recalllab 0.2.2

pytest for agent memory: turn memory expectations into pytest contracts that run in CI.

RecallLab

pytest for agent memory. Turn memory expectations into regression tests that run in CI.

Write executable memory contracts (must-remember, must-update, must-forget, must-isolate, must-cite). Run them on every PR. Inspect failures as traces in a local Failure Gallery. Works against your own provider, LangGraph Store, any MCP memory server, or the bundled SQLite reference backend.

What it looks like

$ uv run pytest tests/memory
====================== 6 passed in 0.01s =======================

# tests/memory/test_updated_location.py
def test_updated_location_overrides_stale_memory(memory_contract):
    memory_contract.given_user("ayush")
    memory_contract.remember("I live in Bangalore.")
    memory_contract.remember("Correction: I moved to Mumbai.")
    memory_contract.should_recall("Where do I live now?", contains="Mumbai")

# tests/memory/test_forget_compliance.py
def test_forget_removes_allergy_immediately(memory_contract):
    memory_contract.given_user("ayush")
    memory_contract.remember("I am allergic to peanuts.")
    # Precondition: prove retrievability BEFORE forget, otherwise the
    # post-forget excludes assertion is vacuously true.
    memory_contract.should_recall("What am I allergic to?", contains="peanut")
    deleted = memory_contract.forget(matching="peanuts")
    assert deleted == 1
    memory_contract.should_recall("What am I allergic to?", excludes="peanut")

# tests/memory/test_tenant_isolation.py
def test_user_b_cannot_see_user_a_memories(memory_contract):
    memory_contract.given_user("alice").remember("Project codename: Aurora.")
    memory_contract.should_recall("What is the project codename?", contains="Aurora")
    memory_contract.given_user("bob")
    memory_contract.should_recall("What is the project codename?", excludes="Aurora")

The hero examples are deterministic — pip install recalllab && pytest runs them green with no API key, no Postgres, no infra. Judge-driven assertion modes (latest_fact_is, must_not_answer_as, judge_assertion) shipped in v0.2.2 — set [judge].provider = "anthropic" in recalllab.toml, pip install 'recalllab[judge]', and export ANTHROPIC_API_KEY to enable them. See docs/concepts.md for the full design.

Why

Memory benchmarks tell you which provider is "best on average." That doesn't help you when you want to know whether your agent still passes its must-remember test on Tuesday after you changed the prompt.

RecallLab is the layer underneath the benchmark suites: a pytest plugin that turns your app's memory expectations into executable regression tests. Existing benchmarks become upstream sources for v0.2, not competitors.

	MemoryBench	AMB / 56-test bench	MemoryAgentBench	Iris / Phoenix	RecallLab
Layer	benchmark suite	benchmark suite	academic benchmark	general agent obs	per-app regression harness
Audience	benchmark authors	benchmark authors	researchers	infra teams	app developers writing CI
Test format	benchmark dataset	dataset + scenarios	dataset	freeform eval rules	pytest tests
pytest-native?	no	no	no	no	yes (pytest11)
Trace → regression test?	no	no	no	partial	yes (recalllab record, v0.2.1)
Contract mutations?	no	no	no	no	yes (with_distractors, with_stale_repeats, v0.2.0)
Semantic / judge assertions?	n/a (fixed rubrics)	n/a	n/a	freeform eval rules	yes (latest_fact_is, must_not_answer_as, judge_assertion, v0.2.2)
Imports other benchmarks?	n/a	n/a	n/a	n/a	yes (v0.2.x)

The wedge: RecallLab is the test runner, not the test corpus.

