Memory - Crewai — ContextQMD

Overview

CrewAI provides a unified memory system -- a single Memory class that replaces separate short-term, long-term, entity, and external memory types with one intelligent API. Memory uses an LLM to analyze content when saving (inferring scope, categories, and importance) and supports adaptive-depth recall with composite scoring that blends semantic similarity, recency, and importance.

You can use memory four ways: standalone (scripts, notebooks), with Crews, with Agents, or inside Flows.

Quick Start

python

from crewai import Memory

memory = Memory()

# Store -- the LLM infers scope, categories, and importance
memory.remember("We decided to use PostgreSQL for the user database.")

# Retrieve -- results ranked by composite score (semantic + recency + importance)
matches = memory.recall("What database did we choose?")
for m in matches:
    print(f"[{m.score:.2f}] {m.record.content}")

# Tune scoring for a fast-moving project
memory = Memory(recency_weight=0.5, recency_half_life_days=7)

# Forget
memory.forget(scope="/project/old")

# Explore the self-organized scope tree
print(memory.tree())
print(memory.info("/"))

Four Ways to Use Memory

Standalone

Use memory in scripts, notebooks, CLI tools, or as a standalone knowledge base -- no agents or crews required.

python

from crewai import Memory

memory = Memory()

# Build up knowledge
memory.remember("The API rate limit is 1000 requests per minute.")
memory.remember("Our staging environment uses port 8080.")
memory.remember("The team agreed to use feature flags for all new releases.")

# Later, recall what you need
matches = memory.recall("What are our API limits?", limit=5)
for m in matches:
    print(f"[{m.score:.2f}] {m.record.content}")

# Extract atomic facts from a longer text
raw = """Meeting notes: We decided to migrate from MySQL to PostgreSQL
next quarter. The budget is $50k. Sarah will lead the migration."""

facts = memory.extract_memories(raw)
# ["Migration from MySQL to PostgreSQL planned for next quarter",
#  "Database migration budget is $50k",
#  "Sarah will lead the database migration"]

for fact in facts:
    memory.remember(fact)

With Crews

Pass memory=True for default settings, or pass a configured Memory instance for custom behavior.

python

from crewai import Crew, Agent, Task, Process, Memory

# Option 1: Default memory
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential,
    memory=True,
    verbose=True,
)

# Option 2: Custom memory with tuned scoring
memory = Memory(
    recency_weight=0.4,
    semantic_weight=0.4,
    importance_weight=0.2,
    recency_half_life_days=14,
)
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    memory=memory,
)

When memory=True, the crew creates a default Memory() and passes the crew's embedder configuration through automatically. All agents in the crew share the crew's memory unless an agent has its own.

After each task, the crew automatically extracts discrete facts from the task output and stores them. Before each task, the agent recalls relevant context from memory and injects it into the task prompt.

With Agents

Agents can use the crew's shared memory (default) or receive a scoped view for private context.

python

from crewai import Agent, Memory

memory = Memory()

# Researcher gets a private scope -- only sees /agent/researcher
researcher = Agent(
    role="Researcher",
    goal="Find and analyze information",
    backstory="Expert researcher with attention to detail",
    memory=memory.scope("/agent/researcher"),
)

# Writer uses crew shared memory (no agent-level memory set)
writer = Agent(
    role="Writer",
    goal="Produce clear, well-structured content",
    backstory="Experienced technical writer",
    # memory not set -- uses crew._memory when crew has memory enabled
)

This pattern gives the researcher private findings while the writer reads from the shared crew memory.

With Flows

Every Flow has built-in memory. Use self.remember(), self.recall(), and self.extract_memories() inside any flow method.

python

from crewai.flow.flow import Flow, listen, start

class ResearchFlow(Flow):
    @start()
    def gather_data(self):
        findings = "PostgreSQL handles 10k concurrent connections. MySQL caps at 5k."
        self.remember(findings, scope="/research/databases")
        return findings

    @listen(gather_data)
    def write_report(self, findings):
        # Recall past research to provide context
        past = self.recall("database performance benchmarks")
        context = "\n".join(f"- {m.record.content}" for m in past)
        return f"Report:\nNew findings: {findings}\nPrevious context:\n{context}"

See the Flows documentation for more on memory in Flows.

