kagura-memory 0.20.1

Python SDK for Kagura Memory Cloud — memory management, document ingestion (PDF → memory graph), and R2 file storage for AI agents

Memory SDK — Python client for Kagura Memory Cloud

What is this?

This SDK connects your Python code to Kagura Memory Cloud, giving AI assistants the ability to remember, search, and learn from past interactions — and to ingest documents (PDFs, URLs) directly into a searchable memory graph. It provides four clients for different use cases:

Client	Protocol	Use Case
KaguraAgent	MCP + LLM	AI-powered — auto-decides what to remember/recall from conversations
KaguraClient	MCP (JSON-RPC)	Direct memory ops — remember, recall, explore, reference, forget
ResourceClient	REST API	External data ingestion — push data from Slack, CI/CD, CRM into Kagura
FilesClient	REST + presigned PUT	File uploads with sha256 integrity binding (R2)
FileIngestor	CLI + SDK	Document ingestion — PDF text → memory graph + R2 archive (Phase 1; image/PPT/Excel in Phase 2)

60-second demo

Turn a PDF into a structured graph of memories — one overview memory plus per-section summaries, linked via declared_link edges. The original file is archived to your workspace's storage so you can always pull the bytes back.

pip install 'kagura-memory[ingest-pdf]'
kagura auth login
kagura ingest ./report.pdf

kagura recall "report findings" -k 5        # search across sections
kagura files download-url <file_id>          # short-lived GET on the original

Phase 1 ingests text only. A vision provider (Gemini 2.5 Flash by default) is configured and validated, but the orchestrator does not currently call it — no extractor in Phase 1 emits images, so neither _image.preprocess() nor Provider.describe_image() is invoked. Image-based OCR memories land in Phase 2. Pass --no-vision to skip provider configuration entirely, or --dry-run to see token / cost estimates without calling an LLM.

Installation

pip install kagura-memory                   # core SDK
pip install 'kagura-memory[ingest-pdf]'     # adds PDF ingestion support
# or
uv add kagura-memory

Quick Start

Configuration

Copy the example and fill in your credentials:

cp .kagura.json.example .kagura.json
# Edit .kagura.json — set api_key and mcp_url

Used by the CLI (kagura commands) and load_config() in Python code:

{
  "api_key": "kagura_your_api_key",
  "mcp_url": "http://localhost:8080/mcp/w/{workspace_id}",
  "model": "gpt-5.4-nano",
  "context_id": "auto"
}

Or use environment variables: KAGURA_API_KEY, KAGURA_MCP_URL, KAGURA_MODEL, KAGURA_CONTEXT_ID

Get your API key from the Kagura Memory Cloud Web UI: Integrations > API Keys

KaguraAgent — AI-Powered Memory

Let the AI analyze conversations and automatically decide what to remember and recall:

from kagura_memory import KaguraAgent, Session, Message

agent = KaguraAgent(api_key="kagura_...", model="gpt-5.4-nano")

session = Session(messages=[
    Message(role="user", content="FastAPIでOAuth2を実装したい"),
    Message(role="assistant", content="Authlibを使うパターンが推奨です..."),
    Message(role="user", content="なるほど、これ覚えておいて"),
])

async with agent:
    result = await agent.process(session, deep=True, verbose=2)
    print(f"Remembered: {len(result.remembered)}, Recalled: {len(result.recalled)}")

Supports OpenAI, Claude, Gemini via LiteLLM, and Ollama for local models:

# Local LLM via Ollama (no cloud API key needed)
agent = KaguraAgent(api_key="kagura_...", model="ollama/qwen3:30b")

Ollama Local Model Requirements

Model	Size	Context	Min VRAM	Recommended GPU
qwen3:30b (recommended)	19 GB	256K	24 GB	RTX 4090 or equivalent
qwen3:14b	9.3 GB	40K	16 GB	RTX 4080 or equivalent

Recommended minimum: qwen3:30b on an RTX 4090 (24 GB VRAM) or equivalent.

Smaller models (< 30B parameters) may produce lower quality memory analysis — summaries may lack searchable keywords, and recall query generation may be less precise.

