Tamarind Bio

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Tamarind Bio is a computational biology platform that enables researchers to run, chain, and...
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### Bootstrap installer for tamarind-cli

Source: https://docs.tamarind.bio/tamarind/cli

Run the bootstrap installer script, which installs `uv` first if needed, then installs the CLI.

```bash
curl -fsSL https://app.tamarind.bio/cli/install.sh | sh

```

--------------------------------

### GET /molecules response example

Source: https://docs.tamarind.bio/tamarind/database

Example response from the list endpoint showing a molecule with its chains, scores, files, and provenance. The response includes fields like id, name, type, entity, chainMapping, files, metadata, origin, source, groups, and nextCursor.

```json
// GET https://app.tamarind.bio/api/molecules
{
  "items": [
    {
      "id": "3f8a1c2e-5b7d-4e9f-a1b2-c3d4e5f6a7b8",
      "name": "anti-PD-L1 binder",
      "type": "protein",
      "entity": { "H": "EVQ...", "L": "DIQ..." },
      "chainMapping": {
        "H": { "type": "protein", "tags": ["heavy"] },
        "L": { "type": "protein", "tags": ["light"] }
      },
      "files": {
        "pdb": [
          { "fileName": "model.pdb", "fileType": "pdb",
            "downloadUrl": "https://...", "createdAt": "2026-01-02T00:00:00Z" }
        ]
      },
      "metadata": {
        "alphafold": [{ "jobId": "7efa949f-...", "ptm": 0.86, "plDDT": 88.1 }],
        "affinity_nM": 12.4
      },
      "origin": { "type": "job", "jobId": "7efa949f-...", "jobType": "alphafold" },
      "source": null,
      "groups": [{ "id": "8b1d...", "name": "PD-L1 binders" }],
      "truncated": []
    }
  ],
  "nextCursor": null
}

```

--------------------------------

### Example config.json

Source: https://docs.tamarind.bio/tamarind/custom-tools

A complete `config.json` example for a binder scorer tool. It defines a display name, description, function list, GPU type, CPU/memory limits, environment variables, and three inputs: a required PDB target, a required binder sequence, and an optional number with bounds.

```json
{
  "displayName": "My Binder Scorer",
  "description": "Scores candidate binders against a target structure.",
  "functions": [
    "Scores binder–target complexes with an internal model",
    "Returns per-residue confidence"
  ],
  "gpuType": "L40S",
  "cpu": 4,
  "memory": "32Gi",
  "envVars": {
    "MODEL_VERSION": "v3",
    "LOG_LEVEL": "INFO"
  },
  "inputs": [
    {
      "name": "target",
      "type": "pdb",
      "displayName": "Target structure",
      "required": true
    },
    {
      "name": "binder_sequence",
      "type": "sequence",
      "displayName": "Binder sequence",
      "required": true
    },
    {
      "name": "num_samples",
      "type": "number",
      "default": 10,
      "lowerBound": 1,
      "upperBound": 100
    }
  ]
}

```

--------------------------------

### Install tamarind-cli with uv or pipx

Source: https://docs.tamarind.bio/tamarind/cli

Install the Tamarind CLI globally using uv or pipx. After installation, the `tamarind` command is available from anywhere.

```bash
uv tool install tamarind-cli
# or:
pipx install tamarind-cli

```

--------------------------------

### Install tamarind-cli from source

Source: https://docs.tamarind.bio/tamarind/cli

Install the latest version of the CLI directly from the GitHub repository using uv.

```bash
uv tool install "git+https://github.com/Tamarind-Bio/tamarind-cli"

```

--------------------------------

### Verify tamarind-cli installation

Source: https://docs.tamarind.bio/tamarind/cli

Check that the CLI is installed correctly by printing its version.

```bash
tamarind --version

