AI Agent Persistent Memory: LangChain, AutoGen & CrewAI that Actually Remember

The stateless agent problem

LangChain, AutoGen, and CrewAI are brilliant at reasoning. But they all share a fatal flaw: agents are stateless by default.

Run a 10-step CrewAI workflow on Monday. Run it again on Tuesday. Your agents start from zero. They'll make the same mistakes, ask the same clarifying questions, and rediscover the same solutions — again.

The typical workaround is to dump everything into the context window. That costs tokens. It exceeds context limits. It's not searchable. And it doesn't work across different agent runs or different team members' agents.

What you need is a persistent, queryable memory layer — separate from the context window, shared across runs, and fast enough to not be a bottleneck.

Cachly Agent Memory: how it works

Cachly provides three memory primitives for AI agents:

• Key-Value Memory — store and retrieve any structured data by key. Persists across runs, restarts, and scale-out. Used for agent state, intermediate results, and workflow checkpoints.
• Semantic Memory — store facts by content, retrieve by meaning. No exact key needed. CachlySemanticMemory uses pgvector for similarity search. Works offline with local embeddings via Ollama.
• Chat History — persistent message store with TTL and session isolation. Drop-in replacement for ConversationBufferWindowMemory in LangChain.

LangChain integration

3 lines to replace the default in-memory store:

from cachly_agents import CachlyMemory, CachlySemanticMemory
from langchain.agents import initialize_agent

# Persistent chat history — survives restarts and scale-out
memory = CachlyMemory(
    cachly_url=os.environ["CACHLY_URL"],
    session_id="user-42",
    ttl=86400 * 7,  # 7 days
)

# Semantic memory — recall by meaning, not key
semantic = CachlySemanticMemory(
    cachly_url=os.environ["CACHLY_URL"],
    namespace="product-knowledge",
    embed_fn=openai_embed,  # or None for free Ollama
)

# Store a fact
semantic.store("Our refund policy is 30 days, no questions asked")

# Recall by meaning in any future run
facts = semantic.recall("what is the return policy?", top_k=3)

# Standard LangChain agent with persistent memory
agent = initialize_agent(
    tools=tools,
    llm=llm,
    memory=memory,
    agent="conversational-react-description",
)

CrewAI: shared crew knowledge base

CrewAI agents can share a knowledge base — every researcher agent stores findings, every analyst reads them, every writer synthesizes them. Across runs. Across crew restarts. Across different deployments.

from cachly_agents import CachlyCrewMemory

memory = CachlyCrewMemory(
    cachly_url=os.environ["CACHLY_URL"],
    crew_id="market-research-crew",
)

# Researcher agent stores findings
memory.store(
    key="competitor:acme:pricing",
    value={"price": 99, "tier": "pro", "updated": "2026-04-28"},
    tags=["competitor", "pricing"],
)

# Analyst agent reads across runs
findings = memory.search_by_tag("pricing")
snapshot = memory.get_snapshot()  # full crew context

# Writer agent recalls by meaning
relevant = memory.semantic_recall(
    "What do we know about competitor pricing?"
)

AutoGen: multi-agent memory with TTL

AutoGen's conversation model means agents share context through the message thread. But that context disappears when the conversation ends. Cachly adds a durable memory layer that persists the important parts:

from cachly_agents import CachlyAutoGenMemory
from autogen import AssistantAgent, UserProxyAgent

memory = CachlyAutoGenMemory(
    cachly_url=os.environ["CACHLY_URL"],
    conversation_id="project-x-planning",
)

# Inject memory into system prompts
assistant = AssistantAgent(
    name="planner",
    system_message=(
        "You are a senior engineer. "
        + memory.get_context_prompt(top_k=10)
    ),
    llm_config=llm_config,
)

# After each conversation, store key decisions
memory.store_decisions(conversation_result)  # auto-extracted

Performance: why Valkey beats a vector DB

Most persistent memory solutions for agents use a vector database (Pinecone, Weaviate, Qdrant) for semantic search. That adds a network hop, a separate service to manage, and $30–200/month in additional costs.

Cachly stores semantic vectors in pgvector alongside your structured data. The semantic search endpoint has p99 latency of 3–8ms — fast enough to use on every agent turn without adding noticeable latency. For non-semantic lookups, Valkey delivers sub-millisecond reads.

For offline or air-gapped deployments, set CACHLY_EMBED_PROVIDER=ollama and point it at your local Llama model. Zero external API calls, full semantic search capability.

Get started

pip install cachly-agents

# Then set:
# CACHLY_URL=redis://:token@host:port

# Optional semantic search:
# CACHLY_VECTOR_URL=https://api.cachly.dev/v1/sem/token
# CACHLY_EMBED_PROVIDER=openai   # or mistral, cohere, gemini, ollama

Free tier: 25 MB, 1 instance, no credit card. That's enough for a full CrewAI research workflow with semantic memory across dozens of runs.