Pydantic Collab
A Pydantic AI library for validated multi-agent handoffs, consultation, and shared context.
Per-dimension scores and reasoning
Evidence: The framework provides memory permission control (r/rw), but no user confirmation mechanism; data flow transparency is partially addressed via context passing controls (include_conversation etc.) in README and tests, but data flow is not explicitly documented; sensitive data handling is not specifically addressed; dependency security: pyproject.toml lists dependencies but no lock file or security audit; external effects: tool calls and visualization may have side effects, but not clearly stated; rollback mechanism not mentioned; source attribution: MIT license and copyright notice exist, but publisher is unverified. Deductions: lack of user confirmation, dedicated sensitive data handling, rollback; insufficient dependency security evidence; external effects not fully explained.
Evidence: Code and tests are consistent in several aspects, such as memory permission validation and context passing control; dependency availability: dependency list is clear, but no lock file; failure messages: tests do not explicitly show error handling messages. Deductions: insufficient failure message evidence, missing dependency lock.
Evidence: README provides multiple use cases (pipeline, star, mesh) and examples; capability boundaries are defined via topology and tool definitions; trigger precision: tool calls and handoffs have clear parameters, but trigger conditions are not detailed; environment fit: supports Python 3.11+, but OS compatibility not specified. Deductions: insufficient trigger precision evidence, limited environment fit info.
Evidence: README is well-structured with installation, quick start, examples; install notes are clear (pip install); naming stability: API names are consistent in docs, but no version history; examples and FAQ: multiple examples provided, but no FAQ; known limitations not explicitly listed; license is MIT, but version changelog missing; maintenance responsibility: CI and tests exist, but maintainers not clearly identified. Deductions: missing version changelog, known limitations, FAQ; maintenance responsibility unclear.
Evidence: Output usability: result object and visualization provided; marginal value: multi-agent collaboration features, but comparison with other frameworks lacking; cost-benefit: no performance or cost data. Deductions: insufficient cost-benefit evidence, marginal value comparison not thorough.
Evidence: README feature claims partially correspond to code and tests, but implementation details not provided; cross-source corroboration: tests and docs corroborate each other, but independent verification lacking; fact-inference separation: not explicitly distinguished in docs. Deductions: insufficient claim traceability, limited cross-source corroboration, fact-inference not clearly separated.
- Publisher identity is unverified; assess supply chain risks carefully.
- Dependencies are not locked, posing supply chain attack risks.
- Lack of user confirmation mechanism; automatic execution may cause unintended side effects.
- Sensitive data handling is not specifically addressed; evaluate as needed.
- No rollback mechanism; recovery after failure is difficult.
What does this agent do, and when should you use it?
pydantic-collab is a Python multi-agent orchestration library built on Pydantic AI, with teams defined through Collab, CollabAgent, and predefined collaboration topologies. It provides PipelineCollab, StarCollab, and MeshCollab, while Collab supports explicit agent_calls and agent_handoffs. Agents can share run-scoped context through AgentMemory, with read-only or read-write access per agent. Calling run_sync returns an object with output, final_agent, execution_path, and usage; execution flow can be printed and topology can be rendered as an image. The documented delivery model is an embedded Python library, not a standalone service, hosted deployment, or CLI runner.
Developers create CollabAgent objects with a name and system_prompt, optionally configuring agent_calls, agent_handoffs, and memory, then compose them with Collab, PipelineCollab, StarCollab, or MeshCollab. During execution, agents can consult another agent through tool calls or transfer control through a handoff, while configured context can include conversation content, thinking, handoff information, and topology information. The host application runs collab.run_sync("Query"), optionally supplying deps, and receives a result with final output, the producing agent, an execution path, and token usage. Async Python functions can be registered with @collab.tool_plain and scoped to named agents; visualize_topology can display a graph or save it as a PNG.
- A support operations team that must classify an incoming customer email, determine urgency, and draft a response can chain Triager and Responder in a PipelineCollab.
- An incident-response team whose first responder, investigator, and communications writer need the same evolving incident context can assign one AgentMemory to their CollabAgent definitions.
- A product lead collecting engineering, design, and support input before issuing a go/no-go recommendation can use a StarCollab.
- A vendor-evaluation group where security, engineering, and legal reviewers need to consult one another can use a MeshCollab.
- A code-review lead who needs security and performance input before transferring the final write-up to a summary writer can configure explicit agent_calls, agent_handoffs, and final_agent in Collab.
What are this agent's strengths and limitations?
- Includes Pipeline, Star, and Mesh topologies while still allowing precise custom relationships through
agent_callsandagent_handoffs. - AgentMemory provides shared context during a run with per-agent
randrwaccess modes. - The result object exposes
execution_path,final_agent, andusage, andprint_execution_flow()renders the execution flow. @collab.tool_plainregisters async Python functions as tools and can restrict them to selected agents.- Topology validation and optional image visualization make collaboration structure inspectable.
- The framework is built on Pydantic AI, so adoption includes its model-configuration and runtime integration approach.
- Examples show Anthropic and OpenAI models, but the source does not document credential configuration, provider-failure handling, or retry behavior.
- Multi-agent execution is bounded by
max_handoffs(default 10) andmax_agent_call_depth(default 3), which may need tuning for complex flows. - Topology image generation requires the additional
pydantic-collab[viz]installation. - No standalone deployment service, hosted runtime, or production identity-and-permission model is documented.
How do you install or deploy this agent?
Run pip install pydantic-collab. The documented package is for Python and is built on Pydantic AI; model use also requires credentials configured for the selected provider in that runtime, although the source does not specify credential names or environment variables. For topology rendering, run pip install pydantic-collab[viz] to install the visualization dependencies.
How do you use this agent?
Create PipelineCollab(agents=[CollabAgent(name="Triager", system_prompt="..."), CollabAgent(name="Responder", system_prompt="...")], model="anthropic:claude-sonnet-4-5"), then call result = collab.run_sync("user input") and read result.output. The documented examples also use openai:gpt-5.2-pro and openai:gpt-4o-mini. To run a repository example, the documented command is uv run --env-file .env examples/01_simple_chain.py.
FAQ
Which model providers are shown?
anthropic:claude-sonnet-4-5, openai:gpt-5.2-pro, and openai:gpt-4o-mini.When does an agent consult another agent versus transfer the task?
agent_calls are consultations in which the caller stays in control. agent_handoffs transfer control, so the caller stops and the receiving agent continues.Can shared memory be access-controlled?
memory argument accepts mappings such as {"notes": "rw", "config": "r"} for read-write and read-only access.How are runaway collaboration loops limited?
max_handoffs and max_agent_call_depth; the documented defaults are 10 and 3 respectively.