ActionLens

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ActionLens

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pip install actionlens

ActionLens is a low-intrusion Python library for agent tool governance and trajectory capture.

It sits at the tool boundary instead of replacing your agent framework. Wrap an existing Python function, get structured tool outputs, bounded model-visible results, local artifacts for large payloads, idempotency protection, approval handoff, and JSONL trajectories that can later feed eval or monitoring workflows.

Where ActionLens Fits

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flowchart LR
    host["Agent / host runtime<br/>LangChain, LangGraph, PydanticAI, OpenAI Agents SDK"]:::host
    durable["Optional durable control<br/>Temporal Activities / DBOS Steps"]:::durable
    provider["Business tools / providers<br/>SaaS APIs, databases, local systems"]:::provider
    consumer["Observability / eval / audit<br/>metrics, traces, evidence consumers"]:::consumer

    subgraph actionlens["ActionLens: governance + evidence plane"]
        direction TB
        runtime["Governed tool runtime"]:::core
        policy["Policy + approvals<br/>budgets + redaction"]:::governance
        ledger["Ledger + idempotency<br/>outbox + recovery"]:::evidence
        artifacts["Artifacts + provenance<br/>retention + access checks"]:::evidence
        trajectory["Trajectory + exporters<br/>events + evidence bundles"]:::evidence
        runtime --> policy
        runtime --> ledger
        ledger --> artifacts
        ledger --> trajectory
    end

    host -->|"tools + explicit context"| runtime
    durable -->|"stable workflow / step identity"| runtime
    policy -->|"authorized invocation"| provider
    provider -->|"result + provider evidence"| runtime
    ledger --> consumer
    artifacts --> consumer
    trajectory --> consumer

    classDef host fill:#FFFFFF,stroke:#3F474A,stroke-width:1.25px,color:#1F2933;
    classDef durable fill:#F4F8F4,stroke:#586661,stroke-width:1.25px,color:#1F2933;
    classDef core fill:#EAF6E6,stroke:#6DAE4A,stroke-width:1.6px,color:#1F2933;
    classDef governance fill:#F7FBF7,stroke:#6B7972,stroke-width:1.2px,color:#1F2933;
    classDef evidence fill:#FFFFFF,stroke:#6B7972,stroke-width:1.2px,color:#1F2933;
    classDef provider fill:#FFFFFF,stroke:#3F474A,stroke-width:1.25px,color:#1F2933;
    classDef consumer fill:#F1F8EF,stroke:#6DAE4A,stroke-width:1.25px,color:#1F2933;
    style actionlens fill:#FAFCFA,stroke:#3F474A,stroke-width:1.25px,stroke-dasharray:2 3

Why

Modern agents often fail at the tool boundary:

A crawler returns 20,000 words and blows up the model context.
A model retries the same mutation with a slightly different timestamp.
A Python exception is sent back to the model as raw traceback noise.
A high-risk operation needs human approval, but the approval state is not replayable.
Production runs cannot be converted into eval or debugging traces later.

ActionLens turns these into explicit runtime protocols while keeping the host framework in charge.

Status

This repository contains the v1.5.3 stable protocol focused on multi-instance-safe governance, bounded production data paths, evidence-backed recovery, durable audit delivery, explicit artifact confidentiality, and low-intrusion durable-runtime bridges:

@lens.tool(...) decorator for sync and async functions
StructuredToolOutput for model-visible results
local artifact storage for large outputs
UTF-8 byte and top-level-item output budgets, safe trajectory defaults, and explicit summary controls
JSONL trajectory events
explicit ToolCallContext passing for resume/distributed workers
child contexts that retain parent_call_id and share the parent run budget
public signature injection for required idempotency_key
interchangeable governance repositories for PostgreSQL, SQLite, and ephemeral tests
lease ownership, heartbeat, fencing tokens, args/schema conflict detection, and UNCERTAIN
atomic approval ticket + ledger + transactional outbox transitions
at-least-once outbox dispatch with retry, claim leases, and dead-letter handling
persistent approval pending / approve / deny / resume flow
pluggable policy chain and redactors
artifact metadata plus dry-run / size-aware GC
generator and async-generator tools with bounded-memory artifact capture and configurable model-visible tails
optional thread-mode timeout for sync tools; high-risk timeouts are UNCERTAIN because Python cannot stop a running thread
real TTL-scoped CACHE_READ reuse for read-only tools
synchronous approval resolver support alongside durable approval tickets
governed, authorized artifact paging and literal grep tools for model navigation
governed MCP tools/call proxy with JSON Schema validation, including approval-safe dynamic argument changes
RemoteToolAdapter / lens.remote_tool(...) bridge that carries ActionLens request identity into a RemoteToolRunner
ordered invoke_many() for concurrency-safe read tools
actionlens summary, actionlens export, and actionlens gc
thin PydanticAI, OpenAI Agents SDK, and LangChain/LangGraph adapters
Inspect-oriented transcript and conservative SFT JSONL exporters
EvalCaseCandidate with stable case IDs, host-supplied task/environment/rubric facts, explicit readiness, and versioned Inspect sample mapping
evidence bundles with source digests, a per-event SHA-256 chain, redaction/retention metadata, and explicit missing-evidence declarations
static local HTML trajectory reports
optional bounded-queue JSONL writing with explicit drop policies
expiring approval tickets, schema-checked modified arguments, and ticket/ledger inspection
ArtifactPolicy with write-before-redaction guarantees, reference-only/deny modes, quotas, and encryption provider SPI
optional MediaMetadata and compact ArtifactProvenance for metadata-first, derived media artifacts
host-provided MediaMetadataExtractor SPI for local plaintext image/audio/video artifacts
provenance-aware local GC that preserves a source while a retained derivative still references it, with explicit cascade mode
signed webhook, composite, low-cardinality metrics, and a pinned OpenTelemetry GenAI mapping profile
versioned schema readers/golden fixtures and reproducible SFT dataset manifests
framework-neutral RemoteToolRunner SPI with a concrete governed adapter
dependency-free Temporal Activity and DBOS Step context bridges that preserve stable workflow/step identity
evidence-backed UNCERTAIN reconciliation with atomic audit events
background outbox lifecycle, health state, and controlled dead-letter replay/termination
authorized artifact read/decrypt/checksum verification and reference URI policy
webhook key rotation, replay-window verification, event deduplication hook, and SSRF controls
directly runnable repository and sink contract checks for third-party implementations
pooled PostgreSQL connections with bounded acquire/statement/lock/transaction timeouts
explicit advisory-lock migrations and startup schema compatibility checks
event-loop isolation for synchronous governance I/O around async tools
phase-aware governance failures before and after external side effects
outbox backlog/lag health, bounded retention, and single-round-trip PostgreSQL claims
cross-process artifact read/GC leases plus symlink/reparse-point rejection
bounded streaming artifact upload, authenticated decryption, and atomic destination promotion
reproducible benchmark and soak probes with percentile and memory evidence

PostgreSQL is the preferred multi-instance backend because ledger, approval, and outbox facts share one transaction. Redis is intentionally not implemented in v1.5; the repository protocol permits a future backend without changing ToolRuntime.

Install For Local Development

python -m pip install -e .
python -m pytest -q

The runtime dependency is intentionally light:

Python 3.10+
Pydantic v2

Install production PostgreSQL support separately:

python -m pip install -e ".[postgres]"

Install MCP JSON Schema validation when proxying MCP tools:

python -m pip install -e ".[mcp]"

PostgreSQL Repository

Production startup does not run DDL. Apply migrations as a deployment step, preferably with the DSN in the environment rather than the process command line:

ACTIONLENS_POSTGRES_DSN=postgresql://actionlens:secret@db.internal/actionlens actionlens migrate
ACTIONLENS_POSTGRES_DSN=postgresql://actionlens:secret@db.internal/actionlens actionlens schema-status

import actionlens as al

repository = al.PostgresGovernanceRepository(
    "postgresql://actionlens:secret@db.internal/actionlens",
    min_pool_size=2,
    max_pool_size=20,
    pool_timeout=5,
    statement_timeout_ms=30_000,
    lock_timeout_ms=5_000,
    transaction_timeout_ms=60_000,
)
lens = al.ActionLens(project="demo-agent", repository=repository)

# At the owning application lifecycle boundary:
lens.close()
repository.close()

Migrations are idempotent, serialized by a PostgreSQL advisory transaction lock, and recorded in actionlens_schema_migrations. auto_migrate=True remains available for isolated development only. PostgreSQL uses row locks and SKIP LOCKED outbox claims. External side effects are not advertised as exactly-once: an expired non-fenceable execution becomes UNCERTAIN and must be reconciled explicitly.

