Echo Memory

Shared memory for AI agents, as a graph in your own database. What Claude Code learns, Cursor and Codex can recall. Every fact records who wrote it and when, and the server never calls a model to store one.

Your agents start every session from zero. The usual fix is a notes file you paste into context, which grows until it is mostly irrelevant to whatever you are asking. Echo Memory is the other shape: facts connected to each other, and a query that returns the few that matter. On the author's own store that is 96.7% less context for the same answer, with the answer still present 87.2% of the time across 1,190 questions.

write_episode                          query_memory
  billing ──uses──▸ Razorpay             "how do we take payments"
    │ written by claude-code               ▸ billing uses Razorpay, not Stripe
    │ supersedes ──▸ Stripe                  written by claude-code, 3 days ago
    └ no model invoked                     ▸ 1,372 tokens, not 41,838

Install

Requires: Python 3.11+ and Docker (for the database).

pipx install echo-mem
echo-memory quickstart

quickstart starts the database, applies the schema, and prints the claude mcp add line that registers it, filled in with the port it actually used. The Postgres image is published, so nothing compiles.

Or use the hosted service and run no database at all:

pipx install echo-mem
echo-memory connect <key>          # a key from https://app.echo-mem.com

Then once per machine, so an agent knows when to record and recall rather than only that the tools exist:

echo-memory install --global

Restart your client afterwards. An MCP server is a long lived process that holds the code and config it started with, and an editable install does not change that.

The PyPI name is echo-mem, not echo-memory. That name belongs to an unrelated hosted product. The import package and the CLI are both echo_memory / echo-memory; only the distribution name differs.

Upgrading

pipx upgrade echo-mem
echo-memory init-db                # apply any new migrations
echo-memory reindex                # rebuild what the new code derives

Both steps, in that order, and neither is optional on a store that predates the version you just installed.

Skipping init-db fails loudly. A read against a schema older than the code raises an undefined column, which is the good case: it stops rather than answering from something it half understands.

Skipping reindex fails silently, which is worse. Derived data that the new code reads differently is still the old data, and nothing errors. Concretely, on an upgrade to 0.5.3 the lexical corpus statistics are treated as stale until rebuilt, so BM25 stays off and your queries keep ranking by ts_rank while the setting reports as on. reindex rebuilds the embeddings and those statistics together. echo-memory health tells you afterwards.

Usage

echo-memory status                 # what each scope holds, and which agents have written
echo-memory health                 # a score, what is weak, and what to do about it
echo-memory dashboard --serve      # the graph, in a browser, localhost only

echo-memory why <fact_id>          # the full audit trail for one fact
echo-memory recall "<question>"    # query the store from a terminal
echo-memory export                 # everything, as JSON

echo-memory install --for cursor   # wire one client, project scoped
echo-memory adopt                  # wire every MCP client on the machine, each with its own id

echo-memory infer-causal-hints     # type the facts whose own sentence states a cause (dry run)

echo-memory eval                   # retrieval quality against your own store
echo-memory eval --context         # what a recall costs against injecting everything
echo-memory eval --context --sweep # the same, as a curve across corpus size
echo-memory eval-external locomo <path>       # LoCoMo, the corpus the published figures use
echo-memory eval-external longmemeval <path>  # LongMemEval, the same
echo-memory calibrate              # is entity resolution trustworthy on your data
echo-memory benchmark              # write, query and digest latency

The seven MCP tools

What you get

A graph, not a list. Entities are nodes and a fact is an edge between two of them. Two sessions that never knew about each other resolve onto the same entity by name, so the second inherits what the first learned.

Bounded retrieval, designed but not built. The plan is that old, rarely read memory demotes into higher level summaries over time, with nothing discarded and every summary still edged back to the facts it came from. None of it exists yet: there is no tiering, no summarisation, and retrieval today walks every active fact in the scope. It is described in docs/designs/ and listed below as v1c, and this paragraph used to claim it in the present tense.

A ranked answer that says why each fact is in it. Every returned fact carries score, its cosine similarity to the query, rank, its position, and matched, the channels that found it. score is deliberately not the fusion number: reciprocal rank values are sums of 1/(k+rank) and mean nothing from one query to the next, while a similarity means the same thing every time, which is what a caller thresholding on it needs. It is computed for every fact returned, including the ones only full text search found, because which channel retrieved a fact is an implementation detail and has no business reaching a field callers read as relevance.

The full text channel ranks by BM25. Postgres's ts_rank counts term occurrences and nothing else: no inverse document frequency, so a word in every fact of a scope counts as much as one in three; no saturation, so repetition scales linearly; no length normalisation, so a long fact is punished for being long. On 324 real prompts the rank 3 and rank 4 scores were identical in 59% of them, and reciprocal rank fusion reads rank position and never the score underneath it, so a channel ordered by scan order handed the fusion a coin flip.

BM25 is computed in SQL rather than installed, so no extension is added to your database. Corpus statistics are rebuilt amortised on write inside the transaction that already holds the scope's lock, and by echo-memory reindex; a scope that has none falls back to ts_rank rather than ranking on statistics it does not have. ECHO_MEMORY_LEXICAL_BM25=0 returns to ts_rank everywhere.

The history the store has always kept is readable. Every fact has carried t_valid and t_invalid since the first migration, and the read path had only ever asked whether a fact is current, so the store paid to keep the whole record of what a scope believed and could not answer the first question a post mortem asks. query_memory takes as_of in unix seconds and every channel honours it: vector, full text, the graph hop, the digest.

Supersession is what makes that more than a curiosity. Writing the same source, target and relation again does not edit the old fact, it ends it: the old edge takes a t_invalid and stays queryable. Nothing is ever rewritten, so the history is real rather than reconstructed, which is the property the memory reconsolidation literature gives up and the reason this project refuses that idea.

Provenance on every fact. Who wrote it, which tool, which project, when, and which reads returned it. A superseded fact is never deleted. It stops being drawn and stays reachable with its history.

Causal typing, and a walk over it. A fact can carry causal_hint: one of caused_by, led_to, enabled_by, blocked_by or contradicts, set by the agent's own read of what the session said and never inferred statistically. trace_cause walks those links and returns chains rather than a ranked list, because the answer to "why did this happen" is an ordered chain and similarity cannot produce one. "A led_to B" and "B caused_by A" are one claim written from opposite ends, and both assemble into the same chain.

A fact with no hint is associative, which is the default and usually correct. An empty answer from trace_cause says which kind of empty it is: nobody recorded a cause here is a different fact about a store than there are no chains.

Filling it in, for a store that predates it. Facts written before 0.5.0 carry no hint, so on an existing store trace_cause has nothing to walk. echo-memory infer-causal-hints re-reads the fact text already stored with your own model (ECHO_MEMORY_LLM_API_KEY, ECHO_MEMORY_LLM_MODEL) and types the edges whose own sentence states the relation. Dry run by default; --write applies what it printed and --clear --write takes it back.