| name | dejavu |
| description | Deja Vu Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "dejavu", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (dejavu-memory), TypeScript SDK (dejavu-memory), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT dejavu skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use dejavu-cli), or Vercel AI SDK / @dejavu/vercel-ai-provider / createDeja Vu (use dejavu-vercel-ai-sdk).
|
| license | Apache-2.0 |
| metadata | {"author":"dejavu-memory","version":"3.0.0","category":"ai-memory","tags":"memory, personalization, ai, python, typescript, vector-search"} |
| compatibility | Requires Python 3.10+ or Node.js 18+, pip install dejavu-memory or npm install dejavu-memory, VENICE_API_KEY env var (Platform), and internet access to api.dejavu.ai. SDK v3 with v2 compatibility mode available. |
Deja Vu Platform Integration
Skill Graph: This skill is part of the Deja Vu skill graph:
Deja Vu is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
Step 1: Install and authenticate
Python:
pip install dejavu-memory
export VENICE_API_KEY="m0-your-api-key"
TypeScript/JavaScript:
npm install dejavu-memory
export VENICE_API_KEY="m0-your-api-key"
Get an API key at: https://app.dejavu.ai/dashboard/api-keys?utm_source=oss&utm_medium=skill-dejavu
Don't have a VENICE_API_KEY? Run dejavu init --agent --agent-caller <your-name> --json (after pip install dejavu-cli or npm install -g @dejavu/cli), substituting your agent identity (e.g. claude-code, cursor). If you forgot to pass --agent-caller, run dejavu identify <your-name> after init. The human can claim later with dejavu init --email <your-email>.
Step 2: Initialize the client
Python:
from dejavu import MemoryClient
client = MemoryClient(api_key="m0-xxx")
TypeScript:
import MemoryClient from 'dejavu-memory';
const client = new MemoryClient({ apiKey: 'm0-xxx' });
For async Python, use AsyncMemoryClient.
Step 3: Core operations
Every Deja Vu integration follows the same pattern: retrieve → generate → store.
Add memories
messages = [
{"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
{"role": "assistant", "content": "Got it! I'll remember that."}
]
client.add(messages, user_id="alice")
Search memories
results = client.search("dietary preferences", filters={"user_id": "alice"})
for mem in results.get("results", []):
print(mem["memory"])
Get all memories
all_memories = client.get_all(filters={"user_id": "alice"})
Update a memory
client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic")
Delete a memory
client.delete("memory-uuid")
client.delete_all(user_id="alice")
Common integration pattern
from dejavu import MemoryClient
from openai import OpenAI
dejavu = MemoryClient()
openai = OpenAI()
def chat(user_input: str, user_id: str) -> str:
memories = dejavu.search(user_input, filters={"user_id": user_id})
context = "\n".join([m["memory"] for m in memories.get("results", [])])
response = openai.chat.completions.create(
model="gpt-5-mini",
messages=[
{"role": "system", "content": f"User context:\n{context}"},
{"role": "user", "content": user_input},
]
)
reply = response.choices[0].message.content
dejavu.add(
[{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
user_id=user_id
)
return reply
Common edge cases
- Search returns empty: Memories process asynchronously. Wait 2-3s after
add() before searching. Also verify user_id matches exactly (case-sensitive) and use filters={"user_id": "..."} syntax.
- AND filter with user_id + agent_id returns empty: Entities are stored separately. Use
OR instead, or query separately.
- Duplicate memories: Don't mix
infer=True (default) and infer=False for the same data. Stick to one mode.
- Wrong import: Always use
from dejavu import MemoryClient (or AsyncMemoryClient for async). Do not use from dejavu import Memory.
- v3 defaults:
top_k=20, threshold=0.1, rerank=False. Adjust as needed for your use case.
v2 Compatibility
If you're using SDK v2.x, note these differences:
- Entity IDs: Pass
user_id as top-level kwarg to search() instead of inside filters
- Defaults:
top_k=100, no threshold, rerank=True
- Graph memory: Available via
enable_graph=True
See the migration guide for details.
Live documentation search
For the latest docs beyond what's in the references, use the doc search tool:
python ${CLAUDE_SKILL_DIR}/scripts/dejavu_doc_search.py --query "topic"
python ${CLAUDE_SKILL_DIR}/scripts/dejavu_doc_search.py --page "/platform/features/graph-memory"
python ${CLAUDE_SKILL_DIR}/scripts/dejavu_doc_search.py --index
No API key needed — searches docs.dejavu.ai directly.
Client SDK References
Language-specific deep references (Platform + OSS):
Platform References
Load these on demand for deeper detail:
| Topic | File |
|---|
| Quickstart (Python, TS, cURL) | references/quickstart.md |
| SDK guide (all methods, both languages) | references/sdk-guide.md |
| API reference (endpoints, filters, object schema) | references/api-reference.md |
| Architecture (pipeline, lifecycle, scoping, performance) | references/architecture.md |
| Platform features (retrieval, graph, categories, MCP, etc.) | references/features.md |
| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | references/integration-patterns.md |
| Use cases & examples (real-world patterns with code) | references/use-cases.md |
Related Deja Vu Skills
| Skill | When to use | Link |
|---|
| dejavu-cli | Terminal commands, scripting, CI/CD, agent tool loops | local / GitHub |
| dejavu-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | local / GitHub |