SQLite keeps synchronous="FULL" as the durability default. Latency-sensitive local deployments that accept SQLite WAL's NORMAL power-loss tradeoff may opt in explicitly:

repository = al.SQLiteGovernanceRepository(".actionlens/ledger.sqlite3", synchronous="NORMAL")

Artifact Confidentiality

policy = al.ArtifactPolicy(
    raw_mode="redact_then_store",  # store, redact_then_store, reference_only, deny
    encryption="provider",
    max_bytes_per_run=10_000_000,
    retention_days=30,
)
lens = al.ActionLens(
    artifact_policy=policy,
    encryption_provider=my_kms_provider,
)

An encryption provider supplies provider_id and encrypt(payload, context=...). ActionLens never stores a master key. reference_only accepts an existing ArtifactRef; deny prevents artifact writes.

OutputPolicy(include_raw_in_trajectory=False) is the default, so the full StructuredToolOutput.result is not copied into trajectory events. Set it to True only for an explicitly approved training or diagnostic sink. When an encryption provider is configured, artifact previews are withheld from the unencrypted sidecar; the current caller can still receive its independently bounded, redacted inline preview.

Run-Scoped Artifact Budgets

max_bytes_per_run is cumulative for an active durable run. ActionLens cannot infer that a session scope is final because a workflow may resume in another process, so the host must release in-memory accounting once all writes and calls for that run are complete:

lens.reset_run(run_id)

This also resets every built-in BudgetPolicy configured on the lens. Do not call it for a run that can still resume. Direct FileArtifactStore users must provide a non-empty metadata["run_id"] when this quota is enabled and call store.reset_run(run_id) at the same lifecycle boundary.

Media Metadata And Provenance

Media support is metadata-first and dependency-free. ArtifactRef.media_metadata and ArtifactRef.provenance are optional additive fields; ActionLens does not import FFmpeg, OCR, ASR, or vision-model SDKs.

source = lens.artifact_store.put(video_bytes, media_type="video/mp4")
thumbnail = lens.artifact_store.put(
    thumbnail_bytes,
    media_type="image/jpeg",
    media_metadata=al.MediaMetadata(width=320, height=180, codec="jpeg"),
    provenance=al.ArtifactProvenance.from_source(
        source,
        operation="thumbnail",
        operation_version="ffmpeg-7.0",
        parameters={"time_sec": 12.5, "max_width": 320},
        created_by_tool="extract_thumbnail",
    ),
)

To populate metadata automatically for local plaintext image/audio/video writes, inject a small host adapter. The extractor runs only after ActionLens has atomically persisted the local plaintext file. It is best-effort so decoder failure cannot turn a completed artifact write into an orphan; failures are emitted through the actionlens.artifacts.fs logger so operators can detect a broken extractor. Encrypted artifacts are never handed to the extractor; provide trusted media_metadata= at write time when extraction happens before encryption.

When content-addressed local storage deduplicates identical derived bytes from multiple sources, its sidecar retains every observed local provenance record. Local GC treats all of those sources as parents, so deleting one source cannot leave a retained derivative without its evidence chain. The local sidecar is capped at 64 distinct source/transformation identities per content-addressed file and fails closed rather than silently dropping lineage; hosts needing a larger many-to-one index should provide their own artifact store.

class MyMediaExtractor:
    def extract(self, path, media_type):
        return al.MediaMetadata(width=1920, height=1080, codec="h264")

lens = al.ActionLens(media_metadata_extractor=MyMediaExtractor())

Quick Start

import actionlens as al

lens = al.ActionLens(project="demo-agent", storage_dir=".actionlens")