Using Ollama Cloud

For Ollama Cloud (hosted, Bearer-authenticated), set the API key in the environment (the ollama signin CLI flow does this automatically) and point ollama_base_url at the cloud endpoint:

export OLLAMA_API_KEY="..."          # or run `ollama signin`

# KaguraAgent — Ollama Cloud
agent = KaguraAgent(
    api_key="kagura_...",
    model="ollama/qwen3:30b",
    ollama_base_url="https://ollama.com",
    # ollama_api_key picks up OLLAMA_API_KEY from env by default;
    # pass it explicitly to override.
)

For ingestion, kagura ingest reads OLLAMA_API_KEY and OLLAMA_API_BASE from the environment (litellm picks them up directly for the ollama_chat/ route):

export OLLAMA_API_KEY="..."
export OLLAMA_API_BASE="https://ollama.com"
kagura ingest report.pdf --text-provider ollama

The ingest provider uses litellm's ollama_chat/ route (system messages preserved), so cloud and local share the same code path.

KaguraClient — Direct Memory Operations

For programmatic control without LLM:

from kagura_memory import KaguraClient

async with KaguraClient(api_key="kagura_...", mcp_url="https://...") as client:
    await client.remember(context_id="dev", summary="OAuth2 pattern", content="Use Authlib...")
    results = await client.recall(context_id="dev", query="OAuth2", k=5)
    await client.explore(context_id="dev", memory_id="uuid", depth=3)

    # Filtered recall — ALL listed tags, optional date range
    results = await client.recall(
        context_id="dev", query="budget",
        filters={"tags": ["予算", "2026"], "tags_match": "all",
                 "created_after": "2026-03-01T00:00:00Z"},
    )

    # Cross-context recall — search several contexts at once
    results = await client.recall(query="auth", context_ids=["ctx-1", "ctx-2"], k=10)

More operations — tag-vocabulary discovery (list_tags), merge_contexts, workspace get_usage, get_memory_stats, find_duplicates, get_embedding_status — are runnable in examples/client_advanced.py; the API Coverage table lists the full surface.

ResourceClient — External Data Ingestion

Push data from external systems into Kagura so AI can search it:

from kagura_memory import ResourceClient, ResourceEventRequest

async with ResourceClient.from_mcp_url(api_key="kagura_...", mcp_url="http://localhost:8080/mcp/w/...") as client:
    # One-call setup: create public context + set resource_id + create token
    token = await client.setup_resource(resource_id="products", summary="Product catalog")
    print(f"Save this token: {token.token}")  # Shown only once!

    event = ResourceEventRequest(
        op="upsert", doc_id="SKU-001", version=1,
        payload={"name": "Wireless Headphones", "price": 79.99},
    )
    await client.ingest_event("products", token.token, event)

    # Check ingestion stats
    stats = await client.get_resource_impact("products")
    print(f"Memories: {stats.memory_count}, Tokens: {stats.token_count}")

See examples/ for complete working examples.

FilesClient — File Uploads with Checksum Binding

Upload files to the workspace's object store via short-lived presigned PUT URLs. The SDK binds the body's sha256 into the PUT signature so the server (memory-cloud v0.15.1+, R2_CHECKSUM_BINDING_ENABLED=true) can reject tampered uploads with 400 BadDigest:

from pathlib import Path
from kagura_memory import FilesClient

async with FilesClient.from_mcp_url(api_key="kagura_...", mcp_url="https://memory.kagura-ai.com/mcp") as client:
    # Upload from a Path (read fully into memory; server caps file size at 100 MiB)
    f = await client.upload(context_id="ctx-uuid", source=Path("./report.pdf"))
    print(f"Uploaded {f.id}, sha256={f.sha256}, size={f.size_bytes}")

    # Upload from bytes — filename is required (server enforces non-empty)
    f2 = await client.upload(context_id="ctx-uuid", source=b"...", filename="payload.bin")

    # Short-lived presigned GET URL
    url = await client.download_url(f.id)

    # List & delete
    page = await client.list(context_id="ctx-uuid", limit=50)
    await client.delete(f.id)

Re-uploading bytes whose sha256 already exists in the workspace returns the existing FileObject (idempotent dedup happy-path) — no exception.