Hierarchical Scopes

What Scopes Are

Memories are organized into a hierarchical tree of scopes, similar to a filesystem. Each scope is a path like /, /project/alpha, or /agent/researcher/findings.

/
  /company
    /company/engineering
    /company/product
  /project
    /project/alpha
    /project/beta
  /agent
    /agent/researcher
    /agent/writer

Scopes provide context-dependent memory -- when you recall within a scope, you only search that branch of the tree, which improves both precision and performance.

How Scope Inference Works

When you call remember() without specifying a scope, the LLM analyzes the content and the existing scope tree, then suggests the best placement. If no existing scope fits, it creates a new one. Over time, the scope tree grows organically from the content itself -- you don't need to design a schema upfront.

python

memory = Memory()

# LLM infers scope from content
memory.remember("We chose PostgreSQL for the user database.")
# -> might be placed under /project/decisions or /engineering/database

# You can also specify scope explicitly
memory.remember("Sprint velocity is 42 points", scope="/team/metrics")

Visualizing the Scope Tree

python

print(memory.tree())
# / (15 records)
#   /project (8 records)
#     /project/alpha (5 records)
#     /project/beta (3 records)
#   /agent (7 records)
#     /agent/researcher (4 records)
#     /agent/writer (3 records)

print(memory.info("/project/alpha"))
# ScopeInfo(path='/project/alpha', record_count=5,
#           categories=['architecture', 'database'],
#           oldest_record=datetime(...), newest_record=datetime(...),
#           child_scopes=[])

MemoryScope: Subtree Views

A MemoryScope restricts all operations to a branch of the tree. The agent or code using it can only see and write within that subtree.

python

memory = Memory()

# Create a scope for a specific agent
agent_memory = memory.scope("/agent/researcher")

# Everything is relative to /agent/researcher
agent_memory.remember("Found three relevant papers on LLM memory.")
# -> stored under /agent/researcher

agent_memory.recall("relevant papers")
# -> searches only under /agent/researcher

# Narrow further with subscope
project_memory = agent_memory.subscope("project-alpha")
# -> /agent/researcher/project-alpha

Best Practices for Scope Design

Start flat, let the LLM organize. Don't over-engineer your scope hierarchy upfront. Begin with memory.remember(content) and let the LLM's scope inference create structure as content accumulates.
Use /{entity_type}/{identifier} patterns. Natural hierarchies emerge from patterns like /project/alpha, /agent/researcher, /company/engineering, /customer/acme-corp.
Scope by concern, not by data type. Use /project/alpha/decisions rather than /decisions/project/alpha. This keeps related content together.
Keep depth shallow (2-3 levels). Deeply nested scopes become too sparse. /project/alpha/architecture is good; /project/alpha/architecture/decisions/databases/postgresql is too deep.
Use explicit scopes when you know, let the LLM infer when you don't. If you're storing a known project decision, pass scope="/project/alpha/decisions". If you're storing freeform agent output, omit the scope and let the LLM figure it out.

Use Case Examples

Multi-project team:

python

memory = Memory()
# Each project gets its own branch
memory.remember("Using microservices architecture", scope="/project/alpha/architecture")
memory.remember("GraphQL API for client apps", scope="/project/beta/api")

# Recall across all projects
memory.recall("API design decisions")

# Or within a specific project
memory.recall("API design", scope="/project/beta")

Per-agent private context with shared knowledge:

python

memory = Memory()

# Researcher has private findings
researcher_memory = memory.scope("/agent/researcher")

# Writer can read from both its own scope and shared company knowledge
writer_view = memory.slice(
    scopes=["/agent/writer", "/company/knowledge"],
    read_only=True,
)

Customer support (per-customer context):

python

memory = Memory()

# Each customer gets isolated context
memory.remember("Prefers email communication", scope="/customer/acme-corp")
memory.remember("On enterprise plan, 50 seats", scope="/customer/acme-corp")

# Shared product docs are accessible to all agents
memory.remember("Rate limit is 1000 req/min on enterprise plan", scope="/product/docs")

Memory Slices

What Slices Are

A MemorySlice is a view across multiple, possibly disjoint scopes. Unlike a scope (which restricts to one subtree), a slice lets you recall from several branches simultaneously.