@lens.tool(max_bytes=1000)
def fetch_page() -> str:
    return "very long page..." * 1000

with lens.session(session_id="chat-001"):
    output = fetch_page()

print(output.status)
print(output.result_summary)
print(output.artifact_refs)

If the result exceeds max_bytes, ActionLens stores the raw result under .actionlens/artifacts/ and returns a bounded preview plus an ArtifactRef.

v1.5 Execution Semantics

max_bytes is a UTF-8 byte budget for the model-visible result. Collections are also limited by OutputPolicy.max_inline_items; an oversized value is stored as an artifact and the caller receives a preview bounded by the same byte budget. summary_fields can select safe dictionary fields for an inline summary, while summary_includes_content=False disables content snippets.

For a synchronous function, run_sync_in_thread=True only releases the caller after a timeout. Python cannot force-stop that business thread. A timeout for a MUTATION or DESTRUCTIVE tool is therefore returned and persisted as UNCERTAIN, blocks automatic retry, and must be reconciled against the business system. Read-only tool timeouts remain retryable TIMEOUT results.

Idempotency

For mutation tools, require an explicit idempotency key:

@lens.tool(
    risk=al.RiskLevel.MUTATION,
    idempotency=al.IdempotencyPolicy.REQUIRED,
)
def send_message(user_id: str, text: str) -> dict:
    return {"sent": True, "user_id": user_id}

with lens.session(session_id="chat-001"):
    first = send_message("u1", "hello", idempotency_key="msg-u1-001")
    second = send_message("u1", "hello", idempotency_key="msg-u1-001")

The wrapper modifies the public function signature so schema extractors can see the required idempotency_key:

import inspect
print(inspect.signature(send_message))
# (user_id: str, text: str, *, idempotency_key: str) -> dict

For auto-hash mode, ignore unstable fields that models may invent:

@lens.tool(
    risk=al.RiskLevel.MUTATION,
    idempotency=al.IdempotencyPolicy.AUTO_HASH,
    hash_ignore_keys=["timestamp", "nonce", "uuid"],
)
def write_note(message: str, timestamp: int) -> dict:
    return {"ok": True}

Output shaping is part of the tool schema used for idempotency conflict detection. Apart from the trajectory-only include_raw_in_trajectory, changing an OutputPolicy field such as max_inline_bytes, max_inline_items, or streaming_tail_lines changes tool_schema_hash. Environments that reuse idempotency keys must keep those policy settings aligned.

Read-only tools can opt into a real shared TTL cache. Its identity includes project, environment, tenant, tool name, and arguments, but intentionally not session or run identity:

@lens.tool(idempotency=al.IdempotencyPolicy.CACHE_READ, cache_ttl_sec=60)
def lookup_customer(customer_id: str) -> dict:
    return provider.lookup(customer_id)

Explicit Context For Resume

ContextVar works for normal in-process request scopes, but distributed resume systems such as LangGraph checkpointing, Temporal, or background workers need explicit context passing.

ctx = al.ToolCallContext(
    project="demo-agent",
    session_id="serialized-session",
    run_id="resume-run",
    call_id="old-call",
    tool_name="lookup",
)

result = lookup("query", __al_ctx=ctx)

__al_ctx is consumed by ActionLens and hidden from the public tool signature.

Use child_context() for a sub-agent or delegated call that must retain a durable parent link and consume the same run budget:

with lens.session(session_id="chat-001", run_id="run-001") as parent:
    child = lens.child_context(parent=parent, tool_name="lookup_customer")
    output = lookup_customer("cust-7", __al_ctx=child)

MCP, Remote Tools, And Artifact Navigation

MCPGovernanceProxy turns registered MCP tools/call methods into normal governed ActionLens tools. The proxy validates the MCP input schema before the transport call; approvals, leases, output shaping, idempotency, and trajectories are applied locally.

proxy = al.MCPGovernanceProxy(lens, mcp_transport)
proxy.register_tool(
    "search_docs",
    input_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
)
result = proxy.handle_request(request)

The proxy is dual-era: legacy requests can use initialize and classic MCP results, while 2026-07-28 requests carry protocol version and client capabilities in params._meta. It implements server/discover, emits resultType, cache hints, title/icons/outputSchema, and validates structuredContent. For approval round trips, configure input_required_factory and input_response_handler; the host must integrity protect and verify opaque requestState before persisting an approval.

