---
title: "Your CrewAI Agents Forget Everything Between Runs. Here's the Fix."
authors: [benfrank241]

date: 2026-03-02
tags: [crewai, agents, python, memory, tutorial]
image: /img/blog/crewai-memory.png
---

CrewAI agents lose all memory when a crew finishes. `hindsight-crewai` plugs into CrewAI's ExternalMemory to persist knowledge across runs -- three lines of setup, and your agents automatically store task outputs and recall relevant context.

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---

## The Problem: Stateless Crews

CrewAI has a memory system. Short-term, long-term, entity memory. It works well within a single `kickoff()`.

Then the process exits.

Next run, the crew starts from zero. Every fact learned, every decision made, every entity discovered -- gone.

This matters when you build crews that run repeatedly:

- A research crew that deepens knowledge over time
- A support crew that remembers customer history
- A planning crew that tracks decisions across sprints

CrewAI's built-in memory backends (RAG storage, SQLite) are designed for single-run persistence. For cross-run, cross-session memory that actually compounds, you need something else.

That's what the `hindsight-crewai` package does. It implements CrewAI's `Storage` interface using Hindsight's memory engine, so your crews remember everything -- across runs, across days, across weeks.

---

## Architecture

Here's how it fits together:

```
CrewAI Crew
  └─ ExternalMemory
       └─ HindsightStorage (implements Storage interface)
            ├─ save()   → Hindsight retain (extract facts, entities, relationships)
            ├─ search() → Hindsight recall (semantic + graph + temporal retrieval)
            └─ reset()  → Hindsight delete_bank + recreate
```

CrewAI calls `save()` after each task completes and `search()` before each task starts. You don't manage the lifecycle -- CrewAI drives it, Hindsight stores it.

Under the hood, Hindsight does more than store text. It extracts structured facts, identifies entities, builds a knowledge graph, and runs multi-strategy retrieval (semantic search, BM25, graph traversal, temporal ranking) with cross-encoder reranking.

Your crew gets a real memory system, not a vector dump.

---

## Step 1 -- Start Hindsight

Install and start the memory server:

```bash
pip install hindsight-all
```

```bash
export HINDSIGHT_API_LLM_API_KEY=YOUR_OPENAI_KEY
hindsight-api
```

This runs locally at `http://localhost:8888` with embedded Postgres, embeddings, and reranking. No external infra needed.

> **Note:** You can also use [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup) and skip the self-hosted setup entirely.

---

## Step 2 -- Install the Integration

```bash
pip install hindsight-crewai
```

This pulls in `hindsight-client` and `crewai` as dependencies.

---

## Step 3 -- Wire It Up

```python
from hindsight_crewai import configure, HindsightStorage
from crewai.memory.external.external_memory import ExternalMemory
from crewai import Agent, Crew, Task

# Point at your Hindsight instance
configure(hindsight_api_url="http://localhost:8888")

# Create agents
researcher = Agent(
    role="Researcher",
    goal="Find accurate, detailed information on the given topic.",
    backstory="You are a thorough researcher who digs deep into topics.",
    llm="openai/gpt-4o-mini",
)

writer = Agent(
    role="Writer",
    goal="Write clear, well-structured content based on research.",
    backstory="You are a technical writer who values clarity and precision.",
    llm="openai/gpt-4o-mini",
)

# Create a task
research_task = Task(
    description="Research the benefits of Rust for CLI tools.",
    expected_output="A detailed summary of Rust's strengths for CLI development.",
    agent=researcher,
)

write_task = Task(
    description="Write a short article based on the research.",
    expected_output="A polished 3-paragraph article.",
    agent=writer,
)

# Create the crew with persistent memory
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    external_memory=ExternalMemory(
        storage=HindsightStorage(
            bank_id="research-crew",
            mission="Track research findings, technical comparisons, and writing preferences.",
        )
    ),
)

crew.kickoff()
```

That's it. After `kickoff()`, every task output is retained in Hindsight. Next time you run this crew, it recalls relevant prior work before starting each task.

