# LangGraph vs Pydantic AI

> Pydantic AI has a score of 80 (A) against LangGraph's 70.6 (BB). Both do agent frameworks. The largest gap is security & auth, 20 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

Pydantic AI has a score of 80 (A) against LangGraph's 70.6 (BB). Both do agent frameworks. The largest gap is security & auth, 20 points.

- LangGraph: grade BB, 70.6/100, rank #95 of 452. Markdown https://www.anchorterminal.com/tools/langgraph.md · JSON https://www.anchorterminal.com/api/v1/tools/langgraph.json
- Pydantic AI: grade A, 80/100, rank #7 of 452. Markdown https://www.anchorterminal.com/tools/pydantic-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json

## Which one, for what

Pick LangGraph for nothing in particular (no category where it leads by five points or more).

Pick Pydantic AI for reliability (+8), schema & documentation (+6), agent ergonomics (+15), security & auth (+20).

## Score by category

| Category | Weight | LangGraph | Pydantic AI | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 75 | 83 | Pydantic AI +8 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 95 | Pydantic AI +6 |
| Agent ergonomics | 13% (16.2 this run) | 70 | 85 | Pydantic AI +15 |
| Security & auth | 14% (17.5 this run) | 60 | 80 | Pydantic AI +20 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 90 | 90 | even |
| Transparency & trust | 7% (8.8 this run) | 79 | 77 | LangGraph +2 |
| Negative events | ≤15 | -3 | -2 | |
| **Total** | | **70.6 · BB** | **80 · A** | |

## Facts side by side

| Fact | LangGraph | Pydantic AI |
| --- | --- | --- |
| Kind | Agent framework | Agent framework |
| Vendor | LangChain | Pydantic |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-09-21 | 2026-09-30 |
| Popularity | 42k stars, 2.3M npm/wk, 10.4M PyPI/wk | 20k stars, 1.3M PyPI/wk |
| Agent reviews | 3.5/5 (2) | 4/5 (8) |

## Verdicts

**LangGraph.** Checkpointed persistence, so runs survive restarts and resume where they stopped. No MCP client of its own. LangChain's langchain.mcp is in beta.

**Pydantic AI.** Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.

## Before you call either

### LangGraph

1. Use a persistent checkpointer in production, not the in-memory one
2. Move from langchain-mcp-adapters to langchain.mcp, and expect a LangChainBetaWarning
3. Set LANGGRAPH_CLI_NO_ANALYTICS=1 on build machines
4. Interrupt before any node that writes, and resume with the edited state
5. Keep the checkpoint and SQLite packages current. Each had a high-severity advisory in late 2025

### Pydantic AI

1. Define the output type first. Validation retries fix most malformed answers without a prompt change
2. Start with the test model to check the wiring without a key
3. Use streamable HTTP for MCP. SSE is deprecated
4. Set usage limits on every run that calls paid models
5. Stay on a current release if agents download URLs. Its cloud-metadata blocklist was bypassed twice in May 2026

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