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langflow-ai/langflow

RepoRadar surfaced langflow-ai/langflow — a code repository — into the Agent workflows section, where it sits at Gold tier with a 'try now' verdict. It is written primarily in Python. Its strongest signal is workflow potential, scored 10.0 out of 10.

Open on GitHub →Back to radar
Score8.8
Popularity100.0
Riskconditional
TierGold
Score breakdown
Usefulness9.0
Novelty7.4
Momentum9.2
Maturity9.3
Open-source/build8.4
Evidence7.2
Workflow potential10.0
Setup ease6.4

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Langflow belongs on RepoRadar because it is one of the most widely adopted open-source paths from prototype to running LLM app. The project is not just a canvas: the docs cover components, agents, MCP server/client support, APIs, Docker, and production deployment. Its scale and current v1.10.2 package make it useful now, while the main caveat is operational discipline around secrets, connected

Who should use it

Teams prototyping LLM workflows before hardening them into services Builders who want a visible graph of components, tools, prompts, and data stores Agent teams comparing MCP server and client wiring inside a broader app builder Developers who need a PyPI-installable framework plus a browser UI for flow editing

Who should skip it

Skip langflow-ai/langflow unless the captured evidence suggests it solves a problem you are actively working on.

About this signal

langflow-ai/langflow is tracked by RepoRadar as a code repository in the Agent workflows section. First seen 2026-07-11; the source record was last checked on 2026-07-11. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, langflow-ai/langflow is strongest on workflow potential (10.0) and maturity (9.3) and weakest on setup ease (6.4) — a profile worth weighing against your own priorities. This page summarizes the public evidence on the linked source page and states where additional review is still needed.

How this item is evaluated

The langflow-ai/langflow record combines a 8.8/10 composite score with separate popularity (100.0), risk (conditional), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.

Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.

Risk explanation

Flows can connect to LLM providers, vector stores, APIs, and MCP tools; evaluate with throwaway credentials and non-sensitive data before exposing a server; A visual builder can make powerful workflows easy to share; review authentication, environment variables, and component permissions before deployment.

Evidence links
Closest alternatives / related signals
agent-workflows llm-apps visual-builder mcp rag langchain python pypi
Verification record

What RepoRadar actually verified

Tested in a bounded workflow

Bounded representative workflow retained by RepoRadar verification harness. Last checked 2026-07-14T05:40:38.018386Z.

failed · cohort-20260714-langflow-lfx-local-graph-workflow

Tester
RepoRadar automated local verification harness
Started
2026-07-14T05:36:15.143533Z
Completed
2026-07-14T05:37:30.460277Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
2 minute(s)
Evidence scope
Bounded setup or capability check
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Created 2 synthetic fixture file(s) inside the disposable work directory; retained hashes prove the exact inputs.
  • Installed pinned Langflow 1.10.2 in an isolated uv environment with tracking disabled.
  • Authored Langflow's official LFX component graph from Chat Input through Text Input and Text Output to Chat Output, then invoked it with the langflow lfx run CLI.
  • Validated the JSON success/type/component fields and exact marker result for the fixed session, retaining both normalized evidence and the CLI transcript.
  • Executed bounded check: Execute a pinned Langflow LFX four-component chat/text graph through the official CLI and verify the synthetic message reaches Chat Output unchanged.
  • Captured the complete sanitized stdout, stderr, exit status, artifact checks, and 75.32-second wall time.
Observed results
  • Check exited 1 after 75.32 seconds; failure output is retained and the verification stage must not advance.
Observed strengths
  • No product strength was established in this failed attempt; diagnostic evidence is retained.
Friction
  • Even this small model-free graph resolves Langflow's substantial application dependency set; the server, UI, persistence, and provider-backed components remain separate operational surfaces.
  • Setup or runtime emitted 457 stderr line(s); the complete warnings/errors are preserved in the retained log.
Limitations
  • This model-free graph validates Langflow component imports, graph construction, local CLI execution, JSON output, and message propagation; it does not start the Langflow server/UI, persist a flow, call an LLM, use agents/retrieval, expose an API/MCP server, or assess multi-user operations.
  • This credential-free disposable workflow does not establish operator use, production scale, model quality, reliability under sustained use, or team adoption.

Pricing assessment: The open-source local Langflow/LFX execution used no account, hosted Langflow service, API key, model provider, or paid inference.

Privacy assessment: Only a synthetic marker and fixed session ID were processed in a disposable local Python process; tracking was disabled and no flow data or prompt was sent externally after package retrieval.