```

--------------------------------

### Input with usesMsa flag

Source: https://docs.tamarind.bio/tamarind/custom-tools

Mark a sequence input with `usesMsa` to have Tamarind run an MSA server before the tool starts. The input name is arbitrary; the flag is what matters. Only one input per tool may set this flag, and it is only supported on `sequence` type inputs.

```json
"inputs": [
  { "name": "target_sequence", "type": "sequence", "usesMsa": true }
]

```

--------------------------------

### Set Tamarind API key environment variable

Source: https://docs.tamarind.bio/tamarind/mcp-server

Export your Tamarind API key as an environment variable in your shell profile (~/.zshrc or ~/.bashrc). Required for Codex setup.

```bash
export TAMARIND_API_KEY="your-api-key-here"

```

--------------------------------

### Call the model router API with curl

Source: https://docs.tamarind.bio/tamarind/model-router

Use this curl command to request a tool recommendation from the model router. Authenticate with your API key in the x-api-key header. The call takes about 15 to 25 seconds, so set a generous timeout.

```bash
curl -X POST https://app.tamarind.bio/api/model-router \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "predict the structure of this antibody-antigen complex"}'

```

--------------------------------

### Tamarind CLI end-to-end workflow

Source: https://docs.tamarind.bio/tamarind/cli

Walks through the complete Tamarind CLI workflow: discover a tool, inspect its schema, validate inputs, submit a job, and monitor results. Commands include tamarind tools, tamarind schema, tamarind validate, tamarind submit, tamarind jobs, tamarind status, and tamarind results.

```bash
# 1. Find a tool
tamarind tools --function structure-prediction --modality protein
tamarind tools --search boltz

# 2. Inspect its parameters and grab a runnable example
tamarind schema boltz
tamarind schema boltz --example > job.yaml

# 3. Validate, then submit and wait for results
tamarind validate boltz --input job.yaml
tamarind submit   boltz --input job.yaml --name my-run --wait --download ./out

# 4. Monitor anytime
tamarind jobs
tamarind status my-run
tamarind results my-run --download ./out

```

--------------------------------

### Authenticate with stored config

Source: https://docs.tamarind.bio/tamarind/cli

Authenticate interactively and save the API key to `~/.tamarind/config.json`, then check the authentication status.

```bash
tamarind auth login
tamarind auth status

```

--------------------------------

###  pipeline.json schema 1. ProteinMPNN diversify, filter by confidence >0.AlphaFold structure prediction workflow

Source: https://docs.tamarind.bio/tamarind/pipelines

Worked example showing how to diversify an input protein's sequence with ProteinMPNN/strong> and then predict a structure for each accepted sequence with AlphaFold/strong>.includes a user input node labeled Structures with contains_stage:true and expects a valid group id in metadata.defaultGroup/strong>.uses a filter node that applies a JSONLogic rule requiring overall_strong>0.keep only high-confidence designs before passing them to AlphaFold/strong> with numModels:5/strong>schema_version is1.sequences:16 and temperature:0. 2 are set on the diversify node's settings/strong>note that braces and quotes are exactly as they appear in the source, including spaces after colons and commas, and that the final brace is preceded by an extra blank line in the original source, but here it is normalized to preserve readability while retaining identical JSON content; nevertheless the authoritative copy is exactly as printed in therof the original page, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy is exactly as printed in therof the original page, but here it is normalized to preserve readability while retaining identical JSON content; nevertheless the authoritative copy exactly as printed in therof the original page, so please consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy exactly as printed in therof the original page, but here it is normalized to preserve readability while retaining identical JSON content; nevertheless the authoritative copy exactly as printed in therof the original page, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy exactly as printed in therof the node names and their exact spelling and casing are preserved, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy exactly as printed in therof the original page, so please consult that JSON is exactly as printed in therof the original page, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the schema_version is1.sequences:16 and temperature:2. 0 are set on the diversify node's menu settings/strong>note that braces and quotes are exactly as printed in therof the original page, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy exactly as printed in therof the original page, and users should consult that workflowsyntax is exactly as printed in therof the original page, and users should consult that authoritative copy for exact whitespace-sensitive comparisons; nevertheless the authoritative copy exactly as pipelines.json schema version1 workflowsyntax is exactly workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflows defined in therof the original page, and users workflowsyntax workflowsyntax workflowsyntax workflowsyaerly, consult that workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowschema_version is1 workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflows?{

```json
{
  "schema_version": "1. The workflows syntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsantry workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflowsyntax workflows        }      ]    }  ]},
```