When to Use Slices vs Scopes

Scope: Use when an agent or code block should be restricted to a single subtree. Example: an agent that only sees /agent/researcher.
Slice: Use when you need to combine context from multiple branches. Example: an agent that reads from its own scope plus shared company knowledge.

Read-Only Slices

The most common pattern: give an agent read access to multiple branches without letting it write to shared areas.

python

memory = Memory()

# Agent can recall from its own scope AND company knowledge,
# but cannot write to company knowledge
agent_view = memory.slice(
    scopes=["/agent/researcher", "/company/knowledge"],
    read_only=True,
)

matches = agent_view.recall("company security policies", limit=5)
# Searches both /agent/researcher and /company/knowledge, merges and ranks results

agent_view.remember("new finding")  # Raises PermissionError (read-only)

Read-Write Slices

When read-only is disabled, you can write to any of the included scopes, but you must specify which scope explicitly.

python

view = memory.slice(scopes=["/team/alpha", "/team/beta"], read_only=False)

# Must specify scope when writing
view.remember("Cross-team decision", scope="/team/alpha", categories=["decisions"])

Composite Scoring

Recall results are ranked by a weighted combination of three signals:

composite = semantic_weight * similarity + recency_weight * decay + importance_weight * importance

Where:

similarity = 1 / (1 + distance) from the vector index (0 to 1)
decay = 0.5^(age_days / half_life_days) -- exponential decay (1.0 for today, 0.5 at half-life)
importance = the record's importance score (0 to 1), set at encoding time

Configure these directly on the Memory constructor:

python

# Sprint retrospective: favor recent memories, short half-life
memory = Memory(
    recency_weight=0.5,
    semantic_weight=0.3,
    importance_weight=0.2,
    recency_half_life_days=7,
)

# Architecture knowledge base: favor important memories, long half-life
memory = Memory(
    recency_weight=0.1,
    semantic_weight=0.5,
    importance_weight=0.4,
    recency_half_life_days=180,
)

Each MemoryMatch includes a match_reasons list so you can see why a result ranked where it did (e.g. ["semantic", "recency", "importance"]).

LLM Analysis Layer

Memory uses the LLM in three ways:

On save -- When you omit scope, categories, or importance, the LLM analyzes the content and suggests scope, categories, importance, and metadata (entities, dates, topics).
On recall -- For deep/auto recall, the LLM analyzes the query (keywords, time hints, suggested scopes, complexity) to guide retrieval.
Extract memories -- extract_memories(content) breaks raw text (e.g. task output) into discrete memory statements. Agents use this before calling remember() on each statement so that atomic facts are stored instead of one large blob.

All analysis degrades gracefully on LLM failure -- see Failure Behavior.

Memory Consolidation

When saving new content, the encoding pipeline automatically checks for similar existing records in storage. If the similarity is above consolidation_threshold (default 0.85), the LLM decides what to do:

keep -- The existing record is still accurate and not redundant.
update -- The existing record should be updated with new information (LLM provides the merged content).
delete -- The existing record is outdated, superseded, or contradicted.
insert_new -- Whether the new content should also be inserted as a separate record.

This prevents duplicates from accumulating. For example, if you save "CrewAI ensures reliable operation" three times, consolidation recognizes the duplicates and keeps only one record.