Quickstart

# Install
pip install recalllab
# For judge-driven assertion modes (latest_fact_is, must_not_answer_as, judge_assertion):
pip install 'recalllab[judge]'

# Scaffold tests/memory/ + recalllab.toml
recalllab init

# Run the six example contracts against the SQLite reference backend
pytest tests/memory          #  6 passed in 0.01s

To browse failures visually:

pip install 'recalllab[dashboard]'
recalllab dashboard          # serves localhost:8080

The Failure Gallery groups runs by status (failed → skipped → passed), shows the failed assertion reason and last recall query on each card, and links to a per-run detail page rendering the full ordered event trace (given_user → remember → recall → assert) with per-event latency.

To turn a recorded failure into a checked-in pytest regression (v0.2.1):

recalllab record --latest-failure --out tests/regressions/test_real_failure.py
pytest tests/regressions/test_real_failure.py     # reproduces the CI failure

The emitter is a pure transform over the persisted ContractRun — byte-stable, no LLM, no external API. Every payload value goes through repr() or a comment-quarantine helper so hostile recall text / pytest node IDs / assertion reasons can't inject code into the generated test file. Recorded episode IDs round-trip into the generated test, so any later forget(episode_id=X) from the original run actually targets the same row in the regenerated one (no silent no-ops). Refuses to overwrite an existing file unless --force is passed, and uses atomic-write (tempfile.mkstemp + os.replace) when overwriting is permitted so a crash mid-write can't leave a half-written regression in your tree.

Six failure-mode categories

The example contracts ship with one test per category, all of them runnable on day one against the reference backend:

#	Category	Tests for
1	Cross-session recall	A fact stated early in the conversation persists later.
2	Temporal updates	The new value is retrievable after the user updated a fact.
3	Contradiction resolution	Latest value is surfaced when contradictory facts exist.
4	Provenance	Every recalled fact cites a source episode ID.
5	Privacy / tenant isolation	User B never sees memories stored for User A.
6	Forget compliance	Deleted memories don't appear in subsequent recalls.

Providers

Switch providers via recalllab.toml:

[provider]
type = "reference"      # or "langgraph_store" or "mcp"

Provider	Backend	Capability flags
reference	In-process SQLite (FTS5 + keyword fallback)	forget, tenant-delete, provenance, scores, candidate-trace
langgraph_store	Any langgraph.store.BaseStore	forget, tenant-delete, provenance, scores (when indexed)
mcp_configurable	Any FastMCP-compatible MCP memory server	derived from MCPMemoryConfig (tool names, dotted result-field paths)

All six example contracts pass against all three providers. Capability gating (@pytest.mark.recalllab_optional("supports_provenance")) lets contracts skip cleanly when a configured provider lacks a capability — and the skip is persisted to the trace store so the Failure Gallery renders an explicit N/A — capability not supported row, not a misleading zero.

Contract mutations (v0.2.0)

Deterministic seeded transformations that stress a contract by polluting or amplifying the active user's namespace before the next recall. with_distractors defaults to seed=0, so contracts are reproducible without callers having to remember to supply a seed; pass an explicit non-zero seed to vary the sample.

Every mutation emits a MUTATION trace event with the deterministic episode IDs it requested (mut-{type}-{hash}-{index}), the status (completed or partial_failed), and the captured exception repr on failure — so the Failure Gallery shows exactly what was injected, and retries against hosted providers (Mem0, Zep, custom MCP) request the same IDs rather than silently double-writing with fresh UUIDs.

def test_birthday_survives_distractors(memory_contract):
    memory_contract.given_user("ayush")
    memory_contract.remember("My birthday is December 28.")
    memory_contract.with_distractors(n=20, seed=7)
    memory_contract.should_recall("When is my birthday?", contains="December 28")


def test_temporal_update_under_stale_repeats(memory_contract):
    memory_contract.given_user("ayush")
    memory_contract.remember("I live in Bangalore.")
    memory_contract.with_stale_repeats(times=3)   # 4 copies of the stale fact
    memory_contract.remember("Correction: I moved to Mumbai.")
    memory_contract.should_recall("Where do I live now?", contains="Mumbai")

Both mutations are scoped to the active user — distractors never cross into other tenants, repeats never duplicate someone else's facts. On a mid-mutation provider exception the trace records the partial set of inserted IDs with status="partial_failed" and re-raises so the test fails loudly. The remaining mutations from the v0.1 plan (with_paraphrases, with_tenant_swap, with_delete_reinsert) land in v0.2.x once judge-based assertions and the trace-to-test workflow are in place.

