loom-harness 0.14.0

The agent harness you can read, replay, and rewind.

Loom

The black box recorder for AI agents.

Record Claude Code, Codex, LangGraph — any agent that speaks the Anthropic or OpenAI API — with one command and zero code changes. Replay failures with zero API calls. Diff prompt changes before merging. Turn every agent bug into a regression test.

pip install loom-harness                       # zero dependencies

loom record -- claude -p "fix the failing test"   # record a real Claude Code session
loom studio session.loom.json                     # time-travel through it in your browser
loom proxy --replay session.loom.json             # serve it back -- zero API calls, fake key works

Everything an agent does is visible in its API traffic — so recording that traffic gives you the whole story, without touching the agent:

You get	What it means
Replay	Re-serve any recorded session with zero API calls — byte-identical.
Time travel	loom studio — scrub through the run, see the context at every step.
Cost accounting	Every model call metered: the exact turn your $23.40 went off the rails.
Diff	Two runs compared at the effect level: where they diverged, and why.
Free CI	Record once; the GitHub Action flags PRs that change agent behavior — no tokens burned.
Firewall	Shield: --deny 'Read(*.env*)' blocks a tool call before the agent ever sees it.
Ask the trace	loom why trace.json "why did it read my .env?" — a debugger agent investigates and answers, citing exact steps.

The package installs as loom-harness, imports as loom (like beautifulsoup4 / bs4).

Record any agent — no migration

loom record black-boxes a single session: it starts a recording proxy on a free port, points ANTHROPIC_BASE_URL (or OPENAI_BASE_URL with --target https://api.openai.com) at it, runs your command unchanged, and writes the trace:

$ loom record -- claude -p "what does loom/effect.py do?"
loom record: proxying https://api.anthropic.com on http://127.0.0.1:52104
...your agent runs exactly as normal...

recorded 3 step(s), 1841 tokens -> session.loom.json
  replay it:  loom replay session.loom.json
  inspect it: loom studio session.loom.json

Want the whole workflow in one command? The flight-recorder demo runs five acts offline — a deploy bot fails, the recording pinpoints the stale context that misled it, heal verifies the fix, impact catches the PR that would re-break it, and the firewall blocks a credentials grab — in about fifteen seconds, no API key.

For anything longer-lived than one command, run the proxy yourself:

loom proxy --save session.loom.json
export ANTHROPIC_BASE_URL=http://127.0.0.1:8788      # Claude Code & friends
# or, for OpenAI-API agents:
loom proxy --save session.loom.json --target https://api.openai.com
export OPENAI_BASE_URL=http://127.0.0.1:8788/v1
# ...run your agent exactly as before

Verified end-to-end: a real Claude Code session recorded through the proxy (its internal calls included, every token accounted), then replayed offline with a fake API key. Streaming works both ways — SSE is relayed live while the trace gets the complete message, and replays synthesize a well-formed stream for streaming clients.

Tool calls ride in the responses and tool results in the next request, so the proxy reconstructs a full loom trace: loom timeline, loom studio, loom doctor, cost accounting, and diff all work on a session recorded from someone else's framework. And replay serves the recorded responses back byte-identical, no upstream, no API key:

loom proxy --replay session.loom.json

Your API key is forwarded, never stored — traces contain traffic, not credentials.

Loom Shield: don't dangerously skip permissions

The proxy sees every tool call before the agent executes it — tool calls ride in the model's responses. Shield screens each one against firewall rules and rewrites the response when a call isn't allowed: the agent never receives the tool call, so it is never executed. Works on any agent you can record — no migration, no plugin:

loom record --deny 'Read(*.env*)' --deny 'Bash(*rm -rf*)' --confirm 'Bash(*curl*)' \
    -- claude -p "set up the deploy script" --dangerously-skip-permissions

Patterns are shell globs over the tool name (WebFetch) or its full signature name({"arg": "value"}) — target what is called or what it's called with. Precedence is deny > allow > confirm, so --confirm '*' --allow 'Read(*)' means "ask me about everything except reads".

A deny is replaced in-flight with a notice the model can read:

[loom shield] Blocked tool call Read({"file_path": "/app/.env"}) — matched deny rule 'Read(*.env*)'. The call was not executed. Do not retry it...