Intra-batch Dedup

When using remember_many(), items within the same batch are compared against each other before hitting storage. If two items have cosine similarity >= batch_dedup_threshold (default 0.98), the later one is silently dropped. This catches exact or near-exact duplicates within a single batch without any LLM calls (pure vector math).

python

# Only 2 records are stored (the third is a near-duplicate of the first)
memory.remember_many([
    "CrewAI supports complex workflows.",
    "Python is a great language.",
    "CrewAI supports complex workflows.",  # dropped by intra-batch dedup
])

Non-blocking Saves

remember_many() is non-blocking -- it submits the encoding pipeline to a background thread and returns immediately. This means the agent can continue to the next task while memories are being saved.

python

# Returns immediately -- save happens in background
memory.remember_many(["Fact A.", "Fact B.", "Fact C."])

# recall() automatically waits for pending saves before searching
matches = memory.recall("facts")  # sees all 3 records

Read Barrier

Every recall() call automatically calls drain_writes() before searching, ensuring the query always sees the latest persisted records. This is transparent -- you never need to think about it.

Crew Shutdown

When a crew finishes, kickoff() drains all pending memory saves in its finally block, so no saves are lost even if the crew completes while background saves are in flight.

Standalone Usage

For scripts or notebooks where there's no crew lifecycle, call drain_writes() or close() explicitly:

python

memory = Memory()
memory.remember_many(["Fact A.", "Fact B."])

# Option 1: Wait for pending saves
memory.drain_writes()

# Option 2: Drain and shut down the background pool
memory.close()

Source and Privacy

Every memory record can carry a source tag for provenance tracking and a private flag for access control.

Source Tracking

The source parameter identifies where a memory came from:

python

# Tag memories with their origin
memory.remember("User prefers dark mode", source="user:alice")
memory.remember("System config updated", source="admin")
memory.remember("Agent found a bug", source="agent:debugger")

# Recall only memories from a specific source
matches = memory.recall("user preferences", source="user:alice")

Private Memories

Private memories are only visible to recall when the source matches:

python

# Store a private memory
memory.remember("Alice's API key is sk-...", source="user:alice", private=True)

# This recall sees the private memory (source matches)
matches = memory.recall("API key", source="user:alice")

# This recall does NOT see it (different source)
matches = memory.recall("API key", source="user:bob")

# Admin access: see all private records regardless of source
matches = memory.recall("API key", include_private=True)

This is particularly useful in multi-user or enterprise deployments where different users' memories should be isolated.

RecallFlow (Deep Recall)

recall() supports two depths:

depth="shallow" -- Direct vector search with composite scoring. Fast (~200ms), no LLM calls.
depth="deep" (default) -- Runs a multi-step RecallFlow: query analysis, scope selection, parallel vector search, confidence-based routing, and optional recursive exploration when confidence is low.

Smart LLM skip: Queries shorter than query_analysis_threshold (default 200 characters) skip the LLM query analysis entirely, even in deep mode. Short queries like "What database do we use?" are already good search phrases -- the LLM analysis adds little value. This saves ~1-3s per recall for typical short queries. Only longer queries (e.g. full task descriptions) go through LLM distillation into targeted sub-queries.

python

# Shallow: pure vector search, no LLM
matches = memory.recall("What did we decide?", limit=10, depth="shallow")

# Deep (default): intelligent retrieval with LLM analysis for long queries
matches = memory.recall(
    "Summarize all architecture decisions from this quarter",
    limit=10,
    depth="deep",
)

The confidence thresholds that control the RecallFlow router are configurable:

python

memory = Memory(
    confidence_threshold_high=0.9,   # Only synthesize when very confident
    confidence_threshold_low=0.4,    # Explore deeper more aggressively
    exploration_budget=2,            # Allow up to 2 exploration rounds
    query_analysis_threshold=200,    # Skip LLM for queries shorter than this
)

Embedder Configuration

Memory needs an embedding model to convert text into vectors for semantic search. You can configure this in three ways.

