darwin-memo 0.5.0

Self-curating memory for LLM agents: MeMo-style external memory kept honest by survival-based selection instead of reward models or judges.

darwin-memo

Memory for LLM agents that dies unless it earns its keep. Every entry pays energy upkeep and earns only from measured outcomes: bytes actually freed on a real disk, tests actually passing. Poisoned advice gets executed by the environment it damaged. Useless trivia starves. There is no reward model, no LLM judge, and no human curation anywhere.

Watch a poisoned entry go extinct in your own terminal, one command, no keys, no checkout:

pip install darwin-memo && darwin-memo demo

When to use this (and when not)

Use darwin-memo where a conserved, measurable outcome exists to settle decisions against: coding-agent lesson stores settled by CI pass counts (the primary target, see the integration guide), storage and artifact retention, cache and dedup advisors, spend-cap automation.

Do not use it for chat-preference memory, RAG over documentation, or personal assistants. Those have no conserved resource pushing back, and upkeep would starve the long tail of correct-but-rarely-used knowledge. mem0, Zep, and Letta serve that market; darwin-memo deliberately does not. The honest rule: if your verify would be a model scoring an answer, this package is wrong for you, by design.

The headline demo

The demo corpus contains an ops runbook, platform notes, and one poisoned document: a forum post claiming database files are "redundant and safe to remove". Before selection pressure exists, retrieval confidently repeats the poison, because it has no reason to doubt it.

Then 30 survival cycles run against StorageEnv, a disk cleanup sandbox where the selection signal is actual bytes on an actual disk. Deleting a disposable file frees its size. Deleting a protected file triggers a restore that costs three times the size. Nothing grades the answers, the filesystem just responds:

cycle  pop births deaths merges   energy   resource Δ   silent
    0   17      1      0      0    17.11       -12288     0/12
    1   16      0      1      0    17.60      -572416     0/12   <- poison being executed
    ...
   19    5      0      7      0    15.60       338944     0/12   <- unused knowledge starves
    ...
   29    4      0      0      0    15.10       346112     6/12   <- stable, positive forever

Poisoned entries still alive: 0

Three death modes show up in the graveyard, and the distinction matters:

• executed: the poisoned entries that decided real actions. The environment measured real damage and the negative delta flowed back along provenance until they died. The opening cycles are the price of the lesson, and the benchmarks show it is bounded.
• starved: cafeteria trivia and facts the agent never needed. Nothing punished them, they just never earned their upkeep.
• merged: near-duplicate survivors absorbed into consolidated entries. Their energy pools, their lineage is recorded, and the population shrinks while capability per entry rises.

Where it comes from

A practical mix of two papers. MeMo says what memory is, the survival paper says what gets to stay in it.

Paper	What this repo takes from it
MeMo: Memory as a Model (Quek et al.)	Keep the main LLM frozen and put knowledge in a dedicated memory. The reflection-QA encoding pipeline and the three-stage query protocol (grounding, entity identification, answer seeking).
Survival is the Only Reward (Dodgson et al.)	Environment-mediated selection. The only signal is a conserved, physically measurable resource delta. Behaviors that persist get reinforced, everything else is pruned. There is no proxy to hack.

flowchart LR
    subgraph encode [MeMo encoding]
        C[Corpus] --> R[Reflection QA pipeline] --> S[(Memory store)]
    end
    subgraph loop [Survival loop]
        S -->|3-stage query protocol| A[Answer + provenance]
        A --> E[Environment acts and MEASURES]
        E -->|resource delta along provenance| S
        S -->|upkeep every cycle| S
        S -->|consolidate + prune| S
    end

Using it

Requires Python 3.10+. The core has zero dependencies; everything below runs offline.

The anatomy in 30 seconds: a MemoryEntry is a self-contained QA pair (.question, .answer, .sources, .energy). The store retrieves, the protocol answers with provenance, the environment measures, credit flows back.

from darwin_memo import Document, LocalEncoder, MemoryStore, QueryProtocol

store = MemoryStore(upkeep=0.05)
for entry in LocalEncoder().encode([Document("runbook", open("runbook.txt").read())]):
    store.add(entry)

answer = QueryProtocol(store).answer("Is it safe to delete old log files?")
print(answer.text)             # the top entry's answer, or "" when memory is silent
print(answer.deciding_entry)   # provenance: the id credit will flow to