Runnable: examples/files_upload.py.

SDK ↔ memory-cloud Compatibility

SDK	Min memory-cloud	Notes
0.15.0 – 0.20.x	0.15.1	FilesClient + R2 checksum binding. list_tags() additionally needs 0.15.4 — MIN_SERVER_VERSION is intentionally not bumped, only that one method requires the newer server.
0.14.x	0.15.1	FilesClient + R2 checksum binding (x-amz-checksum-sha256 on PUT)
0.13.x	0.13.0	Pre-FilesClient

MIN_SERVER_VERSION in src/kagura_memory/client.py is the authoritative floor — the SDK refuses to call newer MCP tools against an older server. When pointing the SDK at a backend with R2_CHECKSUM_BINDING_ENABLED=true, the SDK must be v0.14.0+; older versions don't send the signed checksum header and uploads fail with HTTP 403 SignatureDoesNotMatch.

CLI

Authentication (OAuth2 device flow)

Log in once with kagura auth login — the SDK stores credentials at ~/.kagura/credentials.json (mode 0600) and KaguraClient() plus all kagura CLI commands pick them up automatically:

kagura auth login                                    # default: memory:read + memory:write
kagura auth login --read-only                        # read-only scope
kagura auth login --scope "memory:read profile:read" # custom scope set
kagura auth login --no-browser                       # SSH / headless
kagura auth login --profile work                     # named profile for a second workspace

kagura auth status                                   # show profile, server, expiry, scope
kagura auth refresh                                  # manual token rotation
kagura auth refresh --scope "memory:write"           # incremental consent (re-runs device flow)
kagura auth token                                    # raw access_token to stdout (CI use)
kagura auth logout                                   # revoke + delete profile
kagura auth logout --all --yes                       # remove every profile

Two integration paths:

You want…	Use
CLI / KaguraClient (terminal use, scripts, KaguraAgent)	kagura auth login — refresh happens automatically
Claude Code MCP (Claude Code reads .mcp.json)	kagura setup claude with a long-lived API key from the web UI

Claude Code's MCP client reads its config once at startup and does not refresh tokens — a refresh-aware MCP proxy daemon (kagura-mcp) is tracked as a follow-up so that kagura auth login can eventually power both paths from a single credentials file. For now, use the long-lived API key path for Claude Code and the OAuth path for everything else.

Credential resolution order when KaguraClient() is called with no arguments: KAGURA_API_KEY env (CI / service accounts always win) → KAGURA_PROFILE env or explicit profile= arg → default_profile from ~/.kagura/credentials.json → legacy .kagura.json.

Other commands

# AI-powered (requires LLM API key)
kagura process -m "Remember: FastAPI uses Depends() for DI"

# Direct memory operations
kagura remember -s "FastAPI DI" --content "Use Depends()..." -c dev
kagura recall "dependency injection" -k 10
kagura explore -m "memory-uuid" --depth 3
kagura forget -m "memory-uuid"
kagura contexts

# Resource tokens
kagura resource tokens create -r products -d "Product sync"
kagura resource ingest -r products -k TOKEN --doc-id SKU-001 -V 1 -p '{"name":"Widget"}'
kagura resource ingest-batch -r products -k TOKEN -f events.json
kagura resource stats -r products
kagura resource schema -r products

# Sleep Maintenance — observability + rollback
kagura sleep history <context-id> --limit 5
kagura sleep report <context-id> <report-id>
kagura sleep rollback <context-id> <report-id> -y    # destructive: prompts unless --yes / -y is set

# File uploads (R2 checksum binding)
kagura files upload ./report.pdf -c <context-id>
kagura files list -c <context-id> --limit 50
kagura files download-url <file-id>
kagura files delete <file-id>

# Config
kagura config show

Document ingestion (kagura ingest)

See the 60-second demo above for the happy path. The full option surface:

# Local file or URL → one overview + N sections + R2 archive
kagura ingest ./report.pdf
kagura ingest https://example.com/report.pdf --tags "Q1,research"

