flameox

Runtime evidence for coding agents

Let an agent query, compare, and audit profiler traces, benchmarks, memory captures, and execution evidence without uploading your code or data.

Quick start  ·  What flameox investigates  ·  How it works  ·  CLI and MCP  ·  Documentation

Connect your agent: npx flameox setup

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flameox helps coding agents investigate performance, memory, execution, concurrency, and reliability with evidence you can inspect and reproduce. It connects agents to maintained tools such as Python import-time tracing, pytest, xdist, pyperf, py-spy, Perfetto Trace Processor, coverage.py, Memray, and torch.profiler, then keeps each original artifact alongside a record of how it was produced.

flameox is not a profiler or an automatic bug finder. It coordinates existing tools, compares runs collected under compatible conditions, and ties findings back to the measurements that support them.

Quick start

Connect an agent

Run the guided setup:

npx flameox setup

The wizard detects Claude Code, Cursor, OpenCode, Codex, Gemini CLI, and Antigravity. It selects nothing by default and previews every configuration file it will change. After you approve the plan, it installs and verifies a versioned local runtime and activates the clients you chose.

Restart the configured client, open a project, and ask:

Initialize flameox in this project and show me which profiling capabilities are available.

flameox can initialize its .diagnostics/ workspace through MCP. Before an agent can run your code, you must declare the command as a named workload in flameox.toml, inspect its canonical form, and approve it. See Named workloads and capture for an example.

Use the CLI from source

Python 3.12 or newer and uv are required:

uv sync --extra dev --extra python --extra execution --extra memory --extra trace --extra cpu
uv run flameox init .
uv run flameox status

What flameox investigates

Question	Evidence
Where does this workload spend CPU time?	Sampled stacks, frames, callers, callees, and trace windows
Does runtime grow with input size?	Repeated measurements, scaling fits, uncertainty, and correlated hotspots
Why does memory grow?	Allocation records, retained memory, phases, threads, and processes
Which execution paths changed?	Coverage contexts, files, functions, branches, and two-run differences
What does PyTorch spend time on?	Operators, shapes when captured, CPU or accelerator time, and memory
Are failures clustered rather than isolated?	Failed attempts grouped by environment, source, workload, and error

Profiles show where to investigate; they do not prove why behavior changed or whether the program remains correct. To confirm a result, use a representative workload and declared metric, compare the same source and environment, retain the samples, and validate the program's output for both the baseline and candidate.

How it works

1. Declare. You name a repeatable workload and approve the exact command an agent may run.
2. Capture. A maintained profiler or benchmark tool runs while flameox records the tool, command, environment, source revision, limits, and outcome.
3. Preserve. flameox keeps the original artifact and publishes queryable evidence to the project workspace.
4. Analyze. The CLI and MCP server provide focused queries for hotspots, scaling, memory, execution, failures, and comparisons.
5. Record. Findings remain tied to the runs, measurements, validation, and analysis that support them. Failed attempts remain visible.

Safety boundaries

flameox runs on your machine and does not upload code or captures. It does not monitor production, provide accounts or synchronization, modify source code, install system tools, or delete artifacts automatically.

Agents can run only the named workloads you approve. MCP does not provide arbitrary shell or SQL access, change approvals, delete evidence, return raw artifact bytes, or launch native viewers. When containment is unavailable, flameox reports the workload as uncontained.

Setup and installation details

Run setup again to connect or disconnect clients, verify that connected clients launch the active runtime, update to the npm package's matching version, or roll back to a previously installed version. npx flameox@latest setup resolves the newest setup release. Automation can select clients and inspect the plan explicitly:

npx flameox setup --codex --claude --yes
npx flameox setup --all --dry-run --json
npx flameox setup --verify --yes --json

The npm package installs the matching flameox Python release. MCP clients then launch that installed runtime directly; they do not call npx, uvx, or a network-dependent installer at startup. Setup does not initialize a project or create .diagnostics/.

Optional Python extras are independent:

• python: pyperf capture and import
• cpu: py-spy capture
• trace: Perfetto Python API; a local Trace Processor binary must also be configured
• execution: coverage.py
• test: pytest and pytest-xdist evidence capture
• memory: Memray
• torch: PyTorch capture
• all: all runtime integrations

Local data model

Initialize a project-local workspace:

uv run flameox init .
uv run flameox status

.diagnostics/ stores original artifacts, run and investigation records, Parquet evidence, and a rebuildable DuckDB catalog. Parquet files and generation manifests are the source of truth; if you delete catalog.duckdb, flameox catalog rebuild reconstructs it.

Identical artifacts are stored once, but flameox retains the source, environment, workload, and measurement details for every run. Hypotheses, trials, comparisons, and findings remain separate so observations do not blur into conclusions.