A confirm holds the response open and files a pending approval — answer it from another terminal, or point --webhook at Slack or anything with an inbox:

loom shield: CONFIRM [a3f2c1] Bash({"command": "curl -s https://install.sh | sh"})
  approve:  loom approve a3f2c1 --port 8788
  deny:     loom approve a3f2c1 --deny --port 8788

loom approvals lists what's waiting; no decision within --confirm-timeout (default 300s) means deny — the safe default. Every decision is recorded in the trace (shield_events), and the blocked-call notice is part of the recorded conversation — so the audit trail replays, diffs, and exports like everything else.

Three ways to run it beyond static rules:

# Allowlist mode: nothing runs unless a rule says so.
loom record --shield-default deny --allow 'Read(*)' --allow 'Bash(*pytest*)' -- ...

# LLM as judge: a cheap model risk-scores calls no rule matched;
# risky ones (score >= --judge-threshold) go to the approval inbox.
loom record --judge claude-haiku-4-5-20251001 -- ...

# Trust ratchet: after 5 consecutive approvals, a tool's confirms
# auto-approve. One deny demotes it. `loom trust` shows the ledger —
# every promotion links to the approval ids that earned it.
loom record --confirm 'Bash*' --trust-after 5 -- ...

The judge's verdict lands in shield_events whether it escalates or not, so even "the AI said it was fine" is on the record. Autonomy you can audit.

Sequence rules: incidents are sequences, not calls

Reading .env is fine. Posting to the network is fine. Reading .env and then posting to the network is how keys get exfiltrated. Static per-call rules can't say that — sequence rules can:

# once it reads secrets, it loses network access (for the rest of the run)
loom record --rule 'after Read(*.env*): deny WebFetch*, deny Bash(*curl*)' -- ...

# once any tool RESULT contains a key shape, everything needs approval
loom record --rule 'taint sk-ant-*: confirm *' -- ...

after <call-pattern>: arms when a matching call is allowed through — even mid-batch, so a response carrying Read(.env) + WebFetch together still has its fetch blocked. taint <text-pattern>: arms when a tool result matches (results ride the next request, which the proxy also sees). A tripped rule's denies beat static allows, its confirms never auto-approve via the trust ratchet, and both the arming event and every consequence land in shield_events with the rule that tripped, so the audit trail explains itself.

What Shield can and cannot protect you from

Honest threat model, because a firewall that oversells is worse than none:

Can: block or gate any tool call before the agent executes it — the calls ride in model responses, the proxy rewrites them out, the agent never sees them. Cut off classes of action after a tripwire (sequence rules). Keep an audit trail that replays. This is blast-radius reduction for agents you run with permissions skipped: it turns "oops" into a logged non-event.

Cannot: stop a model from writing secrets into its reply text — that channel doesn't pass through a tool call (--scrub redacts stored traces, but the live response reaches the client). Can't see tools that never touch the API wire (local function calls in a framework you didn't proxy — though tool calls still ride responses, their execution is the client's). Can't out-think a deliberately adversarial model with rules alone — that's what confirm, the judge, and a human in the loop are for. If your agent handles payments or deletes production data, Shield is a layer, not a substitute for idempotency keys, RBAC, and sandboxes.

--sandbox: make the proxy the only door

A proxy alone is a camera on a door in an open field — the agent can make other connections and walk around it. Add the OS sandbox and the field gets a wall:

loom record --sandbox --deny 'Read(*.env*)' \
    --rule 'after Read(*secret*): deny *' -- python my_agent.py

The agent process (and everything it spawns) is denied all network except the proxy port. Now the deny rules and sequence tripwires can't be bypassed, and the trace is the complete account of the model's traffic — not the part that happened to go through the door with the camera. Built in on macOS (sandbox-exec, the same mechanism Claude Code's sandboxing uses); --sandbox-allow host:port opens explicit extra holes when the agent legitimately needs one. Network only — the filesystem is still yours, so pair it with a container when you need that too. On Linux, the same shape by hand:

docker network create --internal jail
docker run --network jail -e ANTHROPIC_BASE_URL=http://proxy:8788 my-agent
docker run --network jail --network bridge --name proxy -p 8788 loom-proxy

Share traces, not secrets

Traces record everything the agent saw — which can include the API key it read out of a config file. Before a trace leaves your machine:

loom scrub session.loom.json            # -> session.scrubbed.loom.json
loom scrub session.loom.json --check    # CI gate: exit 1 if secrets found
loom record --scrub -- ...              # redact at write time: credentials never touch disk

Detection covers known key shapes (Anthropic, OpenAI, GitHub, AWS, Slack, JWTs, PEM blocks, DB_PASSWORD=... assignments); --aggressive adds an entropy detector. With --scrub the agent still sees real values — only the stored trace is redacted.

