Redis agent memory with redis-py

Build a Redis-backed agent memory layer in Python with redis-py, sentence-transformers, and standard Redis commands — working memory in a Hash, long-term semantic recall as JSON with a vector index, and an event log in a Stream.

This guide shows you how to build a small Redis-backed agent memory layer in Python with redis-py and the sentence-transformers library, using only standard Redis commands — no agent-memory SDK, no managed service. It includes a local web server built with the Python standard library so you can send turns at the agent, watch working memory update in place, see semantically similar long-term memories recalled in real time, watch the write-time deduplication skip near-duplicates, and inspect the per-thread event log.

Overview

The memory layer splits across three Redis primitives, each handling one tier:

• Working memory for the active session is a Hash at agent:session:<thread_id> holding the goal, scratchpad, a rolling window of recent turns (as a JSON list inside one field), and a few audit timestamps. One HGETALL returns the whole session in a single round trip; every write refreshes the key's EXPIRE so idle sessions decay on their own.
• Long-term memory is a set of JSON documents at agent:mem:<id>, each carrying the memory text, a 384-dimensional embedding vector, and tag fields for user, namespace, kind (episodic / semantic), and source thread. A single Redis Search index covers the HNSW vector field and every metadata field, so one FT.SEARCH call performs the KNN with the metadata pre-filter in the same round trip. Write-time deduplication runs the same KNN at insert time and skips a new memory whose nearest existing entry is within a tighter threshold.
• Event log for the agent's actions and observations is a Stream at agent:events:<thread_id>, appended with XADD MAXLEN ~ so retention stays bounded automatically, replayed with XREVRANGE.

That gives you:

• A single round trip per tier: one HGETALL for the session, one FT.SEARCH for recall, one XADD for the event log.
• Sub-millisecond reads on every step of the agent loop, so the memory layer doesn't dominate the per-step latency.
• Per-tier decay: short TTLs on working memory, longer on episodic memories, no TTL on semantic memories. Combined with a database-level eviction policy (LFU is the common choice), memory stays bounded under pressure.
• Scoping enforced inside the query: a recall query for user=alice will never see user=bob's memories, because the TAG filter goes into the same FT.SEARCH call as the KNN.

How it works

Each turn through the agent loop touches all three tiers in one pass: append to working memory, recall similar long-term memories, write the turn back as a new memory (with deduplication), and append one event to the log.

Per-turn flow

1. The application calls embedder.encode_one(text) to turn the incoming turn into a 384-dimensional float32 vector.
2. session.append_turn(thread_id, role, content) reads the per-thread Hash with HGETALL, appends the new turn to the rolling window in application code, trims it back to the configured maximum, and writes the Hash back with an HSET + EXPIRE pipeline. The session TTL refreshes on every write so an active thread stays alive.
3. memory.recall(vec, user=..., namespace=..., k=5) runs FT.SEARCH with a TAG pre-filter and a KNN 5 clause. Redis returns the closest matching memories together with their cosine distances; memories beyond the recall threshold are dropped before they reach the agent so an unrelated query doesn't surface confident-looking false positives.
4. memory.remember(text, vec, user=..., namespace=..., kind=...) runs the same KNN with a tighter dedup threshold. If an existing memory is within the threshold, the new write is skipped and the existing memory's hit_count is incremented with JSON.NUMINCRBY; otherwise a fresh JSON document is written with JSON.SET and a per-kind EXPIRE — episodic defaults to seven days, semantic has no TTL by default.
5. event_log.record(thread_id, action, detail) appends one entry to the per-thread Stream with XADD MAXLEN ~, bounding retention to roughly a thousand entries per thread without an explicit cleanup job.

The embedding is computed once and reused for steps 3 and 4 — there's no point encoding the same text twice. Recall runs before the write, so the agent doesn't see its own just-written turn echoed back as a recalled memory.