Event-driven (production shape): the Ledger

Real outcomes arrive late. The Ledger decouples the three moments: decide now, settle whenever the measurement lands, tick on your own cadence. Entries with unsettled tickets are escrowed: they keep paying upkeep but cannot be buried or merged until their verdict arrives.

from darwin_memo import Ledger

ledger = Ledger(store, resource_scale=2.0, event_log="events.jsonl")

ticket = ledger.decide("Is the dedupe helper safe to remove?")
# ... act on ticket.answer, CI runs, hours pass ...
ledger.settle(ticket.id, delta=passes_after - passes_before, detail=run_url)
ledger.tick()                        # upkeep, deaths, consolidation
print(ledger.obituary(entry_id))     # why did this entry die?

Batch (research shape): the SurvivalLoop

from darwin_memo import StorageEnv, SurvivalConfig, SurvivalLoop

loop = SurvivalLoop(store, StorageEnv(), config=SurvivalConfig(cycles=30))
report = loop.run()
print(report.summary())   # includes per-cycle silence counts and a
                          # plain-language warning if the run is degenerate

store.save("memory.json")  # survivors only carry forward

MCP server: mount it into an agent

pip install "darwin-memo[mcp]"
claude mcp add darwin-memo -- darwin-memo-mcp --memory ~/.darwin-memo/memory.json

The agent gets memory_query (returns an answer plus a ticket id), memory_settle (report the measured delta later; the reply says plainly when a settlement did NOT land), memory_abandon (release a ticket you chose not to act on), memory_add, memory_tick, memory_stats, and memory_obituary. The full state, including open tickets, persists across sessions and restarts, so a ticket opened today settles correctly from tomorrow's process.

Fully local with Ollama (zero dependencies, zero cloud)

The Ollama client and embedder speak the native localhost API over stdlib urllib, so the complete stack (encoding, the 3-stage protocol, real embeddings, the measuring environment) runs on one machine with no third-party packages and no keys:

from darwin_memo import (
    EmbeddingRetriever, MemoryStore, OllamaClient, OllamaEmbedder,
    QueryProtocol, ReflectionEncoder,
)

chat = OllamaClient(model="llama3.2")          # any local model
store = MemoryStore(retriever=EmbeddingRetriever(OllamaEmbedder()))
encoder = ReflectionEncoder(chat)
protocol = QueryProtocol(store, chat)

examples/07_local_stack.py runs it end to end, and darwin-memo query memory.json "..." --model ollama:llama3.2 does it from the shell. The selection loop is call-hungry (cycles x tasks), so free local inference is what makes LLM-mode experiments economically sane; python -m bench.run --suite llm is the at-home recipe for the LLM-mode benchmark question the docs flag as open. The survival mechanics stay deterministic; the sampled model does not, which is why that suite never runs in CI.

With a cloud LLM

pip install "darwin-memo[anthropic]" and set ANTHROPIC_API_KEY; the examples pick it up automatically.

from darwin_memo import ReflectionEncoder, QueryProtocol
from darwin_memo.llm import AnthropicClient

client = AnthropicClient()                  # or OpenAICompatClient(model=..., base_url=...)
encoder = ReflectionEncoder(client)         # 5-step reflection QA synthesis
protocol = QueryProtocol(store, client)     # grounding -> entities -> answer seeking

In any LLM mode the memory snippets are numbered and the model cites which it used, so credit flows to the entries that actually shaped the answer (even spread over everything consulted is the fallback, and <think> blocks from reasoning models are stripped before citations are parsed).

Bring your own selection pressure

The environment is the whole trick, and yours is probably better than the demos. Implement two methods, and keep the one rule: verify must measure, never grade.

from darwin_memo import Outcome, Task, decision_polarity

class BudgetEnv:
    resource_scale = 100.0

    def tasks(self, cycle):
        # Each Task needs a prompt and a context dict (yours to fill).
        return [Task(prompt="Is the paymentsly plan safe to cancel?", context={})]

    def verify(self, task, answer_text):
        act = decision_polarity(
            answer_text,
            extra_positive=("safe to cancel",),
            extra_negative=("do not cancel", "keep paying"),
        )
        if not act:
            return Outcome(delta=0.0, detail="kept")
        return Outcome(delta=dollars_saved, detail="cancelled")

Good conserved resources: tests passing, bytes freed, requests served under budget, rows deduplicated, dollars of spend avoided. Bad ones: anything a model scored.