# Preview cost / sections without calling any LLM
kagura ingest ./report.pdf --dry-run

# Skip vision-provider configuration entirely (Phase 1 already ingests
# text only; this becomes meaningful once Phase 2 image extraction lands)
kagura ingest ./report.pdf --no-vision

# Storage: skip the R2 archive (no file_id stamped on the overview memory)
kagura ingest ./report.pdf --no-archive

# Machine-readable output for scripts
kagura ingest ./report.pdf --json

Exit codes: 0 when the overview memory is created (per-section errors are still 0, they show up in result.errors); 1 when the overview itself fails (corrupted PDF, network error, etc.); 0 for any --dry-run invocation.

For the SDK-level FileIngestor API, see examples/ingest_pdf.py.

Provider configuration (env vars, picked up automatically via litellm):

• ANTHROPIC_API_KEY — text summarization (default model claude-sonnet-4-6 via the claude preset)
• GEMINI_API_KEY — reserved for vision OCR (default model gemini/gemini-2.5-flash via the gemini preset); not invoked in Phase 1
• Override per invocation: --text-provider {claude|gemini|ollama}, --vision-provider {claude|gemini|ollama}

Claude Code Integration

Use Kagura Memory as an MCP server in Claude Code:

cp .mcp.json.example .mcp.json
# Edit .mcp.json — set workspace_id and API key

Or use the CLI directly:

kagura process -m "今日の学び：FastAPIのDIはDepends()を使う"

API Coverage

Operation	SDK Client	Protocol	Auth
Memory (remember/recall/forget/explore/reference)	KaguraClient	MCP	API Key
Context (create/update/list/get_context_info)	KaguraClient	MCP	API Key
Workspace (get_usage)	KaguraClient	MCP	API Key
Search config (update_search_config)	KaguraClient	MCP	API Key
Embedding status (get_embedding_status)	KaguraClient	REST	API Key
Memory stats (get_memory_stats)	KaguraClient	REST	API Key
Duplicate detection (find_duplicates)	KaguraClient	REST	API Key
Context delete	—	Web UI only	Session
Sleep Maintenance (history / report / rollback)	KaguraClient	MCP	API Key
Resource Token (create/list/update/revoke)	ResourceClient	REST API	API Key
Resource Event ingestion	ResourceClient	REST API	Resource Token
Resource Impact (stats)	ResourceClient	REST API	API Key
Resource Schema	ResourceClient	REST API	API Key
File upload / download-url / delete / list	FilesClient	REST + presigned PUT	API Key
Account erasure (GDPR Art.17 / APPI)	—	Web UI only	Session

Context deletion and account erasure are intentionally Web UI only — destructive operations require session authentication and confirmation. kagura sleep rollback runs over the MCP API Key but is itself destructive (reverses edge creation, merges, importance updates, promotions, and archives) and the CLI requires --yes to skip the interactive confirmation. The server commits per-action without a Saga, so a 5xx response after partial success means SOME actions may have been reversed before the error surfaced — re-run kagura sleep report to inspect the post-failure state.

Development

git clone https://github.com/kagura-ai/kagura-memory-python-sdk.git
cd kagura-memory-python-sdk
uv sync --dev

uv run ruff check src/ tests/   # Lint
uv run ruff format src/ tests/  # Format
uv run pyright src/              # Type check
uv run pytest tests/ -v          # Test

Development with Claude Code

This project is developed with Claude Code:

/quality                # Run lint, format, type check, tests
/simplify               # Review for reuse, quality, efficiency
/self-review            # Pre-PR self-review
/self-maint             # Audit .claude/ config against codebase
/test                   # Run the test suite
/release <level>        # Bump version, tag, push, create GitHub Release
/kagura-memory:guide    # SDK usage reference (kagura-memory plugin)

Typical flow: Issue → Branch → Implement → /quality → /simplify → /self-review → PR → Merge → /release

Links

• Kagura Memory Cloud — the server this SDK connects to
• Releases — changelogs
• Issues — bug reports & feature requests

License

MIT License — see LICENSE for details.