The session store

AgentSession wraps the working-memory Hash and the rolling turn window (source):

import redis
from session_store import AgentSession

r = redis.Redis(host="localhost", port=6379, decode_responses=False)
session = AgentSession(
    redis_client=r,
    key_prefix="agent:session:",
    default_ttl_seconds=3600,  # one hour
    max_turns=20,              # rolling window per thread
)

thread_id = session.new_thread_id()
session.start(thread_id, user="alice", agent="demo-agent",
              goal="Plan next week's meetings.")
session.append_turn(thread_id, role="user",
                    content="Schedule a budget review with finance.")
state = session.load(thread_id)
print(state.turn_count, len(state.recent_turns), state.ttl_seconds)

The data model is one Hash per thread. The rolling turn window is stored as a JSON string in a single field so the whole session loads in one HGETALL — the hash never grows in size or field count as the conversation goes on.

agent:session:9f3d2a4b8c61
  thread_id=9f3d2a4b8c61
  user=alice
  agent=demo-agent
  goal=Plan next week's meetings.
  scratchpad=Need to confirm finance's availability.
  turn_count=4
  created_ts=1715990400.12
  last_active_ts=1715990650.83
  recent_turns=[{"role":"user","content":"...","ts":...}, ...]

Every write — start, append_turn, set_scratchpad — runs the HSET and EXPIRE inside a MULTI / EXEC block, so a connection drop between the two writes can't leave the session without a TTL.

The long-term memory store

LongTermMemory owns the JSON documents, the vector index, the recall query, and the write-time deduplication (source):

import numpy as np
from long_term_memory import LongTermMemory
from embeddings import LocalEmbedder

memory = LongTermMemory(
    redis_client=r,
    index_name="agentmem:idx",
    key_prefix="agent:mem:",
    dedup_threshold=0.20,   # cosine distance — tight at write time
    recall_threshold=0.55,  # looser at read time
)
embedder = LocalEmbedder()
memory.create_index()  # idempotent

# Write a memory. The same KNN that powers recall also runs here
# at a tighter threshold so paraphrases of the same fact collapse.
vec = embedder.encode_one("The user prefers light mode in editors.")
result = memory.remember(
    text="The user prefers light mode in editors.",
    embedding=np.asarray(vec, dtype=np.float32),
    user="alice",
    namespace="default",
    kind="semantic",
    source_thread="9f3d2a4b8c61",
)
print(result.deduped, result.id, result.existing_distance)

# Recall against a later question.
q = embedder.encode_one("Which theme does this user like?")
hits = memory.recall(
    query_embedding=np.asarray(q, dtype=np.float32),
    user="alice",
    namespace="default",
    k=5,
)
for h in hits:
    print(f"{h.distance:.3f} [{h.kind}] {h.text}")

Data model

Each memory is a JSON document at agent:mem:<id>. The embedding is a JSON array of floats so the document is human-readable from redis-cli; FT.SEARCH still expects the query vector as raw float32 bytes, regardless of how the indexed document stores it.

agent:mem:7c3f8a1b9e02
{
  "id": "7c3f8a1b9e02",
  "user": "alice",
  "namespace": "default",
  "kind": "semantic",
  "source_thread": "9f3d2a4b8c61",
  "text": "The user prefers light mode in editors.",
  "embedding": [0.013, -0.041, ...],
  "created_ts": 1715990400.12,
  "hit_count": 0
}

The Redis Search index is declared on the JSON document type with as_name aliases so the query syntax stays compact:

FT.CREATE agentmem:idx
  ON JSON PREFIX 1 agent:mem:
  SCHEMA
    $.text          AS text          TEXT
    $.user          AS user          TAG
    $.namespace     AS namespace     TAG
    $.kind          AS kind          TAG
    $.source_thread AS source_thread TAG
    $.created_ts    AS created_ts    NUMERIC SORTABLE
    $.hit_count     AS hit_count     NUMERIC SORTABLE
    $.embedding     AS embedding     VECTOR HNSW 6
                                       TYPE FLOAT32 DIM 384
                                       DISTANCE_METRIC COSINE

The query

Both recall and dedup are the same hybrid query: a TAG pre-filter in parentheses followed by =>[KNN k @embedding $vec]. With DIALECT 2, Redis applies the filter first and KNN-ranks only the matching documents.