Open retained test log →

passed · cohort-20260714-langflow-lfx-local-graph-workflow-v2

Tester
RepoRadar automated local verification harness
Started
2026-07-14T05:38:27.040189Z
Completed
2026-07-14T05:40:38.018386Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
3 minute(s)
Evidence scope
Bounded representative workflow
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Created 2 synthetic fixture file(s) inside the disposable work directory; retained hashes prove the exact inputs.
  • Installed pinned Langflow 1.10.2 with its compatible LiteLLM 1.91.3 dependency override in an isolated uv environment with tracking disabled.
  • Authored Langflow's official LFX component graph from Chat Input through Text Input and Text Output to Chat Output, then invoked it with the langflow lfx run CLI.
  • Validated the JSON success/type/component fields and exact marker result for the fixed session, retaining both normalized evidence and the CLI transcript.
  • Executed bounded check: Execute a pinned Langflow LFX four-component chat/text graph through the official CLI and verify the synthetic message reaches Chat Output unchanged.
  • Captured the complete sanitized stdout, stderr, exit status, artifact checks, and 130.98-second wall time.
Observed results
  • Command exited 0 after 130.98 seconds.
  • Langflow executed the four-component, three-edge local graph and returned the exact input marker as a successful Chat Output message.
  • Expected marker 'CHECK_OK graph=chat-text-text-chat components=4 edges=3 result=REPORADAR_LANGFLOW_FIXTURE_7319 telemetry=off' was observed in retained output.
  • Validated result.json: 8 required marker(s) present and 0 excluded marker(s) absent; size and SHA-256 are retained.
  • Validated transcript.json: 4 required marker(s) present and 0 excluded marker(s) absent; size and SHA-256 are retained.
Observed strengths
  • LFX components can be composed and executed headlessly with machine-readable output, providing a testable path below the visual builder and hosted-model layers.
Friction
  • Even this small model-free graph resolves Langflow's substantial application dependency set; LiteLLM 1.92.0 also required an unavailable MSVC linker, so this run pins 1.91.3, and the server, UI, persistence, and provider-backed components remain separate operational surfaces.
  • Setup or runtime emitted 158 stderr line(s); the complete warnings/errors are preserved in the retained log.
Limitations
  • This model-free graph validates Langflow component imports, graph construction, local CLI execution, JSON output, and message propagation; it uses the compatible LiteLLM 1.91.3 override because 1.92.0 cannot build without an MSVC linker here, and it does not start the Langflow server/UI, persist a flow, call an LLM, use agents/retrieval, expose an API/MCP server, or assess multi-user operations.
  • This credential-free disposable workflow does not establish operator use, production scale, model quality, reliability under sustained use, or team adoption.

Pricing assessment: The open-source local Langflow/LFX execution used no account, hosted Langflow service, API key, model provider, or paid inference.

Privacy assessment: Only a synthetic marker and fixed session ID were processed in a disposable local Python process; tracking was disabled and no flow data or prompt was sent externally after package retrieval.

Open retained test log →

Verification sources

Longitudinal intelligence

How this decision record is moving

Raw history JSON →

26 dated snapshots retained from 2026-07-11 through 2026-08-13; see the snapshot index for explicit coverage gaps. Stars, version, release, pricing, integration, risk, maintenance, verdict, score, and momentum fields remain explicit even when a source has not reported them. Repository momentum is a normalized 0–10 RepoRadar signal; GitHub stars appear only where the popularity monitor retained exact timestamped observations.

RepoRadar score8.8 current · +0.0 net
Repository momentum9.6 current · +0.4 net
GitHub stars (observed)153,189 current · +1,492 net
GitHub stars153,189 exact observation
Versionv1.11.3
Last release2026-08-11T23:14:46Z
Maintenanceactive
Current riskconditional
Current verdicttry now
Pricing baselineNo structured commercial pricing baseline
Pricing checkedNot applicable or not recorded
Pricing freshnessNo dated commercial pricing review
Integrations baselineNo structured integrations recorded

Recent dated points

DateScoreMomentumStarsRiskVerdictMaintenance
2026-08-138.89.6153,189conditionaltry nowactive
2026-08-128.89.6153,098conditionaltry nowactive
2026-08-118.89.6153,046conditionaltry nowactive
2026-08-108.89.6153,010conditionaltry nowactive
2026-08-098.89.6152,966conditionaltry nowactive
2026-08-088.89.6152,952conditionaltry nowactive
2026-08-078.89.6152,781conditionaltry nowactive
2026-08-068.89.2152,896conditionaltry nownot recorded
2026-08-058.89.2152,865conditionaltry nownot recorded
2026-08-048.89.6152,781conditionaltry nowactive
2026-08-038.89.6152,781conditionaltry nowactive
2026-08-028.89.6152,741conditionaltry nowactive

Why the record changed

stars changed

Stars changed: 153098 → 153189.

stars changed

Stars changed: 153046 → 153098.

version changed

Version changed: v1.11.2 → v1.11.3.

stars changed

Stars changed: 153010 → 153046.

stars changed

Stars changed: 152966 → 153010.

stars changed

Stars changed: 152952 → 152966.

version changed

Version changed: v1.11.1 → v1.11.2.

stars changed

Stars changed: 152781 → 152952.

stars changed

Stars changed: 152896 → 152781.

stars changed

Reconstructed from adjacent retained daily snapshots; no upstream cause is inferred. Stars changed: 152865 → 152896.

stars changed

Reconstructed from adjacent retained daily snapshots; no upstream cause is inferred. Stars changed: 152781 → 152865.

stars changed

Stars changed: 152741 → 152781.