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+---
+title: "Give Your OpenAI App a Memory in 5 Minutes"
+authors: [hindsight]
+date: 2026-03-05
+tags: [memory, openai, python, docker, rag, llm, vector, embedding]
+image: /img/blog/add-memory-to-openai-application.png
+hide_table_of_contents: true
+---
+
+Build a ChatGPT-style chatbot with persistent memory using the OpenAI SDK and Hindsight. Three API calls — `retain()`, `recall()`, `reflect()` — and your app remembers users across restarts, no vector database or RAG pipeline required.
+
+<!-- truncate -->
+
+## TL;DR
+
+You'll build a ChatGPT-style chatbot that:
+
+- Remembers facts across restarts
+- Recalls relevant context automatically
+- Synthesizes long-term knowledge on demand
+
+All with three API calls:
+
+- `retain()` — store memories
+- `recall()` — retrieve relevant ones
+- `reflect()` — synthesize across everything
+
+No vector database. No embedding pipeline. No RAG plumbing.
+
+Copy, paste, run.
+
+---
+
+## The Problem: Your Chatbot Has Amnesia
+
+You build a chatbot with OpenAI:
+
+```python
+messages = [{"role": "system", "content": "You are a helpful assistant."}]
+```
+
+It works perfectly.
+
+Until you restart the process.
+
+Now it remembers nothing.
+
+You can serialize `messages` to disk. But then:
+
+- Context windows fill up
+- Token costs explode
+- You start truncating history
+- The assistant forgets early decisions
+
+We ran into this building Jerri, our internal AI project manager at Vectorize.
+
+Jerri lives in Slack. It ingests meeting transcripts, tracks action items, and answers "what did we decide about X?" Without persistent memory, every session started from zero.
+
+That's not memory. That's stateless autocomplete.
+
+What you actually need:
+
+1. Store facts as they happen
+2. Retrieve only what's relevant
+3. Synthesize when necessary
+
+That's what we're building.
+
+---
+
+## Architecture
+
+Here's the entire loop:
+
+```
+User message
+     ↓
+recall(query)      ← pull relevant memories
+     ↓
+OpenAI completion  ← inject memory into system prompt
+     ↓
+retain(exchange)   ← store conversation
+     ↓
+Response
+```
+
+Three calls. Same pattern Jerri runs in production.
+
+---
+
+## Step 1 — Start the Memory Layer
+
+Install:
+
+```bash
+pip install hindsight-all
+```
+
+Start the server:
+
+```bash
+export HINDSIGHT_API_LLM_API_KEY=YOUR_OPENAI_KEY
+
+hindsight-api
+```
+
+It runs locally at `http://localhost:8888`.
+
+It includes:
+
+- Embedded Postgres
+- Fact extraction
+- Semantic search
+- Knowledge graph
+- Synthesis engine
+
+No external infrastructure.
+
+> **Prefer not to self-host?** [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup) gives you the same API with no setup — just swap `base_url` for your Cloud endpoint.
+
+---
+
+## Step 2 — Baseline Chat (No Memory)
+
+Let's start with the broken version:
+
+```python
+from openai import OpenAI
+
+openai = OpenAI()
+messages = [{"role": "system", "content": "You are a helpful assistant."}]
+
+while True:
+    user_input = input("You: ")
+    if user_input in ("quit", "exit"):
+        break
+
+    messages.append({"role": "user", "content": user_input})
+
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages,
+    )
+
+    reply = response.choices[0].message.content
+    messages.append({"role": "assistant", "content": reply})
+
+    print(reply)
+```
+
+Works great.
+
+Restart it.
+
+Ask: "What's my name?"
+
+Blank stare.
+
+---
+
+## Step 3 — Add `retain()`
+
+Create a memory bank:
+
+```python
+from hindsight_client import Hindsight
+
+hindsight = Hindsight(base_url="http://localhost:8888")
+
+hindsight.create_bank(
+    bank_id="chatbot",
+    name="Chatbot Memory",
+    reflect_mission="Remember user preferences and important facts.",
+)
+```
+
+Now retain every exchange:
+
+```python
+hindsight.retain(
+    bank_id="chatbot",
+    content=f"User: {user_input}\nAssistant: {reply}",
+)
+```
+
+That's it.
+
+Hindsight extracts facts, identifies entities, builds relationships, and stores them in a knowledge graph. You don't manage any of that.
+
+---
+
+## Step 4 — Add `recall()`
+
+Before calling OpenAI, retrieve relevant memories:
+
+```python
+memories = hindsight.recall(
+    bank_id="chatbot",
+    query=user_input,
+    budget="low",
+)
+
+memory_context = "\n".join(r.text for r in memories.results)
+```
+
+Inject into the system prompt:
+
+```python
+system_prompt = "You are a helpful assistant."