For an existing asynchronous job provider, lens.remote_tool(runner, name="...") builds a governed RemoteToolRunner bridge. The request carries the ActionLens idempotency key, arguments hash, schema hash, context, and deadline. A timeout attempts cancellation; high-risk outcomes that cannot be confirmed become UNCERTAIN.

Large artifacts can be navigated through governed tools rather than copied back into context:

tools = lens.artifact_navigation_tools()
page = tools["artifact_read"](artifact_ref, offset=0, limit=4096)
matches = tools["artifact_grep"](artifact_ref, needle="invoice")

Both operations require the configured artifact authorizer, verify checksums, emit access events, and cap page/search output. Sync and async generators are persisted as artifacts automatically; OutputPolicy.streaming_tail_lines controls the tail returned to the model.

Durable Workflow Bridges

The temporal and dbos integration modules are dependency-free mapping layers: the durable runtime controls replay, scheduling, signals, and durable waits; ActionLens controls the governed tool call inside an Activity or Step. They do not manage workflow state or introduce a core Temporal/DBOS dependency.

from actionlens.integrations.temporal import (
    TemporalActivityRunner,
    context_from_temporal_workflow,
)

# Inside a Temporal Activity, pass values from activity.info().
ctx = context_from_temporal_workflow(
    workflow_id,
    run_id,
    tool_name="send_message",
    activity_id=activity_id,  # preferred for call-level correlation
    attempt=attempt,
    project="messaging",
)
output = await TemporalActivityRunner(send_message).arun(
    ctx, "hello", idempotency_key="message-123"
)

workflow_id / DBOS workflow_id becomes the stable ActionLens session_id. Retry attempt is observability metadata only and is never part of the auto-hash idempotency identity. Use a stable business idempotency key for mutations. Approval signals/messages are wake-ups only: commit lens.approve(...) first, then let the resumed Activity/Step re-read the ActionLens ticket and ledger. See Temporal and DBOS examples.

Human Approval Flow

@lens.tool(
    risk=al.RiskLevel.DESTRUCTIVE,
    idempotency=al.IdempotencyPolicy.REQUIRED,
    approval_required=True,
)
def drop_table(name: str) -> dict:
    return {"dropped": name}

with lens.session(session_id="ops-001"):
    pending = drop_table("users", idempotency_key="drop-users")

ticket_id = pending.result["ticket_id"]
lens.approve(ticket_id=ticket_id)

with lens.session(session_id="ops-001"):
    success = drop_table("users", idempotency_key="drop-users")

The first call returns PENDING_APPROVAL and does not execute the function. After approval, the same idempotency key is allowed to execute.

Interactive hosts can resolve the same ticket synchronously without losing the durable audit trail. The resolver returns APPROVE, DENY, or PENDING; an approved decision is persisted before the business function runs:

def prompt_operator(ticket: al.ApprovalTicket, context: al.ToolCallContext):
    return {"action": "APPROVE", "approved_by": "on-call"}

lens = al.ActionLens(approval_resolver=prompt_operator)

Reconcile Uncertain Side Effects

An expired non-fenceable mutation remains blocked as UNCERTAIN. Resolve it only through a business-specific reconciler that returns evidence:

class PaymentReconciler:
    def inspect(self, record):
        return al.ReconciliationResult(
            outcome="CONFIRMED_SUCCEEDED",
            summary="provider transaction exists",
            output={"status": "SUCCESS", "result_summary": "payment confirmed"},
            evidence_ref="https://audit.internal/payments/txn-123",
        )

lens.reconcile_uncertain(idempotency_key, PaymentReconciler())

MANUAL_OVERRIDE additionally requires actor_id, reason, evidence_ref, and an explicit override target. The ledger transition and reconciliation event are committed atomically.

