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* Add Memory Base API: models, migrations, service, endpoints, and tests Introduces Memory Base (MB) — a per-flow knowledge base that auto-captures conversation history and ingests it into a Chroma vector store on configurable thresholds. Backend changes: - MemoryBase + MemoryBaseSession DB models with full CRUD - Three Alembic migrations: base tables, merge head, phase-2 fields (embedding_model, preprocessing, preproc_model, preproc_instructions) - MemoryBaseService: create/list/get/update/delete, session tracking, pending-message cursor logic, mismatch detection, regenerate - ingest_memory_task: async Chroma ingestion with cursor advance on success - REST API (/api/v1/memories): CRUD, flush, sessions, mismatch, regenerate - Flow output hook: on_flow_output() triggers auto-capture after each run - Plumbing in build.py, endpoints.py, workflow.py to call on_flow_output - deps.py: expose get_memory_base_service() - kb_helpers.py: FS/metadata helpers used by MB service Authored-By: Debojit Kaushik <kaushik.debojit@gmail.com> Add dedupe_key idempotency enforcement for MB ingestion jobs Centralizes idempotency into JobService.create_job() with a null-safe check, removing the redundant pre-flight logic from MemoryBaseService. Key changes: - services/jobs/exceptions.py: new DuplicateJobError(RuntimeError) — raised when a QUEUED/IN_PROGRESS/COMPLETED job with the same dedupe_key exists; FAILED/CANCELLED are retryable and are excluded - services/jobs/__init__.py: exports DuplicateJobError - services/jobs/service.py: null-guarded dedup query inside create_job() within the same session_scope as the insert (minimizes TOCTOU window) - services/database/models/jobs/model.py: dedupe_key field -> index=True - alembic/versions/36aa87831162: adds dedupe_key column + ix_job_dedupe_key index to job table with checkfirst guards - services/memory_base/service.py: updated key format to "ingestion:{mb_id}:{session_id}:{first_msg_id}" for namespace isolation; removed _has_non_retryable_job_for_dedupe_key and _has_active_job methods and all call sites; DuplicateJobError catch in _maybe_trigger() for silent skip on auto-capture; split regenerate() catch clauses - api/v1/memories.py: explicit DuplicateJobError catch before RuntimeError in flush_memory_base() for semantic clarity (both return 409) Co-Authored-By: Debojit Kaushik <kaushik.debojit@gmail.com> Checkpointing working version of MBs. TODO: User separation, Get messages endpoint, MB resumption midway through a chat for a session, tests. Added messages endpoint for Memory Bases. Added pagination to sessions endpoint. Modifed messages model to include ingestion related attributes. Added unit tests, fixed linting issues and formatting issues. Aligned Workflows API, /run endpoint, playground to all work with Memory Bases. Created DB models asociated with tracking memory base state with sessions and jobs. Added tests, created unit tests for service and task files related to MemoryBases. Improved concurrency of jobs, added (memory_base_id, session_id) locking to serialize jobs in case the job creation cadence moves ahead of ingestion jobs. Improved concurrency handling and moved the pending check to be live inside the ingestion job rather than a snapshot before triggering the job. Consolidated all migrations related to memory_bases into one idempotent version and serialized all migrations form release along with memory_bases for cleanliness and maintainability. Introduced advisory locking to address multi worker environment, added unique constraint to MB creation, added sanitization check to KB pathnames to avoid illegal directory creation. Added partial write rollback for ChromaDB, aligned same session is used for each job to avoid dangling advisory locks. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Eric Hare <ericrhare@gmail.com>
Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
✨ Highlight features
- Visual builder interface to quickly get started and iterate.
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Deploy as an MCP server and turn your flows into tools for MCP clients.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
🖥️ Langflow Desktop
Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don't need to manage Python environments or install packages manually. Available for Windows and macOS.
⚡️ Quickstart
Install locally (recommended)
Requires Python 3.10–3.13 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install langflow -U
The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.
Run
To start Langflow, run:
uv run langflow run
Langflow starts at http://127.0.0.1:7860.
That's it! You're ready to build with Langflow! 🎉
📦 Other install options
Run from source
If you've cloned this repository and want to contribute, run this command from the repository root:
make run_cli
For more information, see DEVELOPMENT.md.
Docker
Start a Langflow container with default settings:
docker run -p 7860:7860 langflowai/langflow:latest
Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.
🛡️ Security
For security information, see our Security Policy.
🚀 Deployment
Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.
⭐ Stay up-to-date
Star Langflow on GitHub to be instantly notified of new releases.
👋 Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.