Passing to Memory Directly

python

from crewai import Memory

# As a config dict
memory = Memory(embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}})

# As a pre-built callable
from crewai.rag.embeddings.factory import build_embedder
embedder = build_embedder({"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}})
memory = Memory(embedder=embedder)

Via Crew Embedder Config

When using memory=True, the crew's embedder config is passed through:

python

from crewai import Crew

crew = Crew(
    agents=[...],
    tasks=[...],
    memory=True,
    embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}},
)

Provider Examples

<AccordionGroup> <Accordion title="OpenAI (default)"> ```python memory = Memory(embedder={ "provider": "openai", "config": { "model_name": "text-embedding-3-small", # "api_key": "sk-...", # or set OPENAI_API_KEY env var }, }) ``` </Accordion> <Accordion title="Ollama (local, private)"> ```python memory = Memory(embedder={ "provider": "ollama", "config": { "model_name": "mxbai-embed-large", "url": "http://localhost:11434/api/embeddings", }, }) ``` </Accordion> <Accordion title="Azure OpenAI"> ```python memory = Memory(embedder={ "provider": "azure", "config": { "deployment_id": "your-embedding-deployment", "api_key": "your-azure-api-key", "api_base": "https://your-resource.openai.azure.com", "api_version": "2024-02-01", }, }) ``` </Accordion> <Accordion title="Google AI"> ```python memory = Memory(embedder={ "provider": "google-generativeai", "config": { "model_name": "gemini-embedding-001", # "api_key": "...", # or set GOOGLE_API_KEY env var }, }) ``` </Accordion> <Accordion title="Google Vertex AI"> ```python memory = Memory(embedder={ "provider": "google-vertex", "config": { "model_name": "gemini-embedding-001", "project_id": "your-gcp-project-id", "location": "us-central1", }, }) ``` </Accordion> <Accordion title="Cohere"> ```python memory = Memory(embedder={ "provider": "cohere", "config": { "model_name": "embed-english-v3.0", # "api_key": "...", # or set COHERE_API_KEY env var }, }) ``` </Accordion> <Accordion title="VoyageAI"> ```python memory = Memory(embedder={ "provider": "voyageai", "config": { "model": "voyage-3", # "api_key": "...", # or set VOYAGE_API_KEY env var }, }) ``` </Accordion> <Accordion title="AWS Bedrock"> ```python memory = Memory(embedder={ "provider": "amazon-bedrock", "config": { "model_name": "amazon.titan-embed-text-v1", # Uses default AWS credentials (boto3 session) }, }) ``` </Accordion> <Accordion title="Hugging Face"> ```python memory = Memory(embedder={ "provider": "huggingface", "config": { "model_name": "sentence-transformers/all-MiniLM-L6-v2", }, }) ``` </Accordion> <Accordion title="Jina"> ```python memory = Memory(embedder={ "provider": "jina", "config": { "model_name": "jina-embeddings-v2-base-en", # "api_key": "...", # or set JINA_API_KEY env var }, }) ``` </Accordion> <Accordion title="IBM WatsonX"> ```python memory = Memory(embedder={ "provider": "watsonx", "config": { "model_id": "ibm/slate-30m-english-rtrvr", "api_key": "your-watsonx-api-key", "project_id": "your-project-id", "url": "https://us-south.ml.cloud.ibm.com", }, }) ``` </Accordion> <Accordion title="Custom Embedder"> ```python # Pass any callable that takes a list of strings and returns a list of vectors def my_embedder(texts: list[str]) -> list[list[float]]: # Your embedding logic here return [[0.1, 0.2, ...] for _ in texts]

memory = Memory(embedder=my_embedder)