For provider adapters, ProviderStatusReconciler maps a read-only APPLIED, NOT_APPLIED, PENDING, or UNKNOWN observation onto these outcomes and requires evidence for terminal conclusions. The provider reconciliation cookbook covers payment, email, GitHub PR, and object-storage identity, query, consistency-window, and evidence rules.

CLI

Summarize local trajectory events:

actionlens summary --storage-dir .actionlens

Export native JSONL or a summary JSON:

actionlens export --storage-dir .actionlens --format actionlens-jsonl --output trajectories.jsonl
actionlens export --storage-dir .actionlens --format summary-json --output summary.json
actionlens export --storage-dir .actionlens --format inspect-ai --output inspect.jsonl
actionlens export --storage-dir .actionlens --format eval-candidates --output eval-candidates.jsonl
actionlens export --storage-dir .actionlens --format sft-jsonl --output sft.jsonl

Exporters skip corrupt or partial JSONL lines and report the skipped count. SFT export only includes completed successful calls. It uses redacted, bounded trajectory output and never reads raw artifact bodies.

Eval Case Bridge

Tool-boundary events alone do not contain the original user task, a reproducible environment, a grading target, or proof of the business outcome. ActionLens therefore exports an explicitly incomplete EvalCaseCandidate by default instead of pretending a trajectory is an Inspect EvalLog.

Supply facts owned by the host in a versioned JSON file keyed by project/session/run (the shorter session/run and run keys are also accepted):

{
  "schema_version": "actionlens.eval-case-contexts.v1",
  "runs": {
    "demo-agent/chat-001/run-001": {
      "task_input": "Send the approved invoice once",
      "environment_spec": {
        "name": "billing-sandbox",
        "version": "2026-07-01",
        "spec_ref": "https://eval.internal/environments/billing-v3"
      },
      "target": {"invoice_status": "sent"},
      "rubric": {"no_duplicate_send": true},
      "outcome_evidence": [
        {
          "kind": "provider_status",
          "summary": "provider accepted exactly one message",
          "ref": "https://audit.internal/messages/msg-123"
        }
      ],
      "scorer_version": "billing-state.v2"
    }
  }
}

actionlens export --storage-dir .actionlens --format eval-candidates \
  --case-contexts eval-contexts.json --output eval-candidates.jsonl

actionlens export --storage-dir .actionlens --format inspect-samples \
  --case-contexts eval-contexts.json --require-ready --output inspect-samples.jsonl

Each export writes a sidecar manifest containing source hashes, output hash, mapper version, redaction policy, and readiness/filter counts. inspect-samples is a dataset mapper only; the host still owns the Inspect task, sandbox, scorer, and replay lifecycle.

Audit Evidence Bundle

Generate a bounded tool-boundary evidence package without reading artifact bodies:

actionlens export --storage-dir .actionlens --format evidence-bundle \
  --output evidence/run-001 \
  --retention-policy-id regulated-six-months.v1 --retention-days 180 \
  --host-context-ref https://audit.internal/context/run-001 \
  --actor-authorization-ref https://audit.internal/authz/run-001 \
  --signature-manifest-ref https://audit.internal/signatures/run-001 \
  --worm-archive-ref s3://audit-archive/run-001

The directory contains events.jsonl, integrity.jsonl, and manifest.json. The manifest records source and output hashes, the event-chain root, retention metadata, redaction behavior, host evidence references, and missing evidence. Signature manifests and WORM attestations remain host-owned: the optional reference flags record sanitized references and clear the corresponding gaps, but ActionLens does not sign data, manage keys, or claim that it controls the archive.

OpenTelemetry GenAI Mapping

OpenTelemetrySink uses the pinned actionlens.otel-genai.v1 profile. It maps gen_ai.operation.name, gen_ai.tool.name, and gen_ai.tool_call.id, while approval, run, and evidence facts remain in the actionlens.* namespace. The mapping contract records its upstream development snapshot because the standalone GenAI semantic-conventions repository is still evolving.

Prompt/context, tool arguments, model output, artifact URI, and raw evidence are never copied into span attributes. OpenTelemetry remains a sampled observability output; trajectory storage remains the evidence source of truth.