Ask the trace what happened

loom why session.loom.json "why did it install the wrong package?"
# At seq 4 the model ran pip install requests-html because the tool result
# at seq 3 truncated the error message; the actual failure was...

loom why spins up a debugger agent whose tools read the trace — timeline, individual effects, per-turn token costs, context-health checkup, shield decisions — and answers with seq numbers you can jump to in loom studio, fork, or bisect. The diagnosis is itself a loom run: save it, replay it, ask why about the why.

Search every run you've ever recorded

Traces are files — commit them, rsync them, drop them in a shared folder. loom search makes the folder queryable, no service to run:

loom search runs/ 'cost>50000'          # the expensive ones
loom search runs/ 'tool:Bash failed'    # failed runs that shelled out
loom search runs/ 'shield:deny'         # runs where the firewall fired
loom search runs/ 'database migration'  # free text over prompts + outputs
loom lake runs/ --open                  # cost dashboard for the whole corpus

The index is a SQLite file inside the directory, refreshed incrementally by mtime. loom lake renders a self-contained HTML dashboard: total spend, failed runs, shield blocks, every run ranked by token cost.

Loom is also a harness

Recording is the front door. Underneath is a full agent framework whose kernel is a few hundred lines you can read in an afternoon — every nondeterministic step (model calls, tools, human input, even the clock) flows through a single Effect boundary, so agents built on Loom get superpowers traces alone can't give you: fork a run at any turn and take a different branch, sweep counterfactuals in batch, bisect to the turn that went wrong, and heal — automatically find and verify the context repair that fixes a failed run.

Quickstart (works offline, no API key)

from loom import Agent, tool
from loom.providers import ModelResponse, ScriptedProvider, ToolCall

@tool
def add(a: int, b: int) -> int:
    "Add two numbers."
    return a + b

# A deterministic offline "model" so the example runs with no key.
provider = ScriptedProvider([
    ModelResponse(tool_calls=[ToolCall("t1", "add", {"a": 2, "b": 3})], stop_reason="tool_use"),
    ModelResponse(text="The answer is 5.", stop_reason="end_turn"),
])

agent = Agent(model=provider, tools=[add])
run = agent.run("What is 2 + 3?")

print(run.output)          # -> The answer is 5.
run.print_timeline()       # step-by-step trace

Use a real model

from loom import Agent, tool

@tool
def get_weather(city: str) -> str:
    "Get the current weather for a city."
    return f"It's sunny in {city}."

agent = Agent(model="claude-opus-4-8", tools=[get_weather])  # needs ANTHROPIC_API_KEY
run = agent.run("What's the weather in Tokyo?")
print(run.output)

Structured output

Give the agent a type; get a validated object back. The schema rides in the system prompt, and the final answer is parsed at the Effect boundary — a failed parse feeds the error back to the model and retries, and every retry is an ordinary recorded effect, so validated runs replay deterministically:

from dataclasses import dataclass

@dataclass
class Weather:
    city: str
    temp_c: float
    rain: bool

agent = Agent(model="claude-opus-4-8", output_type=Weather)  # or TypedDict / pydantic
run = agent.run("Weather in Tokyo?")
run.parsed          # Weather(city='Tokyo', temp_c=21.0, rain=False)
run.parsed.temp_c   # a real float, validated -- not a string you hope is a number

Retries exhausted (output_retries, default 2) sets run.stop_reason == "invalid_output" instead of raising — inspect the trace to see exactly what the model kept saying.

Time travel

run = agent.run("Plan a 3-day trip to Rome.")

# Save the trace (git-friendly JSON) and replay it later for free.
run.save("trip.loom.json")
replay = run.replay()                 # zero API calls, identical output

# Rewind to turn 1, change the context, take a different branch.
branch = run.fork(at=1, edit=lambda ctx: ctx.add_user("Actually, make it Paris."))

