context-profiler

The evidence layer for context engineering. Profile before you prune.

context-profiler turns raw provider requests, observability exports, and agent trajectories into evidence about how context grows, repeats, and concentrates — so you know what to compact and where it's safe to cut.

Why

Compression tools (LLMLingua, /compact, Mem0) execute blindly. They don't tell you what's redundant, what's safe to remove, or what downstream references will break. context-profiler fills the missing step:

trace → profile → human review → prune/compact decision

Built for both humans and agents:

• HTML reports — interactive timeline, icicle, persistence heatmap, tools, diff, findings
• JSON contracts — stable issue codes and evidence for automated agent workflows
• Trace-source agnostic — same analysis across OpenAI, Anthropic, Langfuse, and public trajectory datasets

Report Views

Icicle Token distribution per request. Semantic color by role, diff mode for additions/removals.	Persistence Which content blocks survive across turns. Blue = token cost. Red = compact candidate.
Tools Which tools dominate the context budget and their invocation details.	Findings Issue codes with severity, evidence, and actionable recommendations.

Findings Across Public Datasets

Profiled on real multi-turn agent trajectories from public benchmarks:

All examples are included in examples/ with conversion scripts and pre-converted session files.

Install

For agent/CLI use, prefer an isolated executable install:

pipx install context-profiler
# or
uv tool install context-profiler
context-profiler --version
which -a context-profiler  # ensure a stale executable is not shadowing pipx/uv

Or install from source:

git clone https://github.com/Turdot/context-profiler.git
cd context-profiler
uv tool install -e .
# for local development in this repo:
PYTHONPATH=src uv run context-profiler --version

Quick Start

Analyze a multi-turn agent session (SWE-agent trajectory included):

context-profiler analyze examples/swe_agent/session.jsonl --format openai --html report.html

Analyze a raw provider request:

context-profiler analyze request.json --format auto
context-profiler diagnose request.json --format auto --json

Analyze a Langfuse export:

context-profiler validate trace.json --format langfuse --json
context-profiler diagnose trace.json --format langfuse --json
context-profiler analyze trace.json --format langfuse --html report.html

Fetch a Langfuse trace through the public API, then analyze it:

TRACE_ID="<trace-id>"
HOST="${LANGFUSE_HOST%/}"
OUT="/tmp/langfuse-trace-${TRACE_ID}"
mkdir -p "$OUT"

curl -fsS \
  -u "$LANGFUSE_PUBLIC_KEY:$LANGFUSE_SECRET_KEY" \
  "$HOST/api/public/traces/$TRACE_ID" \
  -o "$OUT/trace.json"

curl -fsS \
  -u "$LANGFUSE_PUBLIC_KEY:$LANGFUSE_SECRET_KEY" \
  "$HOST/api/public/observations?traceId=$TRACE_ID&limit=100&page=1" \
  -o "$OUT/observations-page-1.json"

context-profiler diagnose "$OUT/trace.json" --format langfuse --json

Analyze a public academic agent trajectory:

context-profiler diagnose examples/agent-trace/sample.json --format agent-trace --json
context-profiler analyze examples/agent-trace/sample.json --format agent-trace --html report.html

Generate an interactive report:

context-profiler analyze session.jsonl --html report.html

CLI Output

The terminal report gives a quick read on context budget, repeated content, and tool hotspots before you open the HTML report.

╭──────────────────────────────────────────────────────────────────────────────╮
│ context-profiler  |  mode: snapshot  |  source:                              │
│ tests/fixtures/repeated_tool_calls.json                                      │
╰──────────────────────────────────────────────────────────────────────────────╯

⚠ Warnings
  • Content duplication: 476 redundant tokens (60.2% of total)

Token Distribution
  Category                  Tokens    % of Total
  Total Input                  791          100%
    System Prompt               13          1.6%
    Tool Definitions            83         10.5%
    Messages (assistant)       609         77.0%
    Messages (tool)             70          8.8%
    Messages (user)             16          2.0%

  Top Tools by Token Usage
      generate_canvas_component    595    75.2%

Agent-Friendly CLI Harness

context-profiler is strict about supported formats but helpful when input does not match. Agents can discover contracts and adapt unsupported traces without asking users to reshape data manually.

