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.

The paper

Attacking the Curator: Curation-Targeted Attacks on Agent Memory, and What Survives Them. An adversary that corrupts the settlement signal rather than injecting poison — denial of memory — measured against six curation mechanisms across attack budgets and seeds, with exact paired permutation tests and Holm-Bonferroni correction.

It reports its negative results as prominently as its positive ones. Absent an attacker the ledger buys leanness and cost, not accuracy; and across 2,115 evaluated SWE-Bench-CL tasks, no memory arm beat carrying no memory at all.

• The paper and its threat model
• Reproduction package — every printed number is re-derived from committed per-seed runs in CI, so a table that drifts from its evidence fails the build
• Cite it with the BibTeX in Citations below

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.

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?

Seeing it: the local dashboard

darwin-memo doctor memory.json     # why is nothing earning?
darwin-memo ui memory.json         # population, graveyard, economics

doctor reads the event log and names which failure mode a store hit instead of leaving three of them looking identical. ui serves the same data as a read-only dashboard on localhost: population and energy over time, the graveyard split by cause of death, and the resource- versus-upkeep accounting. Read-only and loopback-only, so there is nothing to authenticate.

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, memory_obituary, and memory_audit (read the event log). 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.

Two more failure modes, how to pick a conserved resource, how to price a mistake from a real cost, and how to table-test verify before running any loop are in docs/custom-environments.md — the full guide this section condenses, with two worked environments to read.

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.

Temporal awareness

Survival selection culls a stale entry only after it causes damage, so every consult surface carries the time dimension instead of waiting for the world to hurt:

• Surfaced answers carry an age line per entry: UTC timestamp when recorded, born tick, last settled tick. Entries persisted before timestamps existed render as "age unknown" rather than faking a date.
• When retrieval returns near-duplicate entries (the same similarity machinery and threshold consolidation uses), nothing is silently preferred: the group surfaces together, each entry with its dates, newest first, marked as conflicting/overlapping advice. Mechanical throughout, no LLM judges anything.
• Recency-weighted ranking is opt-in: pass a half-life in ticks (store.retrieve(..., half_life=20), --half-life 20 on query and ledger decide, half_life on the MCP memory_query tool) and scores halve for every half-life since an entry last settled. A pure ranking concern: balances and credit assignment never see it.
• kind and source filters (--kind, --source) narrow the candidate population before ranking and compose with everything above.

Benchmarks

Survival is benchmarked against five 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.

(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, 27x the damage, runs that end 7.7M underwater: 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 (officially: a paired permutation test cannot tell them apart); 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% flaky-CI noise (good changes reporting red), survival's true outcomes are byte-identical to its noise-free run in every seed (29 of 30 seeds at 10-20%) while every strike counter collapses (k=1 loses essentially all benign capability by 5%; the strongest variant, strikes-reset-on-success, halves by 10%; every gap holds at adjusted p < 0.005). The suite also publishes the costs: lying rewards delay the poison's execution (median kill cycle 0 to 3 as symmetric noise rises to the half-lies extreme, where 2 of 30 seeds never kill it), and past roughly one lie in three the ledger itself degrades hard, benign capability down to 0.26 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.
• AGENTS.md / CLAUDE.md: the cross-tool memory convention has no schema, no expiry and no pruning — files only grow. darwin-memo render projects a store that has been pruned by measured outcomes into the file your agent already reads.
• Claude Code: darwin-memo render projects the store into the auto-memory MEMORY.md Claude Code reads at session start, inside its 200-line / 25KB ceiling, or into an index plus topic files with --split-dir.
• OpenClaw: mount over MCP, or claim the memory slot with openclaw-memory-darwin: measured (not self-reported) settlement from agent_end outcomes.
• OpenAI Agents SDK: a dependency-free DarwinMemoSession implements the SDK's Session protocol (transcript replay as honest JSONL) and adds the long-term layer the SDK leaves vacant: opt-in consult/settle against a lesson store, deltas always measured by the host.
• 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).

Organic memory (experimental, opt-in)

An adaptive, brain-like layer, complete through Phase 4: memories connected by relevance-weighted links, shrinking to a gist when unused and expanding to full detail on recall, with a recall spreading one hop and strengthening the links it travels — all on earned/measured signals, no judge. OrganicMemory(store) is the facade; store_related(store, entry_id, k) is the one-shot primitive.

Phases 1–3 are additive and read-only with respect to survival: relatedness is mechanical cosine, value is still earned by the ledger. Phase 4 (earned importance) is the exception and is opt-in — it biases ranking by default, and slows upkeep only if you pass om.upkeep_scale() to charge_upkeep. That makes usage a retention signal, which this repo's own salience_matched arm measured at a 0.20 poison kill rate against random eviction's 0.80; read docs/organic.md before wiring it. Zero-dependency by default; pip install darwin-memo[organic] adds a turbovec ANN backend for scale.

Documentation

The docs index links everything. The operator set: the tuning guide (the load-bearing knobs, failure symptoms, evidence-backed starting points per profile), the API reference (Python surface, CLI, MCP tools, exceptions), and the store format (the on-disk JSON, the event log and its rotation, the sidecars, the compatibility policy).

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
python examples/07_local_stack.py      # the whole stack on Ollama, no cloud
python examples/08_evm_settler.py      # on-chain balance deltas as the signal
python examples/09_your_own_corpus.py  # your documents instead of the demo's

09 is the one to read when the demo works and your own files do not: it takes a directory, and it shows the retrieval floor rejecting a question phrased in structural words rather than hiding it.

Five environments ship. Three measure a resource: 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). Two price inaction, which the other three score at zero: RentedStorageEnv and RentedTestSuiteEnv charge for holding on rather than only for acting, because several conclusions here rest on inaction being free and that is a property of the world, not of curation.

Writing your own is the load-bearing task, and it has a guide: docs/custom-environments.md.

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.

The distill benchmark arm (python -m bench.run --suite distill, opt-in, needs torch/transformers/peft/datasets) turns this into measured evidence: it distills the energy-ledger survivor set, the unfiltered raw set, and the LLM-judge-kept set into separate LoRA models and scores each by containment — good_recall (does the model recall the surviving facts?) and poison_reproduction (does it emit the buried poison?). The result is survival selection working as a data filter for parametric memory: the survivor-distilled model recalls the good facts and reproduces none of the poison, while the raw-distilled model reproduces it — because the poison was in its training set. See docs/benchmarks.md.

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

To cite darwin-memo itself, or the paper it ships:

@software{simoes2026darwinmemo,
  title  = {darwin-memo: self-curating memory for LLM agents},
  author = {Sim\~oes, Roger},
  year   = {2026},
  url    = {https://github.com/rogermsc/darwin-memo}
}

@misc{simoes2026attacking,
  title  = {Attacking the Curator: Curation-Targeted Attacks on Agent
            Memory, and What Survives Them},
  author = {Sim\~oes, Roger},
  year   = {2026},
  url    = {https://github.com/rogermsc/darwin-memo/blob/main/paper/main.tex}
}

Both entries are provisional: there is no archival deposit yet, so neither carries a DOI. CITATION.cff is the machine-readable version and says the same thing.

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

@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