# Find the first turn whose output looks wrong.
bad_turn = run.bisect(lambda text: "error" not in text.lower())

Conversations

run.ask() continues a conversation with full context — as one growing trace. The recorded history replays for free; only the new exchange runs live.

run1 = agent.run("Where is order A123?")
run2 = run1.ask("Can I get a refund?")     # knows about A123
run3 = run2.ask("How long will it take?")  # knows everything so far

run3.print_timeline()   # the whole conversation, one trace
run3.replay()           # replays end-to-end, zero API calls
run3.fork(at=1, ...)    # rewind the conversation itself: "what if the user had asked X?"

Human-in-the-loop

A human's answer is nondeterminism like any other — so Loom records it as an effect. Add the built-in ask_human() tool and you get pausable, auditable approval flows with no extra machinery:

from loom import Agent, Run, ask_human

agent = Agent(model=..., tools=[ask_human()])
run = agent.run("Refund $500 on order A123.")

run.paused              # True -- the agent asked for approval
run.pending             # "Approve $500 refund for A123?"
run.save("pending.loom.json")               # answer it tomorrow

loaded = Run.load("pending.loom.json", agent=agent)
done = loaded.resume("yes, approved")       # continues from exactly where it paused
done.replay()           # the human decision is in the trace -- fully auditable

For interactive use, pass a handler instead: Agent(..., on_human=input).

Streaming, parallel tools, async

# Stream tokens as they arrive (recorded effect is still the full response;
# replays return instantly without re-streaming).
provider = AnthropicProvider("claude-opus-4-8", on_token=print)

# Run one turn's tool calls concurrently (opt-in). Results are recorded in
# call order, so the trace stays deterministic and replayable.
agent = Agent(model=..., tools=[fetch_a, fetch_b], parallel_tools=True)

# Embed in async apps (FastAPI etc.).
run = await agent.arun("...")

Rough weather: retries, timeouts, turn caps

agent = Agent(
    model=...,
    model_retries=2,      # rate limits / 5xx / dropped connections retry
    retry_backoff=1.0,    # ...with exponential backoff (1s, 2s, 4s...)
    tool_timeout=30,      # a hung tool records "ERROR: TimeoutError" and the run continues
    max_steps=20,         # hard turn cap per episode
)

Retries happen inside the effect boundary: the recorded effect is the final successful response, so a run that weathered two rate limits replays byte-identically to one that didn't. Auth and bad-request errors never retry. A timed-out tool becomes an ERROR: result the model can react to — same as any other tool failure — and the worker is abandoned, not awaited.

Loom Studio: visual time travel

loom studio opens any saved trace as a self-contained HTML time-travel viewer — scrub (or press play) to watch the run unfold, see the exact context the model saw at every step, hover the cost strip to find the expensive turns, and copy a ready-made fork snippet from any effect. No external assets, safe to attach to a bug report or email to a teammate:

loom studio run.loom.json        # writes run.loom.html and opens it
loom export run.loom.json        # just write the file

Policy: control the agent before it acts

Every tool call flows through one chokepoint, so one policy gates them all:

agent = Agent(model=..., tools=[...], policy=Policy(
    allow=["read_*", "search_*"],    # run freely
    confirm=["delete_*", "send_*"],  # pause for human approval (reuses resume())
    deny=["drop_db"],                # blocked outright, never executed
    budget_tokens=50_000,            # hard spend cap; run stops resumably
))

run = agent.run("clean up old data")
run.intents()      # [{"tool": "delete_orders", "status": "blocked"}, ...]
run.proceed()      # continue a budget-stopped run after raising the cap

Policy(dry_run=True) stubs every non-allowlisted tool with a "would call ..." marker — audit what an agent would do before granting real access. Approvals are recorded human effects, so approved runs replay deterministically and every decision is auditable in the trace.

Effect cache: iterate without paying twice

cache = EffectCache("dev-cache.jsonl")     # persistent (or EffectCache() in-memory)
agent = Agent(model=..., cache=cache)
agent.run("same prompt")    # pays for the model call
agent.run("same prompt")    # zero API calls -- served by input hash

Only model effects are cached by default (tools have side effects); opt in with kinds=("model", "tool:*").

Model A/B: rerun and diff

run_b = run.rerun(model="claude-haiku-4-5")   # same conversation, same tools
print(run.diff(run_b).summary())              # where and why the models diverged

Durable runs (crash recovery)

With a journal, every effect hits disk the moment it's recorded — one JSON line per effect, flushed immediately. If the process dies mid-run (crash, kill, deploy), nothing you paid for is lost:

agent = Agent(model=..., tools=[...], journal="task.jsonl")
agent.run("Migrate the database.")     # 💥 process dies at turn 17

# later, any process:
run = Run.recover("task.jsonl", agent=agent)

The journaled prefix replays for free; only the unfinished tail runs live. Model calls and tool side effects that already happened are never re-executed — the same exactly-once guarantee replay gives, extended across process death. Recovery is idempotent: recovering a finished run just replays it. A torn final line (crash mid-write) is detected and ignored.