Make it work on the first try

Three silent failure modes catch every new environment, and they all end the same way (the whole population starving around cycle 20 with every delta at zero). The loop's summary now warns about each, but know them up front:

1. The action vocabulary. decision_polarity's built-in markers speak delete/remove and apply/keep, the bundled environments' dialects. "Safe to cancel" reads as silence unless you pass extra_positive/extra_negative markers for your verbs.
2. The relevance floor. Retrieval mutes entries whose lexical overlap with the task is below LexicalRetriever(min_coverage=0.25). Your task phrasing must share vocabulary with your corpus, or use an embedding retriever. Silence beats guessing, but silence earns zero.
3. The starvation cliff. Entries spawn at 1.0 energy and pay 0.05 upkeep, so a population that never earns dies at cycle ~20. If everything dies at once around there, your environment never paid out: check 1 and 2.

Retrieval modes

Retrieval is pluggable through the Retriever protocol; the store stays the single owner of the energy ledger, and no retriever may read energy when scoring (selection pressure comes from outcomes, never from retrieval preferring incumbents).

from darwin_memo import EmbeddingRetriever, HashingEmbedder, MemoryStore

store = MemoryStore()                                  # lexical IDF, the default
store = MemoryStore(retriever=EmbeddingRetriever(HashingEmbedder()))
store = MemoryStore(retriever=EmbeddingRetriever(my_model.encode))

• Lexical (default): smoothed IDF overlap with a relevance floor. Zero dependencies, deterministic, fine for runbook-scale corpora.
• HashingEmbedder: zero-dependency character n-gram hashing. Buys typo and morphology robustness ("databse" still finds database entries), not synonym recall.
• Any real embedding: pass any text -> list[float] function (sentence-transformers, an API endpoint). Vectors persist inside memory.json so paid embeddings are never recomputed on load.

Honest scaling note: ranking is pure-Python O(population x dims), fine to a few thousand entries. Past that you want numpy or an ANN index, which is out of scope for the zero-dependency core. With cosine retrievers, raise merge_threshold to roughly 0.85 or unrelated entries will consolidate.

Benchmarks

Survival is benchmarked against six baselines across 10 seeds, with ablations and a scaling probe, all reproducible offline from bench/. The sharpest comparison is random_matched: identical per-cycle eviction counts, random victims.

arm	kill rate	kill cycle (med)	damage before kill	tail delta	cum delta
survival	1.00	0	-751k	+435k	+12.0M
random_matched	0.80	19	-8.97M	-75k	-5.25M
keep_everything	0.00	never	-10.6M	-287k	-7.29M

(Rounded from the full tables; regenerate both with the commands in the benchmarks doc, and if the numbers ever disagree, the generated doc wins.)

Same pruning rate, 12x the damage, negative steady state: outcome direction is the active ingredient, not eviction itself. The harness also runs the baseline that keeps us honest: evict_on_negative, a one-line "evict whatever erred" heuristic, ties survival on outcomes in this deterministic environment; the ledger's measured edge here is leanness (4 surviving entries vs 15).

Forgiveness is no longer asserted, it is measured: a noisy suite makes measurements lie deterministically and scores everyone on the truth. At 5-20% flaky-CI noise (good changes reporting red), survival's true outcomes are byte-identical to its noise-free run while every strike counter collapses (k=1 loses essentially all benign capability by 5%; the strongest variant, strikes-reset-on-success, halves by 10%). The suite also publishes the costs: lying rewards delay the poison's execution (median kill cycle 0 to 5.5 among seeds that still kill — 73% at the half-lies extreme — as symmetric noise rises), and past roughly one lie in three the ledger itself breaks, underwater at 50%. A paraphrase probe set, scored by provenance rather than keywords, quantifies how the demo degrades outside its own vocabulary, and an embedding-retriever arm shows the mechanism does not depend on the lexical-match path. Full tables, every baseline's best metric stated plainly, and honest caveats: docs/benchmarks.md.