+
+if memory_context:
+    system_prompt += "\n\nRelevant past context:\n" + memory_context
+```
+
+Now:
+
+1. Tell it your name
+2. Restart
+3. Ask again
+
+It remembers. Because `recall` injects relevant past facts into the prompt.
+
+---
+
+## Step 5 — Add `reflect()` for Synthesis
+
+`recall` returns facts. `reflect` returns reasoning.
+
+For questions like:
+
+- "What do you know about me?"
+- "Summarize our conversations."
+- "What patterns do you see?"
+
+Use reflect:
+
+```python
+reflection = hindsight.reflect(
+    bank_id="chatbot",
+    query=user_input,
+)
+
+memory_context = reflection.text
+```
+
+Reflect traverses the knowledge graph, runs an LLM reasoning chain, and synthesizes across memories.
+
+In Jerri, reflect powers weekly summaries, sprint reviews, and cross-meeting analysis. In your chatbot, it handles "step back and think" queries.
+
+---
+
+## Full Working Example
+
+Copy this into `chat.py`:
+
+```python
+from openai import OpenAI
+from hindsight_client import Hindsight
+
+openai = OpenAI()
+hindsight = Hindsight(base_url="http://localhost:8888")
+
+hindsight.create_bank(
+    bank_id="chatbot",
+    name="Chatbot Memory",
+    reflect_mission="Remember user preferences and key facts.",
+)
+
+SYSTEM_PROMPT = "You are a helpful assistant with long-term memory."
+
+SYNTHESIS_KEYWORDS = [
+    "summarize",
+    "what do you know about me",
+    "what have we talked about",
+]
+
+
+def get_memory_context(user_input):
+    if any(k in user_input.lower() for k in SYNTHESIS_KEYWORDS):
+        reflection = hindsight.reflect(
+            bank_id="chatbot",
+            query=user_input,
+        )
+        return reflection.text
+
+    memories = hindsight.recall(
+        bank_id="chatbot",
+        query=user_input,
+        budget="low",
+    )
+    return "\n".join(r.text for r in memories.results)
+
+
+def main():
+    conversation = []
+
+    print("Chat with memory. Type 'quit' to exit.\n")
+
+    while True:
+        user_input = input("You: ")
+        if user_input in ("quit", "exit"):
+            break
+
+        memory_context = get_memory_context(user_input)
+
+        conversation.append({"role": "user", "content": user_input})
+
+        system = SYSTEM_PROMPT
+        if memory_context:
+            system += "\n\nRelevant context:\n" + memory_context
+
+        messages = [{"role": "system", "content": system}] + conversation
+
+        response = openai.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages,
+        )
+
+        reply = response.choices[0].message.content
+        conversation.append({"role": "assistant", "content": reply})
+
+        print(f"\nAssistant: {reply}\n")
+
+        hindsight.retain(
+            bank_id="chatbot",
+            content=f"User: {user_input}\nAssistant: {reply}",
+        )
+
+
+if __name__ == "__main__":
+    main()
+```
+
+Run:
+
+```bash
+export OPENAI_API_KEY=YOUR_KEY
+python chat.py
+```
+
+Restart it. It still remembers.
+
+---
+
+## Production Lessons (From Building Jerri)
+
+**1. Retain after responding.** Otherwise the assistant remembers questions but not answers.
+
+**2. Use `budget="low"` for chat loops.** Sub-second latency. Upgrade only when needed.
+
+**3. One bank per user in multi-user apps.** Otherwise memories leak across users.
+
+**4. Set a mission.** Fact extraction quality depends heavily on it.
+
+**5. Start by retaining everything.** Optimize later.
+
+---
+
+## When to Use This Pattern
+
+**Use it if:**
+
+- You need cross-session memory
+- You want synthesis across time
+- You don't want to build RAG infra
+
+**Don't use it if:**
+
+- You only need single-session context
+- You're storing structured database records
+
+This solves the space between "chat history" and "knowledge base."
+
+---
+
+## The Pattern to Remember
+
+- `retain` — after responding
+- `recall` — before responding
+- `reflect` — when synthesizing
+
+That's the loop.
+
+That's what powers Jerri across weeks of meetings.
+
+That's what you just built in 15 minutes.
+
+---
+
+## Next Steps
+
+- **Add per-user banks** with unique `bank_id` per user
+- **Use tags** for scoped memory (`tags` on retain, `tags_match` on recall)
+- **Add structured JSON output** to reflect with `response_schema`
+- **Inspect memories in the web UI** at `localhost:9999` via Docker
+- **Try [hosted Hindsight](https://ui.hindsight.vectorize.io/signup)** instead of self-hosting
+
+Persistent memory turns a chatbot into an agent.
+
+Now yours remembers.
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