Pydantic Collab
A Pydantic AI library for validated multi-agent handoffs, consultation, and shared context.
- Source repo
- Unfold-Security/pydantic-collab
- Stars
- ★ 33
- Last updated
- 2mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 48/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- 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.
- Not a fit if
- Teams needing a hosted service or standalone deployment
- Teams wanting to avoid Pydantic AI and cloud model providers
- Situations requiring production identity and permission controls
- Source review
- 48/100 · Major gaps 1 safety controls not found
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.
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.
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 does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Pydantic Collab This agent | 48 · Major gaps | Library / SDKFree + model costs | ★ 33 | 2mo ago | Python | OpenAI API · Claude API |
| Marvin – A Python Framework for AI Workflows | 53 · Major gaps | Library / SDKFree + model costs | ★ 6.2k | 12d ago | Python | OpenAI API |
| Continuous Claude | 46 · Major gaps | Agent plugin / skillFree + model costs | ★ 3.9k | 9mo ago | Python | Claude Code |
| Swarm (Experimental, Educational) | 58 · Major gaps | Library / SDKFree + model costs | ★ 22k | 6mo ago | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
The README shows configurable memory read/write permissions, handoff context settings, and handoff/call-depth limits, supporting some least-privilege and data-flow controls (least_privilege=2, data_flow_transparency=2). The framework is presented for collaboration without human intervention, and examples run models and register tools without confirmation or approval steps, so user_confirmation=1 and external_effects=1. Context controls are described, but sensitive-data safeguards, log retention, and secret handling are not, so sensitive_data_handling=1. pyproject.toml specifies dependency ranges and CI has read-only repository permissions, but the packet shows no vulnerability audit or security update policy, so dependency_security=1. No rollback mechanism is documented, so rollback=0. The README, project metadata, and license provide attribution clues, but the publisher is unverified and maintenance/release responsibility is unclear, so source_attribution=1.
The architecture account in AGENTS.md, README feature descriptions, and test samples on memory and context control are broadly consistent, supporting self_consistency=2, though illustrative examples do not independently verify every claim. Core dependencies have version ranges and uv.lock is referenced, but the lockfile contents are not in the evidence, so dependency_availability=1. The packet shows execution limits and some test assertions for errors, but gives little user-facing failure-message or recovery guidance, so failure_messages=1.
The README covers common pipeline, star, mesh, and custom collaboration scenarios, with examples for tools, dependencies, and visualization, so audience_and_scenarios=2. It explains topology, handoffs versus tool calls, context passing, and some execution boundaries, so capability_boundaries=2. Adoption is left largely to the reader; there is no clear applicability boundary or integration trigger guidance, so trigger_precision=1. Python requirements, installation, optional features, and sync/async usage are documented, so environment_fit=2.
The README, project metadata, and contributor guide provide useful structure and navigation, so information_architecture=2. pip installation, optional visualization installation, and a uv example command are provided, so install_notes=2. Package, module, and example naming are consistent, so naming_stability=2. The README includes topology, configuration, results, and use-case examples but no FAQ, so examples_and_faq=2. Execution limits are stated, but known security, compatibility, and operational limitations are not systematically described, so known_limitations=1. The MIT license text is complete, so license=3. Dynamic version sourcing is identified, but no changelog is shown, so versioning_changelog=1. Contribution guidance describes PR expectations but does not identify maintainers or support responsibility, so maintenance_responsibility=1.
The result object exposes final output, final agent, execution path, usage, and a flow diagram, and the README includes workflow examples, supporting output_usability=2. Organizing multiple agents into validated topologies with controlled handoffs and context is a clear added capability, so marginal_value=2. The materials do not quantify model-call costs or savings, so cost_benefit=1.
Feature claims have corresponding clues in the README, project configuration, and relevant test themes, supporting claim_traceability=2. Documentation, test samples, and package configuration provide some cross-checking for architecture, dependencies, and context controls, so cross_source_corroboration=2. AGENTS.md mixes architecture, contributor guidance, and product capability statements without always separating implemented facts from assertions; the packet also cannot independently substantiate badge or coverage claims, so fact_inference_separation=1.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Examples target agent collaboration without human intervention. Before use, review each agent’s available tools, shared memory, handoff context, and the data handling of configured model providers.
- The packet does not include the lockfile, full source, or full test suite; coverage badges and test claims are not verified by this static review.
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Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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0.2.1 v0.2.1
Release notes excerpt
Features: - Collab Can now be initialized with CollabAgents without description - README.md updated with more relevant and different examples - Added Tests for 99% Code Coverage and code coverage check in the CI.View on GitHub ↗ -
0.2.0 v0.2.0
Release notes excerpt
Added: * AgentMemory Option - You can define a memory that agents can see and add to based on their permissions, which they can share with each other or just save for later runs of themselves. * You can now Initialize Agents directly through CollabAgent. Instead of doing CollabAgent(Agent('openai', sys...)) you can now do CollabAgent('openai..', name='..', system_prompt='...', agent_calls=(...))View on GitHub ↗ -
0.1.2 v0.1.2
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.What does the framework cost?
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