---

## Step 4 -- Run It Again

Second run, different topic:

```python
research_task = Task(
    description="Research how Go compares to Rust for CLI tools.",
    expected_output="A comparison of Go vs Rust for CLI development.",
    agent=researcher,
)
```

Now the researcher has context from the first run. It knows what it already found about Rust. The writer remembers the style and structure from the previous article.

Third run:

```python
research_task = Task(
    description="Which language should I pick for a new CLI tool?",
    expected_output="A recommendation based on all prior research.",
    agent=researcher,
)
```

The crew now draws on two prior research sessions. Knowledge compounds.

---

## Step 5 -- Add Reflect for Deeper Synthesis

CrewAI's Storage interface has `save` and `search`. But Hindsight also supports `reflect` -- a synthesis operation that reasons across all relevant memories instead of returning raw facts.

Since `reflect` doesn't map to the Storage interface, it's exposed as a CrewAI Tool:

```python
from hindsight_crewai import HindsightReflectTool

reflect_tool = HindsightReflectTool(
    bank_id="research-crew",
    budget="mid",
    reflect_context="You are helping a development team evaluate programming languages.",
)

researcher = Agent(
    role="Researcher",
    goal="Provide deep, synthesized analysis on technical topics.",
    backstory="You are a senior researcher. Use the hindsight_reflect tool to review what you already know before starting new research.",
    tools=[reflect_tool],
    llm="openai/gpt-4o-mini",
)
```

When the agent calls `hindsight_reflect`, it gets a synthesized, reasoned response that draws on the full knowledge graph -- not just the top-k vector matches.

---

## Per-Agent Memory Banks

By default, all agents share one bank. If you want each agent to have isolated memory:

```python
storage = HindsightStorage(
    bank_id="research-crew",
    per_agent_banks=True,
)
```

The researcher writes to `research-crew-researcher`, the writer to `research-crew-writer`. Each agent builds its own knowledge base.

For full control, use a custom resolver:

```python
storage = HindsightStorage(
    bank_id="research-crew",
    bank_resolver=lambda base, agent: f"{base}-{agent.lower()}" if agent else base,
)
```

---

## Pitfalls and Edge Cases

**1. Bank ID collisions.** If multiple unrelated crews share a `bank_id`, their memories mix. Use unique bank IDs per crew or project.

**2. Large task outputs.** CrewAI passes the full task output to `save()`. If your tasks produce very long outputs, Hindsight handles the chunking, but retain latency increases. Set a reasonable `expected_output` length in your task definitions.

**3. Recall budget tuning.** The default `budget="mid"` balances speed and thoroughness. For latency-sensitive crews, use `"low"`. For deep analysis, use `"high"`. Budget affects how many retrieval strategies run and how much reranking happens.

**4. Async event loop conflicts.** CrewAI runs inside an async event loop. The integration handles this transparently via a dedicated thread pool, but if you're also doing async work in custom tools, avoid calling `hindsight-client` directly from the same event loop. Use the `HindsightStorage` and `HindsightReflectTool` abstractions instead.

---

## Recap

- `hindsight-crewai` gives CrewAI agents persistent, compounding memory
- It implements CrewAI's `Storage` interface, so integration is three lines
- Memories are automatically stored after tasks and recalled before tasks
- `HindsightReflectTool` adds on-demand synthesis for deeper reasoning
- Per-agent banks let you isolate or share knowledge as needed

The integration handles the hard parts: async compatibility, thread safety, fact extraction, multi-strategy retrieval. You just point it at a bank and let your crews learn.

---

## Next Steps

- **Try it locally**: `pip install hindsight-all hindsight-crewai` and run the example above
- **Use Hindsight Cloud**: Skip self-hosting with a [free account](https://ui.hindsight.vectorize.io/signup)
- **Add tags for scoped memory**: Use `tags` on retain and `recall_tags` on search to partition memories by project, environment, or topic
- **Inspect memories in the web UI**: Run `hindsight-control-plane` locally or use the cloud dashboard to browse facts, entities, and mental models
- **Combine with per-agent banks**: Give specialized agents their own memory while sharing a common bank for cross-agent knowledge