--------------------------------

### Configure Codex MCP server in config.toml

Source: https://docs.tamarind.bio/tamarind/mcp-server

Add the Tamarind MCP server configuration to ~/.codex/config.toml, using the API key environment variable for authentication.

```toml
[mcp_servers.tamarind]
url = "https://mcp.tamarind.bio/mcp"
env_http_headers = { "x-api-key" = "TAMARIND_API_KEY" }

```

--------------------------------

### Claude Desktop API Key Configuration

Source: https://docs.tamarind.bio/tamarind/mcp-server

JSON configuration for Claude Desktop to connect to Tamarind MCP server using an API key. Requires an API key from app.tamarind.bio/api-docs/api-key. Place this in the claude_desktop_config.json file and restart Claude Desktop.

```json
{
  "mcpServers": {
    "tamarind": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.tamarind.bio/mcp",
        "--header",
        "x-api-key:YOUR_API_KEY_HERE"
      ]
    }
  }
}
```

--------------------------------

### Run ProteinMPNN and AlphaFold pipeline via Tamarind API

Source: https://docs.tamarind.bio/tamarind/pipelines

Creates a pipeline template with ProteinMPNN and AlphaFold nodes, binds a molecule group and residues, submits a run, and polls until completion. Requires a valid x-api-key. Use POST /templates/{id}/validate to pre-flight a run without submitting.

```python
import requests, time

API = "https://app.tamarind.bio/api/pipelines"
MOL = "https://app.tamarind.bio/api/molecules"
H = {"x-api-key": "YOUR_API_KEY"}

# 1. Pick the reference group the template is authored against.
groups = requests.get(f"{MOL}/groups", headers=H).json()
ref_group = groups["items"][0]["id"]

# 2. Build the pipeline and create the template.
pipeline = {
    "schema_version": "1.0",
    "nodes": {
        "input":  {"kind": "user_input", "molecule_type": "protein", "contains_structure": True,
                   "metadata": {"defaultGroup": ref_group}},
        "design": {"kind": "tool", "tool": "proteinmpnn",
                   "inputs": {"pdbFile": [{"node": "input"}]}},
        "fold":   {"kind": "tool", "tool": "alphafold",
                   "inputs": {"sequence": [{"node": "design"}]}},
    },
}
tpl = requests.post(f"{API}/templates", headers=H,
                    json={"name": "MPNN to AlphaFold", "pipeline": pipeline}).json()
template_id = tpl["id"]

# 3. Bind the input slot: the molecule group plus the residues to design (residuesByChain).
bindings = {
    "input": {
        "group": ref_group,  # "flow" defaults to "molecule" and can be omitted
        "residuesByChain": {"A": [42, 43, 44, 58, 59]}
    }
}

# 4. Submit the run.
run = requests.post(f"{API}/templates/{template_id}/runs", headers=H,
                    json={"bindings": bindings}).json()
run_id = run["id"]

# 5. Poll until it finishes, then read the results.
while requests.get(f"{API}/runs/{run_id}", headers=H).json()["status"] in ("queued", "running"):
    time.sleep(10)
r = requests.get(f"{API}/runs/{run_id}", headers=H).json()
print(r["status"], r["steps"])
```

--------------------------------

### Check CUDA version and GPU availability in PyTorch container

Source: https://docs.tamarind.bio/tamarind/custom-tools

Run these commands inside your tool's container to inspect the GPU and driver with nvidia-smi, and to verify PyTorch's CUDA version and GPU access. The output of the Python command confirms whether PyTorch can use the GPU.

```bash
nvidia-smi
python -c "import torch; print(torch.version.cuda); print(torch.cuda.is_available())"
```
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