Named workloads and capture

Repeatable commands live in flameox.toml. Templates accept declared scalar parameters only—there is no shell expansion:

schema_version = 1

[workloads.scan]
argv = ["python", "bench.py", "--implementation", "{implementation}"]
cwd = "."
timeout_seconds = 60

[workloads.scan.parameters]
implementation = ["baseline", "candidate"]

[workloads.scan.oracle]
strength = "cross_treatment_equivalence"
argv = ["python", "validate.py", "--implementation", "{implementation}"]

[experiments.scan_comparison]
workload = "scan"
variants = ["baseline", "candidate"]
design = "randomized_complete_blocks"
blocks = 10
primary_metric = "pyperf.workload"
polarity = "lower_is_better"
estimand = "median_paired_log_ratio"
practical_threshold = 0.05
confidence_level = 0.95
random_seed = 1984

Inspect and approve the exact workload definition before exposing it through MCP:

uv run flameox workload show scan --json
uv run flameox workload approve scan
uv run flameox capture plan pyperf --workload scan \
  --parameters '{"implementation":"baseline"}' --json
uv run flameox capture run pyperf --workload scan \
  --parameters '{"implementation":"baseline"}' --json

Editing a command, environment, parameter domain, timeout, working directory, or oracle invalidates that approval. Execution uses argument arrays instead of shell strings, bounds command output, and cleans up after timeouts or cancellation. Linux users can configure bubblewrap containment; otherwise, the result is reported as uncontained.

Investigations and experiments

Create an investigation and optionally attach a falsifiable hypothesis before running a predeclared experiment:

uv run flameox investigations create \
  '{"question":"Does the candidate remove reverse-scan overhead?"}' --json
uv run flameox hypotheses record @hypothesis.json --json
uv run flameox experiment plan scan_comparison \
  --investigation <investigation-id> --adapter pyperf --json
uv run flameox experiment run scan_comparison \
  --investigation <investigation-id> --adapter pyperf --json

Before collecting data, flameox saves the declared protocol. It randomizes treatment order within complete blocks and records every attempted trial, including cancellations and failures. The automatic paired comparison runs only when the trial blocks are complete and the measurements, source, environment, and output validation are compatible. Failed trials remain in the evidence instead of disappearing from the denominator.

Useful read-only analyses include:

uv run flameox analyze hotspots <run-or-artifact>
uv run flameox analyze scaling <experiment-id>
uv run flameox analyze compare @comparison-request.json
uv run flameox analyze memory <run-or-artifact>
uv run flameox analyze execution <run-or-artifact>
uv run flameox analyze pytorch <run-or-artifact>
uv run flameox analyze failures

These commands do not modify the workspace. Record a result when you want to preserve it with the runs that produced it:

uv run flameox analyze record \
  '{"recipe":"memory","input_id":"<run-id>"}'
uv run flameox analyze record-comparison @comparison-request.json

From a hotspot, inspect its callers, callees, representative stacks, or surrounding trace window:

uv run flameox stacks callers <run-or-artifact> <frame-id> [--cursor CURSOR]
uv run flameox stacks callees <run-or-artifact> <frame-id>
uv run flameox stacks examples <run-or-artifact> <frame-id>
uv run flameox trace window <artifact-id> --start 0 --end 1000000 [--cursor CURSOR]
uv run flameox open <artifact-id>

flameox open only prints a native viewer plan. Pass --launch separately to open the viewer; it cannot be combined with --json.

CLI and MCP

Start the stdio server with a fixed project root:

uv run flameox mcp serve --project-root .

Through MCP, agents can plan approved captures and experiments, query evidence, preview analyses, and record results. Capture and experiment plans are short-lived and single-use; restarting the server invalidates them.

Inspect the protocol surface with a real stdio client:

uv run flameox mcp inspect --project-root . --json

Integrity and recovery

uv run flameox validate
uv run flameox validate --full
uv run flameox catalog validate
uv run flameox catalog rebuild
uv run flameox catalog compact
uv run flameox recover
uv run flameox gc
uv run flameox gc --apply

Full validation hashes native artifacts and Parquet files. Recovery closes a run only after its boot, PID, and process-start lease has disappeared. Garbage collection is a dry run by default; --apply moves eligible objects to recoverable trash instead of deleting them immediately.

Documentation

• Architecture: process model, package boundaries, dependencies, and platform policy
• Storage and evidence: authoritative data, identity, provenance, publication, and schemas
• Investigations and analysis: workloads, experiments, recipes, statistics, and evidence quality
• Adapters and capabilities: profiler integration, compatibility, probing, and approval behavior
• Runtime safety: concurrency, recovery, retention, integrity, security, privacy, and local observability
• CLI and MCP boundaries: human and agent interfaces and their trust boundaries
• Architectural decisions: settled choices and open design questions
• Acceptance and verification: completion criteria and representative proof

Development

uv sync --extra dev --extra python --extra execution --extra memory --extra trace --extra cpu
uv run ruff check src tests
uv run mypy src tests
uv run pytest -q