session Task spans contain:
agentId- identifier for the sub-agent runtotalTokens,totalToolUseCount- aggregated metricscontent- full agent response/summarytool_input.prompt- original task prompttool_input.subagent_type- agent type (e.g., "oracle")- Start/end timestamps
Sub-agent sessions contain:
session_id(equals orphaned traceroot_span_id)- Start/end timestamps
- All internal spans and tool calls
Correlation strategy:
- Export parent session traces (query parent
root_span_id) - Export sub-agent traces (query all sessions created within parent's time window)
- Match by:
- Timing: Task span end ≈ sub-agent session end
- Metadata:
subagent_typefrom Task prompt - IDs: SubagentStop hook provides
session_id(can be captured and logged)
Architecture Insight
SessionStart input is intentionally minimal - it contains no prompt or tool context:
interface SessionStartInput {
session_id: string;
type: "start" | "resume" | "compact" | "clear";
cwd: string;
env: { [key: string]: string How to use braintrust-tracing on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add braintrust-tracing
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches braintrust-tracing from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate braintrust-tracing. Access the skill through slash commands (e.g., /braintrust-tracing) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★63 reviews- ★★★★★Chinedu Sethi· Dec 20, 2024
braintrust-tracing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arya Gupta· Dec 16, 2024
braintrust-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Evelyn Ghosh· Dec 12, 2024
Keeps context tight: braintrust-tracing is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Fatima Diallo· Dec 12, 2024
braintrust-tracing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Dec 4, 2024
braintrust-tracing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 23, 2024
braintrust-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Robinson· Nov 19, 2024
Solid pick for teams standardizing on skills: braintrust-tracing is focused, and the summary matches what you get after install.
- ★★★★★Anika Sethi· Nov 11, 2024
braintrust-tracing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yusuf Anderson· Nov 7, 2024
braintrust-tracing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Olivia Taylor· Nov 7, 2024
braintrust-tracing has been reliable in day-to-day use. Documentation quality is above average for community skills.
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