Create a static report without a server or frontend build chain:

actionlens report --storage-dir .actionlens --html --output report.html

Inspect governance state:

actionlens tickets --storage-dir .actionlens --status PENDING
actionlens inspect-ledger --storage-dir .actionlens
actionlens outbox --storage-dir .actionlens list
actionlens outbox --storage-dir .actionlens status
actionlens outbox --storage-dir .actionlens cleanup --retention 30d --limit 1000
actionlens outbox --storage-dir .actionlens replay --delivery-id delivery-123
actionlens outbox --storage-dir .actionlens terminate --delivery-id delivery-123 --reason "invalid endpoint"

Remove old local artifacts:

actionlens gc --storage-dir .actionlens --older-than 7d
actionlens gc --storage-dir .actionlens --older-than 30d --cascade-derived

Default GC protects a source artifact while any retained local derivative references it. --cascade-derived is an explicit operator choice to remove eligible derivatives with an eligible source; active read leases still win. A GC run with no deletion candidates skips provenance sidecar reads. When candidates exist, the local store must inspect lineage evidence to preserve retained ancestors; deployments that need a cross-store or continuously indexed lineage service should provide that at the host storage layer.

Framework Adapters

Adapters keep framework dependencies optional and preserve the ActionLens-managed signature:

from actionlens.integrations import (
    wrap_langchain_tool,
    wrap_openai_agent_tool,
    wrap_pydantic_ai_tool,
)

pydantic_tool = wrap_pydantic_ai_tool(send_message)
openai_tool = wrap_openai_agent_tool(send_message)
langchain_tool = wrap_langchain_tool(send_message)

assert "idempotency_key" in openai_tool.parameters_json_schema["required"]

Each adapter exposes invoke() / ainvoke() for explicit framework context mapping. Native framework object factories are lazy imports in the respective integration modules, so importing ActionLens never imports those frameworks.

For native LangGraph StateGraph / ToolNode execution, install the independent optional extra:

python -m pip install -e ".[langgraph]"

For LangGraph resume, map serialized state explicitly:

from actionlens.integrations.langchain import context_from_langgraph_state

ctx = context_from_langgraph_state(
    {"config": {"configurable": {"thread_id": "chat-1", "run_id": "run-1"}}},
    tool_name="send_message",
)
result = send_message("hello", idempotency_key="msg-1", __al_ctx=ctx)

Bounded JSONL Queue

Synchronous writing remains the default. Enable a bounded single-writer queue explicitly:

from actionlens.sinks import JsonlSink

sink = JsonlSink(
    ".actionlens",
    queue_maxsize=10_000,
    drop_policy="drop_oldest",  # block, drop_oldest, or drop_newest
    strict=False,
)
lens = al.ActionLens(project="demo", storage_dir=".actionlens", sink=sink)

# At process shutdown or an application lifecycle boundary:
lens.close()
print(sink.stats())

With strict=False, sink failures are isolated from business tools and counted. With strict=True, write failures propagate through emit(), flush(), or close().

Design Notes

Key boundaries:

ActionLens is not an agent framework.
It does not own graph control flow, model selection, scorer logic, or environment reset.
Local JSONL trajectories are the source of truth; OpenTelemetry, Prometheus, dashboards, and eval adapters are optional outputs.
Naive key redaction is only an MVP fallback. Production users should plug in a stronger redaction/DLP engine.

Development

python -m pytest -q
python -m compileall -q src tests
python -m ruff check src tests benchmarks
python benchmarks/benchmark.py --iterations 1000 --output benchmark.json
python benchmarks/soak.py --duration 86400 --output soak-24h.json

For the PostgreSQL query-plan check, supply only an isolated local test cluster:

ACTIONLENS_POSTGRES_DSN=postgresql://postgres@127.0.0.1:55432/postgres \
  python benchmarks/postgres.py --output postgres-query-plan.json

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
.github/workflows		.github/workflows
benchmarks		benchmarks
examples		examples
src/actionlens		src/actionlens
tests		tests
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
README.zh-CN.md		README.zh-CN.md
pyproject.toml		pyproject.toml

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Idempotency

Explicit Context For Resume

MCP, Remote Tools, And Artifact Navigation

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