What's guaranteed — and what isn't. The journal is two-phase: an intent line is flushed before a side effect executes, the effect line lands after its result is known. So every recorded effect replays exactly once — but a crash can still land in the window between a tool starting and its result hitting disk, and no journal on earth can know from the log alone whether that tool ran. Loom doesn't pretend otherwise: recovery finds the dangling intent and raises UnfinishedEffect instead of silently re-executing, telling you exactly which tool was in flight. You check the outside world, then Run.recover(..., on_unfinished="retry") to accept the re-execution. Harness-internal effects (model calls, memory recalls, compaction) retry silently — a repeated model call costs tokens, not correctness. For tools that are genuinely destructive and non-idempotent (payments, sends, deletes), give the tool itself an idempotency key; Loom makes the ambiguity visible and exactly as wide as one effect, but only the outside system can close it.

Context-rot detection — and self-healing

Context rot (stale, bloated, unused context) is the leading cause of agent failures. Loom can diagnose it after the fact — and test the repairs:

report = run.checkup()
print(report.summary())
# 2 finding(s) in 688 tokens of context:
#   [high] oversized: tool:fetch result is 675 tokens (98% of context)
#   [warn] unused: tool:fetch result never referenced by any later answer

healed = run.heal(check=lambda text: "ERROR" not in text)
healed.output      # "The answer is 42."     <- fixed
healed.healed_by   # "redact-oversized-0"    <- and it names the culprit

heal() is the loop nobody else can run: checkup flags suspects → each one becomes a fork that redacts it → only the divergent tail re-runs → the first branch that passes your check wins. Diagnosis to verified fix, automatically. Also available for any saved trace: loom doctor run.loom.json.

And every repair can grow your test suite — pass regression_dir and the healed branch is saved as a golden trace, ready for loom test and verify_replay:

healed = run.heal(check, regression_dir="tests/regressions/")
healed.regression_path   # tests/regressions/healed-3fa1b2c4d5.loom.json

Every bug becomes a test, automatically.

Trace memory: agents that learn from their own history

Every run leaves a complete trace — so a directory of traces is recallable experience. Before a run starts, the most similar past runs (with their outcomes) are injected into context, recorded as a "memory" effect so replays reproduce exactly what was recalled:

memory = TraceMemory("runs/", auto_store=True)   # completed runs become experience
agent = Agent(model=..., tools=[...], memory=memory)
agent.run("Migrate the staging database.")       # walks in knowing what worked last time

Compaction: long-horizon runs that don't rot

When history outgrows a threshold, it's summarized into one pinned item — and the summarization is itself a recorded effect, so compacted runs replay deterministically:

agent = Agent(model=..., compact_after=8000, compact_keep=4)

Self-correction: a critic at the boundary

Give the agent a (cheaper) reviewer. Every final answer is scored as a recorded "critic" effect — a low score rewinds the turn with the critique in context, and the model tries again. The failed attempt, the verdict, and the retry are all in the trace: self-correction you can replay and audit.

agent = Agent(model="claude-opus-4-8", critic="claude-haiku-4-5", critic_threshold=0.6)
run = agent.run("Capital of France?")
run.print_timeline()
#  [0] model   The capital of France is Lyon.
#  [1] critic  {"score": 0.2, "critique": "Lyon is not the capital."}
#  [2] model   The capital of France is Paris.      <- caught by its own reviewer
#  [3] critic  {"score": 0.95, "critique": "Correct."}

And when the answer really matters, deliberate: sample N candidates and let the critic pick. Samples are "sample" effects, not turns — fork and bisect semantics stay intact:

agent = Agent(model="claude-opus-4-8", critic="claude-haiku-4-5", deliberate=3)

Spend compute exactly where you need confidence — and replay the whole deliberation later for free.

