Migrate LangChain, LangGraph, and Deep Agents to Pydantic AI

Preserve behavior, not framework shape. Migrate the smallest behaviorally complete slice and leave application infrastructure outside that slice unchanged. LangChain, LangGraph, and Deep Agents are one source ecosystem; Pydantic AI, pydantic_graph, and Pydantic AI Harness are the matching target ecosystem, and this skill maps across all of it.

Work from the running application

1. Read repository instructions, dependency files, tests, and the actual runtime entrypoints. Identify the installed LangChain, LangGraph, Deep Agents, Pydantic AI, and Harness versions.
2. Trace one representative request through prompts, retrieval, model and tool calls, state, persistence, interrupts, emitted events, tracing/metrics callbacks, and the public result. Inspect every caller and sibling endpoint that consumes the migrated component; a narrow implementation slice can still have several public contracts. Include keyword parameter names and the sync, async, callback, and streaming forms callers actually use. Record only contracts those paths actually use.
3. Run the cheapest useful baseline. When the migration surface is broad or unclear, search dependency files and source for langchain, langgraph, langsmith, and deepagents, then confirm findings against imports, factories, and call sites. Resolve where create_deep_agent comes from: the upstream package and a vendored copy that reproduces its contracts both count as Deep Agents.
4. Classify the slice before choosing a target:
   • Chain or LCEL pipeline: keep deterministic retrieval and transformation in plain Python; use a Pydantic AI agent only where a model/tool loop adds value.
   • LangChain agent: normally use one reusable pydantic_ai.Agent with typed dependencies, tools, and outputs.
   • Direct LangGraph workflow: use plain async Python for simple fixed control flow, or pydantic_graph when explicit typed nodes and branching remain useful. Treat persistence as a separate design decision.
   • Deep Agents harness: create_deep_agent bundles file tools, shell execution, subagent delegation, summarization, skills, memory, permissions, prompt caching, and approval middleware on top of the LangChain agent loop, and a harness profile can add or hide more. Migrate the loop with the LangChain mappings, then map each bundled feature the slice actually uses to a Harness capability, a core primitive, or an application service with Deep Agents Mapping. Inventory implicit defaults the application code never mentions.
   • Product runtime: retain queues, configured database backends, sandboxes, auth, schedulers, webhooks, tracing, and transport adapters unless the user placed them in scope. Extend an existing application seam before creating a parallel persistence or provider subsystem.
5. Add or preserve deterministic characterization tests, then migrate one vertical slice behind the existing public boundary.
6. Run the original tests and focused parity tests. Classify each observed contract by its evidence; never describe the migration as one-to-one merely because the happy path or trace shape looks similar.

Read Concept Mapping for the detected source features. Read Deep Agents Mapping whenever deepagents is in the slice. Read Semantic Gaps only for state, middleware, retries, approval, concurrency, streaming, or other behavior where similar-looking APIs may differ. Use Workaround Recipes after a concrete gap is identified, not as a mandatory checklist. Read Logfire Verification when adding observability, comparing source and target runs, or debugging a semantic difference. Read Verification and Cutover before a production cutover.