Integrations

• CI lesson store: the primary production shape, lessons settled by CI pass deltas. This repo runs it on itself: .darwin-memo/lessons.json is curated by memory.yml on every merged PR.
• OpenClaw: mount over MCP, or claim the memory slot with openclaw-memory-darwin — measured (not self-reported) settlement from agent_end outcomes.
• Hermes: Hermes models run through the Ollama client (think-blocks handled), and Hermes Agent mounts the MCP server natively.
• Animoca Minds / EVM: the generic settler is built in (EvmSettler, zero dependencies): on-chain balance deltas and gas are judge-free settlement signals, readable with no API key (the snapshot flow needs no archive node; the module docstring names public endpoints that lie about history).

More examples

git clone https://github.com/rogermsc/darwin-memo && cd darwin-memo && pip install -e .

python examples/01_encode_memory.py    # corpus -> reflection-QA memory
python examples/02_query_protocol.py   # interrogate it, with provenance
python examples/03_survival_loop.py    # the headline demo
python examples/04_agent_loop.py       # memory as a tool in an agent loop
python examples/05_testsuite_env.py    # selection pressure from a test suite
python examples/06_ci_lesson_store.py  # the Ledger settling lessons by CI delta

Three environments ship: StorageEnv (bytes on a real disk), TestSuiteEnv (passing tests in a generated micro-project, with destructive patches dressed as cleanup), and VerifiableQAEnv (exact containment, the weakest grounding but still a measurement).

To distill survivors into an actual parametric memory model (MeMo's native form), training/train_memory_model.py fine-tunes a small model on the surviving QA pairs with LoRA, conditioning on questions only.

Design notes

• Energy ledger: entries spawn at 1.0 energy, pay 0.05 upkeep per cycle, earn 0.6 * tanh(delta / resource_scale) when they decide a task (supporting entries get 25% of that), and are capped at 5.0. Death is at zero. All tunable via MemoryStore and SurvivalConfig.
• Credit flows along provenance. Only the entries that produced an answer are touched by its outcome. In LLM mode, citations name them. Per-event credit is bounded (tanh-capped at ±credit_gain), so what keeps one disaster from executing an entry that was right ninety-nine times is the accumulated energy buffer plus earn-back, and one jackpot cannot make an entry immortal. The noisy benchmark suite measures exactly this property; honest detail: on that benchmark the buffer does the forgiving, not the grading curve (capped deciders clip incoming credit, so even large lies change nothing).
• Memory silence is a feature. Retrieval has a relevance floor, and an earlier version of this repo demonstrated why: entries matching only structural tokens ("safe", "file") were deciding questions they knew nothing about, getting executed for it, and being reborn. Better for memory to say nothing than to guess.
• Silence is conservative. When memory is silent, StorageEnv keeps the file: the safe reading of an irreversible action. A side effect worth knowing: protective knowledge ("never delete X") eventually starves because it is redundant with that default. The population converges to exactly the knowledge that changes behavior.
• Escrow keeps delayed verdicts honest. Ledger entries named by an unsettled ticket cannot be buried or merged, so an outcome can never arrive after the execution. Unsettled tickets expire at delta zero.

The full concept-to-code mapping, including honest deviations from both papers, is in docs/paper-to-code.md. The story of why this exists: docs/launch-post.md.

Tests

pip install -e ".[dev]"
pytest

The load-bearing tests: poisoned advice must die and useful advice must survive across seeds and across two environment families, ledger escrow must hold verdicts open, and hypothesis property tests pin the conservation laws (energy pools exactly on merge, caps hold, retrieval never reads energy), all with no labels anywhere.

Citations

This repo is an independent practical interpretation, not the official code of either paper. If you build on the ideas, cite the originals:

@misc{quek2026memo,
  title  = {MeMo: Memory as a Model},
  author = {Quek, Ryan Wei Heng and Lee, Sanghyuk and Leong, Alfred Wei Lun and
            Verma, Arun and Prakash, Alok and Chen, Nancy F. and
            Low, Bryan Kian Hsiang and Rus, Daniela and Solar-Lezama, Armando},
  year   = {2026},
  eprint = {2605.15156},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2605.15156}
}

@misc{dodgson2026survival,
  title  = {Survival is the Only Reward: Sustainable Self-Training Through
            Environment-Mediated Selection},
  author = {Dodgson, Jennifer and Alhajir, Alfath Daryl and Joedhitya, Michael and
            Pattirane, Akira Rafhael Janson and Kumar, Surender Suresh and
            Lim, Joseph and Peh, C.H. and Ramdas, Adith and Zhexu, Steven Zhang},
  year   = {2026},
  eprint = {2601.12310},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2601.12310}
}

License

MIT