Skills: the toolbox grows itself

Your trace lake is full of tool sequences that demonstrably worked. Mine them into skills — macro-tools the agent can call in one step next time:

from loom.skills import mine, save

runs = [Run.load(p, agent=agent) for p in glob("runs/*.loom.json")]
skills = mine(runs)          # sequences seen in >= 2 successful runs
skills[0].name               # "skill_geocode_then_forecast"
skills[0].params             # ["city", "coords"]  <- learned by comparing runs

agent2 = Agent(model=..., tools=[*tools, *[s.as_tool(tools) for s in skills]])

Parameterization is learned by comparison: argument values that varied across the mined runs become parameters, values that never changed are baked in. Every skill carries its provenance (support = how many recorded runs prove it) — the agent's habits have receipts.

The clock is an effect too

agent = Agent(model=..., clock=True)   # the model knows today's date
run = agent.run("What day is it tomorrow?")
run.replay()                           # ...and the replay sees the ORIGINAL date

loom.now() and loom.random() complete the promise: at harness level they are recorded effects (replays serve the recorded value); inside a tool they return real values on purpose — a tool either runs live (fresh time is correct) or not at all (its recorded result already embeds the time it saw).

Impact: change your prompt without fear

Every team has the same fear: touch the system prompt and something, somewhere, silently breaks. loom impact is snapshot testing for agents — replay your recorded corpus against the changed configuration and see exactly which runs are affected and where, before paying for a single API call:

$ loom impact fixtures/ --agent myproject.agents:support_agent
inputs-differ    fixtures/refund.loom.json (first at seq 0)
    3 effect(s) see different inputs, starting with 'model'
unchanged        fixtures/greeting.loom.json
    every recorded effect gets identical inputs

1 of 2 recorded run(s) affected

Dry mode (free) recomputes every effect's input hash under the new config and reports the first divergence. Add --live to re-run affected conversations and see how the outputs change, not just where. Exit code 1 when anything is affected — drop it straight into CI. Python API: loom.impact.assess.

The GitHub Action

Lock recorded behavior into every PR — the impact report lands as a comment and the check fails when a prompt/config change touches recorded runs:

jobs:
  agent-ci:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
      - uses: evanl666/loom@main
        with:
          traces: tests/agent_traces
          agent: myapp.agents:build_agent

❌ Loom: this change affects recorded agent runs

inputs-differ    tests/agent_traces/refund.loom.json (first at seq 0)
    3 effect(s) see different inputs, starting with 'model'
1 of 2 recorded run(s) affected

💸 this change makes your agent 12.0% more expensive in input tokens (~12,380 -> ~13,860 across 2 recorded run(s))

The cost line is a real regression check, still with zero API calls: the action sizes every recorded conversation's model inputs under the PR branch and under the base branch (same ~4 chars/token estimator on both sides, so the delta is apples-to-apples) and reports the difference. Prompt bloat shows up in review, priced, before it ships.

Dry mode costs nothing (no API calls). Add live: 'true' to also show how outputs change. This repo dogfoods the action on its own demo traces (.github/workflows/agent-ci.yml).

Agent CI: loom test and loom watch

loom test fixtures/            # verify a suite of saved traces (exit 1 on failure)
loom watch task.jsonl          # follow a running agent's journal live (tail -f)

For full behavioral regression in your test suite (zero API calls):

from loom import verify_replay
def test_agent_fixtures():
    for path in glob("fixtures/*.loom.json"):
        verify_replay(path, agent=build_agent())

The strict-replay guarantee. Replay never consults the model, so "the output matched" alone would prove very little. verify_replay and Run.replay() are therefore strict by default: every effect's inputs — system prompt, rebuilt messages, tool schemas — are re-derived from your current agent and hashed against the recording. A passing strict replay means this configuration is input-equivalent to the one that recorded the trace; a prompt tweak or an added tool fails at the exact seq where inputs first diverge, with a pointer to loom impact for the per-run report. replay(strict=False) remains for deliberately walking an old log with a changed config (e.g. replaying without the original tools).

Trace format stability. Every trace carries a version field. Fields are only ever added within a version — readers ignore unknown keys, so older looms read newer traces of the same version fine. The version bumps only when the meaning of existing data changes (v2: tool schemas joined the effect-key hash), and loading a trace from a different version warns with exactly what to expect instead of failing or silently lying.