# Discover supported formats
context-profiler formats list --json
context-profiler formats describe cursor-jsonl --json

# Discover canonical contracts
context-profiler schema trace --json
context-profiler schema diagnosis --json

# Validate and normalize
context-profiler validate trace.json --format auto --json
context-profiler normalize trace.json --from auto --json

# Diagnose for agent consumption; '-' reads JSON/JSONL from stdin
context-profiler diagnose trace.json --format auto --json

If validation fails, the JSON response includes errors[].agent_action and next_steps so the agent can convert the trace into ContextTrace.

Agent Skill Distribution

This repository ships an analyze-agent-context skill for Cursor, Claude Code, and other Agent Skills / Open Plugins compatible tools.

The skill does not make context-profiler fetch traces itself. It teaches agents to fetch Langfuse trace ids with the Langfuse public API via curl, then route the fetched JSON into context-profiler for diagnosis whenever the user asks to analyze a trace, loop, transcript, agent run, context growth, stale context, or tool bloat. It intentionally avoids langfuse-cli for trace fetching because the CLI may omit fields needed for complete analysis.

Canonical skill:

skills/analyze-agent-context/SKILL.md

Plugin manifests:

.plugin/plugin.json
.claude-plugin/marketplace.json

Supported Inputs

Use context-profiler formats list --json for the current machine-readable registry.

For agent-transcript, analysis is intentionally marked partial: hidden system prompts, rules, tool definitions, MCP schemas, and provider compaction may not be present.

Example Diagnosis

{
  "issues": [
    {
      "code": "TOOL_USE_DOMINATES_CONTEXT",
      "severity": "critical",
      "message": "Tool inputs dominate the visible context."
    },
    {
      "code": "TOP_TOOL_CONTEXT_HOTSPOT",
      "message": "ApplyPatch is the largest visible tool context hotspot."
    }
  ],
  "diff_hints": [
    {
      "type": "large_addition",
      "request_index": 76,
      "evidence": {
        "added_tokens": 7473,
        "top_added_tool": "ApplyPatch"
      }
    }
  ]
}

Academic trajectory sample:

context-profiler analyze examples/agent-trace/sample.json --format agent-trace --html report.html

Total Input: 11.7K
Messages (assistant): 10.7K
Tool: python_interpreter 2.9K
Warnings: Content duplication 2.3K redundant tokens

Examples

See examples/README.md for runnable fixtures and conversion patterns.

Recommended demo order:

1. Raw OpenAI/Anthropic request.
2. Cursor or Claude Code transcript.
3. Langfuse trace export.
4. Multi-turn academic trajectories such as pagarsky/agent-trace, cx-cmu/agent_trajectories, or SWE-agent traces.

Research Context

context-profiler is motivated by recent work showing that long-horizon agents are constrained not only by model quality, but also by how their working context is retained, compressed, and reused across turns.

Related work:

• ByteDance Seed — Scaling Long-Horizon LLM Agent via Context-Folding
  Studies context management for long-horizon agents through folding and summarizing intermediate sub-trajectories. This motivates context-profiler's focus on turn-to-turn context diffs, retained observations, and compression/pruning evidence.
• SWE-agent — Agent-Computer Interfaces Enable Automated Software Engineering
  Shows the importance of the agent-computer interface for software-engineering agents, motivating analysis of tool calls, terminal output, and artifact churn.
• WebArena — A Realistic Web Environment for Building Autonomous Agents
  Demonstrates the value of realistic multi-step agent trajectories, motivating support for loop/transcript analysis rather than only single prompt snapshots.

Docs

• CLI harness design
• Roadmap

What It Does Not Do

• It does not fetch traces from Langfuse, Hugging Face, Cursor, or Claude Code.
• It does not replay agent loops.
• It does not execute tools.
• It does not replace observability platforms.
• It does not pretend agent transcripts are exact raw provider requests.

Development

PYTHONPATH=src uv run --with pytest pytest tests/test_smoke.py -v

Acknowledgements

This project is inspired by and learned from:

• context-lens — local proxy for capturing and visualizing LLM API calls
• ContextFlame — flamegraph-based token profiling for Claude Code
• speedscope — the icicle / flamegraph UI design is inspired by speedscope's interactive visualization

License

MIT