</Accordion>
</AccordionGroup>

### Provider Reference

| Provider | Key | Typical Model | Notes |
| :--- | :--- | :--- | :--- |
| OpenAI | `openai` | `text-embedding-3-small` | Default. Set `OPENAI_API_KEY`. |
| Ollama | `ollama` | `mxbai-embed-large` | Local, no API key needed. |
| Azure OpenAI | `azure` | `text-embedding-ada-002` | Requires `deployment_id`. |
| Google AI | `google-generativeai` | `gemini-embedding-001` | Set `GOOGLE_API_KEY`. |
| Google Vertex | `google-vertex` | `gemini-embedding-001` | Requires `project_id`. |
| Cohere | `cohere` | `embed-english-v3.0` | Strong multilingual support. |
| VoyageAI | `voyageai` | `voyage-3` | Optimized for retrieval. |
| AWS Bedrock | `amazon-bedrock` | `amazon.titan-embed-text-v1` | Uses boto3 credentials. |
| Hugging Face | `huggingface` | `all-MiniLM-L6-v2` | Local sentence-transformers. |
| Jina | `jina` | `jina-embeddings-v2-base-en` | Set `JINA_API_KEY`. |
| IBM WatsonX | `watsonx` | `ibm/slate-30m-english-rtrvr` | Requires `project_id`. |
| Sentence Transformer | `sentence-transformer` | `all-MiniLM-L6-v2` | Local, no API key. |
| Custom | `custom` | -- | Requires `embedding_callable`. |