Sweep: cheap counterfactuals

sweep is the batch version of fork: test N hypotheses from the same rewind point in one call. Every branch replays the shared prefix for free — you only pay for each divergent tail. Ten variants of a 20-turn run forked at turn 18 cost 10×2 turns, not 10×20.

sweep = run.sweep(at=3, variants=[
    None,                                   # control (no edit)
    lambda ctx: ctx.items.pop(2),           # hypothesis: drop the stale item
    lambda ctx: setattr(ctx, "budget", 2000),  # hypothesis: tighten the budget
], labels=["control", "drop-stale", "tight-budget"])

sweep.print_compare()
#   base         turns=5  live_tokens=0     diverged_at=-  ...ERROR...
#   control      turns=5  live_tokens=812   diverged_at=-  ...ERROR...
#   drop-stale   turns=4  live_tokens=655   diverged_at=6  The answer is 42.   <- fixed!
#   tight-budget turns=5  live_tokens=790   diverged_at=6  ...ERROR...

Diff: "it worked yesterday"

loom diff compares two runs at the effect level and tells you not just where they diverged but why — because every recorded step carries a hash of its inputs:

• kinds-differ — control flow diverged (a different action was taken)
• inputs-differ — same action, but the context drifted
• results-differ — same action, same inputs, different outcome

d = run.diff(other_run)
print(d.summary())
# identical prefix: 5 step(s)
# first divergence:
#   step 5 [inputs-differ]
#     a model: calls search({"q": "order status"})
#     b model: I don't have access to orders.

Record a fixture suite, re-run against a new model or prompt, diff — that's regression testing for agents.

Why the Effect boundary?

The kernel routes every model call, tool call, and side effect through one function, Recorder.run(...). In record mode it executes and logs the result; in replay mode it returns the logged result without executing. That single chokepoint is the whole trick — replay, fork, bisect, and cost metering all fall out of it for free. Read loom/effect.py — it's ~120 lines.

Bring your own model

A provider is anything with one method:

class MyProvider:
    name = "mine"
    model = "my-model"
    def complete(self, system: str, messages: list[dict], tools: list[dict]) -> ModelResponse:
        ...

Ships with:

• ScriptedProvider, RuleProvider — offline, no deps (used in all examples)
• AnthropicProvider — pip install "loom-harness[anthropic]", needs ANTHROPIC_API_KEY
• OpenAIProvider — pip install "loom-harness[openai]"; works with OpenAI and any OpenAI-compatible server via base_url (vLLM, Ollama, LM Studio, Together, Groq, OpenRouter, …):

from loom import Agent
from loom.providers import OpenAIProvider

# OpenAI
agent = Agent(provider=OpenAIProvider("gpt-4o"))
# A local model (Ollama / vLLM) — same code, different base_url
agent = Agent(provider=OpenAIProvider("llama3.1", base_url="http://localhost:11434/v1", api_key="x"))

MCP: bring your tool ecosystem

Any Model Context Protocol server plugs in as ordinary tools (pip install "loom-harness[mcp]"):

from loom.mcp import MCPServer

with MCPServer("npx", ["-y", "@modelcontextprotocol/server-filesystem", "."]) as fs:
    agent = Agent(model="claude-opus-4-8", tools=fs.tools())
    run = agent.run("What's in this directory?")
    run.save("fs.loom.json")

Because MCP calls cross the same Effect boundary as everything else, they are recorded like any tool call — which means a trace recorded against a live MCP server replays with the server gone. Your CI verifies filesystem, database, or browser-driving agent behavior with zero MCP processes running.

Subagents

Any agent can be exposed as a tool for another agent. The child runs with its own isolated context, and its steps nest into the same trace — so replay, fork, and bisect keep working across delegation.

researcher = Agent(model=..., tools=[search], name="researcher")
lead = Agent(model=..., tools=[researcher.as_tool()])

run = lead.run("Summarize the latest on X.")
run.print_timeline()      # the researcher's turns show up indented under the lead
run.replay()              # deterministic through the delegation, zero API calls

The parent only ever sees the delegated result, not the child's internal steps — context stays clean. See examples/04_subagents.py.