FT.SEARCH agentmem:idx
  "(@user:{alice} @namespace:{default} @kind:{semantic})
     =>[KNN 5 @embedding $vec AS distance]"
  PARAMS 2 vec <384-float32-bytes>
  SORTBY distance
  RETURN 8 user namespace kind source_thread text created_ts hit_count distance
  DIALECT 2

distance is the cosine distance (0 means identical, 2 means opposite). Recall and dedup share the same query shape; only the threshold differs — strict at write time so the index doesn't fill with paraphrases of the same fact, looser at read time so the agent gets a wider net of relevant memories.

Per-kind TTLs

remember resolves the entry's TTL from the memory's kind:

You can override per write with ttl_seconds=... on remember, or pass a different ttl_by_kind={...} map to the LongTermMemory constructor — for example, to give semantic memories a six-month TTL while leaving episodic memories at seven days.

The event log

AgentEventLog is a thin wrapper over a per-thread Redis Stream (source):

from event_log import AgentEventLog

events = AgentEventLog(redis_client=r, max_len=1000)
events.record(thread_id, action="turn_appended:user",
              detail="Schedule a budget review with finance.")
events.record(thread_id, action="memory_written",
              detail="wrote 7c3f8a1b9e02 as semantic")

for event in events.recent(thread_id, count=20):
    print(event.action, event.detail)

record calls XADD with maxlen=~1000. The tilde lets Redis trim in whole-node units instead of exactly-N units, which is much cheaper at the cost of overshooting the bound by up to a node's worth — the right tradeoff for an audit log where exact length doesn't matter.

The Stream is independent of the session Hash and the long-term JSON documents: it answers "what just happened" without competing with either of those for indexing or memory budget. Consumer groups (not used in this demo) would let downstream workers — summarizers, consolidators, audit pipelines — replay the log without losing position.

Concurrency caveats

The three helpers above trade correctness under heavy concurrency for clarity. Each is fine on a single-process demo, but lifting the code into a real multi-worker agent surfaces three races worth knowing about:

• Working memory is read-modify-write. AgentSession.append_turn calls HGETALL, mutates the recent_turns list in application code, and writes the Hash back with HSET. Two concurrent turns on the same thread can both read the same recent_turns, append different entries, and write back — last writer wins, the other turn is silently lost. The robust fix is either a WATCH / MULTI / EXEC loop around the read-modify-write or a small Lua script that does the append atomically server-side.

• Long-term dedup is not atomic. LongTermMemory.remember runs a FT.SEARCH KNN lookup, decides whether the candidate is a duplicate, and (if not) calls JSON.SET. Two workers seeing the same fact in flight can each fail to see the other's not-yet-committed write and both insert a new memory. The pragmatic fix is to accept that the index will occasionally hold near-duplicates and run a background consolidator that periodically scans for memory pairs within a tight distance and merges them, rather than trying to make the write itself atomic.

• The active thread is server state. The demo server keeps a single current_thread_id that /new_thread and /reset mutate under a lock; handle_turn reads it outside that lock, so a turn racing with a thread rotation can apply to the previous thread. This is cosmetic for a one-user browser demo. A multi-user agent would carry the thread id on the request itself rather than as shared server state.

Those caveats are deliberate. A more conservative implementation would obscure the Redis-shaped parts of the pattern; the demo prioritizes a small, readable code path that maps directly onto the commands in the prose above.