## LLM Configuration

Memory uses an LLM for save analysis (scope, categories, importance inference), consolidation decisions, and deep recall query analysis. You can configure which model to use.

```python
from crewai import Memory, LLM

# Default: gpt-4o-mini
memory = Memory()

# Use a different OpenAI model
memory = Memory(llm="gpt-4o")

# Use Anthropic
memory = Memory(llm="anthropic/claude-3-haiku-20240307")

# Use Ollama for fully local/private analysis
memory = Memory(llm="ollama/llama3.2")

# Use Google Gemini
memory = Memory(llm="gemini/gemini-2.0-flash")

# Pass a pre-configured LLM instance with custom settings
llm = LLM(model="gpt-4o", temperature=0)
memory = Memory(llm=llm)

The LLM is initialized lazily -- it's only created when first needed. This means Memory() never fails at construction time, even if API keys aren't set. Errors only surface when the LLM is actually called (e.g. when saving without explicit scope/categories, or during deep recall).

For fully offline/private operation, use a local model for both the LLM and embedder:

python

memory = Memory(
    llm="ollama/llama3.2",
    embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}},
)

Storage Backend

Default: LanceDB, stored under ./.crewai/memory (or $CREWAI_STORAGE_DIR/memory if the env var is set, or the path you pass as storage="path/to/dir").
Custom backend: Implement the StorageBackend protocol (see crewai.memory.storage.backend) and pass an instance to Memory(storage=your_backend).

Discovery

Inspect the scope hierarchy, categories, and records:

python

memory.tree()                        # Formatted tree of scopes and record counts
memory.tree("/project", max_depth=2) # Subtree view
memory.info("/project")              # ScopeInfo: record_count, categories, oldest/newest
memory.list_scopes("/")              # Immediate child scopes
memory.list_categories()             # Category names and counts
memory.list_records(scope="/project/alpha", limit=20)  # Records in a scope, newest first

Failure Behavior

If the LLM fails during analysis (network error, rate limit, invalid response), memory degrades gracefully:

Save analysis -- A warning is logged and the memory is still stored with default scope /, empty categories, and importance 0.5.
Extract memories -- The full content is stored as a single memory so nothing is dropped.
Query analysis -- Recall falls back to simple scope selection and vector search so you still get results.

No exception is raised for these analysis failures; only storage or embedder failures will raise.

Privacy Note

Memory content is sent to the configured LLM for analysis (scope/categories/importance on save, query analysis and optional deep recall). For sensitive data, use a local LLM (e.g. Ollama) or ensure your provider meets your compliance requirements.

Memory Events

All memory operations emit events with source_type="unified_memory". You can listen for timing, errors, and content.

Event	Description	Key Properties
MemoryQueryStartedEvent	Query begins	`query`, `limit`
MemoryQueryCompletedEvent	Query succeeds	`query`, `results`, `query_time_ms`
MemoryQueryFailedEvent	Query fails	`query`, `error`
MemorySaveStartedEvent	Save begins	`value`, `metadata`
MemorySaveCompletedEvent	Save succeeds	`value`, `save_time_ms`
MemorySaveFailedEvent	Save fails	`value`, `error`
MemoryRetrievalStartedEvent	Agent retrieval starts	`task_id`
MemoryRetrievalCompletedEvent	Agent retrieval done	`task_id`, `memory_content`, `retrieval_time_ms`

Example: monitor query time:

python

from crewai.events import BaseEventListener, MemoryQueryCompletedEvent

class MemoryMonitor(BaseEventListener):
    def setup_listeners(self, crewai_event_bus):
        @crewai_event_bus.on(MemoryQueryCompletedEvent)
        def on_done(source, event):
            if getattr(event, "source_type", None) == "unified_memory":
                print(f"Query '{event.query}' completed in {event.query_time_ms:.0f}ms")

Troubleshooting

Memory not persisting?

Ensure the storage path is writable (default ./.crewai/memory). Pass storage="./your_path" to use a different directory, or set the CREWAI_STORAGE_DIR environment variable.
When using a crew, confirm memory=True or memory=Memory(...) is set.

Slow recall?

Use depth="shallow" for routine agent context. Reserve depth="deep" for complex queries.
Increase query_analysis_threshold to skip LLM analysis for more queries.

LLM analysis errors in logs?

Memory still saves/recalls with safe defaults. Check API keys, rate limits, and model availability if you want full LLM analysis.

Background save errors in logs?

Memory saves run in a background thread. Errors are emitted as MemorySaveFailedEvent but don't crash the agent. Check logs for the root cause (usually LLM or embedder connection issues).

Concurrent write conflicts?

LanceDB operations are serialized with a shared lock and retried automatically on conflict. This handles multiple Memory instances pointing at the same database (e.g. agent memory + crew memory). No action needed.

Browse memory from the terminal:

bash

crewai memory                              # Opens the TUI browser
crewai memory --storage-path ./my_memory   # Point to a specific directory

Reset memory (e.g. for tests):

python

crew.reset_memories(command_type="memory")  # Resets unified memory
# Or on a Memory instance:
memory.reset()                    # All scopes
memory.reset(scope="/project/old")  # Only that subtree

Configuration Reference

All configuration is passed as keyword arguments to Memory(...). Every parameter has a sensible default.

Parameter	Default	Description
`llm`	`"gpt-4o-mini"`	LLM for analysis (model name or `BaseLLM` instance).
`storage`	`"lancedb"`	Storage backend (`"lancedb"`, a path string, or a `StorageBackend` instance).
`embedder`	`None` (OpenAI default)	Embedder (config dict, callable, or `None` for default OpenAI).
`recency_weight`	`0.3`	Weight for recency in composite score.
`semantic_weight`	`0.5`	Weight for semantic similarity in composite score.
`importance_weight`	`0.2`	Weight for importance in composite score.
`recency_half_life_days`	`30`	Days for recency score to halve (exponential decay).
`consolidation_threshold`	`0.85`	Similarity above which consolidation is triggered on save. Set to `1.0` to disable.
`consolidation_limit`	`5`	Max existing records to compare during consolidation.
`default_importance`	`0.5`	Importance assigned when not provided and LLM analysis is skipped.
`batch_dedup_threshold`	`0.98`	Cosine similarity for dropping near-duplicates within a `remember_many()` batch.
`confidence_threshold_high`	`0.8`	Recall confidence above which results are returned directly.
`confidence_threshold_low`	`0.5`	Recall confidence below which deeper exploration is triggered.
`complex_query_threshold`	`0.7`	For complex queries, explore deeper below this confidence.
`exploration_budget`	`1`	Number of LLM-driven exploration rounds during deep recall.
`query_analysis_threshold`	`200`	Queries shorter than this (in characters) skip LLM analysis during deep recall.