CLI

loom record -- claude -p "..."                      # black-box a real agent session
loom record --deny 'Read(*.env*)' -- claude ...     # ...with the Shield firewall active
loom proxy --save session.loom.json                 # long-lived recording proxy (or --replay)
loom approvals && loom approve a3f2c1               # the Shield confirm inbox
loom studio trip.loom.json                          # open the time-travel viewer
loom run "What is 2 + 3?" --model claude-opus-4-8   # run an agent
loom timeline trip.loom.json                        # inspect a saved trace
loom replay trip.loom.json                          # replay offline
loom diff yesterday.loom.json today.loom.json       # where + why two runs diverged
loom export trip.loom.json                          # self-contained HTML trace viewer
loom doctor trip.loom.json                          # check a trace for context rot
loom heal run.loom.json --agent app:agent --forbid ERROR --save-regression tests/regressions
loom impact fixtures/ --agent app:agent             # which recorded runs a change affects
loom skills mine runs/ --save skills.json           # crystallize proven tool sequences
loom test fixtures/                                 # verify saved traces (CI)
loom watch task.jsonl                               # follow a running agent's journal

FAQ

Can I use Loom with my existing Claude Code / LangGraph / CrewAI / OpenAI-SDK agent?

Yes — that's the front door. loom record -- <your command> (or loom proxy) records any agent that speaks the Anthropic or OpenAI API with zero code changes, and everything that works on a trace works on that recording: replay, Studio, timeline, diff, doctor, cost accounting.

Then why would I build on the harness?

Recording captures what did happen; the harness can also run what didn't. Fork, sweep, heal, live rerun, and resume all need Loom to re-execute the divergent tail of a run — that requires your agent's loop and tools to live inside the Effect boundary. A recorded trace of someone else's agent has no loop to hand control back to. Think Git: git log works on any history you import, but branching needs your work to happen in the repo. Migrating is deliberately cheap — tools are plain decorated functions, the Agent is a thin loop, so porting is usually a dozen lines.

Do I pay for replays?

No. Replay serves every model and tool result from the recorded log — zero API calls, zero tokens. That's also why forks and sweeps are cheap: the shared prefix replays free and you only pay for the divergent tail.

Is a trace tied to one vendor?

The trace format is vendor-neutral JSON (ModelResponse, tool results, input hashes). Providers translate at the edge; the kernel and the traces never import an SDK.

Status

v0.10 — alpha, on PyPI as loom-harness. Recording proxy (loom record / loom proxy, Anthropic + OpenAI dialects, streaming both ways), Shield firewall + approval inbox, Studio time-travel viewer, GitHub Action, kernel, time-travel (replay/fork/bisect), sweep, diff, subagents, conversations, human-in-the-loop, streaming, parallel tools, context-rot checkup/heal, durable runs, policy, effect cache, trace memory, compaction, structured output, critic/deliberate, skills, impact analysis, and MCP are complete and tested. See Roadmap.

Roadmap

• Subagents (isolated context, nested traces) ✅ shipped
• OpenAI-compatible provider ✅ shipped
• Sweep (batch counterfactual forks) ✅ shipped
• Trace diff (loom diff) ✅ shipped
• Conversations (run.ask) ✅ shipped
• Human-in-the-loop as an effect (pause / resume) ✅ shipped
• Streaming, parallel tools, arun ✅ shipped
• HTML trace export ✅ shipped
• Context-rot checkup + self-healing (run.heal) ✅ shipped
• Durable runs (write-ahead journal + Run.recover) ✅ shipped
• Policy at the boundary (deny/confirm/dry-run/budget) + intents() ✅ shipped
• Effect-level caching ✅ shipped
• Model A/B (run.rerun) + edits persisted as effects ✅ shipped
• loom test & loom watch ✅ shipped
• Trace memory + context compaction ✅ shipped
• PyPI release (pip install loom-harness) ✅ shipped
• Structured output (output_type=, validation-retry at the boundary) ✅ shipped
• Impact analysis (loom impact — snapshot testing for config changes) ✅ shipped
• Heal-to-test (heal(regression_dir=) — every bug becomes a test) ✅ shipped
• MCP servers as tools (loom-harness[mcp]) ✅ shipped
• Clock & randomness as effects (loom.now, loom.random, Agent(clock=True)) ✅ shipped
• Critic gate + deliberate mode (replayable self-correction) ✅ shipped
• Skill crystallization (loom.skills.mine — proven sequences become tools) ✅ shipped
• loom proxy / loom record — record any Anthropic- or OpenAI-API agent (streaming included), replay offline ✅ shipped
• Loom CI GitHub Action — impact reports as PR comments ✅ shipped
• Loom Studio — time-travel debugger UI (loom studio) ✅ shipped
• Loom Shield — an agent firewall on the proxy: deny/confirm patterns for any agent, no migration ✅ shipped
• loom fuzz — chaos engineering for agents (fault injection at any effect)

License

MIT