Pre-seeding long-term memory

In a real deployment the memory store fills up organically as the agent reasons over user turns: each turn produces zero or more memories that flow into the store, with deduplication catching repeats. For the demo, seed_memory.py pre-loads a small set of mixed semantic and episodic memories so the very first recall query returns something useful (source):

from seed_memory import seed
from long_term_memory import LongTermMemory
from embeddings import LocalEmbedder

memory = LongTermMemory()
embedder = LocalEmbedder()
memory.create_index()
seed(memory, embedder, user="default", namespace="default")

The seed list mixes long-lived facts and preferences (semantic) with snapshots of past sessions (episodic), so the Kind to write control in the demo has something to switch between when a new turn is being remembered.

The interactive demo

demo_server.py runs a ThreadingHTTPServer on port 8086. The HTML page exposes three live panels — working memory, recalled memories, event log — plus a memories table for admin actions. Endpoints:

The server holds one LocalEmbedder, one AgentSession, one LongTermMemory, and one AgentEventLog for the lifetime of the process. The "current thread" is a class attribute that the New thread button rotates — every browser session inherits the same thread until you explicitly start a new one.

Run the demo locally

1. Clone the redis/docs repository and change into the example directory:

   git clone https://github.com/redis/docs.git
   cd docs/content/develop/use-cases/agent-memory/redis-py
   

2. Install the dependencies:

   pip install redis sentence-transformers numpy
   

3. Make sure a Redis instance with Redis Search and Redis JSON is running locally on port 6379. Redis Stack ships both, or Redis 8 with the Search and JSON modules enabled.

4. Start the demo server. The first run downloads the all-MiniLM-L6-v2 model (~80 MB) into the local Hugging Face cache:

   python demo_server.py
   

5. Open http://localhost:8086 and try some turns:

   • "Remind me which theme I prefer in editors." — paraphrase of a seeded semantic memory ("The user dislikes dark mode and prefers a high-contrast light theme..."). You should see that memory recalled with a cosine distance around 0.47, comfortably under the 0.55 default recall threshold.
   • "What did we discuss about the order-routing outage?" — paraphrase of a seeded episodic memory; the postmortem memory should recall around 0.44. Switch the Kind to write dropdown to skip so the question itself doesn't enter long-term memory.
   • "I prefer concise answers without filler phrases." — paraphrase of a seeded semantic memory. Switch the Kind to write dropdown to semantic so the dedup KNN runs in the same kind as the seed (dedup is scoped per kind, on purpose, so an episodic write can't collapse onto a semantic memory). You should then see the write deduped onto the existing memory at a cosine distance around 0.15, with hit_count ticking up in the memories table.
   • "My favorite color is teal." — unrelated to any seed; nothing recalls above the threshold (every seed lands above 0.8), and the new memory is written as episodic (or semantic, depending on the dropdown) under a fresh id.
   • Switch the User field to bob and re-ask any of the above — recall returns nothing because the seed memories live under default. That's the TAG pre-filter at work inside FT.SEARCH.
   • Slide the Recall threshold down to 0.30 to see borderline paraphrases drop out of the recall set, then back up to 0.70 to watch them return.

   all-MiniLM-L6-v2 puts a faithful paraphrase in the 0.15 – 0.50 cosine-distance range, a loose paraphrase or related topic in the 0.50 – 0.80 range, and unrelated queries above 0.8 — which is what motivates the 0.55 default recall threshold and the 0.20 default dedup threshold. A stricter embedding model (or a domain-tuned one) would let you tighten both; a noisier one would push them up. The right thresholds are always a function of the model, the corpus, and how conservative the agent needs to be about accepting a memory as a match.

The server is read/write against your local Redis. The default memory index is agentmem:idx, JSON keys live under agent:mem:, session Hashes under agent:session:, and event Streams under agent:events:. Useful flags:

• --no-reset — keep the existing long-term memories across restarts instead of dropping and re-seeding.
• --session-ttl-seconds — change the working-memory TTL (default 3600).
• --dedup-threshold — change the cosine-distance cutoff for write-time deduplication.
• --recall-threshold — change the default cosine-distance cutoff for recall.