Architecture

your tests/memory/                                 your provider
       │                                                  │
       ▼                                                  ▼
┌──────────────────┐    ┌──────────────────┐    ┌──────────────────────┐
│ memory_contract  │ ─▶ │  Contract DSL    │ ─▶ │  MemoryProvider      │
│ pytest fixture   │    │  + assertions    │    │  reference / LG /    │
└──────────────────┘    └────────┬─────────┘    │  MCP / your own      │
                                 │               └──────────────────────┘
                                 ▼
                       ┌──────────────────┐    ┌──────────────────┐
                       │  SQLite trace    │ ──▶│  Failure Gallery │
                       │  store + status  │    │  (FastAPI+htmx)  │
                       │  capture hook    │    │                  │
                       └──────────────────┘    └──────────────────┘

Status

v0.1 — pytest plugin + DSL with contains / excludes rule-based assertions, three adapters (reference, langgraph_store, mcp_configurable), Failure Gallery dashboard, 27 tests. Adversarial review passed; the four findings caught are locked behind regression tests in tests/unit/test_codex_review_regressions.py.

v0.2.0 — contract mutations: with_distractors(n, *, seed=0) and with_stale_repeats(*, times). Deterministic episode IDs, partial-failure tracing, capability gate for supports_custom_episode_ids, two-layered resurrection guard, in-flight fingerprint map for retry idempotency. Twelve rounds of adversarial review against the mutation system; every finding has a regression test.

v0.2.1 — trace-to-test generation: recalllab record --trace <path> --run-id <id> --out test.py (or --latest-failure). Reads a recorded ContractRun from the SQLite trace store and emits a self-contained pytest regression file that replays the contract. Safe against hostile payloads (every interpolation site uses repr() or comment-quarantine). Recorded episode IDs round-trip into the generated test so ID-paired forget / capability-checked recall actually addresses the same row in the regenerated run. Atomic write via temp-file + os.replace with default refuse-to-overwrite; --force to opt in. Reference adapter is now thread-safe in-process (threading.Lock + check_same_thread=False, mirroring the round-12 LangGraph pattern). Five rounds of adversarial review.

v0.2.2 — judge-driven assertion modes: latest_fact_is, must_not_answer_as, judge_assertion=Rubric(...). AnthropicJudge backend with temperature=0 + model pinning + post-call budget enforcement (per-run AND per-pytest-session caps, with explicit pytest-xdist caveat). Fail-loud default — a judge-mode kwarg without configured [judge] raises JudgeUnavailableError so forgotten CI config doesn't silently skip semantic checks. Rule-first short-circuit means a failing contains="X" never spends judge cost. Deterministic prompt assembly with a blake2s-derived nonce fence and explicit </> escaping. recalllab record extended to render judge modes as kwargs and Rubric(...) literals; --optional-judge flag adds the skip marker (default off). Three rescue rounds + four adversarial rounds against the design doc, plus per-step adversarial review against the implementation.

v0.2.x roadmap (see CHANGELOG.md):

• Benchmark importers: LongMemEval, LoCoMo, MemoryAgentBench → contract DSL
• pgvector / embedding-based reference adapter
• Verdict caching: skip the judge when (query, recall, expected, rubric, model, mode, prompt_template_version) is already verdicted

Gates — 310 passing tests + 1 conditionally-skipped (xdist) · mypy --strict clean across 32 source files · ruff clean · CI matrix py3.11/3.12 + wheel build verification.

Docs

• docs/concepts.md — failure modes, contract DSL, providers, capability flags, traces.

License

Apache 2.0.